From 591d803c81a898f9c35524f3e47be76c22aa6382 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 10:46:34 -0400
Subject: [PATCH 01/33] docs: add async scheduling epic UML reference

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 .../AsyncSchedulingEpicComponent.png          | Bin 0 -> 53001 bytes
 .../AsyncSchedulingEpicIssueMap.png           | Bin 0 -> 27924 bytes
 .../AsyncSchedulingEpicRuntimeSequence.png    | Bin 0 -> 107275 bytes
 ...ncSchedulingRequestAdmissionClassModel.png | Bin 0 -> 70268 bytes
 ...ncSchedulingSupportContractsClassModel.png | Bin 0 -> 48023 bytes
 ...AsyncSchedulingTaskAdmissionClassModel.png | Bin 0 -> 60292 bytes
 architecture/async-scheduling/README.md       |  47 ++
 .../async-scheduling-epic.puml                | 584 ++++++++++++++++++
 8 files changed, 631 insertions(+)
 create mode 100644 architecture/async-scheduling/AsyncSchedulingEpicComponent.png
 create mode 100644 architecture/async-scheduling/AsyncSchedulingEpicIssueMap.png
 create mode 100644 architecture/async-scheduling/AsyncSchedulingEpicRuntimeSequence.png
 create mode 100644 architecture/async-scheduling/AsyncSchedulingRequestAdmissionClassModel.png
 create mode 100644 architecture/async-scheduling/AsyncSchedulingSupportContractsClassModel.png
 create mode 100644 architecture/async-scheduling/AsyncSchedulingTaskAdmissionClassModel.png
 create mode 100644 architecture/async-scheduling/README.md
 create mode 100644 architecture/async-scheduling/async-scheduling-epic.puml

diff --git a/architecture/async-scheduling/AsyncSchedulingEpicComponent.png b/architecture/async-scheduling/AsyncSchedulingEpicComponent.png
new file mode 100644
index 0000000000000000000000000000000000000000..4d4a20804aec5bbbfa4af33bd2ea99369db3a33a
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diff --git a/architecture/async-scheduling/AsyncSchedulingEpicRuntimeSequence.png b/architecture/async-scheduling/AsyncSchedulingEpicRuntimeSequence.png
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diff --git a/architecture/async-scheduling/AsyncSchedulingSupportContractsClassModel.png b/architecture/async-scheduling/AsyncSchedulingSupportContractsClassModel.png
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diff --git a/architecture/async-scheduling/README.md b/architecture/async-scheduling/README.md
new file mode 100644
index 000000000..fe15eb1bb
--- /dev/null
+++ b/architecture/async-scheduling/README.md
@@ -0,0 +1,47 @@
+# Async Scheduling Epic UML
+
+Reference diagrams for the async scheduling epic tracked by issue 645. These diagrams are intentionally design artifacts for future implementation PRs, not runtime documentation.
+
+## Source
+
+- [async-scheduling-epic.puml](async-scheduling-epic.puml): PlantUML source for every diagram on this page.
+
+## Component View
+
+![Component view](AsyncSchedulingEpicComponent.png)
+
+## Task Admission Contracts
+
+![Task admission class model](AsyncSchedulingTaskAdmissionClassModel.png)
+
+## Request Admission Contracts
+
+![Request admission class model](AsyncSchedulingRequestAdmissionClassModel.png)
+
+## Capacity, Telemetry, and Evidence Contracts
+
+![Support contracts class model](AsyncSchedulingSupportContractsClassModel.png)
+
+## Runtime Sequence
+
+![Runtime sequence](AsyncSchedulingEpicRuntimeSequence.png)
+
+## Issue Dependency Map
+
+![Issue dependency map](AsyncSchedulingEpicIssueMap.png)
+
+## Render
+
+```bash
+plantuml architecture/async-scheduling/async-scheduling-epic.puml
+```
+
+The expected design spine is:
+
+```text
+SchedulingMetadata -> TaskSchedulingResolver -> CompletionTracker
+-> FairTaskQueue -> TaskAdmissionController -> AsyncTaskScheduler
+-> ModelRequestExecutor -> RequestAdmissionController -> provider/model endpoint
+```
+
+Task admission and request admission each have explicit controller, queue, policy, and lease/state boundaries where applicable. Telemetry observes scheduler admission and request admission separately, then issue 648 correlates the two timelines through the runtime correlation provider.
diff --git a/architecture/async-scheduling/async-scheduling-epic.puml b/architecture/async-scheduling/async-scheduling-epic.puml
new file mode 100644
index 000000000..181ff702c
--- /dev/null
+++ b/architecture/async-scheduling/async-scheduling-epic.puml
@@ -0,0 +1,584 @@
+@startuml AsyncSchedulingEpicComponent
+title Data Designer Async Scheduling Epic - Component View
+
+left to right direction
+skinparam componentStyle rectangle
+skinparam shadowing false
+skinparam packageStyle rectangle
+skinparam linetype ortho
+skinparam nodesep 40
+skinparam ranksep 45
+
+legend right
+  Epic issue 645 target shape.
+  Issue tags show primary ownership.
+  Solid arrows are runtime/data flow.
+  Dashed arrows are observability or policy inputs.
+endlegend
+
+actor "Generator / Plugin Author" as Author
+actor "Operator / Integrator" as Operator
+
+component "Generator Metadata\nColumnGenerator\nSchedulingMetadata\nTaskSchedulingResolver\nissues 641, 646, 653, 652" as MetadataStage
+component "Task Readiness and Ordering\nCompletionTracker\nFairTaskQueue\nSchedulableTask" as TaskQueueStage
+component "Task Admission\nTaskAdmissionController\nTaskAdmissionPolicy\nTaskAdmissionLease\nissues 644, 650, 651" as TaskAdmissionStage
+component "AsyncTaskScheduler\nexecution owner" as SchedulerStage
+component "Model Request Boundary\nModelRequestExecutor" as ModelBoundaryStage
+component "Request Admission\nRequestAdmissionController\nRequestFairQueue\nRequestAdmissionPolicy\nAdaptiveRequestLimitState\nissue 657" as RequestAdmissionStage
+component "Provider / Model Endpoint" as ProviderStage
+
+component "Capacity Plan\nAsyncCapacityPlan\nTaskAdmissionConfig\nRequestAdmissionConfig\nissue 654" as CapacityStage
+component "Telemetry and Correlation\nSchedulerAdmissionEventSink\nRequestAdmissionEventSink\nRuntimeCorrelationProvider\nCorrelated Runtime View\nissues 635, 647, 648" as TelemetryStage
+component "Benchmark and Future Design\nbenchmark harness\nresource-vector design\nissues 649, 651" as EvidenceStage
+
+Author --> MetadataStage
+MetadataStage -right-> TaskQueueStage : resolved task DTOs
+TaskQueueStage -right-> TaskAdmissionStage : selected ready work
+TaskAdmissionStage -right-> SchedulerStage : task lease
+SchedulerStage -right-> ModelBoundaryStage : execute task code
+ModelBoundaryStage -right-> RequestAdmissionStage : model request item
+RequestAdmissionStage -right-> ModelBoundaryStage : request lease
+ModelBoundaryStage -right-> ProviderStage : admitted model call
+
+CapacityStage ..> TaskAdmissionStage
+CapacityStage ..> RequestAdmissionStage
+TaskAdmissionStage ..> TelemetryStage
+RequestAdmissionStage ..> TelemetryStage
+SchedulerStage ..> TelemetryStage
+TelemetryStage --> Operator
+EvidenceStage ..> CapacityStage
+EvidenceStage ..> TaskAdmissionStage
+EvidenceStage ..> RequestAdmissionStage
+
+note bottom of TaskAdmissionStage
+  Controller owns scheduler leases.
+  Policy decides admissibility.
+  FairTaskQueue owns only ready-work ordering.
+end note
+
+note bottom of MetadataStage
+  SchedulingMetadata is static declaration only.
+  It does not encode queue depth, AIMD state,
+  admitted limits, RequestDomain, or runtime pressure.
+end note
+
+@enduml
+
+@startuml AsyncSchedulingTaskAdmissionClassModel
+title Data Designer Async Scheduling Epic - Task Admission Contracts
+
+left to right direction
+skinparam classAttributeIconSize 0
+skinparam shadowing false
+skinparam packageStyle rectangle
+skinparam linetype ortho
+
+package "Metadata contracts" {
+  class ColumnGenerator {
+    +get_scheduling_metadata()
+  }
+
+  class SchedulingMetadata {
+    +kind
+    +identity
+    +weight
+  }
+
+  class TaskSchedulingResolver {
+    +resolve(generator)
+  }
+
+  class ResolvedTaskScheduling {
+    +group
+    +resource_request
+  }
+}
+
+package "Task scheduling contracts" {
+  class CompletionTracker {
+    +ready_frontier()
+    +mark_complete(task)
+  }
+
+  class TaskGroupSpec {
+    +key
+    +weight
+  }
+
+  class SchedulerResourceRequest {
+    +amounts
+  }
+
+  class SchedulableTask {
+    +task
+    +group
+    +resource_request
+  }
+
+  class FairTaskQueue {
+    +enqueue(item)
+    +select_next(is_eligible)
+    +commit(selection)
+    +view()
+  }
+
+  interface TaskAdmissionPolicy {
+    +is_eligible(item, queue, view)
+    +on_acquire(lease)
+    +on_release(lease)
+  }
+
+  class StrictFairTaskAdmissionPolicy {
+    +enforce hard per-group limits
+  }
+
+  class BoundedBorrowTaskAdmissionPolicy {
+    +borrow_debt(group, resource)
+  }
+
+  class TaskAdmissionController {
+    +is_eligible(item, queue)
+    +try_acquire(item, queue)
+    +release(lease)
+    +view()
+  }
+
+  class TaskAdmissionDecision {
+    +lease
+    +denied_reason
+  }
+
+  class TaskAdmissionLease {
+    +item
+    +resources
+    +acquired_at
+  }
+
+  class AsyncTaskScheduler <<boundary>> {
+    +coordinate_ready_work()
+    +execute_admitted_task()
+  }
+}
+
+package "Task config" {
+  class TaskAdmissionConfig {
+    +submission_capacity
+    +resource_limits
+  }
+}
+
+ColumnGenerator --> SchedulingMetadata
+TaskSchedulingResolver --> SchedulingMetadata
+TaskSchedulingResolver --> ResolvedTaskScheduling
+ResolvedTaskScheduling --> TaskGroupSpec
+ResolvedTaskScheduling --> SchedulerResourceRequest
+ResolvedTaskScheduling --> SchedulableTask
+CompletionTracker --> FairTaskQueue : ready tasks
+FairTaskQueue --> TaskAdmissionController : QueueSelection
+TaskAdmissionController --> TaskAdmissionPolicy
+StrictFairTaskAdmissionPolicy ..|> TaskAdmissionPolicy
+BoundedBorrowTaskAdmissionPolicy ..|> TaskAdmissionPolicy
+TaskAdmissionController --> TaskAdmissionDecision
+TaskAdmissionDecision --> TaskAdmissionLease
+TaskAdmissionLease --> SchedulableTask
+AsyncTaskScheduler --> CompletionTracker
+AsyncTaskScheduler --> FairTaskQueue
+AsyncTaskScheduler --> TaskAdmissionController
+TaskAdmissionController ..> TaskAdmissionConfig : reads config
+
+note right of SchedulingMetadata
+  Static declaration only.
+  Must not encode queue depth,
+  AIMD state, admitted limits,
+  RequestDomain, or runtime pressure.
+end note
+
+note bottom of SchedulerResourceRequest
+  Replaces legacy needs_llm_wait-style booleans
+  once the resource-vector design lands.
+end note
+
+@enduml
+
+@startuml AsyncSchedulingRequestAdmissionClassModel
+title Data Designer Async Scheduling Epic - Request Admission Contracts
+
+left to right direction
+skinparam classAttributeIconSize 0
+skinparam shadowing false
+skinparam packageStyle rectangle
+skinparam linetype ortho
+
+package "Model request boundary" {
+  class ModelRequestExecutor <<boundary>> {
+    +execute(request)
+  }
+
+  class ProviderModelEndpoint <<external>> {
+    +request()
+  }
+}
+
+package "Request admission contracts" {
+  class RequestResourceKey {
+    +provider_name
+    +model_id
+    +domain
+  }
+
+  class RequestGroupSpec {
+    +key
+    +weight
+  }
+
+  class RequestAdmissionItem {
+    +resource
+    +group
+    +timeout
+  }
+
+  class RequestFairQueue <<internal>> {
+    +enqueue(item)
+    +select_next(is_eligible)
+    +commit(selection)
+    +view()
+  }
+
+  interface RequestAdmissionPolicy <<internal>> {
+    +is_eligible(item, queue, limits)
+    +on_success(lease)
+    +on_rate_limited(lease, retry_after)
+    +on_failure(lease)
+  }
+
+  class AdaptiveRequestLimitState {
+    +current_limit(resource)
+    +effective_max(resource)
+    +record_success(resource)
+    +record_rate_limit(resource, retry_after)
+    +record_failure(resource)
+  }
+
+  class RequestAdmissionController {
+    +try_acquire(item)
+    +acquire_sync(item)
+    +acquire_async(item)
+    +release_success(lease)
+    +release_rate_limited(lease, retry_after)
+    +release_failure(lease)
+    +pressure_snapshots()
+  }
+
+  class AdaptiveRequestAdmissionController {
+    +AIMD reduce/increase behavior
+  }
+
+  class RequestAdmissionDecision <<union>> {
+    RequestAdmissionLease | RequestAdmissionDenied
+  }
+
+  class RequestAdmissionLease {
+    +item
+    +acquired_at
+    +current_limit
+    +effective_max
+  }
+
+  class RequestAdmissionDenied {
+    +item
+    +reason
+    +retry_after_seconds
+    +available_after
+    +snapshot
+  }
+
+  class RequestPressureSnapshotProvider {
+    +snapshot(resource)
+    +snapshots()
+    +global_snapshot(provider, model)
+    +global_snapshots()
+  }
+}
+
+package "Request config" {
+  class RequestAdmissionConfig {
+    +resources
+    +initial_limit
+    +max_limit
+  }
+}
+
+ModelRequestExecutor --> RequestAdmissionController
+RequestAdmissionController --> RequestFairQueue
+RequestAdmissionController --> RequestAdmissionPolicy
+RequestAdmissionPolicy --> AdaptiveRequestLimitState
+AdaptiveRequestAdmissionController --|> RequestAdmissionController
+AdaptiveRequestAdmissionController --> RequestFairQueue
+AdaptiveRequestAdmissionController --> RequestAdmissionPolicy
+AdaptiveRequestAdmissionController --> AdaptiveRequestLimitState
+RequestAdmissionController --> RequestAdmissionDecision
+RequestAdmissionDecision --> RequestAdmissionLease
+RequestAdmissionDecision --> RequestAdmissionDenied
+RequestAdmissionController --> RequestPressureSnapshotProvider
+RequestAdmissionController ..> RequestAdmissionConfig : reads config
+RequestAdmissionItem --> RequestResourceKey
+RequestAdmissionItem --> RequestGroupSpec
+RequestAdmissionLease --> RequestAdmissionItem
+RequestAdmissionDenied --> RequestAdmissionItem
+ModelRequestExecutor --> ProviderModelEndpoint
+
+note bottom of RequestFairQueue
+  Internal waiter ordering.
+  This is not a second public wrapper around
+  RequestAdmissionController.
+end note
+
+note right of RequestAdmissionController
+  Owns concrete provider/model/domain
+  request admission at model-call time.
+  Does not own DAG readiness or task
+  scheduler admission.
+  No durable Throttle*, needs_llm_wait,
+  or held_llm_wait compatibility concepts.
+end note
+
+@enduml
+
+@startuml AsyncSchedulingSupportContractsClassModel
+title Data Designer Async Scheduling Epic - Capacity, Telemetry, and Evidence Contracts
+
+left to right direction
+skinparam classAttributeIconSize 0
+skinparam shadowing false
+skinparam packageStyle rectangle
+skinparam linetype ortho
+
+package "Capacity planning (issue 654)" {
+  class RunConfigRuntimeArgs <<input>> {
+    +env
+    +runtime_args
+    +run_config
+  }
+
+  class AsyncCapacityPlan {
+    +buffer_size
+    +row_group_concurrency
+    +submission_capacity
+    +task_admission
+    +request_admission
+  }
+
+  class TaskAdmissionConfig {
+    +submission_capacity
+    +resource_limits
+  }
+
+  class RequestAdmissionConfig {
+    +resources
+    +initial_limit
+    +max_limit
+  }
+}
+
+package "Telemetry and correlation (issues 635, 647, 648)" {
+  class RuntimeCorrelation {
+    +run_id
+    +row_group
+    +task_column
+    +task_type
+    +scheduling_group_kind
+    +scheduling_group_identity_hash
+  }
+
+  class RuntimeCorrelationProvider {
+    +set(context)
+    +reset(token)
+    +current()
+  }
+
+  class SchedulerAdmissionEventSink {
+    +emit(event)
+  }
+
+  class RequestAdmissionEventSink {
+    +emit(event)
+  }
+
+  class CorrelatedRuntimeView {
+    +join scheduler and request timelines
+  }
+}
+
+package "Evidence and future design (issues 649, 650, 651)" {
+  class AsyncSchedulingBenchmarkHarness {
+    +run_ab(baseline_ref, candidate_ref)
+    +emit_json_csv_artifacts()
+  }
+
+  class SchedulerResourceVectorDesign {
+    +static task resources
+    +read-only request pressure
+  }
+
+  class BoundedBorrowTaskAdmissionPolicy {
+    +borrow_debt(group, resource)
+  }
+}
+
+RunConfigRuntimeArgs --> AsyncCapacityPlan
+AsyncCapacityPlan --> TaskAdmissionConfig
+AsyncCapacityPlan --> RequestAdmissionConfig
+SchedulerAdmissionEventSink ..> RuntimeCorrelationProvider : attach current context
+RequestAdmissionEventSink ..> RuntimeCorrelationProvider : attach current context
+RuntimeCorrelationProvider --> RuntimeCorrelation
+SchedulerAdmissionEventSink --> CorrelatedRuntimeView
+RequestAdmissionEventSink --> CorrelatedRuntimeView
+AsyncSchedulingBenchmarkHarness ..> AsyncCapacityPlan
+AsyncSchedulingBenchmarkHarness ..> SchedulerAdmissionEventSink
+AsyncSchedulingBenchmarkHarness ..> RequestAdmissionEventSink
+AsyncSchedulingBenchmarkHarness ..> BoundedBorrowTaskAdmissionPolicy
+SchedulerResourceVectorDesign ..> BoundedBorrowTaskAdmissionPolicy
+
+@enduml
+
+@startuml AsyncSchedulingEpicRuntimeSequence
+title Data Designer Async Scheduling Epic - Runtime Sequence
+
+skinparam shadowing false
+skinparam sequenceMessageAlign center
+
+actor User
+participant "ColumnGenerator" as Gen
+participant "TaskSchedulingResolver\nissue 646" as Resolver
+participant "CompletionTracker" as Tracker
+participant "AsyncTaskScheduler" as Scheduler
+participant "FairTaskQueue" as Queue
+participant "TaskAdmissionController\nissue 644" as TaskAdmit
+participant "TaskAdmissionPolicy" as TaskPolicy
+participant "ModelRequestExecutor" as Executor
+participant "RequestAdmissionController\nissue 657" as ReqAdmit
+participant "RequestFairQueue" as ReqQueue
+participant "RequestAdmissionPolicy" as ReqPolicy
+participant "AdaptiveRequestLimitState" as ReqLimits
+participant "Provider / Model" as Provider
+participant "SchedulerAdmissionEventSink\nissue 647" as SchedEvents
+participant "RequestAdmissionEventSink\nissue 635" as ReqEvents
+participant "RuntimeCorrelationProvider\nissue 648" as Correlation
+
+User -> Gen : declare columns / plugins
+Gen -> Resolver : get_scheduling_metadata()
+Resolver -> Resolver : resolve TaskGroupSpec\nand SchedulerResourceRequest
+Resolver -> Tracker : provide SchedulableTask inputs
+
+Scheduler -> Tracker : ready_frontier()
+Tracker -> Queue : enqueue ready SchedulableTask
+Scheduler -> Queue : select_next(TaskAdmit.is_eligible)
+Queue -> TaskAdmit : is_eligible(candidate, queue.view)
+TaskAdmit -> TaskPolicy : evaluate candidate against leases/capacity
+TaskPolicy --> TaskAdmit : eligible or denied
+TaskAdmit --> Queue : eligibility result
+Queue --> Scheduler : QueueSelection
+Scheduler -> TaskAdmit : try_acquire(selection.item, queue.view)
+TaskAdmit -> TaskPolicy : on_acquire if admitted
+TaskAdmit --> Scheduler : TaskAdmissionLease
+Scheduler -> Queue : commit(selection)
+Scheduler -> Correlation : set RuntimeCorrelation
+TaskAdmit -> SchedEvents : lease_acquired
+SchedEvents -> Correlation : current()
+
+Scheduler -> Executor : execute task
+Executor -> ReqAdmit : acquire_async(RequestAdmissionItem)
+ReqAdmit -> ReqQueue : enqueue waiter if needed
+ReqAdmit -> ReqQueue : select_next(ReqPolicy.is_eligible)
+ReqQueue --> ReqAdmit : RequestQueueSelection
+ReqAdmit -> ReqPolicy : evaluate resource/group against limits
+ReqPolicy -> ReqLimits : read current/effective limit
+ReqLimits --> ReqPolicy : limit snapshot
+ReqPolicy --> ReqAdmit : eligible or denied
+ReqAdmit -> ReqQueue : commit(selection) if admitted
+ReqAdmit -> ReqEvents : wait_started / lease_acquired
+ReqEvents -> Correlation : current()
+ReqAdmit --> Executor : RequestAdmissionLease
+Executor -> Provider : concrete model request
+Provider --> Executor : response or rate limit
+
+alt success
+  Executor -> ReqAdmit : release_success(lease)
+  ReqAdmit -> ReqPolicy : on_success(lease)
+  ReqPolicy -> ReqLimits : record_success(resource)
+else rate limit
+  Executor -> ReqAdmit : release_rate_limited(lease, retry_after)
+  ReqAdmit -> ReqPolicy : on_rate_limited(lease, retry_after)
+  ReqPolicy -> ReqLimits : record_rate_limit(resource, retry_after)
+else non-rate-limit failure or cancellation
+  Executor -> ReqAdmit : release_failure(lease)
+  ReqAdmit -> ReqPolicy : on_failure(lease)
+  ReqPolicy -> ReqLimits : record_failure(resource)
+end
+
+ReqAdmit -> ReqQueue : wake/select next waiter
+ReqAdmit -> ReqEvents : request admission event
+ReqEvents -> Correlation : current()
+Executor --> Scheduler : generated value / error
+Scheduler -> TaskAdmit : release TaskAdmissionLease
+TaskAdmit -> TaskPolicy : on_release(lease)
+TaskAdmit -> SchedEvents : lease_released / completed
+SchedEvents -> Correlation : current()
+Scheduler -> Correlation : reset RuntimeCorrelation
+
+@enduml
+
+@startuml AsyncSchedulingEpicIssueMap
+title Data Designer Async Scheduling Epic - Issue Dependency Map
+
+skinparam componentStyle rectangle
+skinparam shadowing false
+skinparam linetype ortho
+
+component "issue 641\nSchedulingMetadata" as I641
+component "issue 646\nTaskSchedulingResolver" as I646
+component "issue 653\nRemove legacy hints" as I653
+component "issue 652\nDocs" as I652
+component "issue 644\nTask admission" as I644
+component "issue 654\nAsyncCapacityPlan" as I654
+component "issue 657\nRequestAdmissionController" as I657
+component "issue 635\nRequest admission telemetry" as I635
+component "issue 647\nScheduler admission telemetry" as I647
+component "issue 648\nCorrelation" as I648
+component "issue 649\nBenchmark harness" as I649
+component "issue 650\nBounded borrow task policy" as I650
+component "issue 651\nResource-vector design" as I651
+
+I641 --> I646
+I646 --> I653
+I653 --> I652
+I646 --> I644
+I641 --> I654
+I646 --> I654
+I644 --> I654
+I654 --> I657
+I644 --> I657
+I657 --> I635
+I654 ..> I635 : metric vocabulary
+I644 --> I647
+I635 --> I647
+I657 --> I648
+I635 --> I648
+I647 --> I648
+I644 --> I649
+I649 --> I650
+I644 --> I650
+I641 --> I651
+I646 --> I651
+I644 --> I651
+I649 --> I651
+I650 --> I651
+
+note bottom
+  Native GitHub subissue order:
+  issue 641 -> issue 646 -> issue 653 -> issue 652 -> issue 644
+  -> issue 654 -> issue 657 -> issue 635 -> issue 647
+  -> issue 648 -> issue 649 -> issue 650 -> issue 651
+end note
+
+@enduml

From c03bedc3055a92e4fa537c53545660f8a722e771 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 11:55:31 -0400
Subject: [PATCH 02/33] docs: align async scheduling issue map

---
 .../AsyncSchedulingEpicIssueMap.png           | Bin 27924 -> 28601 bytes
 .../async-scheduling-epic.puml                |   3 +++
 2 files changed, 3 insertions(+)

diff --git a/architecture/async-scheduling/AsyncSchedulingEpicIssueMap.png b/architecture/async-scheduling/AsyncSchedulingEpicIssueMap.png
index ab3772d7cd0b7c0cab779e9112351b8344bbf193..b18e095337092a737ff5a0e59633bcd3d0dacc50 100644
GIT binary patch
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diff --git a/architecture/async-scheduling/async-scheduling-epic.puml b/architecture/async-scheduling/async-scheduling-epic.puml
index 181ff702c..02e865713 100644
--- a/architecture/async-scheduling/async-scheduling-epic.puml
+++ b/architecture/async-scheduling/async-scheduling-epic.puml
@@ -550,7 +550,10 @@ component "issue 650\nBounded borrow task policy" as I650
 component "issue 651\nResource-vector design" as I651
 
 I641 --> I646
+I641 --> I653
+I641 --> I652
 I646 --> I653
+I646 --> I652
 I653 --> I652
 I646 --> I644
 I641 --> I654

From 7125435106d93f5f51247760b048cb58a723568c Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 12:27:55 -0400
Subject: [PATCH 03/33] docs: base async scheduling artifacts in plans

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 .../AsyncSchedulingEpicComponent.png            | Bin 53001 -> 0 bytes
 plans/645/AsyncSchedulingEpicComponent.png      | Bin 0 -> 52837 bytes
 .../645}/AsyncSchedulingEpicIssueMap.png        | Bin
 .../645}/AsyncSchedulingEpicRuntimeSequence.png | Bin
 ...syncSchedulingRequestAdmissionClassModel.png | Bin
 ...syncSchedulingSupportContractsClassModel.png | Bin
 .../AsyncSchedulingTaskAdmissionClassModel.png  | Bin
 .../async-scheduling => plans/645}/README.md    |   6 +++---
 .../645}/async-scheduling-epic.puml             |   4 ++--
 9 files changed, 5 insertions(+), 5 deletions(-)
 delete mode 100644 architecture/async-scheduling/AsyncSchedulingEpicComponent.png
 create mode 100644 plans/645/AsyncSchedulingEpicComponent.png
 rename {architecture/async-scheduling => plans/645}/AsyncSchedulingEpicIssueMap.png (100%)
 rename {architecture/async-scheduling => plans/645}/AsyncSchedulingEpicRuntimeSequence.png (100%)
 rename {architecture/async-scheduling => plans/645}/AsyncSchedulingRequestAdmissionClassModel.png (100%)
 rename {architecture/async-scheduling => plans/645}/AsyncSchedulingSupportContractsClassModel.png (100%)
 rename {architecture/async-scheduling => plans/645}/AsyncSchedulingTaskAdmissionClassModel.png (100%)
 rename {architecture/async-scheduling => plans/645}/README.md (87%)
 rename {architecture/async-scheduling => plans/645}/async-scheduling-epic.puml (98%)

diff --git a/architecture/async-scheduling/AsyncSchedulingEpicComponent.png b/architecture/async-scheduling/AsyncSchedulingEpicComponent.png
deleted file mode 100644
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diff --git a/architecture/async-scheduling/AsyncSchedulingEpicIssueMap.png b/plans/645/AsyncSchedulingEpicIssueMap.png
similarity index 100%
rename from architecture/async-scheduling/AsyncSchedulingEpicIssueMap.png
rename to plans/645/AsyncSchedulingEpicIssueMap.png
diff --git a/architecture/async-scheduling/AsyncSchedulingEpicRuntimeSequence.png b/plans/645/AsyncSchedulingEpicRuntimeSequence.png
similarity index 100%
rename from architecture/async-scheduling/AsyncSchedulingEpicRuntimeSequence.png
rename to plans/645/AsyncSchedulingEpicRuntimeSequence.png
diff --git a/architecture/async-scheduling/AsyncSchedulingRequestAdmissionClassModel.png b/plans/645/AsyncSchedulingRequestAdmissionClassModel.png
similarity index 100%
rename from architecture/async-scheduling/AsyncSchedulingRequestAdmissionClassModel.png
rename to plans/645/AsyncSchedulingRequestAdmissionClassModel.png
diff --git a/architecture/async-scheduling/AsyncSchedulingSupportContractsClassModel.png b/plans/645/AsyncSchedulingSupportContractsClassModel.png
similarity index 100%
rename from architecture/async-scheduling/AsyncSchedulingSupportContractsClassModel.png
rename to plans/645/AsyncSchedulingSupportContractsClassModel.png
diff --git a/architecture/async-scheduling/AsyncSchedulingTaskAdmissionClassModel.png b/plans/645/AsyncSchedulingTaskAdmissionClassModel.png
similarity index 100%
rename from architecture/async-scheduling/AsyncSchedulingTaskAdmissionClassModel.png
rename to plans/645/AsyncSchedulingTaskAdmissionClassModel.png
diff --git a/architecture/async-scheduling/README.md b/plans/645/README.md
similarity index 87%
rename from architecture/async-scheduling/README.md
rename to plans/645/README.md
index fe15eb1bb..bdfdac6a4 100644
--- a/architecture/async-scheduling/README.md
+++ b/plans/645/README.md
@@ -33,14 +33,14 @@ Reference diagrams for the async scheduling epic tracked by issue 645. These dia
 ## Render
 
 ```bash
-plantuml architecture/async-scheduling/async-scheduling-epic.puml
+plantuml plans/645/async-scheduling-epic.puml
 ```
 
-The expected design spine is:
+The expected design spine is data provenance plus scheduler-owned control:
 
 ```text
 SchedulingMetadata -> TaskSchedulingResolver -> CompletionTracker
--> FairTaskQueue -> TaskAdmissionController -> AsyncTaskScheduler
+-> AsyncTaskScheduler coordinates FairTaskQueue + TaskAdmissionController
 -> ModelRequestExecutor -> RequestAdmissionController -> provider/model endpoint
 ```
 
diff --git a/architecture/async-scheduling/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
similarity index 98%
rename from architecture/async-scheduling/async-scheduling-epic.puml
rename to plans/645/async-scheduling-epic.puml
index 02e865713..56f750bc0 100644
--- a/architecture/async-scheduling/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -21,7 +21,7 @@ actor "Operator / Integrator" as Operator
 
 component "Generator Metadata\nColumnGenerator\nSchedulingMetadata\nTaskSchedulingResolver\nissues 641, 646, 653, 652" as MetadataStage
 component "Task Readiness and Ordering\nCompletionTracker\nFairTaskQueue\nSchedulableTask" as TaskQueueStage
-component "Task Admission\nTaskAdmissionController\nTaskAdmissionPolicy\nTaskAdmissionLease\nissues 644, 650, 651" as TaskAdmissionStage
+component "Task Admission\nTaskAdmissionController\nTaskAdmissionPolicy\nTaskAdmissionLease\nissue 644" as TaskAdmissionStage
 component "AsyncTaskScheduler\nexecution owner" as SchedulerStage
 component "Model Request Boundary\nModelRequestExecutor" as ModelBoundaryStage
 component "Request Admission\nRequestAdmissionController\nRequestFairQueue\nRequestAdmissionPolicy\nAdaptiveRequestLimitState\nissue 657" as RequestAdmissionStage
@@ -29,7 +29,7 @@ component "Provider / Model Endpoint" as ProviderStage
 
 component "Capacity Plan\nAsyncCapacityPlan\nTaskAdmissionConfig\nRequestAdmissionConfig\nissue 654" as CapacityStage
 component "Telemetry and Correlation\nSchedulerAdmissionEventSink\nRequestAdmissionEventSink\nRuntimeCorrelationProvider\nCorrelated Runtime View\nissues 635, 647, 648" as TelemetryStage
-component "Benchmark and Future Design\nbenchmark harness\nresource-vector design\nissues 649, 651" as EvidenceStage
+component "Benchmark and Future Design\nbenchmark harness\nbounded-borrow policy\nresource-vector design\nissues 649, 650, 651" as EvidenceStage
 
 Author --> MetadataStage
 MetadataStage -right-> TaskQueueStage : resolved task DTOs

From 05522430316ddd3b45796b783354678ab92fb225 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 12:30:28 -0400
Subject: [PATCH 04/33] docs: clarify async scheduler control ownership

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 plans/645/AsyncSchedulingEpicComponent.png | Bin 52837 -> 51739 bytes
 plans/645/README.md                        |   7 ++++---
 plans/645/async-scheduling-epic.puml       |   7 ++++---
 3 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/plans/645/AsyncSchedulingEpicComponent.png b/plans/645/AsyncSchedulingEpicComponent.png
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diff --git a/plans/645/README.md b/plans/645/README.md
index bdfdac6a4..6875e083d 100644
--- a/plans/645/README.md
+++ b/plans/645/README.md
@@ -36,12 +36,13 @@ Reference diagrams for the async scheduling epic tracked by issue 645. These dia
 plantuml plans/645/async-scheduling-epic.puml
 ```
 
-The expected design spine is data provenance plus scheduler-owned control:
+The expected runtime control owner is `AsyncTaskScheduler`:
 
 ```text
 SchedulingMetadata -> TaskSchedulingResolver -> CompletionTracker
--> AsyncTaskScheduler coordinates FairTaskQueue + TaskAdmissionController
--> ModelRequestExecutor -> RequestAdmissionController -> provider/model endpoint
+AsyncTaskScheduler -> FairTaskQueue.select_next(...)
+AsyncTaskScheduler -> TaskAdmissionController.try_acquire(...)
+AsyncTaskScheduler -> ModelRequestExecutor -> RequestAdmissionController -> provider/model endpoint
 ```
 
 Task admission and request admission each have explicit controller, queue, policy, and lease/state boundaries where applicable. Telemetry observes scheduler admission and request admission separately, then issue 648 correlates the two timelines through the runtime correlation provider.
diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index 56f750bc0..741bd61de 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -12,7 +12,7 @@ skinparam ranksep 45
 legend right
   Epic issue 645 target shape.
   Issue tags show primary ownership.
-  Solid arrows are runtime/data flow.
+  Solid arrows are runtime calls or data flow.
   Dashed arrows are observability or policy inputs.
 endlegend
 
@@ -33,8 +33,9 @@ component "Benchmark and Future Design\nbenchmark harness\nbounded-borrow policy
 
 Author --> MetadataStage
 MetadataStage -right-> TaskQueueStage : resolved task DTOs
-TaskQueueStage -right-> TaskAdmissionStage : selected ready work
-TaskAdmissionStage -right-> SchedulerStage : task lease
+SchedulerStage -left-> TaskQueueStage : select_next(...)
+SchedulerStage -down-> TaskAdmissionStage : try_acquire(...)
+TaskAdmissionStage -up-> SchedulerStage : TaskAdmissionLease
 SchedulerStage -right-> ModelBoundaryStage : execute task code
 ModelBoundaryStage -right-> RequestAdmissionStage : model request item
 RequestAdmissionStage -right-> ModelBoundaryStage : request lease

From 451c1ffbc5907434d296578733c57500c6affc52 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 12:36:39 -0400
Subject: [PATCH 05/33] docs: align task admission class flow

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 ...AsyncSchedulingTaskAdmissionClassModel.png | Bin 60292 -> 62306 bytes
 plans/645/async-scheduling-epic.puml          |   6 +++---
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/plans/645/AsyncSchedulingTaskAdmissionClassModel.png b/plans/645/AsyncSchedulingTaskAdmissionClassModel.png
index 12b41310c65b3a6d7047018b9cefa6ce13ca88a4..41cdbff3d24f3263bd964d64a214ebef497c6f30 100644
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diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index 741bd61de..39f2ed7ef 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -175,7 +175,7 @@ ResolvedTaskScheduling --> TaskGroupSpec
 ResolvedTaskScheduling --> SchedulerResourceRequest
 ResolvedTaskScheduling --> SchedulableTask
 CompletionTracker --> FairTaskQueue : ready tasks
-FairTaskQueue --> TaskAdmissionController : QueueSelection
+FairTaskQueue --> TaskAdmissionController : is_eligible(candidate)
 TaskAdmissionController --> TaskAdmissionPolicy
 StrictFairTaskAdmissionPolicy ..|> TaskAdmissionPolicy
 BoundedBorrowTaskAdmissionPolicy ..|> TaskAdmissionPolicy
@@ -183,8 +183,8 @@ TaskAdmissionController --> TaskAdmissionDecision
 TaskAdmissionDecision --> TaskAdmissionLease
 TaskAdmissionLease --> SchedulableTask
 AsyncTaskScheduler --> CompletionTracker
-AsyncTaskScheduler --> FairTaskQueue
-AsyncTaskScheduler --> TaskAdmissionController
+AsyncTaskScheduler --> FairTaskQueue : select_next returns QueueSelection
+AsyncTaskScheduler --> TaskAdmissionController : try_acquire(selection.item)
 TaskAdmissionController ..> TaskAdmissionConfig : reads config
 
 note right of SchedulingMetadata

From f617101c19fd17bb4de44b5c2e839995a1e3ffe0 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 12:44:56 -0400
Subject: [PATCH 06/33] Update plans/645/async-scheduling-epic.puml

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---
 plans/645/async-scheduling-epic.puml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index 39f2ed7ef..1970e7215 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -138,8 +138,8 @@ package "Task scheduling contracts" {
   }
 
   class TaskAdmissionController {
-    +is_eligible(item, queue)
-    +try_acquire(item, queue)
+    +is_eligible(item, view)
+    +try_acquire(item, view)
     +release(lease)
     +view()
   }

From 844bfbba0193b85e6b7d81b704b3ea02edb2045f Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 12:45:10 -0400
Subject: [PATCH 07/33] Update plans/645/async-scheduling-epic.puml

Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---
 plans/645/async-scheduling-epic.puml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index 1970e7215..c363f87f7 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -184,7 +184,7 @@ TaskAdmissionDecision --> TaskAdmissionLease
 TaskAdmissionLease --> SchedulableTask
 AsyncTaskScheduler --> CompletionTracker
 AsyncTaskScheduler --> FairTaskQueue : select_next returns QueueSelection
-AsyncTaskScheduler --> TaskAdmissionController : try_acquire(selection.item)
+AsyncTaskScheduler --> TaskAdmissionController : try_acquire(selection.item, queue.view)
 TaskAdmissionController ..> TaskAdmissionConfig : reads config
 
 note right of SchedulingMetadata

From ad7bb877b6c181afcba8732e32ee0980a685266f Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 12:54:14 -0400
Subject: [PATCH 08/33] docs: add async scheduling source-of-truth plan

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 plans/645/AsyncSchedulingEpicIssueMap.png     | Bin 28601 -> 31055 bytes
 ...ncSchedulingRequestAdmissionClassModel.png | Bin 70268 -> 70505 bytes
 ...AsyncSchedulingTaskAdmissionClassModel.png | Bin 62306 -> 76776 bytes
 plans/645/README.md                           |  18 +-
 plans/645/architecture.md                     |  79 ++++++++
 plans/645/async-scheduling-epic.puml          |  34 +++-
 plans/645/benchmark-plan.md                   | 160 +++++++++++++++
 plans/645/capacity-model.md                   |  75 +++++++
 plans/645/contracts.md                        | 189 ++++++++++++++++++
 plans/645/issue-map.md                        |  67 +++++++
 plans/645/migration-and-cleanup.md            | 106 ++++++++++
 plans/645/observability.md                    | 151 ++++++++++++++
 plans/645/request-admission.md                | 118 +++++++++++
 plans/645/task-admission.md                   | 133 ++++++++++++
 14 files changed, 1124 insertions(+), 6 deletions(-)
 create mode 100644 plans/645/architecture.md
 create mode 100644 plans/645/benchmark-plan.md
 create mode 100644 plans/645/capacity-model.md
 create mode 100644 plans/645/contracts.md
 create mode 100644 plans/645/issue-map.md
 create mode 100644 plans/645/migration-and-cleanup.md
 create mode 100644 plans/645/observability.md
 create mode 100644 plans/645/request-admission.md
 create mode 100644 plans/645/task-admission.md

diff --git a/plans/645/AsyncSchedulingEpicIssueMap.png b/plans/645/AsyncSchedulingEpicIssueMap.png
index b18e095337092a737ff5a0e59633bcd3d0dacc50..28f230c8cdaee5a9b9a7c2aa0fe75d0d263a7d78 100644
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diff --git a/plans/645/AsyncSchedulingRequestAdmissionClassModel.png b/plans/645/AsyncSchedulingRequestAdmissionClassModel.png
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diff --git a/plans/645/AsyncSchedulingTaskAdmissionClassModel.png b/plans/645/AsyncSchedulingTaskAdmissionClassModel.png
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diff --git a/plans/645/README.md b/plans/645/README.md
index 6875e083d..d5a7acf31 100644
--- a/plans/645/README.md
+++ b/plans/645/README.md
@@ -1,6 +1,20 @@
-# Async Scheduling Epic UML
+# Async Scheduling Architecture Plan
 
-Reference diagrams for the async scheduling epic tracked by issue 645. These diagrams are intentionally design artifacts for future implementation PRs, not runtime documentation.
+Source-of-truth architecture plan for the async scheduling epic tracked by issue 645. The UML file is the visual index; the Markdown files in this directory are the durable spec. GitHub issues should point back here and focus on implementation sequencing, quality gates, tests, and evidence.
+
+If an issue body and this plan disagree, update this plan first, then adjust the issue to reference the corrected section.
+
+## Spec
+
+- [Architecture](architecture.md): target system shape, ownership boundaries, invariants, and non-goals.
+- [Contracts](contracts.md): durable DTO, protocol, event, and config names.
+- [Capacity model](capacity-model.md): layered capacity vocabulary and ownership.
+- [Task admission](task-admission.md): scheduler-owned ready selection, task leases, policy hooks, bounded borrowing, and resource-vector direction.
+- [Request admission](request-admission.md): model-call admission, AIMD controller shape, dynamic request semantics, and no legacy throttle names.
+- [Observability](observability.md): scheduler events, request events, runtime correlation, snapshots, and cardinality rules.
+- [Benchmark plan](benchmark-plan.md): scenarios, metrics, A/B baselines, and required artifacts.
+- [Migration and cleanup](migration-and-cleanup.md): legacy-name removal, grep gates, and no-shim rules.
+- [Issue map](issue-map.md): how the GitHub issues map to this source-of-truth plan.
 
 ## Source
 
diff --git a/plans/645/architecture.md b/plans/645/architecture.md
new file mode 100644
index 000000000..0e9ffe3e4
--- /dev/null
+++ b/plans/645/architecture.md
@@ -0,0 +1,79 @@
+# Async Scheduling Architecture
+
+This plan moves Data Designer's async engine from implicit scheduling behavior to explicit, layered admission control. The target architecture separates static generator resource metadata, dependency readiness, ready-work ordering, scheduler-level task admission, concrete model-request admission, capacity diagnostics, and runtime observability.
+
+The guiding rule is: each layer owns one question and speaks through typed boundaries.
+
+## Source Of Truth
+
+The Markdown files in `plans/645` are the source of truth for this epic. The UML in [async-scheduling-epic.puml](async-scheduling-epic.puml) is the visual index and must be kept aligned with these files. GitHub issues should reference this plan and own implementation sequencing, validation commands, acceptance gates, and PR-level evidence.
+
+## Target Shape
+
+The durable runtime flow is:
+
+```text
+ColumnGenerator / plugin
+  -> SchedulingMetadata
+  -> TaskSchedulingResolver
+  -> CompletionTracker
+  -> AsyncTaskScheduler
+  -> FairTaskQueue.select_next(TaskAdmissionController.is_eligible)
+  -> TaskAdmissionController.try_acquire(selection.item)
+  -> TaskAdmissionLease
+  -> ModelRequestExecutor
+  -> RequestAdmissionController.acquire_async(RequestAdmissionItem)
+  -> RequestAdmissionLease
+  -> provider/model endpoint
+```
+
+This is not a passive pipeline where `FairTaskQueue` or `TaskAdmissionController` pushes work into the scheduler. `AsyncTaskScheduler` is the execution owner. It asks the readiness tracker for work, asks the queue to select a candidate through an admission eligibility callback, asks the task admission controller for a lease, commits the queue selection, executes the task, and releases the lease.
+
+## Layer Responsibilities
+
+`SchedulingMetadata` is a generator-facing static resource declaration. It describes the resource shape a generator expects, such as local work or model-backed work. It does not expose queue internals, admitted limits, request domains, AIMD state, or runtime pressure.
+
+`TaskSchedulingResolver` is the internal bridge from generator metadata to scheduler inputs. It produces `ResolvedTaskScheduling`, including `TaskGroupSpec` and `SchedulerResourceRequest`, and appends scheduler-owned flow identity such as output columns. It is the only scheduler grouping bridge once the legacy resolver is removed.
+
+`CompletionTracker` owns dependency readiness. It reports the ready frontier and completion state. It does not order ready work, admit resources, or inspect provider/model pressure.
+
+`FairTaskQueue` owns ready-work membership and ordering. Its selection operation is non-mutating and takes an eligibility callback supplied by scheduler admission. It does not own dependency readiness, admitted counts, provider metadata, request admission, or policy state.
+
+`TaskAdmissionController` owns scheduler-level task leases and resource accounting. `TaskAdmissionPolicy` decides whether a queued task is eligible under the current queue and admission views. The controller consumes resolved scheduler inputs and must not inspect generators, configs, model registries, or provider registries directly.
+
+`AsyncTaskScheduler` owns runtime control flow. It wires readiness, queue selection, task admission, worker spawn, task execution, salvage/retry behavior, shutdown, and lease release.
+
+`ModelRequestExecutor` is the durable model-call boundary. It maps each concrete provider/model/domain call to a `RequestAdmissionItem`, acquires a request lease, calls the provider, records request timing, and releases that exact lease on success, rate limit, failure, cancellation, or unexpected exception.
+
+`RequestAdmissionController` owns request-level provider/model/domain admission. `AdaptiveRequestAdmissionController` is the V1 AIMD-backed implementation. Internal `RequestFairQueue`, `RequestAdmissionPolicy`, and `AdaptiveRequestLimitState` are implementation components of this controller, not a second public layer.
+
+`SchedulerAdmissionEventSink` and `RequestAdmissionEventSink` observe their own layers separately. `RuntimeCorrelationProvider` supplies shared runtime context, and `CorrelatedRuntimeView` joins timelines without collapsing the two telemetry systems.
+
+## Two-Stage Admission
+
+Task admission controls when ready dataset work may become a running worker. Request admission controls concrete provider/model/domain calls at the moment they are made.
+
+The split is required because arbitrary custom Python can make zero, one, or many model calls dynamically. A task's metadata may help group and schedule the task, but it is not a promise of exact request count and must not reserve every future model call up front.
+
+Task admission may later consume request pressure snapshots as read-only policy input. It must not pre-acquire request permits, emulate AIMD, or wrap provider/model/domain request admission.
+
+## Core Invariants
+
+- Scheduler-level work is not spawned until `TaskAdmissionController` returns a `TaskAdmissionLease`.
+- `FairTaskQueue.select_next(...)` does not remove work or mutate virtual-time state. `commit(selection)` is the only queue operation that removes the selected task.
+- If `try_acquire(...)` succeeds but `commit(selection)` fails, the scheduler releases the task lease before retrying.
+- Every task lease and request lease is released exactly once in all success, failure, retry, cancellation, shutdown, and salvage paths.
+- Root/from-scratch work uses the same ready queue and task-admission path as downstream work.
+- Request admission happens only at concrete model-call time through `ModelRequestExecutor`.
+- Scheduler telemetry and request telemetry remain independently useful when the other subsystem is disabled.
+- Capacity and benchmark artifacts must distinguish dependency readiness, ready ordering, scheduler admission wait, request admission wait, provider execution, cooldown/rate-limit behavior, and task completion.
+
+## Non-Goals
+
+- Do not collapse task admission and request admission into one subsystem.
+- Do not expose scheduler internals as plugin API.
+- Do not put provider retry, cooldown, or AIMD behavior into `AsyncTaskScheduler` or `TaskAdmissionController`.
+- Do not put DAG readiness, row-group lifecycle, or task ordering into `RequestAdmissionController`.
+- Do not configure OpenTelemetry SDKs or exporters in core runtime.
+- Do not add public capacity knobs before benchmark evidence and docs justify them.
+- Do not keep durable compatibility shims or aliases for replaced scheduler/request-admission names at epic completion.
diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index c363f87f7..fb52b2ffa 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -123,6 +123,17 @@ package "Task scheduling contracts" {
     +view()
   }
 
+  class QueueSelection {
+    +item
+    +sequence
+  }
+
+  class QueueView {
+    +queued_total
+    +queued_by_group
+    +has_queued_peer(group)
+  }
+
   interface TaskAdmissionPolicy {
     +is_eligible(item, queue, view)
     +on_acquire(lease)
@@ -138,12 +149,18 @@ package "Task scheduling contracts" {
   }
 
   class TaskAdmissionController {
-    +is_eligible(item, view)
-    +try_acquire(item, view)
+    +is_eligible(item, queue_view)
+    +try_acquire(item, queue_view)
     +release(lease)
     +view()
   }
 
+  class TaskAdmissionView {
+    +resources_available
+    +leased_by_group
+    +running_by_group
+  }
+
   class TaskAdmissionDecision {
     +lease
     +denied_reason
@@ -175,10 +192,13 @@ ResolvedTaskScheduling --> TaskGroupSpec
 ResolvedTaskScheduling --> SchedulerResourceRequest
 ResolvedTaskScheduling --> SchedulableTask
 CompletionTracker --> FairTaskQueue : ready tasks
+FairTaskQueue --> QueueSelection : returns
+FairTaskQueue --> QueueView : view()
 FairTaskQueue --> TaskAdmissionController : is_eligible(candidate)
 TaskAdmissionController --> TaskAdmissionPolicy
 StrictFairTaskAdmissionPolicy ..|> TaskAdmissionPolicy
 BoundedBorrowTaskAdmissionPolicy ..|> TaskAdmissionPolicy
+TaskAdmissionController --> TaskAdmissionView
 TaskAdmissionController --> TaskAdmissionDecision
 TaskAdmissionDecision --> TaskAdmissionLease
 TaskAdmissionLease --> SchedulableTask
@@ -267,7 +287,7 @@ package "Request admission contracts" {
     +release_success(lease)
     +release_rate_limited(lease, retry_after)
     +release_failure(lease)
-    +pressure_snapshots()
+    +pressure
   }
 
   class AdaptiveRequestAdmissionController {
@@ -547,6 +567,7 @@ component "issue 635\nRequest admission telemetry" as I635
 component "issue 647\nScheduler admission telemetry" as I647
 component "issue 648\nCorrelation" as I648
 component "issue 649\nBenchmark harness" as I649
+component "issue 660\nArchitecture docs" as I660
 component "issue 650\nBounded borrow task policy" as I650
 component "issue 651\nResource-vector design" as I651
 
@@ -570,6 +591,11 @@ I657 --> I648
 I635 --> I648
 I647 --> I648
 I644 --> I649
+I652 --> I660
+I654 --> I660
+I657 --> I660
+I648 --> I660
+I649 --> I660
 I649 --> I650
 I644 --> I650
 I641 --> I651
@@ -582,7 +608,7 @@ note bottom
   Native GitHub subissue order:
   issue 641 -> issue 646 -> issue 653 -> issue 652 -> issue 644
   -> issue 654 -> issue 657 -> issue 635 -> issue 647
-  -> issue 648 -> issue 649 -> issue 650 -> issue 651
+  -> issue 648 -> issue 649 -> issue 660 -> issue 650 -> issue 651
 end note
 
 @enduml
diff --git a/plans/645/benchmark-plan.md b/plans/645/benchmark-plan.md
new file mode 100644
index 000000000..d5615656e
--- /dev/null
+++ b/plans/645/benchmark-plan.md
@@ -0,0 +1,160 @@
+# Benchmark Plan
+
+The benchmark harness turns architecture claims into reusable evidence. It prevents each implementation PR from inventing one-off scripts and makes fairness/throughput tradeoffs explicit.
+
+## Harness Requirements
+
+Provide a repo-local benchmark entrypoint that can compare two refs or checkouts.
+
+Required inputs:
+
+- baseline ref
+- candidate ref
+- scenario
+- record count
+- buffer size
+- row-group concurrency
+- task admission capacity
+- request latency knobs
+- warmups
+- measured iterations
+- output directory
+- seed
+
+Required artifacts:
+
+- JSON and CSV outputs
+- concise Markdown summary
+- baseline and candidate commit SHAs
+- command lines
+- machine/runtime information
+- environment knobs
+- `AsyncCapacityPlan`
+- per-layer observed maxima
+- completion timeline
+- ready-idle/utilization timeline
+- deterministic output hashes where applicable
+
+The sync path can be used as a correctness/hash oracle, not as the timing baseline for async scheduling policy.
+
+## Scenario Matrix
+
+### Queue And Admission Microbench
+
+Compare old `admit_next + release` behavior with new `select_next + try_acquire + commit + release` behavior.
+
+Matrix:
+
+- task counts: 1k, 10k, 100k
+- group counts: 1, 8, 64, 256
+- resource mixes: local only, resource-bound, mixed local/resource-bound, stateful/exclusive if included
+
+Metrics:
+
+- p50/p95 admission cycle cost
+- enqueue/select/acquire/commit/release breakdown
+- total CPU time
+- peak memory
+- scaling by group count
+
+### Heavy-Root Downstream Benchmark
+
+Required shape:
+
+```text
+true_from_scratch_root_slow -> downstream_fast
+```
+
+Optional secondary shape:
+
+```text
+seed -> root_slow -> downstream_fast
+```
+
+Required metrics:
+
+- first downstream-ready time
+- first downstream-dispatch time
+- ready-but-not-running gap
+- root over-admission debt after first downstream-ready timestamp
+- time to first completed record
+- time to 50 percent completed records
+- p95 row completion time
+- final wall time
+- max ready-idle gap by group/resource
+- active-capacity integral
+
+This scenario must exercise true root/from-scratch dispatch, not only downstream slow tasks.
+
+### Hidden-Waiter Proof
+
+After task admission lands:
+
+```text
+max(spawned_waiting_for_llm_lease) == 0
+```
+
+Required monotonic timeline fields:
+
+- selected_at
+- lease_acquired_at
+- worker_spawned_at
+- model_request_started_at
+- model_request_completed_at
+- lease_released_at
+
+Scheduler events own selected/lease/spawn/release. Request/model instrumentation owns model request start/complete.
+
+### Idle And Utilization Proxy
+
+Use mock endpoint pools with request start/end events.
+
+Metrics:
+
+- active-capacity integral
+- max ready-idle gap while work is available
+- initial idle gap after first downstream-ready task
+
+### End-To-End A/B Timing
+
+Run paired A/B trials with warmup and at least five measured iterations for:
+
+- narrow serial workflow
+- wide independent roots
+- dual model generate-to-judge workflow
+- heavy-root workflow
+- dynamic request-count custom generator workflow
+
+### Request Dynamic-Call Benchmark
+
+Use custom generators that make zero, one, and many model calls per task, including branch-dependent request counts.
+
+Metrics:
+
+- request admission acquire/release overhead
+- queue wait
+- event emission overhead
+- emitted event count
+- CPU time
+- memory
+- end-to-end timing
+
+## Baselines
+
+| Consumer | Baseline | Candidate |
+| --- | --- | --- |
+| Task admission | `origin/main` or the implementation PR merge-base before `TaskAdmissionController` | task-admission PR |
+| Bounded borrow | accepted lease-only task-admission SHA | bounded-borrow PR |
+| Resource vector | accepted bounded-borrow SHA or named policy baseline | resource-vector policy PR |
+
+## Evidence Thresholds
+
+Neutral scenarios should be no worse than 5 percent mean wall time unless the PR explicitly justifies a fairness/utilization tradeoff.
+
+Heavy-root scenarios should show reduced downstream ready-to-dispatch lag versus the named baseline when the candidate claims to improve heavy-root behavior.
+
+Every run must show no permit leaks and deterministic output equality where applicable.
+
+## CI Smoke
+
+The harness should have a small deterministic smoke mode using mock endpoints. It writes machine-readable artifacts and does not require live providers.
diff --git a/plans/645/capacity-model.md b/plans/645/capacity-model.md
new file mode 100644
index 000000000..af1ff60f2
--- /dev/null
+++ b/plans/645/capacity-model.md
@@ -0,0 +1,75 @@
+# Capacity Model
+
+The async engine uses layered capacity. Each layer has a different owner and meaning. The epic goal is to make these layers visible, non-overlapping, and traceable in runtime artifacts.
+
+## Layer Vocabulary
+
+| Layer | Owner | Meaning |
+| --- | --- | --- |
+| Engine selection | Dataset builder / interface | Selects async or sync execution. Not a capacity control. |
+| Record window | Dataset builder | Controls row grouping, checkpoint granularity, and memory shape. |
+| Row-group admission | Async scheduler | Bounds row groups in flight. |
+| Task-stage admission | `TaskAdmissionController` | Bounds scheduler-spawned work and scheduler-level resource pressure. |
+| Request-stage admission | `RequestAdmissionController` | Bounds concrete provider/model/domain requests when they are made. |
+| Static provider cap | Model config / metadata | User-declared provider/model upper bound and scheduling weight source. |
+| Adaptive provider cap | `AdaptiveRequestAdmissionController` | Runtime AIMD limit under the static provider cap. |
+| Transport pool | HTTP/model client adapter | Socket/session pool sizing. Not scheduling or fairness policy. |
+
+## AsyncCapacityPlan
+
+`AsyncCapacityPlan` is the run-level explanation of capacity. It should record:
+
+- `buffer_size`
+- row-group concurrency
+- task admission capacity
+- task resource limits
+- request admission resources
+- static provider/model caps used by the workflow
+- adaptive request-admission config
+- transport/session pool values if they remain distinct
+- source of each value, such as default constant, model metadata, run config, request admission state, or environment selection
+
+The plan is emitted for diagnostics, traces, benchmarks, and operator documentation. It does not admit work by itself.
+
+## Ownership Rules
+
+Task admission capacity is scheduler-level capacity. It controls when a ready task can become a running worker.
+
+Request admission capacity is provider/model/domain request capacity. It controls when a concrete model call can execute.
+
+`max_parallel_requests` remains the user-facing static provider/model cap and scheduling metadata weight source. `AdaptiveRequestAdmissionController.current_limit` is the runtime adaptive request cap.
+
+HTTP transport pools may be larger than the static provider cap. They are transport sizing, not effective request concurrency.
+
+`DATA_DESIGNER_ASYNC_ENGINE` is an execution path selector. It is not a capacity knob.
+
+`RunConfig.buffer_size` shapes record windows and row groups. It is not a request-concurrency knob.
+
+## Transitional Values
+
+Any current `max_llm_wait_tasks`, `needs_llm_wait`, or `held_llm_wait` concept is transitional. At epic completion these names must either be gone or replaced by explicit scheduler-resource terminology in `TaskAdmissionConfig` and `AsyncCapacityPlan`.
+
+If a distinct task-stage LLM backpressure resource remains, it must be derived from actually used resolved scheduling metadata, not every registered model alias. It must be described as scheduler task-stage pressure, not provider request concurrency.
+
+## Alias And Provider Semantics
+
+Scheduling metadata may use model aliases to derive static resource identity and weight. Alias metadata should deduplicate aliases that resolve to the same provider/model/generation resource before summing weight.
+
+Request admission resources are provider/model/domain scoped. A provider/model may have a global effective static cap while each request domain has its own adaptive state. The capacity plan must make that distinction visible.
+
+V1 does not define a cross-domain aggregate AIMD provider cap beyond the documented provider/model effective static cap unless a later issue explicitly adds that policy.
+
+## Observability Requirements
+
+Operators should be able to answer:
+
+- Which capacity values were used for this run?
+- Was progress limited by dependency readiness, queue ordering, task admission, request admission, provider cooldown, or provider execution?
+- What static provider caps and adaptive request limits were active?
+- Were transport pools distinct from request caps?
+
+Benchmarks and traces must include `AsyncCapacityPlan` plus per-layer observed maxima.
+
+## Public Knob Rule
+
+Do not add a new public capacity knob until benchmark evidence shows a specific need and the docs explain its layer. Prefer clear defaults, internal configs, and diagnostics first.
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
new file mode 100644
index 000000000..5e35a73fe
--- /dev/null
+++ b/plans/645/contracts.md
@@ -0,0 +1,189 @@
+# Contracts
+
+This file records the durable names and semantics used by the async scheduling architecture. The exact implementation can evolve, but these names are the spec vocabulary for the epic.
+
+## Metadata Contracts
+
+`ColumnGenerator.get_scheduling_metadata()` returns generator-facing scheduling metadata. It is additive and non-abstract so existing generators keep working.
+
+`SchedulingMetadata` is a static declaration with:
+
+- `kind`: initial values are `local`, `model`, and `custom_model`.
+- `identity`: deterministic tuple of broad-to-specific resource identity values.
+- `weight`: positive static capacity hint.
+
+`SchedulingMetadataError` is the typed failure path for metadata resolution. It can carry fallback metadata when partial resolution is safe.
+
+Rules:
+
+- Metadata identity is resource identity, not a queue key.
+- Metadata cannot encode queue depth, admitted limits, runtime pressure, request domains, AIMD state, or provider cooldown.
+- Multi-alias metadata deduplicates aliases that resolve to the same provider/model/generation resource before summing weight.
+- Alias ordering is canonicalized so equivalent configs produce equivalent metadata.
+
+## Scheduler Input Contracts
+
+`TaskSchedulingResolver` consumes `SchedulingMetadata` and produces scheduler-internal inputs. It owns per-run metadata caching and scheduler flow-identity composition.
+
+`ResolvedTaskScheduling` contains:
+
+- `group: TaskGroupSpec`
+- `resource_request: SchedulerResourceRequest`
+
+`TaskGroupSpec` contains a scheduler-internal task group key and static weight.
+
+`SchedulerResourceRequest` contains scheduler-level task-stage resources:
+
+```text
+amounts: Mapping[SchedulerResourceKey, int]
+```
+
+The first implementation can model submission and LLM-wait style resources. Future resource-vector work may add provider/model, local, GPU, or other scheduler resources, but those remain scheduler-internal unless a later design explicitly changes the public contract.
+
+`SchedulableTask` contains:
+
+- task payload
+- task group
+- scheduler resource request
+
+## Queue Contracts
+
+`FairTaskQueue` owns ready-task membership and ready ordering:
+
+```text
+enqueue(item)
+select_next(is_eligible) -> QueueSelection | None
+commit(selection) -> SchedulableTask | None
+view() -> QueueView
+```
+
+`QueueSelection` returns from `FairTaskQueue` to `AsyncTaskScheduler`. It is not delivered to `TaskAdmissionController`.
+
+`QueueSelection` contains the selected item and an opaque sequence/version used by `commit(selection)` to detect stale selections.
+
+`QueueView` is read-only policy input. It exposes queued totals and queued counts by group, including whether a group has queued peers.
+
+## Task Admission Contracts
+
+`TaskAdmissionController` owns task-stage resource accounting and leases:
+
+```text
+is_eligible(item, queue_view) -> bool
+try_acquire(item, queue_view) -> TaskAdmissionLease | None
+release(lease)
+view() -> TaskAdmissionView
+```
+
+`TaskAdmissionPolicy` owns the decision rule:
+
+```text
+is_eligible(item, queue_view, admission_view) -> bool
+on_acquire(lease)
+on_release(lease)
+```
+
+`TaskAdmissionLease` contains:
+
+- `item`
+- `resources`
+- `acquired_at`
+
+`TaskAdmissionView` exposes task-stage resource availability plus leased/running counts by group.
+
+`TaskAdmissionDecision` is the richer decision shape for implementations that need denial reasons. V1 may expose `try_acquire(...)` as a non-blocking `TaskAdmissionLease | None` helper, but the spec vocabulary treats denials as typed scheduler-admission outcomes for events, tests, and debugging.
+
+`TaskAdmissionConfig` contains scheduler task-stage capacity values such as `submission_capacity` and resource limits.
+
+## Request Admission Contracts
+
+`ModelRequestExecutor` maps concrete model calls into request-admission items and owns exact lease release around provider execution.
+
+`RequestResourceKey` identifies a concrete provider/model/domain request resource:
+
+- `provider_name`
+- `model_id`
+- `domain`
+
+`RequestGroupSpec` contains the request resource key and static weight.
+
+`RequestAdmissionItem` contains:
+
+- request resource
+- request group
+- optional timeout
+
+`RequestAdmissionController` owns request-level admission:
+
+```text
+try_acquire(item) -> RequestAdmissionDecision
+acquire_sync(item) -> RequestAdmissionLease
+acquire_async(item) -> RequestAdmissionLease
+release_success(lease)
+release_rate_limited(lease, retry_after)
+release_failure(lease)
+pressure -> RequestPressureSnapshotProvider
+```
+
+`RequestAdmissionDecision` is a union of `RequestAdmissionLease` and `RequestAdmissionDenied`.
+
+`RequestAdmissionLease` contains:
+
+- item
+- acquired timestamp
+- current adaptive limit
+- effective max
+
+`RequestAdmissionDenied` contains:
+
+- item
+- reason
+- retry-after or available-after timing where applicable
+- optional snapshot
+
+`AdaptiveRequestAdmissionController` is the V1 concrete request controller. It owns AIMD behavior through internal `RequestFairQueue`, `RequestAdmissionPolicy`, and `AdaptiveRequestLimitState`.
+
+`RequestPressureSnapshotProvider` exposes read-only request pressure:
+
+```text
+snapshot(resource)
+snapshots()
+global_snapshot(provider, model)
+global_snapshots()
+```
+
+It has no mutation or admission methods.
+
+`RequestAdmissionConfig` is the durable request-admission tuning/config vocabulary. `ThrottleConfig` and `RunConfig.throttle` are not durable names.
+
+## Telemetry And Correlation Contracts
+
+`SchedulerAdmissionEventSink` emits scheduler admission events.
+
+`RequestAdmissionEventSink` emits request admission events.
+
+`RuntimeCorrelation` contains primitive runtime context:
+
+- run id
+- row group
+- task column
+- task type
+- scheduling group kind
+- scheduling group identity hash
+
+`RuntimeCorrelationProvider` owns set/reset/current behavior, likely through context variables. It must not require request admission protocols to import scheduler types.
+
+`CorrelatedRuntimeView` joins scheduler and request timelines for diagnostics, benchmarks, and future operator views.
+
+## Capacity Contracts
+
+`AsyncCapacityPlan` records computed per-run capacity values:
+
+- buffer size
+- row-group concurrency
+- submission capacity
+- task admission config
+- request admission config
+- static provider/model caps when available
+- source of each value
+
+The capacity plan explains observed runtime behavior. It is not itself a policy engine.
diff --git a/plans/645/issue-map.md b/plans/645/issue-map.md
new file mode 100644
index 000000000..d321b2364
--- /dev/null
+++ b/plans/645/issue-map.md
@@ -0,0 +1,67 @@
+# Issue Map
+
+This file maps GitHub issues to the source-of-truth plan sections. Issues should reference these files and own implementation/quality gates rather than restating the full architecture.
+
+## Source-Of-Truth Rule
+
+Use this directory for architecture, contracts, naming, invariants, and cross-cutting decisions.
+
+Use GitHub issues for:
+
+- implementation slices
+- dependencies and target branch
+- acceptance criteria for that slice
+- tests and validation commands
+- benchmark evidence required by that slice
+- PR-specific cleanup gates
+
+## Issues
+
+| Issue | Implementation Focus | Source Sections |
+| --- | --- | --- |
+| #641 | Add `SchedulingMetadata` and generator override contract | [architecture.md](architecture.md), [contracts.md](contracts.md), [task-admission.md](task-admission.md) |
+| #646 | Ingest metadata into scheduler grouping through `TaskSchedulingResolver` | [architecture.md](architecture.md), [contracts.md](contracts.md), [task-admission.md](task-admission.md) |
+| #653 | Remove legacy hint resolver path | [migration-and-cleanup.md](migration-and-cleanup.md), [contracts.md](contracts.md) |
+| #652 | Document plugin-facing metadata behavior | [architecture.md](architecture.md), [contracts.md](contracts.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
+| #644 | Implement task admission lease boundary | [task-admission.md](task-admission.md), [contracts.md](contracts.md), [benchmark-plan.md](benchmark-plan.md) |
+| #654 | Implement and document capacity vocabulary and snapshots | [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md) |
+| #657 | Refactor model-call throttling into request admission | [request-admission.md](request-admission.md), [contracts.md](contracts.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
+| #635 | Instrument request admission state | [observability.md](observability.md), [request-admission.md](request-admission.md), [contracts.md](contracts.md) |
+| #647 | Instrument scheduler admission state | [observability.md](observability.md), [task-admission.md](task-admission.md), [contracts.md](contracts.md) |
+| #648 | Correlate scheduler and request observability | [observability.md](observability.md), [architecture.md](architecture.md) |
+| #649 | Build reusable benchmark harness | [benchmark-plan.md](benchmark-plan.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md) |
+| #660 | Produce final user/operator docs | [architecture.md](architecture.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
+| #650 | Implement bounded-borrow task policy | [task-admission.md](task-admission.md), [benchmark-plan.md](benchmark-plan.md), [capacity-model.md](capacity-model.md) |
+| #651 | Design resource-vector/provider-aware policy | [task-admission.md](task-admission.md), [capacity-model.md](capacity-model.md), [benchmark-plan.md](benchmark-plan.md) |
+
+## Dependency Order
+
+The implementation order remains:
+
+```text
+#641 -> #646 -> #653 -> #652 -> #644 -> #654 -> #657
+-> #635 -> #647 -> #648 -> #649 -> #660 -> #650 -> #651
+```
+
+#641 and #644 may proceed independently only while #646 preserves the adapter contract between them. The accepted end state is `SchedulingMetadata` feeding task admission through `TaskSchedulingResolver`.
+
+## Issue Body Cleanup Pattern
+
+When revising issue bodies, keep:
+
+- priority
+- dependency metadata
+- target branch
+- short problem statement
+- links to the relevant plan sections
+- implementation checklist
+- tests and validation commands
+- evidence requirements
+- acceptance criteria specific to the slice
+
+Remove or shorten:
+
+- duplicated contract definitions
+- duplicated architecture diagrams
+- broad cross-cutting non-goals already captured here
+- stale naming decisions superseded by this plan
diff --git a/plans/645/migration-and-cleanup.md b/plans/645/migration-and-cleanup.md
new file mode 100644
index 000000000..6d21bfb72
--- /dev/null
+++ b/plans/645/migration-and-cleanup.md
@@ -0,0 +1,106 @@
+# Migration And Cleanup
+
+The epic is not complete until replaced names and compatibility paths are removed from production code and current docs.
+
+## Scheduling Metadata Cleanup
+
+Remove or collapse the legacy `SchedulingHintResolver` path after `SchedulingMetadata` and `TaskSchedulingResolver` are stable.
+
+Accepted end states:
+
+- delete `SchedulingHintResolver`, or
+- refactor/rename it into a metadata-oriented adapter where all model/provider inference lives behind `ColumnGenerator.get_scheduling_metadata()` and typed `SchedulingMetadataError` fallback behavior.
+
+Unacceptable end state:
+
+- a parallel fallback that independently introspects generators, configs, model registries, aliases, or admitted policy data under the old resolver contract.
+
+Final search gate should have no production/current-doc matches for:
+
+```text
+SchedulingHintResolver
+SchedulingHint
+_model_aliases_for_generator
+```
+
+Independent scheduler-side `is_llm_bound` fallback is also migration-only and should be folded behind metadata/resource requests by epic completion.
+
+## Request Admission Cleanup
+
+The durable request-admission names are:
+
+- `ModelRequestExecutor`
+- `RequestAdmissionController`
+- `AdaptiveRequestAdmissionController`
+- `RequestAdmissionConfig`
+- `RunConfig.request_admission`
+- `RequestDomain`
+
+Final search gate should have no production/current-doc matches for:
+
+```text
+ThrottleManager
+ThrottleDomain
+ThrottleConfig
+RunConfig.throttle
+throttle_manager.py
+```
+
+Historical changelog text may remain only if it is clearly marked as historical and not presented as current API.
+
+## Task-Stage Wait Cleanup
+
+The durable architecture does not include:
+
+```text
+needs_llm_wait
+held_llm_wait
+max_llm_wait_tasks
+```
+
+If a scheduler-level resource remains for LLM-bound work, it must be represented through `SchedulerResourceRequest`, `TaskAdmissionConfig`, and `AsyncCapacityPlan`, with names that describe scheduler task-stage pressure rather than request concurrency.
+
+## Compatibility Shim Rule
+
+Do not leave production compatibility aliases, subclasses, adapters, reexports, docs paths, or durable tests for replaced names at epic completion.
+
+Temporary names may exist inside a PR only if the same PR removes them before merge.
+
+## Gate Semantics
+
+Before the migration issues land, stale-name matches can exist as current-state evidence.
+
+By #653 close, legacy scheduling-hint production paths are gone and tests have moved to metadata/resolver coverage.
+
+By #657 close, request-admission code has no production `Throttle*` aliases, exports, modules, or durable tests.
+
+By #645 close, public/current docs use only the durable architecture vocabulary. Historical changelog or dev-note text can remain only when explicitly marked historical.
+
+## Documentation Cleanup
+
+Current user/operator docs should use:
+
+- `SchedulingMetadata`
+- `TaskSchedulingResolver`
+- `TaskAdmissionController`
+- `TaskAdmissionPolicy`
+- `TaskAdmissionLease`
+- `ModelRequestExecutor`
+- `RequestAdmissionController`
+- `AdaptiveRequestAdmissionController`
+- `RequestAdmissionConfig`
+- `RuntimeCorrelationProvider`
+
+Docs must not present scheduler internals as plugin API. Plugin-facing docs should describe metadata only, then link to architecture docs for maintainers/operators.
+
+## Validation Commands
+
+Adjust paths as files move, but final PRs should include searches equivalent to:
+
+```bash
+rg "SchedulingHintResolver|SchedulingHint|_model_aliases_for_generator|is_llm_bound" packages docs fern
+rg "ThrottleManager|ThrottleDomain|ThrottleConfig|RunConfig\\.throttle|throttle_manager\\.py" packages docs fern
+rg "needs_llm_wait|held_llm_wait|max_llm_wait_tasks" packages docs fern
+```
+
+Any remaining hit must be intentionally historical, not a current implementation or docs path.
diff --git a/plans/645/observability.md b/plans/645/observability.md
new file mode 100644
index 000000000..9520dbc47
--- /dev/null
+++ b/plans/645/observability.md
@@ -0,0 +1,151 @@
+# Observability
+
+Observability must explain which layer is limiting progress without collapsing scheduler admission and request admission into one subsystem.
+
+## Separate Event Streams
+
+`SchedulerAdmissionEventSink` emits scheduler-owned task admission events.
+
+`RequestAdmissionEventSink` emits provider/model/domain request admission events.
+
+Both sinks are generic first. OpenTelemetry, structured logs, dashboards, benchmarks, and debug tools are adapters or consumers.
+
+Sink failures must never interrupt generation. Event data can be collected under locks, but event emission should happen after locks are released.
+
+## Scheduler Admission Events
+
+Scheduler events describe dependency-ready work moving through ready ordering, task admission, worker spawn, and task lease release.
+
+Representative event kinds:
+
+- ready enqueued
+- selected
+- lease acquired
+- worker spawned
+- admission blocked
+- group capped
+- stale selection
+- retry deferred
+- non-retryable dropped
+- cancelled
+- salvage redispatched
+- queue drained
+- lease released
+- completed
+
+Scheduler snapshots include:
+
+- queued total
+- queued by group
+- admitted/running by group
+- submission available
+- scheduler resources available
+
+Scheduler events must make `spawned_waiting_for_llm_lease` derivable and zero after the task-admission lease boundary lands.
+
+## Request Admission Events
+
+Request events describe provider/model/domain request admission and AIMD behavior.
+
+Representative event kinds:
+
+- registered
+- effective cap changed
+- queue formed
+- wait started
+- queue wait completed
+- queue wait timeout
+- queue drained
+- rate limited
+- limit decreased
+- limit increased
+- soft ceiling recovered
+- fully recovered
+- failure released
+
+Request snapshots include:
+
+- request resource
+- effective max
+- current limit
+- in-flight count
+- waiters
+- cooldown remaining
+- rate-limit ceiling
+- consecutive rate limits
+
+Global provider/model snapshots capture effective static caps and aliases.
+
+## Runtime Correlation
+
+`RuntimeCorrelationProvider` carries current task context while the scheduler executes a task. The likely implementation is a context variable with set/reset/current behavior.
+
+`RuntimeCorrelation` contains primitive values only:
+
+- run id
+- row group
+- task column
+- task type
+- scheduling group kind
+- scheduling group identity hash
+
+`ModelRequestExecutor` and event sinks read the current correlation context at event emission time. `AdaptiveRequestAdmissionController` remains keyed by provider/model/domain resources and does not import scheduler types.
+
+`CorrelatedRuntimeView` joins the timelines for diagnostics and benchmarks.
+
+## Joined Timeline
+
+The joined timeline should distinguish:
+
+```text
+dependency readiness
+ready enqueued
+selected by fair queue
+task lease acquired
+worker spawned
+request admission wait started
+request lease acquired
+provider request started
+provider request completed
+request lease released
+task completed
+task lease released
+```
+
+Runs should be diagnosable as limited by dependency readiness, ready-queue fairness, scheduler capacity, request-admission wait, provider cooldown/rate-limit behavior, transport/provider execution, or downstream completion.
+
+## Cardinality And Safety
+
+Metric-safe dimensions:
+
+- event kind
+- scheduler resource kind
+- request admission event kind
+- provider name
+- model id
+- request domain
+- algorithm
+
+Trace-only or sampled fields:
+
+- run id
+- row group
+- task column
+- task type
+- scheduling group identity hash
+- queued maps by group
+
+Never emit:
+
+- prompts
+- completions
+- row values
+- dataset records
+- secrets
+- raw provider response bodies
+- raw exception payloads
+- unbounded request IDs as metric labels
+
+## OpenTelemetry Rule
+
+Core runtime may provide an OTel bridge that depends on API-level primitives, but it must not configure OTel SDKs, exporters, or collectors. Applications embedding Data Designer own exporter configuration.
diff --git a/plans/645/request-admission.md b/plans/645/request-admission.md
new file mode 100644
index 000000000..ce2575683
--- /dev/null
+++ b/plans/645/request-admission.md
@@ -0,0 +1,118 @@
+# Request Admission
+
+Request admission controls concrete provider/model/domain calls at the moment they are made. It is separate from task admission because task-level scheduling cannot predict every model call inside arbitrary generator Python.
+
+## Runtime Shape
+
+```text
+ModelRequestExecutor
+  -> RequestAdmissionController.acquire_async(RequestAdmissionItem)
+  -> RequestAdmissionLease
+  -> provider/model endpoint
+  -> release_success | release_rate_limited | release_failure
+```
+
+`ModelRequestExecutor` is the durable model-call boundary. It maps each concrete call to a request resource, acquires a lease, calls the provider, records timing, and releases the exact lease.
+
+## Dynamic Requests
+
+Custom generators may make zero, one, or many model requests depending on row data, branches, retries, validation failures, tool calls, or helper functions. `SchedulingMetadata` can describe static resource shape for task grouping, but it is not an exact request-count promise.
+
+Therefore:
+
+- task admission must not pre-acquire request permits
+- request admission happens at concrete model-call time
+- each acquired request lease is released exactly once
+- request-level wait and provider execution timing remain visible separately
+
+## Durable Names
+
+The durable interface name is `RequestAdmissionController`.
+
+The durable V1 implementation name is `AdaptiveRequestAdmissionController`.
+
+The durable model-call boundary name is `ModelRequestExecutor`.
+
+The durable config vocabulary is `RequestAdmissionConfig` and `RunConfig.request_admission` if a run config surface exists.
+
+Do not keep production aliases, shims, subclasses, adapters, exports, docs paths, or durable tests for:
+
+- `ThrottleManager`
+- `ThrottleDomain`
+- `ThrottleConfig`
+- `RunConfig.throttle`
+- `throttle_manager.py`
+
+## Request Resource Model
+
+`RequestResourceKey` identifies:
+
+- provider name
+- model id
+- request domain
+
+`RequestDomain` includes domains such as chat, embedding, image, and healthcheck.
+
+`RequestAdmissionItem` contains resource, group, and optional timeout. `RequestGroupSpec` contains resource key and weight.
+
+`RequestAdmissionDecision` is `RequestAdmissionLease | RequestAdmissionDenied`.
+
+`RequestAdmissionLease` records the item, acquired timestamp, current adaptive limit, and effective max.
+
+`RequestAdmissionDenied` records item, reason, retry timing, availability timing, and optional snapshot.
+
+`RequestAdmissionController.pressure` exposes the read-only `RequestPressureSnapshotProvider`.
+
+## AdaptiveRequestAdmissionController
+
+`AdaptiveRequestAdmissionController` is the AIMD-backed request controller. It owns:
+
+- request fair queueing
+- request admission policy
+- adaptive request limit state
+- provider/model/domain in-flight counts
+- waiters
+- cooldown state
+- rate-limit cascades
+- additive increase and multiplicative decrease
+- request pressure snapshots
+
+Internal `RequestFairQueue`, `RequestAdmissionPolicy`, and `AdaptiveRequestLimitState` are part of the single canonical request-admission implementation. They are not a second public wrapper around request admission.
+
+## Release Classification
+
+`ModelRequestExecutor` releases the exact acquired lease through:
+
+- `release_success(lease)` after provider success
+- `release_rate_limited(lease, retry_after)` after provider rate-limit response
+- `release_failure(lease)` after non-rate-limit failure, cancellation, timeout, or unexpected exception
+
+The release path is responsible for exactly-once accounting. Key-only release paths are not durable.
+
+## Request Pressure Snapshots
+
+`RequestPressureSnapshotProvider` exposes read-only state to diagnostics, benchmarks, telemetry, and future task policies.
+
+Domain snapshots include:
+
+- request resource
+- effective max
+- current limit
+- in-flight count
+- waiters
+- blocked-until timing
+- cooldown remaining
+- rate-limit ceiling
+- consecutive rate limits
+
+Global snapshots include provider/model effective caps and aliases.
+
+Task admission may read these snapshots as advisory input in later policy work. It must not mutate request state or emulate request admission.
+
+## Non-Goals
+
+- Do not make request admission aware of DAG dependencies.
+- Do not make request admission own row-group lifecycle or ready-work ordering.
+- Do not replace AIMD with token-bucket or leaky-bucket behavior in V1.
+- Do not require static prediction of all model calls.
+- Do not make task-level `TaskAdmissionController` responsible for provider retry, cooldown, or AIMD updates.
diff --git a/plans/645/task-admission.md b/plans/645/task-admission.md
new file mode 100644
index 000000000..cf594a6d8
--- /dev/null
+++ b/plans/645/task-admission.md
@@ -0,0 +1,133 @@
+# Task Admission
+
+Task admission controls when dependency-ready dataset work may become a running worker. It is scheduler-level admission, not provider/model request admission.
+
+## Control Owner
+
+`AsyncTaskScheduler` is the control owner. Its dispatch loop follows this shape:
+
+```python
+selection = queue.select_next(admission.is_eligible)
+if selection is None:
+    wait_for_wake()
+    return
+
+lease = admission.try_acquire(selection.item, queue.view())
+if lease is None:
+    wait_for_wake()
+    return
+
+committed = queue.commit(selection)
+if committed is None:
+    admission.release(lease)
+    wake_dispatch_loop()
+    return
+
+spawn_worker(committed, lease)
+```
+
+`FairTaskQueue` selects candidates. `TaskAdmissionController` leases scheduler resources. The scheduler coordinates both.
+
+## Queue Semantics
+
+`FairTaskQueue` owns ready-work ordering only.
+
+Rules:
+
+- `select_next(...)` is non-mutating.
+- `select_next(...)` calls the eligibility callback with candidates and queue view.
+- `QueueSelection` returns to `AsyncTaskScheduler`.
+- `commit(selection)` removes the selected task and advances queue state.
+- The queue does not track admitted/running counts after this epic.
+- The queue does not inspect model registries, provider pressure, or request-admission state.
+
+## Admission Semantics
+
+`TaskAdmissionController` owns:
+
+- scheduler-resource availability
+- task-stage leases
+- admitted/running resource counts
+- per-group accounting used by policy
+- release on every worker terminal path
+
+`TaskAdmissionPolicy` owns:
+
+- eligibility decisions
+- acquisition/release policy callbacks
+- strict fair admission
+- bounded-borrow behavior
+- future resource-vector policy decisions
+
+`TaskAdmissionController` consumes `SchedulableTask`, `SchedulerResourceRequest`, `QueueView`, and `TaskAdmissionView`. It must not inspect `ColumnGenerator`, config layout, model registry, or provider registry directly.
+
+## V1 Lease Boundary
+
+The first task-admission implementation is lease-only and behavior-preserving. It centralizes resource ownership without changing fairness policy beyond what is required to eliminate hidden waiters and make root work visible.
+
+V1 includes:
+
+- submission capacity for scheduler-spawned work
+- task-stage LLM-wait style resource if a distinct scheduler-stage resource remains
+- current per-group admitted/running cap behavior
+
+V1 excludes:
+
+- row-group admission
+- concrete provider/model/domain request admission
+- public runtime knobs
+- distributed scheduling
+- token budgets
+- provider retry and AIMD behavior
+
+## Root And From-Scratch Work
+
+Root/from-scratch tasks must become `SchedulableTask`s and enter the same `FairTaskQueue` as downstream ready tasks. They must acquire scheduler-level leases through `TaskAdmissionController`.
+
+No root dispatch path should bypass:
+
+- ready queue membership
+- queue selection
+- task admission
+- lease release accounting
+- scheduler admission telemetry
+
+This is required for heavy-root live-traffic evidence and later bounded-borrow policy.
+
+## Resource Handoff
+
+Resource-bound work must not become a spawned worker that waits for scheduler-level resources. The lease is acquired before spawn.
+
+Non-resource-bound work holds the relevant scheduler lease until worker completion. Resource-bound work holds the scheduler resource lease that represents the V1 task-stage resource request. Durable `needs_llm_wait` and `held_llm_wait` fields are not part of the target architecture.
+
+## Bounded Borrow Policy
+
+`BoundedBorrowTaskAdmissionPolicy` is the first behavior-changing follow-up after the lease boundary. It limits how far one group may borrow ahead while no peer group is queued.
+
+Policy inputs:
+
+- `QueueView`: queued counts and peer pressure.
+- `TaskAdmissionView`: leased/running counts and resources available.
+- `TaskGroupSpec`: group key and weight.
+- candidate `SchedulerResourceRequest`.
+
+Policy constraints:
+
+- Single-group workloads remain live.
+- A solo heavy group may borrow only to its borrow ceiling.
+- When peer queue pressure exists and a group has borrow debt, that group receives no further admissions while an eligible peer has queued work and the required resource is available.
+- The policy must not traverse the DAG inside `FairTaskQueue`.
+- No public knob is added until benchmark evidence supports it.
+
+## Resource-Vector Direction
+
+Future policy work may use `SchedulerResourceKey` and `SchedulerResourceRequest` for multi-resource admission. Candidate resources include submission, LLM-wait, provider/model task-stage hints, local resources, and GPU slots if reliable metadata exists.
+
+Resource-vector policy must:
+
+- remain scheduler-internal unless a later design explicitly changes public metadata fields
+- consume resolved metadata from `TaskSchedulingResolver`
+- avoid duplicating provider/model/domain AIMD request admission
+- use `RequestPressureSnapshotProvider` only as read-only pressure input
+- preserve single-resource and single-group liveness
+- produce benchmark evidence through the benchmark harness

From 59dbb47a17c3e728b16ef156cf03607f98a723a7 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 13:57:19 -0400
Subject: [PATCH 09/33] docs: tighten async scheduling plan contracts

Resolve the second review pass over plans/645 by making the Markdown spec and UML source agree on task admission, request admission, capacity, telemetry, benchmark, migration, and issue-map contracts.

Key updates include canonical event names, richer AsyncCapacityPlan fields, request waiter and cancellation semantics, timed wakeups, retry/salvage lease ordering, and clearer public/internal documentation boundaries.

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 plans/645/AsyncSchedulingEpicComponent.png    | Bin 51739 -> 61721 bytes
 plans/645/AsyncSchedulingEpicIssueMap.png     | Bin 31055 -> 38107 bytes
 .../AsyncSchedulingEpicRuntimeSequence.png    | Bin 107275 -> 262691 bytes
 ...ncSchedulingRequestAdmissionClassModel.png | Bin 70505 -> 120203 bytes
 ...ncSchedulingSupportContractsClassModel.png | Bin 48023 -> 90775 bytes
 ...AsyncSchedulingTaskAdmissionClassModel.png | Bin 76776 -> 118400 bytes
 plans/645/README.md                           |  39 +-
 plans/645/architecture.md                     |  64 +-
 plans/645/async-scheduling-epic.puml          | 606 ++++++++++++++----
 plans/645/benchmark-plan.md                   |  95 ++-
 plans/645/capacity-model.md                   |  52 +-
 plans/645/contracts.md                        | 381 ++++++++++-
 plans/645/issue-map.md                        |  24 +-
 plans/645/migration-and-cleanup.md            |  25 +-
 plans/645/observability.md                    | 131 ++--
 plans/645/request-admission.md                | 121 +++-
 plans/645/task-admission.md                   |  79 ++-
 17 files changed, 1347 insertions(+), 270 deletions(-)

diff --git a/plans/645/AsyncSchedulingEpicComponent.png b/plans/645/AsyncSchedulingEpicComponent.png
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diff --git a/plans/645/AsyncSchedulingRequestAdmissionClassModel.png b/plans/645/AsyncSchedulingRequestAdmissionClassModel.png
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diff --git a/plans/645/AsyncSchedulingTaskAdmissionClassModel.png b/plans/645/AsyncSchedulingTaskAdmissionClassModel.png
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diff --git a/plans/645/README.md b/plans/645/README.md
index d5a7acf31..56d55fd7d 100644
--- a/plans/645/README.md
+++ b/plans/645/README.md
@@ -4,22 +4,35 @@ Source-of-truth architecture plan for the async scheduling epic tracked by issue
 
 If an issue body and this plan disagree, update this plan first, then adjust the issue to reference the corrected section.
 
+This directory is the maintainer source of truth while the epic is active. Issue 660 promotes the stabilized V1 content into current user/operator architecture docs and marks older pre-epic scheduling descriptions as historical or removes them.
+
 ## Spec
 
 - [Architecture](architecture.md): target system shape, ownership boundaries, invariants, and non-goals.
 - [Contracts](contracts.md): durable DTO, protocol, event, and config names.
 - [Capacity model](capacity-model.md): layered capacity vocabulary and ownership.
 - [Task admission](task-admission.md): scheduler-owned ready selection, task leases, policy hooks, bounded borrowing, and resource-vector direction.
-- [Request admission](request-admission.md): model-call admission, AIMD controller shape, dynamic request semantics, and no legacy throttle names.
+- [Request admission](request-admission.md): model-call admission, AIMD controller shape, dynamic request semantics, and replacement of pre-epic request-control names.
 - [Observability](observability.md): scheduler events, request events, runtime correlation, snapshots, and cardinality rules.
 - [Benchmark plan](benchmark-plan.md): scenarios, metrics, A/B baselines, and required artifacts.
 - [Migration and cleanup](migration-and-cleanup.md): legacy-name removal, grep gates, and no-shim rules.
 - [Issue map](issue-map.md): how the GitHub issues map to this source-of-truth plan.
 
+## Read This First
+
+Recommended reading paths:
+
+- Implementers: [Architecture](architecture.md), [Contracts](contracts.md), then the topic file for the issue being implemented.
+- Plugin documentation authors: [Contracts](contracts.md#metadata-contracts), [Architecture](architecture.md#audience-and-api-boundaries), and [Migration and cleanup](migration-and-cleanup.md#documentation-cleanup).
+- Operators and performance reviewers: [Capacity model](capacity-model.md), [Observability](observability.md), and [Benchmark plan](benchmark-plan.md).
+- Issue owners: [Issue map](issue-map.md), then the linked source sections for the issue.
+
 ## Source
 
 - [async-scheduling-epic.puml](async-scheduling-epic.puml): PlantUML source for every diagram on this page.
 
+The PNG files in this directory are generated review artifacts. The PlantUML file is authoritative for diagram source. Any PR that changes the UML should regenerate the PNGs and include them in the same diff, or explicitly state why rendering was unavailable.
+
 ## Component View
 
 ![Component view](AsyncSchedulingEpicComponent.png)
@@ -53,10 +66,26 @@ plantuml plans/645/async-scheduling-epic.puml
 The expected runtime control owner is `AsyncTaskScheduler`:
 
 ```text
-SchedulingMetadata -> TaskSchedulingResolver -> CompletionTracker
-AsyncTaskScheduler -> FairTaskQueue.select_next(...)
-AsyncTaskScheduler -> TaskAdmissionController.try_acquire(...)
-AsyncTaskScheduler -> ModelRequestExecutor -> RequestAdmissionController -> provider/model endpoint
+ColumnGenerator.get_scheduling_metadata()
+  -> SchedulingMetadata
+  -> TaskSchedulingResolver
+  -> ResolvedTaskScheduling
+  -> SchedulableTask inputs
+
+AsyncTaskScheduler
+  -> CompletionTracker.ready_frontier()
+  -> FairTaskQueue.enqueue(...)
+  -> FairTaskQueue.select_next(scheduler-owned eligibility callback)
+  -> TaskAdmissionController.try_acquire(...)
+  -> FairTaskQueue.commit(...)
+  -> execute admitted task/generator code
+
+Admitted task/generator code
+  -> model facade/provider boundary
+  -> ModelRequestExecutor.execute_attempt(...) per concrete request attempt
+  -> RequestAdmissionController.acquire_async(...)
+  -> provider/model endpoint
+  -> RequestAdmissionController.release(lease, outcome)
 ```
 
 Task admission and request admission each have explicit controller, queue, policy, and lease/state boundaries where applicable. Telemetry observes scheduler admission and request admission separately, then issue 648 correlates the two timelines through the runtime correlation provider.
diff --git a/plans/645/architecture.md b/plans/645/architecture.md
index 0e9ffe3e4..6ebfdf362 100644
--- a/plans/645/architecture.md
+++ b/plans/645/architecture.md
@@ -10,24 +10,39 @@ The Markdown files in `plans/645` are the source of truth for this epic. The UML
 
 ## Target Shape
 
-The durable runtime flow is:
+The durable data-preparation flow is:
 
 ```text
 ColumnGenerator / plugin
+  -> ColumnGenerator.get_scheduling_metadata()
   -> SchedulingMetadata
   -> TaskSchedulingResolver
-  -> CompletionTracker
-  -> AsyncTaskScheduler
-  -> FairTaskQueue.select_next(TaskAdmissionController.is_eligible)
-  -> TaskAdmissionController.try_acquire(selection.item)
-  -> TaskAdmissionLease
-  -> ModelRequestExecutor
+  -> ResolvedTaskScheduling
+  -> SchedulableTask inputs
+```
+
+The durable runtime control flow is:
+
+```text
+AsyncTaskScheduler
+  -> CompletionTracker.ready_frontier()
+  -> FairTaskQueue.enqueue(...)
+  -> FairTaskQueue.select_next(scheduler-owned eligibility callback)
+  -> TaskAdmissionController.try_acquire(selection.item, queue_view)
+  -> FairTaskQueue.commit(selection)
+  -> execute admitted task/generator code
+
+admitted task/generator code
+  -> model facade/provider boundary
+  -> ModelRequestExecutor.execute_attempt(...)
   -> RequestAdmissionController.acquire_async(RequestAdmissionItem)
-  -> RequestAdmissionLease
   -> provider/model endpoint
+  -> RequestAdmissionController.release(lease, outcome)
 ```
 
-This is not a passive pipeline where `FairTaskQueue` or `TaskAdmissionController` pushes work into the scheduler. `AsyncTaskScheduler` is the execution owner. It asks the readiness tracker for work, asks the queue to select a candidate through an admission eligibility callback, asks the task admission controller for a lease, commits the queue selection, executes the task, and releases the lease.
+This is not a passive pipeline where `CompletionTracker`, `FairTaskQueue`, or `TaskAdmissionController` pushes work into the scheduler. `AsyncTaskScheduler` is the execution owner. It asks the readiness tracker for work, enqueues ready tasks, asks the queue to select a candidate through an admission eligibility callback, asks the task admission controller for a lease, commits the queue selection, executes the admitted task, and releases the lease.
+
+`ModelRequestExecutor` is not a scheduler task wrapper. It is reached only when admitted task/generator code makes a concrete model call through the model facade/provider boundary. A task may make zero, one, or many concrete calls; each call attempt receives request admission independently.
 
 ## Layer Responsibilities
 
@@ -35,20 +50,41 @@ This is not a passive pipeline where `FairTaskQueue` or `TaskAdmissionController
 
 `TaskSchedulingResolver` is the internal bridge from generator metadata to scheduler inputs. It produces `ResolvedTaskScheduling`, including `TaskGroupSpec` and `SchedulerResourceRequest`, and appends scheduler-owned flow identity such as output columns. It is the only scheduler grouping bridge once the legacy resolver is removed.
 
-`CompletionTracker` owns dependency readiness. It reports the ready frontier and completion state. It does not order ready work, admit resources, or inspect provider/model pressure.
+`CompletionTracker` owns dependency readiness. It reports the ready frontier and completion state to `AsyncTaskScheduler`. It does not enqueue into the ready queue, order ready work, admit resources, or inspect provider/model pressure.
 
 `FairTaskQueue` owns ready-work membership and ordering. Its selection operation is non-mutating and takes an eligibility callback supplied by scheduler admission. It does not own dependency readiness, admitted counts, provider metadata, request admission, or policy state.
 
-`TaskAdmissionController` owns scheduler-level task leases and resource accounting. `TaskAdmissionPolicy` decides whether a queued task is eligible under the current queue and admission views. The controller consumes resolved scheduler inputs and must not inspect generators, configs, model registries, or provider registries directly.
+`TaskAdmissionController` owns scheduler-level task leases and resource accounting. `TaskAdmissionPolicy` decides whether a queued task is eligible under the current queue and admission views. The controller consumes resolved scheduler inputs and its engine-internal `TaskAdmissionConfig`; it must not inspect generators, user config layout, model registries, or provider registries directly.
 
 `AsyncTaskScheduler` owns runtime control flow. It wires readiness, queue selection, task admission, worker spawn, task execution, salvage/retry behavior, shutdown, and lease release.
 
-`ModelRequestExecutor` is the durable model-call boundary. It maps each concrete provider/model/domain call to a `RequestAdmissionItem`, acquires a request lease, calls the provider, records request timing, and releases that exact lease on success, rate limit, failure, cancellation, or unexpected exception.
+`ModelRequestExecutor` is the durable model-call boundary. It maps each concrete provider/model/domain call attempt to a `RequestAdmissionItem`, acquires a request lease, calls the provider, records request timing, and releases that exact lease with a classified outcome on success, rate limit, failure, cancellation, timeout, or unexpected exception.
 
 `RequestAdmissionController` owns request-level provider/model/domain admission. `AdaptiveRequestAdmissionController` is the V1 AIMD-backed implementation. Internal `RequestFairQueue`, `RequestAdmissionPolicy`, and `AdaptiveRequestLimitState` are implementation components of this controller, not a second public layer.
 
 `SchedulerAdmissionEventSink` and `RequestAdmissionEventSink` observe their own layers separately. `RuntimeCorrelationProvider` supplies shared runtime context, and `CorrelatedRuntimeView` joins timelines without collapsing the two telemetry systems.
 
+## Audience And API Boundaries
+
+The plan uses several contract categories. Keeping them separate prevents internal scheduling mechanics from becoming accidental plugin API.
+
+| Audience | Durable surface | Must not expose |
+| --- | --- | --- |
+| Plugin authors | `ColumnGenerator.get_scheduling_metadata()` and `SchedulingMetadata` | queue state, task leases, request domains, AIMD state, runtime pressure |
+| Users/operators | documented run config fields, `AsyncCapacityPlan`, benchmark and telemetry artifacts | internal queue/policy classes, per-lease mutation APIs |
+| Engine implementers | scheduler/request admission protocols, DTOs, policies, snapshots, events | config-layer imports from engine runtime |
+| Diagnostics and benchmarks | event DTOs, snapshots, correlation view, capacity plan | prompts, completions, row data, secrets, unbounded IDs as metric labels |
+
+Package ownership follows Data Designer's structural layering:
+
+| Package | Owns |
+| --- | --- |
+| `data-designer-config` | public configuration DTOs and generator-facing metadata, including `SchedulingMetadata`, metadata validation errors, and future stable config surfaces only after an issue explicitly promotes them to public API |
+| `data-designer-engine` | scheduler runtime DTOs, task/request admission controllers, queues, policies, leases, snapshots, events, capacity plan construction, and benchmark harness internals |
+| `data-designer` | public interface wiring, CLI/operator presentation, and integration docs; it may consume engine/config contracts but must not make engine internals plugin API |
+
+When a contract is shared across packages, the lower package owns the data definition and the higher package owns presentation or orchestration. Engine code may import config contracts; config code must not import engine runtime protocols.
+
 ## Two-Stage Admission
 
 Task admission controls when ready dataset work may become a running worker. Request admission controls concrete provider/model/domain calls at the moment they are made.
@@ -57,14 +93,18 @@ The split is required because arbitrary custom Python can make zero, one, or man
 
 Task admission may later consume request pressure snapshots as read-only policy input. It must not pre-acquire request permits, emulate AIMD, or wrap provider/model/domain request admission.
 
+In V1, a task waiting inside request admission keeps its scheduler task lease until the task reaches a terminal outcome. This makes request wait visible without adding yield/reacquire complexity to the lease boundary. The cross-provider optimization target, where tasks blocked on one cooled-down provider do not occupy every scheduler slot while another provider has ready work, belongs to #651's provider/resource-aware task policy or an explicit later yield/reacquire design.
+
 ## Core Invariants
 
 - Scheduler-level work is not spawned until `TaskAdmissionController` returns a `TaskAdmissionLease`.
 - `FairTaskQueue.select_next(...)` does not remove work or mutate virtual-time state. `commit(selection)` is the only queue operation that removes the selected task.
+- `select_next(...)`, `try_acquire(...)`, and `commit(selection)` are coordinated by `AsyncTaskScheduler` under a single dispatch critical section or an equivalent versioned-selection protocol.
 - If `try_acquire(...)` succeeds but `commit(selection)` fails, the scheduler releases the task lease before retrying.
 - Every task lease and request lease is released exactly once in all success, failure, retry, cancellation, shutdown, and salvage paths.
 - Root/from-scratch work uses the same ready queue and task-admission path as downstream work.
 - Request admission happens only at concrete model-call time through `ModelRequestExecutor`.
+- Provider retries are visible to request admission: each outbound attempt either re-enters `ModelRequestExecutor` or is owned by a retry loop inside it that acquires/releases per attempt.
 - Scheduler telemetry and request telemetry remain independently useful when the other subsystem is disabled.
 - Capacity and benchmark artifacts must distinguish dependency readiness, ready ordering, scheduler admission wait, request admission wait, provider execution, cooldown/rate-limit behavior, and task completion.
 
diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index fb52b2ffa..dcb12a02e 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -19,12 +19,14 @@ endlegend
 actor "Generator / Plugin Author" as Author
 actor "Operator / Integrator" as Operator
 
-component "Generator Metadata\nColumnGenerator\nSchedulingMetadata\nTaskSchedulingResolver\nissues 641, 646, 653, 652" as MetadataStage
-component "Task Readiness and Ordering\nCompletionTracker\nFairTaskQueue\nSchedulableTask" as TaskQueueStage
+component "Plugin Metadata Surface\nColumnGenerator.get_scheduling_metadata\nSchedulingMetadata\nissues 641, 652" as MetadataStage
+component "Scheduler Metadata Resolver\nTaskSchedulingResolver\nResolvedTaskScheduling\nissues 646, 653" as ResolverStage
+component "Dependency Readiness\nCompletionTracker" as ReadinessStage
+component "Ready Work Ordering\nFairTaskQueue\nQueueView\nQueueSelection" as TaskQueueStage
 component "Task Admission\nTaskAdmissionController\nTaskAdmissionPolicy\nTaskAdmissionLease\nissue 644" as TaskAdmissionStage
 component "AsyncTaskScheduler\nexecution owner" as SchedulerStage
-component "Model Request Boundary\nModelRequestExecutor" as ModelBoundaryStage
-component "Request Admission\nRequestAdmissionController\nRequestFairQueue\nRequestAdmissionPolicy\nAdaptiveRequestLimitState\nissue 657" as RequestAdmissionStage
+component "Model Request Boundary\nModel facade/provider boundary\nModelRequestExecutor\nper concrete request attempt" as ModelBoundaryStage
+component "Request Admission\nRequestAdmissionController interface\nAdaptiveRequestAdmissionController\nRequestFairQueue / Policy / LimitState\nissue 657" as RequestAdmissionStage
 component "Provider / Model Endpoint" as ProviderStage
 
 component "Capacity Plan\nAsyncCapacityPlan\nTaskAdmissionConfig\nRequestAdmissionConfig\nissue 654" as CapacityStage
@@ -32,11 +34,13 @@ component "Telemetry and Correlation\nSchedulerAdmissionEventSink\nRequestAdmiss
 component "Benchmark and Future Design\nbenchmark harness\nbounded-borrow policy\nresource-vector design\nissues 649, 650, 651" as EvidenceStage
 
 Author --> MetadataStage
-MetadataStage -right-> TaskQueueStage : resolved task DTOs
-SchedulerStage -left-> TaskQueueStage : select_next(...)
-SchedulerStage -down-> TaskAdmissionStage : try_acquire(...)
+MetadataStage -right-> ResolverStage : static metadata
+ResolverStage -right-> ReadinessStage : SchedulableTask inputs
+SchedulerStage -left-> ReadinessStage : ready_frontier()
+SchedulerStage -left-> TaskQueueStage : enqueue / select_next / commit
+SchedulerStage -down-> TaskAdmissionStage : eligibility / try_acquire
 TaskAdmissionStage -up-> SchedulerStage : TaskAdmissionLease
-SchedulerStage -right-> ModelBoundaryStage : execute task code
+SchedulerStage -right-> ModelBoundaryStage : admitted task makes model call
 ModelBoundaryStage -right-> RequestAdmissionStage : model request item
 RequestAdmissionStage -right-> ModelBoundaryStage : request lease
 ModelBoundaryStage -right-> ProviderStage : admitted model call
@@ -85,6 +89,13 @@ package "Metadata contracts" {
     +weight
   }
 
+  class SchedulingMetadataError {
+    +code
+    +message
+    +fallback
+    +diagnostics
+  }
+
   class TaskSchedulingResolver {
     +resolve(generator)
   }
@@ -98,6 +109,7 @@ package "Metadata contracts" {
 package "Task scheduling contracts" {
   class CompletionTracker {
     +ready_frontier()
+    +mark_enqueued(task_ids)
     +mark_complete(task)
   }
 
@@ -110,36 +122,57 @@ package "Task scheduling contracts" {
     +amounts
   }
 
+  class SchedulerResourceKey {
+    +kind
+    +identity
+  }
+
   class SchedulableTask {
+    +task_id
     +task
     +group
     +resource_request
   }
 
   class FairTaskQueue {
-    +enqueue(item)
-    +select_next(is_eligible)
-    +commit(selection)
-    +view()
+    +enqueue(items) -> Sequence[task_id]
+    +select_next(is_eligible) -> QueueSelection | None
+    +commit(selection) -> SchedulableTask | None
+    +view() -> QueueView
   }
 
   class QueueSelection {
     +item
-    +sequence
+    +queue_view
+    +sequence_version
   }
 
   class QueueView {
     +queued_total
     +queued_by_group
-    +has_queued_peer(group)
+    +queued_demand_by_group_resource
+    +first_candidate_resource_by_group
+    +queued_peer_demand_by_resource
   }
 
   interface TaskAdmissionPolicy {
-    +is_eligible(item, queue, view)
-    +on_acquire(lease)
+    +evaluate(item, queue_view, admission_view)
+    +on_acquire(lease, decision)
     +on_release(lease)
   }
 
+  class TaskAdmissionPolicyDecision {
+    +allowed
+    +reason
+    +available_after
+    +diagnostics
+  }
+
+  class PolicyStateDelta {
+    +borrow_debt_delta
+    +diagnostics
+  }
+
   class StrictFairTaskAdmissionPolicy {
     +enforce hard per-group limits
   }
@@ -149,27 +182,52 @@ package "Task scheduling contracts" {
   }
 
   class TaskAdmissionController {
-    +is_eligible(item, queue_view)
-    +try_acquire(item, queue_view)
-    +release(lease)
-    +view()
+    +is_eligible(item, queue_view) -> bool
+    +try_acquire(item, queue_view) -> TaskAdmissionDecision
+    +release(lease) -> ReleaseResult
+    +view() -> TaskAdmissionView
+    +explain_blocked(queue_view) -> TaskAdmissionBlockSummary
   }
 
   class TaskAdmissionView {
-    +resources_available
-    +leased_by_group
-    +running_by_group
+    +limits_by_resource
+    +available_by_resource
+    +leased_by_resource
+    +leased_by_group_resource
+    +running_by_group_resource
+    +policy_debt_by_group_resource
+  }
+
+  class TaskAdmissionBlockSummary {
+    +queued_count
+    +dominant_reasons
+    +available_after
+    +diagnostics
+  }
+
+  class TaskAdmissionDecision <<union>> {
+    TaskAdmissionLease | TaskAdmissionDenied
   }
 
-  class TaskAdmissionDecision {
-    +lease
-    +denied_reason
+  class TaskAdmissionDenied {
+    +item
+    +reason
+    +available_after
+    +snapshot
   }
 
   class TaskAdmissionLease {
+    +lease_id
     +item
     +resources
     +acquired_at
+    +controller_generation
+  }
+
+  class ReleaseResult {
+    +released
+    +reason
+    +diagnostics
   }
 
   class AsyncTaskScheduler <<boundary>> {
@@ -182,29 +240,36 @@ package "Task config" {
   class TaskAdmissionConfig {
     +submission_capacity
     +resource_limits
+    +policy_config
   }
 }
 
 ColumnGenerator --> SchedulingMetadata
+SchedulingMetadataError --> SchedulingMetadata : optional fallback
 TaskSchedulingResolver --> SchedulingMetadata
 TaskSchedulingResolver --> ResolvedTaskScheduling
 ResolvedTaskScheduling --> TaskGroupSpec
 ResolvedTaskScheduling --> SchedulerResourceRequest
+SchedulerResourceRequest --> SchedulerResourceKey
 ResolvedTaskScheduling --> SchedulableTask
-CompletionTracker --> FairTaskQueue : ready tasks
+AsyncTaskScheduler --> CompletionTracker : ready_frontier / mark_enqueued
 FairTaskQueue --> QueueSelection : returns
 FairTaskQueue --> QueueView : view()
-FairTaskQueue --> TaskAdmissionController : is_eligible(candidate)
 TaskAdmissionController --> TaskAdmissionPolicy
+TaskAdmissionPolicy --> TaskAdmissionPolicyDecision
+TaskAdmissionPolicy --> PolicyStateDelta
+TaskAdmissionController --> TaskAdmissionBlockSummary
 StrictFairTaskAdmissionPolicy ..|> TaskAdmissionPolicy
 BoundedBorrowTaskAdmissionPolicy ..|> TaskAdmissionPolicy
 TaskAdmissionController --> TaskAdmissionView
 TaskAdmissionController --> TaskAdmissionDecision
+TaskAdmissionController --> ReleaseResult
 TaskAdmissionDecision --> TaskAdmissionLease
+TaskAdmissionDecision --> TaskAdmissionDenied
 TaskAdmissionLease --> SchedulableTask
 AsyncTaskScheduler --> CompletionTracker
-AsyncTaskScheduler --> FairTaskQueue : select_next returns QueueSelection
-AsyncTaskScheduler --> TaskAdmissionController : try_acquire(selection.item, queue.view)
+AsyncTaskScheduler --> FairTaskQueue : enqueue / select_next / commit
+AsyncTaskScheduler --> TaskAdmissionController : eligibility callback / try_acquire
 TaskAdmissionController ..> TaskAdmissionConfig : reads config
 
 note right of SchedulingMetadata
@@ -215,10 +280,16 @@ note right of SchedulingMetadata
 end note
 
 note bottom of SchedulerResourceRequest
-  Replaces legacy needs_llm_wait-style booleans
+  Replaces legacy hidden-wait booleans
   once the resource-vector design lands.
 end note
 
+note bottom of FairTaskQueue
+  select_next receives an opaque scheduler-owned
+  eligibility predicate. The queue owns ordering only;
+  it does not depend on TaskAdmissionController.
+end note
+
 @enduml
 
 @startuml AsyncSchedulingRequestAdmissionClassModel
@@ -232,7 +303,7 @@ skinparam linetype ortho
 
 package "Model request boundary" {
   class ModelRequestExecutor <<boundary>> {
-    +execute(request)
+    +execute_attempt(request)
   }
 
   class ProviderModelEndpoint <<external>> {
@@ -247,6 +318,23 @@ package "Request admission contracts" {
     +domain
   }
 
+  class RequestResourceResolver {
+    +resolve_provider_model(...)
+    +resolve_request_resource(...)
+  }
+
+  class ProviderModelKey {
+    +provider_name
+    +model_id
+  }
+
+  enum RequestDomain {
+    chat
+    embedding
+    image
+    healthcheck
+  }
+
   class RequestGroupSpec {
     +key
     +weight
@@ -256,38 +344,65 @@ package "Request admission contracts" {
     +resource
     +group
     +timeout
+    +event_context
+  }
+
+  class RequestWaiter {
+    +waiter_id
+    +item
+    +enqueued_at
+    +deadline
+    +completion_handle
+  }
+
+  class RequestEventContext {
+    +captured_correlation
+    +task_execution_id
+    +request_attempt_id
   }
 
   class RequestFairQueue <<internal>> {
-    +enqueue(item)
-    +select_next(is_eligible)
-    +commit(selection)
-    +view()
+    +enqueue(waiter)
+    +select_next(is_eligible) -> RequestQueueSelection | None
+    +commit(selection) -> RequestWaiter | None
+    +remove(waiter_id)
+    +view() -> RequestQueueView
+  }
+
+  class RequestQueueSelection {
+    +waiter
+    +item
+    +waiter_id
+    +queue_view
+    +sequence_version
+  }
+
+  class RequestQueueView {
+    +queued_total
+    +queued_by_group
+    +queued_demand_by_resource
+    +aggregate_waiters
   }
 
   interface RequestAdmissionPolicy <<internal>> {
-    +is_eligible(item, queue, limits)
-    +on_success(lease)
-    +on_rate_limited(lease, retry_after)
-    +on_failure(lease)
+    +is_eligible(item, queue_view, limits)
+    +on_release(lease, outcome)
   }
 
   class AdaptiveRequestLimitState {
-    +current_limit(resource)
-    +effective_max(resource)
-    +record_success(resource)
-    +record_rate_limit(resource, retry_after)
-    +record_failure(resource)
+    +current_limit(domain_resource)
+    +effective_max(domain_resource)
+    +aggregate_in_flight(provider, model)
+    +record_acquire(resource)
+    +record_outcome(resource, outcome)
   }
 
-  class RequestAdmissionController {
-    +try_acquire(item)
-    +acquire_sync(item)
-    +acquire_async(item)
-    +release_success(lease)
-    +release_rate_limited(lease, retry_after)
-    +release_failure(lease)
-    +pressure
+  interface RequestAdmissionController {
+    +try_acquire(item) -> RequestAdmissionDecision
+    +acquire_sync(item) -> RequestAdmissionLease
+    +acquire_async(item) -> RequestAdmissionLease
+    +release(lease, outcome) -> ReleaseResult
+    +pressure -> RequestPressureSnapshotProvider
   }
 
   class AdaptiveRequestAdmissionController {
@@ -299,20 +414,38 @@ package "Request admission contracts" {
   }
 
   class RequestAdmissionLease {
+    +lease_id
     +item
     +acquired_at
     +current_limit
     +effective_max
+    +controller_generation
   }
 
   class RequestAdmissionDenied {
     +item
     +reason
     +retry_after_seconds
-    +available_after
+    +available_after_monotonic
     +snapshot
   }
 
+  class RequestAdmissionError {
+    +denial
+  }
+
+  class RequestReleaseOutcome {
+    +kind
+    +retry_after_seconds
+    +safe_status_metadata
+  }
+
+  class ReleaseResult {
+    +released
+    +reason
+    +diagnostics
+  }
+
   class RequestPressureSnapshotProvider {
     +snapshot(resource)
     +snapshots()
@@ -325,25 +458,41 @@ package "Request config" {
   class RequestAdmissionConfig {
     +resources
     +initial_limit
-    +max_limit
+    +max_limit_clamp
+    +cooldown_seconds
+    +multiplicative_decrease_factor
+    +additive_increase_step
+    +increase_after_successes
+    +default_queue_wait_timeout
   }
 }
 
 ModelRequestExecutor --> RequestAdmissionController
-RequestAdmissionController --> RequestFairQueue
-RequestAdmissionController --> RequestAdmissionPolicy
+ModelRequestExecutor --> RequestResourceResolver
+RequestResourceResolver --> ProviderModelKey
+RequestResourceResolver --> RequestResourceKey
 RequestAdmissionPolicy --> AdaptiveRequestLimitState
-AdaptiveRequestAdmissionController --|> RequestAdmissionController
+AdaptiveRequestAdmissionController ..|> RequestAdmissionController
 AdaptiveRequestAdmissionController --> RequestFairQueue
 AdaptiveRequestAdmissionController --> RequestAdmissionPolicy
 AdaptiveRequestAdmissionController --> AdaptiveRequestLimitState
 RequestAdmissionController --> RequestAdmissionDecision
+RequestAdmissionController --> RequestReleaseOutcome
+RequestAdmissionController --> ReleaseResult
 RequestAdmissionDecision --> RequestAdmissionLease
 RequestAdmissionDecision --> RequestAdmissionDenied
+RequestAdmissionError --> RequestAdmissionDenied
 RequestAdmissionController --> RequestPressureSnapshotProvider
-RequestAdmissionController ..> RequestAdmissionConfig : reads config
+AdaptiveRequestAdmissionController ..> RequestAdmissionConfig : reads config
 RequestAdmissionItem --> RequestResourceKey
+RequestAdmissionItem --> RequestEventContext
+RequestWaiter --> RequestAdmissionItem
+RequestResourceKey --> ProviderModelKey
+RequestResourceKey --> RequestDomain
 RequestAdmissionItem --> RequestGroupSpec
+RequestFairQueue --> RequestQueueSelection
+RequestFairQueue --> RequestQueueView
+RequestQueueSelection --> RequestWaiter
 RequestAdmissionLease --> RequestAdmissionItem
 RequestAdmissionDenied --> RequestAdmissionItem
 ModelRequestExecutor --> ProviderModelEndpoint
@@ -359,8 +508,8 @@ note right of RequestAdmissionController
   request admission at model-call time.
   Does not own DAG readiness or task
   scheduler admission.
-  No durable Throttle*, needs_llm_wait,
-  or held_llm_wait compatibility concepts.
+  No durable legacy request-control or hidden-wait
+  compatibility concepts.
 end note
 
 @enduml
@@ -382,22 +531,60 @@ package "Capacity planning (issue 654)" {
   }
 
   class AsyncCapacityPlan {
-    +buffer_size
+    +configured
+    +runtime_snapshot
+    +observed_maxima
+  }
+
+  class CapacityValue {
+    +value
+    +source
+    +fallback_from
+    +missing_reason
+  }
+
+  class CapacityObservedMaxima {
+    +row_groups_in_flight
+    +queued_tasks_by_group
+    +task_leases_by_resource
+    +request_waiters_by_resource
+    +request_in_flight_by_resource
+    +provider_model_aggregate_in_flight
+    +request_domain_current_limits
+    +transport_pool_utilization
+  }
+
+  class RowGroupAdmission {
     +row_group_concurrency
-    +submission_capacity
-    +task_admission
-    +request_admission
+    +observed_in_flight
+  }
+
+  class TransportPoolConfig {
+    +pool_limits
+  }
+
+  class ProviderModelStaticCap {
+    +cap
+    +aliases
+    +raw_caps
+    +merge_rule
   }
 
   class TaskAdmissionConfig {
     +submission_capacity
     +resource_limits
+    +policy_config
   }
 
   class RequestAdmissionConfig {
     +resources
     +initial_limit
-    +max_limit
+    +max_limit_clamp
+    +cooldown_seconds
+    +multiplicative_decrease_factor
+    +additive_increase_step
+    +increase_after_successes
+    +default_queue_wait_timeout
   }
 }
 
@@ -409,6 +596,7 @@ package "Telemetry and correlation (issues 635, 647, 648)" {
     +task_type
     +scheduling_group_kind
     +scheduling_group_identity_hash
+    +task_execution_id
   }
 
   class RuntimeCorrelationProvider {
@@ -425,6 +613,34 @@ package "Telemetry and correlation (issues 635, 647, 648)" {
     +emit(event)
   }
 
+  class SchedulerAdmissionEvent {
+    +event_kind
+    +captured_at_monotonic
+    +sequence
+    +captured_correlation
+    +task_id
+    +task_execution_id
+    +task_lease_id
+    +scheduler_resource_key
+    +reason_or_result
+    +snapshot
+    +diagnostics
+  }
+
+  class RequestAdmissionEvent {
+    +event_kind
+    +captured_at_monotonic
+    +sequence
+    +captured_correlation
+    +request_attempt_id
+    +request_lease_id
+    +request_resource
+    +request_group_key
+    +reason_or_outcome
+    +snapshot
+    +diagnostics
+  }
+
   class CorrelatedRuntimeView {
     +join scheduler and request timelines
   }
@@ -434,6 +650,7 @@ package "Evidence and future design (issues 649, 650, 651)" {
   class AsyncSchedulingBenchmarkHarness {
     +run_ab(baseline_ref, candidate_ref)
     +emit_json_csv_artifacts()
+    +verify_final_snapshots()
   }
 
   class SchedulerResourceVectorDesign {
@@ -449,11 +666,18 @@ package "Evidence and future design (issues 649, 650, 651)" {
 RunConfigRuntimeArgs --> AsyncCapacityPlan
 AsyncCapacityPlan --> TaskAdmissionConfig
 AsyncCapacityPlan --> RequestAdmissionConfig
-SchedulerAdmissionEventSink ..> RuntimeCorrelationProvider : attach current context
-RequestAdmissionEventSink ..> RuntimeCorrelationProvider : attach current context
+AsyncCapacityPlan --> CapacityValue
+AsyncCapacityPlan --> CapacityObservedMaxima
+AsyncCapacityPlan --> RowGroupAdmission
+AsyncCapacityPlan --> TransportPoolConfig
+AsyncCapacityPlan --> ProviderModelStaticCap
+SchedulerAdmissionEvent --> RuntimeCorrelation : captured at construction
+RequestAdmissionEvent --> RuntimeCorrelation : captured at construction
+SchedulerAdmissionEventSink --> SchedulerAdmissionEvent
+RequestAdmissionEventSink --> RequestAdmissionEvent
 RuntimeCorrelationProvider --> RuntimeCorrelation
-SchedulerAdmissionEventSink --> CorrelatedRuntimeView
-RequestAdmissionEventSink --> CorrelatedRuntimeView
+CorrelatedRuntimeView --> SchedulerAdmissionEvent : consumes
+CorrelatedRuntimeView --> RequestAdmissionEvent : consumes
 AsyncSchedulingBenchmarkHarness ..> AsyncCapacityPlan
 AsyncSchedulingBenchmarkHarness ..> SchedulerAdmissionEventSink
 AsyncSchedulingBenchmarkHarness ..> RequestAdmissionEventSink
@@ -476,6 +700,7 @@ participant "AsyncTaskScheduler" as Scheduler
 participant "FairTaskQueue" as Queue
 participant "TaskAdmissionController\nissue 644" as TaskAdmit
 participant "TaskAdmissionPolicy" as TaskPolicy
+participant "Admitted Task /\nGenerator Code" as TaskCode
 participant "ModelRequestExecutor" as Executor
 participant "RequestAdmissionController\nissue 657" as ReqAdmit
 participant "RequestFairQueue" as ReqQueue
@@ -487,66 +712,188 @@ participant "RequestAdmissionEventSink\nissue 635" as ReqEvents
 participant "RuntimeCorrelationProvider\nissue 648" as Correlation
 
 User -> Gen : declare columns / plugins
-Gen -> Resolver : get_scheduling_metadata()
+Resolver -> Gen : get_scheduling_metadata()
+alt valid metadata
+  Gen --> Resolver : SchedulingMetadata
+else generator omitted override
+  Resolver -> Resolver : use documented default metadata
+else recoverable metadata error
+  Gen --> Resolver : SchedulingMetadataError(fallback)
+  Resolver -> Resolver : emit metadata diagnostic
+else fatal metadata error
+  Gen --> Resolver : SchedulingMetadataError(no fallback)
+  Resolver --> User : fail before scheduling
+end
 Resolver -> Resolver : resolve TaskGroupSpec\nand SchedulerResourceRequest
 Resolver -> Tracker : provide SchedulableTask inputs
 
 Scheduler -> Tracker : ready_frontier()
-Tracker -> Queue : enqueue ready SchedulableTask
-Scheduler -> Queue : select_next(TaskAdmit.is_eligible)
-Queue -> TaskAdmit : is_eligible(candidate, queue.view)
-TaskAdmit -> TaskPolicy : evaluate candidate against leases/capacity
-TaskPolicy --> TaskAdmit : eligible or denied
-TaskAdmit --> Queue : eligibility result
-Queue --> Scheduler : QueueSelection
-Scheduler -> TaskAdmit : try_acquire(selection.item, queue.view)
-TaskAdmit -> TaskPolicy : on_acquire if admitted
-TaskAdmit --> Scheduler : TaskAdmissionLease
-Scheduler -> Queue : commit(selection)
-Scheduler -> Correlation : set RuntimeCorrelation
-TaskAdmit -> SchedEvents : lease_acquired
-SchedEvents -> Correlation : current()
-
-Scheduler -> Executor : execute task
-Executor -> ReqAdmit : acquire_async(RequestAdmissionItem)
-ReqAdmit -> ReqQueue : enqueue waiter if needed
-ReqAdmit -> ReqQueue : select_next(ReqPolicy.is_eligible)
-ReqQueue --> ReqAdmit : RequestQueueSelection
-ReqAdmit -> ReqPolicy : evaluate resource/group against limits
-ReqPolicy -> ReqLimits : read current/effective limit
-ReqLimits --> ReqPolicy : limit snapshot
-ReqPolicy --> ReqAdmit : eligible or denied
-ReqAdmit -> ReqQueue : commit(selection) if admitted
-ReqAdmit -> ReqEvents : wait_started / lease_acquired
-ReqEvents -> Correlation : current()
-ReqAdmit --> Executor : RequestAdmissionLease
-Executor -> Provider : concrete model request
-Provider --> Executor : response or rate limit
-
-alt success
-  Executor -> ReqAdmit : release_success(lease)
-  ReqAdmit -> ReqPolicy : on_success(lease)
-  ReqPolicy -> ReqLimits : record_success(resource)
-else rate limit
-  Executor -> ReqAdmit : release_rate_limited(lease, retry_after)
-  ReqAdmit -> ReqPolicy : on_rate_limited(lease, retry_after)
-  ReqPolicy -> ReqLimits : record_rate_limit(resource, retry_after)
-else non-rate-limit failure or cancellation
-  Executor -> ReqAdmit : release_failure(lease)
-  ReqAdmit -> ReqPolicy : on_failure(lease)
-  ReqPolicy -> ReqLimits : record_failure(resource)
+Tracker --> Scheduler : un-enqueued ready SchedulableTasks
+Scheduler -> SchedEvents : dependency_ready\n(correlation captured now)
+Scheduler -> Queue : enqueue ready SchedulableTasks
+Queue --> Scheduler : accepted task_ids
+Scheduler -> Tracker : mark_enqueued(accepted task_ids)
+Scheduler -> SchedEvents : ready_enqueued\n(correlation captured now)
+
+loop dispatch while capacity and ready work may exist
+  Scheduler -> Queue : select_next(scheduler eligibility predicate)
+  Queue -> Scheduler : is_eligible(candidate, queue_view)
+  Scheduler -> TaskAdmit : is_eligible(candidate, queue_view)
+  TaskAdmit -> TaskPolicy : evaluate candidate against queue/admission views
+  TaskPolicy --> TaskAdmit : eligible or denied
+  TaskAdmit --> Scheduler : eligibility result
+
+  alt no eligible selection
+    Queue --> Scheduler : None
+    Scheduler -> TaskAdmit : explain_blocked(queue.view())
+    TaskAdmit --> Scheduler : TaskAdmissionBlockSummary
+    Scheduler -> SchedEvents : queue_empty / admission_blocked\n(correlation captured now)
+    Scheduler -> Scheduler : wait_for_wake_or_deadline()
+  else selection returned
+    Queue --> Scheduler : QueueSelection(item, queue_view, sequence_version)
+    Scheduler -> SchedEvents : selected\n(correlation captured now)
+    Scheduler -> TaskAdmit : try_acquire(selection.item, selection.queue_view)
+
+    alt admission denied
+      TaskAdmit --> Scheduler : TaskAdmissionDenied
+      Scheduler -> SchedEvents : admission_denied\n(correlation captured now)
+      Scheduler -> Scheduler : wake_dispatch_loop()
+    else lease acquired
+      TaskAdmit -> TaskPolicy : on_acquire(lease, decision)
+      TaskAdmit --> Scheduler : TaskAdmissionLease
+      Scheduler -> Queue : commit(selection)
+
+      alt stale selection
+        Queue --> Scheduler : None
+        Scheduler -> TaskAdmit : release(lease)
+        TaskAdmit -> TaskPolicy : on_release(lease)
+        Scheduler -> SchedEvents : stale_selection / task_lease_released\n(correlation captured now)
+        Scheduler -> Scheduler : wake_dispatch_loop()
+      else committed
+        Queue --> Scheduler : SchedulableTask
+        Scheduler -> Correlation : set RuntimeCorrelation
+        Scheduler -> SchedEvents : task_lease_acquired\n(correlation captured now)
+
+        alt worker spawn failed
+          Scheduler -> TaskAdmit : release(lease)
+          TaskAdmit -> TaskPolicy : on_release(lease)
+          Scheduler -> SchedEvents : worker_spawn_failed / task_lease_released\n(correlation captured now)
+          Scheduler -> Correlation : reset RuntimeCorrelation
+        else worker spawned
+          Scheduler -> SchedEvents : worker_spawned\n(correlation captured now)
+          Scheduler -> TaskCode : execute admitted task
+
+          loop zero, one, or many concrete model calls
+            TaskCode -> Executor : model call attempt
+            Executor -> Correlation : current()
+            Executor -> ReqAdmit : acquire_async(RequestAdmissionItem\nwith RequestEventContext)
+
+            ReqAdmit -> ReqEvents : request_wait_started\n(correlation captured now)
+
+            alt immediate eligible and no queued waiter selected first
+              ReqAdmit -> ReqPolicy : evaluate resource/group against limits
+              ReqPolicy -> ReqLimits : read domain limit\nand aggregate in-flight
+              ReqLimits --> ReqPolicy : limit snapshot
+              ReqPolicy --> ReqAdmit : eligible
+            else queued waiter path
+              ReqAdmit -> ReqQueue : enqueue waiter
+
+              loop until admitted, timeout, cancel, or shutdown
+                ReqAdmit -> ReqQueue : select_next(ReqPolicy.is_eligible)
+                ReqQueue --> ReqAdmit : RequestQueueSelection | None
+                ReqAdmit -> ReqPolicy : evaluate resource/group against limits
+                ReqPolicy -> ReqLimits : read domain limit\nand aggregate in-flight
+                ReqLimits --> ReqPolicy : limit snapshot
+                ReqPolicy --> ReqAdmit : eligible or denied
+                ReqAdmit -> ReqAdmit : timed wait to next\nblocked_until / timeout
+              end
+            end
+
+            alt no lease: timeout, shutdown, or hard denial
+              ReqAdmit -> ReqQueue : remove waiter if queued
+              ReqAdmit -> ReqEvents : request_wait_timeout / request_acquire_denied\n(correlation captured now)
+              ReqAdmit --> Executor : raise RequestAdmissionError
+            else async cancellation before lease
+              ReqAdmit -> ReqQueue : remove waiter
+              ReqAdmit -> ReqEvents : request_wait_cancelled\n(correlation captured now)
+              ReqAdmit --> Executor : re-raise cancellation
+            else request lease acquired
+              ReqAdmit -> ReqQueue : commit(selection) if queued\nand fulfill selected waiter
+              ReqAdmit -> ReqLimits : record_acquire(resource)
+              note right of ReqAdmit
+                Once record_acquire succeeds,
+                cancellation either delivers the lease
+                for caller cleanup or internally releases
+                it as local_cancelled.
+              end note
+              ReqAdmit -> ReqEvents : request_wait_completed\n(correlation captured now)
+              ReqAdmit -> ReqEvents : request_lease_acquired\n(correlation captured now)
+              ReqAdmit --> Executor : RequestAdmissionLease
+              Executor -> ReqEvents : model_request_started\n(correlation captured now)
+              Executor -> Provider : concrete provider/model attempt
+              Provider --> Executor : response / rate limit / failure
+              Executor -> ReqEvents : model_request_completed\n(correlation captured now)
+
+              alt success
+                Executor -> ReqAdmit : release(lease, success)
+                ReqAdmit -> ReqPolicy : on_release(lease, success)
+                ReqPolicy -> ReqLimits : record_outcome(resource, success)
+              else rate limit
+                Executor -> ReqAdmit : release(lease, rate_limited)
+                ReqAdmit -> ReqPolicy : on_release(lease, rate_limited)
+                ReqPolicy -> ReqLimits : record_outcome(resource, rate_limited)
+                ReqAdmit -> ReqEvents : request_rate_limited / request_limit_decreased\n(correlation captured now)
+              else provider failure or timeout
+                Executor -> ReqAdmit : release(lease, provider_failure/provider_timeout)
+                ReqAdmit -> ReqPolicy : on_release(lease, provider outcome)
+                ReqPolicy -> ReqLimits : record_outcome(resource, provider outcome)
+              else local_cancelled or local_timeout
+                Executor -> ReqAdmit : release(lease, local_cancelled/local_timeout)
+                ReqAdmit -> ReqPolicy : on_release(lease, local outcome)
+                ReqPolicy -> ReqLimits : record_outcome(resource, local outcome)
+              else unexpected exception
+                Executor -> ReqAdmit : release(lease, unexpected_exception)
+                ReqAdmit -> ReqPolicy : on_release(lease, unexpected_exception)
+                ReqPolicy -> ReqLimits : record_outcome(resource, unexpected_exception)
+              end
+
+              ReqAdmit -> ReqQueue : wake/select next waiter
+              ReqAdmit -> ReqEvents : request_lease_released\n(+ request_limit_increased if changed)
+            end
+          end
+
+          TaskCode --> Scheduler : generated value / terminal outcome
+          alt success
+            Scheduler -> Tracker : mark_complete(task)
+            Scheduler -> SchedEvents : task_completed\n(correlation captured now)
+          else retryable failure
+            Scheduler -> SchedEvents : retry_deferred\n(correlation captured now)
+            Scheduler -> Scheduler : record retry requested
+          else non-retryable failure or cancellation
+            Scheduler -> Tracker : mark_complete(task)
+            Scheduler -> SchedEvents : non_retryable_dropped / cancelled\n(correlation captured now)
+          else salvage redispatch
+            Scheduler -> SchedEvents : salvage_redispatched\n(correlation captured now)
+            Scheduler -> Scheduler : record salvage requested
+          end
+
+          Scheduler -> TaskAdmit : release TaskAdmissionLease
+          TaskAdmit -> TaskPolicy : on_release(lease)
+          Scheduler -> SchedEvents : task_lease_released\n(correlation captured now)
+          Scheduler -> Correlation : reset RuntimeCorrelation
+
+          alt retry or salvage replacement requested
+            Scheduler -> Tracker : record replacement work
+            Scheduler -> Scheduler : replacement re-enters ready_frontier path
+          else no replacement
+            Scheduler -> Scheduler : terminal accounting complete
+          end
+        end
+      end
+    end
+  end
 end
 
-ReqAdmit -> ReqQueue : wake/select next waiter
-ReqAdmit -> ReqEvents : request admission event
-ReqEvents -> Correlation : current()
-Executor --> Scheduler : generated value / error
-Scheduler -> TaskAdmit : release TaskAdmissionLease
-TaskAdmit -> TaskPolicy : on_release(lease)
-TaskAdmit -> SchedEvents : lease_released / completed
-SchedEvents -> Correlation : current()
-Scheduler -> Correlation : reset RuntimeCorrelation
-
 @enduml
 
 @startuml AsyncSchedulingEpicIssueMap
@@ -577,6 +924,7 @@ I641 --> I652
 I646 --> I653
 I646 --> I652
 I653 --> I652
+I652 --> I644
 I646 --> I644
 I641 --> I654
 I646 --> I654
@@ -591,11 +939,17 @@ I657 --> I648
 I635 --> I648
 I647 --> I648
 I644 --> I649
+I654 --> I649
+I657 --> I649
+I635 --> I649
+I647 --> I649
+I648 --> I649
 I652 --> I660
 I654 --> I660
 I657 --> I660
 I648 --> I660
 I649 --> I660
+I660 --> I650
 I649 --> I650
 I644 --> I650
 I641 --> I651
@@ -603,12 +957,16 @@ I646 --> I651
 I644 --> I651
 I649 --> I651
 I650 --> I651
+I660 --> I651
 
 note bottom
   Native GitHub subissue order:
   issue 641 -> issue 646 -> issue 653 -> issue 652 -> issue 644
   -> issue 654 -> issue 657 -> issue 635 -> issue 647
   -> issue 648 -> issue 649 -> issue 660 -> issue 650 -> issue 651
+
+  Issues before 649 that need evidence emit provisional artifacts.
+  Issue 649 normalizes and reruns representative scenarios.
 end note
 
 @enduml
diff --git a/plans/645/benchmark-plan.md b/plans/645/benchmark-plan.md
index d5615656e..7048bc645 100644
--- a/plans/645/benchmark-plan.md
+++ b/plans/645/benchmark-plan.md
@@ -2,6 +2,8 @@
 
 The benchmark harness turns architecture claims into reusable evidence. It prevents each implementation PR from inventing one-off scripts and makes fairness/throughput tradeoffs explicit.
 
+Until issue #649 closes, implementation PRs that need scheduling evidence must emit the provisional artifact schema in this file. A minimal deterministic smoke entrypoint and artifact writer should exist before the risky task/request admission implementation slices rely on it; issue #649 formalizes the reusable harness and reruns the provisional evidence against the accepted implementation chain before issue #645 closes. This prevents task/request admission PRs from landing without evidence while still allowing the harness to mature after capacity and telemetry contracts stabilize.
+
 ## Harness Requirements
 
 Provide a repo-local benchmark entrypoint that can compare two refs or checkouts.
@@ -20,6 +22,10 @@ Required inputs:
 - measured iterations
 - output directory
 - seed
+- scenario version
+- harness version
+- mock provider transcript or scripted provider behavior when live providers are not used
+- monotonic clock/retry schedule when deterministic replay is claimed
 
 Required artifacts:
 
@@ -31,12 +37,71 @@ Required artifacts:
 - environment knobs
 - `AsyncCapacityPlan`
 - per-layer observed maxima
+- final task admission snapshot
+- final request admission snapshot, or explicit `not_available_until_issue` marker before #657 lands
 - completion timeline
 - ready-idle/utilization timeline
 - deterministic output hashes where applicable
 
+Final snapshots must prove zero active task leases, zero request leases, zero request waiters, and no resource-specific permit leaks after all terminal paths complete. Before #657 lands, request snapshot fields remain present but can carry `not_available_until_issue: 657` rather than fabricated zeros.
+
 The sync path can be used as a correctness/hash oracle, not as the timing baseline for async scheduling policy.
 
+## Artifact Schema
+
+The provisional and final JSON artifacts use monotonic seconds for timeline fields and stable scenario ids for comparison:
+
+```text
+scenario_id
+artifact_schema_version
+scenario_version
+harness_version
+baseline_sha
+candidate_sha
+inputs
+provider_script
+clock_script
+capacity_plan
+iterations[]
+  wall_time_seconds
+  timeline[]
+    event_kind
+    captured_at_monotonic
+    stream
+    sequence
+    captured_correlation
+      run_id
+      row_group
+      task_column
+      task_type
+      scheduling_group_kind
+      scheduling_group_identity_hash
+      task_execution_id
+    task_id
+    task_execution_id
+    task_lease_id
+    request_attempt_id
+    request_lease_id
+    scheduler_resource_key
+    request_resource_key
+    reason_or_outcome
+  final_task_snapshot
+  final_request_snapshot
+  output_hashes
+derived_metrics
+```
+
+Derived metrics:
+
+- `ready_queue_wait = selected_at - ready_enqueued_at`
+- `task_admission_wait = task_lease_acquired_at - selected_at`
+- `ready_to_lease_gap = task_lease_acquired_at - ready_enqueued_at`
+- `ready_idle_gap` is derived from intervals where dependency-ready work exists, scheduler task capacity is available, and no task lease is acquired. Per-task `selected_at -> task_lease_acquired_at` is task admission overhead, not the starvation metric.
+- `active_capacity_integral = integral(active_leases / configured_capacity) over wall time`
+- `root_over_admission_debt = admitted root work above strict fair share after first downstream-ready timestamp`
+- `hidden_scheduler_resource_waiters` is the count of spawned workers waiting for scheduler-level resources that should have been acquired before spawn. After the task-admission lease boundary lands, the event stream should prove this is zero by showing no worker-spawned event before the corresponding task-lease-acquired event and no pre-epic scheduler-resource wait event for the task.
+- deterministic hashes include generated output values and stable ordering metadata, not timing or event ids
+
 ## Scenario Matrix
 
 ### Queue And Admission Microbench
@@ -91,19 +156,28 @@ This scenario must exercise true root/from-scratch dispatch, not only downstream
 After task admission lands:
 
 ```text
-max(spawned_waiting_for_llm_lease) == 0
+max(hidden_scheduler_resource_waiters) == 0
 ```
 
 Required monotonic timeline fields:
 
+- dependency_ready_at
+- ready_enqueued_at
 - selected_at
-- lease_acquired_at
+- task_lease_acquired_at
 - worker_spawned_at
+- request_wait_started_at
+- request_wait_completed_at
+- request_lease_acquired_at
 - model_request_started_at
 - model_request_completed_at
-- lease_released_at
+- request_lease_released_at
+- task_completed_at
+- task_lease_released_at
+
+Scheduler events own selected/task-lease/spawn/task-completion/task-release. Request/model instrumentation owns request wait, request lease, model request start/complete, and request release.
 
-Scheduler events own selected/lease/spawn/release. Request/model instrumentation owns model request start/complete.
+Immediate request acquisition records `request_wait_started_at == request_wait_completed_at` so the timeline can distinguish a zero wait from missing instrumentation.
 
 ### Idle And Utilization Proxy
 
@@ -124,6 +198,7 @@ Run paired A/B trials with warmup and at least five measured iterations for:
 - dual model generate-to-judge workflow
 - heavy-root workflow
 - dynamic request-count custom generator workflow
+- cross-provider cooldown workflow where provider A is rate-limited or cooling down while provider B has ready independent work
 
 ### Request Dynamic-Call Benchmark
 
@@ -149,12 +224,24 @@ Metrics:
 
 ## Evidence Thresholds
 
+All timing gates use paired same-machine runs with at least five measured iterations unless the scenario explicitly raises that count. Reports include mean, p50, p95, min, max, standard deviation, and a noise-floor note. If standard deviation is large enough to make a threshold ambiguous, the PR must either add iterations or treat the timing claim as inconclusive.
+
 Neutral scenarios should be no worse than 5 percent mean wall time unless the PR explicitly justifies a fairness/utilization tradeoff.
 
 Heavy-root scenarios should show reduced downstream ready-to-dispatch lag versus the named baseline when the candidate claims to improve heavy-root behavior.
 
 Every run must show no permit leaks and deterministic output equality where applicable.
 
+Scenario-specific gates:
+
+- Queue/admission microbench: p95 admission cycle cost must not regress more than 10 percent unless the PR documents a fairness or correctness tradeoff.
+- Heavy-root benchmark: p95 ready-to-dispatch gap for downstream work must improve versus the named baseline when the candidate claims heavy-root fairness; root over-admission debt must be bounded by the configured policy.
+- Hidden-waiter proof: `max(hidden_scheduler_resource_waiters) == 0` across success, failure, cancellation, and salvage paths after task admission lands.
+- Idle/utilization proxy: ready-idle gaps while eligible work and capacity are available must be zero except for documented event-loop scheduling granularity.
+- Dynamic request benchmark: zero/one/many request tasks must produce matching output hashes, request lease counts must equal concrete outbound attempts, and request wait/execute/release timelines must be monotonic.
+- Cross-provider cooldown benchmark: provider B ready work must continue to receive scheduler task leases while provider A is blocked by request cooldown once the provider-aware policy in #651 claims that optimization.
+- Variance: measured iterations must report mean, p50, p95, min, max, and standard deviation. Any acceptance claim based on timing should remain directionally true after removing the fastest and slowest measured iteration.
+
 ## CI Smoke
 
 The harness should have a small deterministic smoke mode using mock endpoints. It writes machine-readable artifacts and does not require live providers.
diff --git a/plans/645/capacity-model.md b/plans/645/capacity-model.md
index af1ff60f2..4ac49db2c 100644
--- a/plans/645/capacity-model.md
+++ b/plans/645/capacity-model.md
@@ -12,7 +12,7 @@ The async engine uses layered capacity. Each layer has a different owner and mea
 | Task-stage admission | `TaskAdmissionController` | Bounds scheduler-spawned work and scheduler-level resource pressure. |
 | Request-stage admission | `RequestAdmissionController` | Bounds concrete provider/model/domain requests when they are made. |
 | Static provider cap | Model config / metadata | User-declared provider/model upper bound and scheduling weight source. |
-| Adaptive provider cap | `AdaptiveRequestAdmissionController` | Runtime AIMD limit under the static provider cap. |
+| Adaptive request-domain limit | `AdaptiveRequestAdmissionController` | Runtime AIMD limit for one provider/model/domain resource under the static provider/model cap. |
 | Transport pool | HTTP/model client adapter | Socket/session pool sizing. Not scheduling or fairness policy. |
 
 ## AsyncCapacityPlan
@@ -24,20 +24,50 @@ The async engine uses layered capacity. Each layer has a different owner and mea
 - task admission capacity
 - task resource limits
 - request admission resources
+- provider/model aggregate static caps
+- provider/model aggregate in-flight maxima
 - static provider/model caps used by the workflow
-- adaptive request-admission config
+- adaptive request-admission config snapshot
+- request-domain adaptive initial/current/effective limits when captured
 - transport/session pool values if they remain distinct
 - source of each value, such as default constant, model metadata, run config, request admission state, or environment selection
 
 The plan is emitted for diagnostics, traces, benchmarks, and operator documentation. It does not admit work by itself.
 
+`AsyncCapacityPlan` uses three sections:
+
+```text
+configured: values computed before or at run start
+runtime_snapshot: point-in-time controller snapshots, nullable until the owning issue lands
+observed_maxima: maxima collected during execution or benchmark replay
+```
+
+Each configured value is a `CapacityValue` with `value`, `source`, `fallback_from`, and `missing_reason`. Fields that depend on request-admission runtime state may be present with `value = None` and `missing_reason` in #654 before #657 lands.
+
+`CapacityValue.source` uses the durable source vocabulary from [contracts.md](contracts.md#capacity-contracts), including `dataset_builder`, `engine_internal_config`, and `adapter_config` for values that do not come from public run config or model metadata.
+
+Source precedence is per-field, not global:
+
+| Field | V1 precedence |
+| --- | --- |
+| `buffer_size` | explicit run config, then documented default |
+| row-group concurrency | existing dataset-builder/runtime setting if present, then documented default |
+| task admission limits | benchmark override for benchmark runs, then engine default |
+| provider/model static cap | canonical model/provider metadata; request-admission config may lower but not raise it |
+| request-domain initial/adaptive settings | benchmark override or engine default, then clamped under provider/model static cap |
+| transport pool | adapter/client config, then documented default |
+
+If a value is missing, the capacity plan records the missing source and fallback used. If no safe fallback exists, construction fails with a typed configuration/metadata error before work is scheduled.
+
 ## Ownership Rules
 
 Task admission capacity is scheduler-level capacity. It controls when a ready task can become a running worker.
 
 Request admission capacity is provider/model/domain request capacity. It controls when a concrete model call can execute.
 
-`max_parallel_requests` remains the user-facing static provider/model cap and scheduling metadata weight source. `AdaptiveRequestAdmissionController.current_limit` is the runtime adaptive request cap.
+`max_parallel_requests` remains the user-facing static provider/model cap and scheduling metadata weight source. `AdaptiveRequestAdmissionController.current_limit` is the runtime adaptive request cap for a request domain.
+
+The provider/model static cap is an aggregate in-flight upper bound across all domains for that provider/model in V1. Domain adaptive limits operate under that aggregate cap. V1 intentionally does not define an aggregate cross-domain AIMD policy; adding one requires a later design that specifies fairness, telemetry, and benchmarks.
 
 HTTP transport pools may be larger than the static provider cap. They are transport sizing, not effective request concurrency.
 
@@ -45,9 +75,17 @@ HTTP transport pools may be larger than the static provider cap. They are transp
 
 `RunConfig.buffer_size` shapes record windows and row groups. It is not a request-concurrency knob.
 
+## Row Groups And Record Windows
+
+`buffer_size` defines the record-window shape used by the dataset builder. Row groups are the concrete execution partitions produced from that windowing behavior.
+
+Row-group admission remains scheduler-owned but is not changed by the V1 task-admission lease boundary. For this epic, #654 records row-group configured concurrency and observed row groups in flight through the `RowGroupAdmission` section of `AsyncCapacityPlan`; it does not introduce a new row-group scheduling policy unless a later issue explicitly does so.
+
+Preview, resume, and checkpoint behavior use the existing dataset-builder partitioning rules. `AsyncCapacityPlan` reports the row-group values that the current engine used rather than redefining those rules.
+
 ## Transitional Values
 
-Any current `max_llm_wait_tasks`, `needs_llm_wait`, or `held_llm_wait` concept is transitional. At epic completion these names must either be gone or replaced by explicit scheduler-resource terminology in `TaskAdmissionConfig` and `AsyncCapacityPlan`.
+Any current hidden LLM-wait task-stage capacity concept is transitional. At epic completion those names must either be gone or replaced by explicit scheduler-resource terminology in `TaskAdmissionConfig` and `AsyncCapacityPlan`.
 
 If a distinct task-stage LLM backpressure resource remains, it must be derived from actually used resolved scheduling metadata, not every registered model alias. It must be described as scheduler task-stage pressure, not provider request concurrency.
 
@@ -57,7 +95,9 @@ Scheduling metadata may use model aliases to derive static resource identity and
 
 Request admission resources are provider/model/domain scoped. A provider/model may have a global effective static cap while each request domain has its own adaptive state. The capacity plan must make that distinction visible.
 
-V1 does not define a cross-domain aggregate AIMD provider cap beyond the documented provider/model effective static cap unless a later issue explicitly adds that policy.
+V1 does not define a cross-domain aggregate AIMD provider cap beyond the documented provider/model effective static cap unless a later issue explicitly adds that policy. The request controller still enforces the static aggregate cap by checking provider/model aggregate in-flight counts before admitting a domain request.
+
+Alias-derived provider/model caps deduplicate aliases that resolve to the same concrete provider/model endpoint. If aliases for the same endpoint specify different `max_parallel_requests` values, V1 uses the minimum as the effective static cap and records every contributing alias and raw cap in `AsyncCapacityPlan`. This min-merge is not a metadata error. Alias resolution is fatal only when endpoint identity is ambiguous or conflicting. If the provider treats generation type as a distinct endpoint, the canonical model id includes that distinction before cap merging.
 
 ## Observability Requirements
 
@@ -70,6 +110,8 @@ Operators should be able to answer:
 
 Benchmarks and traces must include `AsyncCapacityPlan` plus per-layer observed maxima.
 
+Required per-layer maxima include row groups in flight, queued tasks by group/resource, task leases by resource, request waiters by resource, domain in-flight counts, provider/model aggregate in-flight counts, adaptive current limits, and transport pool utilization when available.
+
 ## Public Knob Rule
 
 Do not add a new public capacity knob until benchmark evidence shows a specific need and the docs explain its layer. Prefer clear defaults, internal configs, and diagnostics first.
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index 5e35a73fe..fc4991939 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -1,6 +1,28 @@
 # Contracts
 
-This file records the durable names and semantics used by the async scheduling architecture. The exact implementation can evolve, but these names are the spec vocabulary for the epic.
+This file records the durable names and semantics used by the async scheduling architecture. The exact implementation can evolve, but these names are the normative spec vocabulary for the epic. Topic files may explain behavior, but should not redefine fields or return shapes in ways that conflict with this file.
+
+## Package Ownership
+
+| Contract family | Owning package | Notes |
+| --- | --- | --- |
+| Generator metadata and public config DTOs | `data-designer-config` | `SchedulingMetadata`, metadata validation errors, and exposed run-config fields live here when they are public/user-facing. |
+| Scheduler/request runtime protocols | `data-designer-engine` | queues, controllers, policies, leases, runtime snapshots, event DTOs, capacity plan construction, and benchmark internals live here. |
+| User interface and operator presentation | `data-designer` | consumes config and engine contracts for the public `DataDesigner` interface, CLI, and integrations. |
+
+Config-layer contracts must not import engine runtime protocols. Engine contracts may consume config-layer DTOs.
+
+## Config Surface Status
+
+| Contract | V1 status | Owner |
+| --- | --- | --- |
+| `SchedulingMetadata` | public plugin-facing DTO | `data-designer-config` |
+| `TaskAdmissionConfig` | engine-internal config and benchmark injection surface; not a public `RunConfig` knob in V1 | `data-designer-engine` |
+| `RequestAdmissionConfig` | engine-internal config and benchmark injection surface in V1 | `data-designer-engine` |
+| `RunConfig.request_admission` | reserved future public spelling if evidence justifies a user-facing request-admission knob | not introduced by this epic unless a later issue explicitly changes the public API |
+| `AsyncCapacityPlan` | diagnostic/reporting DTO, emitted to explain a run | `data-designer-engine` |
+
+Public knobs are not added by the task-admission or request-admission implementation slices. Benchmarks may inject capacity values through harness-only configuration so policy evidence can be produced without committing to public API.
 
 ## Metadata Contracts
 
@@ -12,7 +34,7 @@ This file records the durable names and semantics used by the async scheduling a
 - `identity`: deterministic tuple of broad-to-specific resource identity values.
 - `weight`: positive static capacity hint.
 
-`SchedulingMetadataError` is the typed failure path for metadata resolution. It can carry fallback metadata when partial resolution is safe.
+`SchedulingMetadataError` is the typed failure path for metadata resolution. It can carry fallback metadata when partial resolution is safe. The documented default metadata for generators that do not override `get_scheduling_metadata()` is a normal resolver path, not an error fallback.
 
 Rules:
 
@@ -20,6 +42,27 @@ Rules:
 - Metadata cannot encode queue depth, admitted limits, runtime pressure, request domains, AIMD state, or provider cooldown.
 - Multi-alias metadata deduplicates aliases that resolve to the same provider/model/generation resource before summing weight.
 - Alias ordering is canonicalized so equivalent configs produce equivalent metadata.
+- Generators that do not override `get_scheduling_metadata()` receive a documented default metadata value. The default must preserve current behavior and must not infer provider/model pressure dynamically.
+- Invalid `kind`, non-deterministic `identity`, non-positive `weight`, or ambiguous alias resolution raises `SchedulingMetadataError`.
+- Differing `max_parallel_requests` values for aliases that resolve to the same concrete provider/model endpoint are not, by themselves, ambiguous. They merge through the static-cap min rule in the capacity model. Alias ambiguity is fatal when aliases cannot be resolved to one concrete endpoint, resolve to conflicting endpoint identities, or would require hidden dynamic inference to choose a resource.
+- Fallback metadata is safe only when it preserves current scheduling behavior and the resolver can explain the fallback in diagnostics. Ambiguous resource identity or invalid weights are fatal.
+
+Normative V1 metadata shapes:
+
+| Kind | Identity tuple | Weight source | Default/fallback behavior |
+| --- | --- | --- | --- |
+| `local` | `("local", resource_name)` where `resource_name` defaults to `"default"` | positive integer, default `1` | the default for generators that do not override `get_scheduling_metadata()` is `SchedulingMetadata(kind="local", identity=("local", "default"), weight=1)` |
+| `model` | `("model", provider_name, canonical_model_id, generation_kind)` after alias resolution | effective static provider/model capacity hint, normally derived from the model config's `max_parallel_requests` and clamped to at least `1` | safe fallback is allowed only when the resolver can identify the same canonical provider/model resource as the current implementation |
+| `custom_model` | `("custom_model", plugin_namespace, resource_name, version)` with deterministic plugin-provided values | positive plugin-provided capacity hint, defaulting to `1` if omitted | no alias inference is performed unless the plugin returns model aliases through the documented metadata API |
+
+`SchedulingMetadataError` contains:
+
+- `code`
+- `message`
+- optional `fallback: SchedulingMetadata`
+- sanitized `diagnostics`
+
+If `fallback` is present, the resolver may continue and must emit diagnostics. If `fallback` is absent, metadata resolution is fatal before scheduler inputs are created.
 
 ## Scheduler Input Contracts
 
@@ -38,20 +81,33 @@ Rules:
 amounts: Mapping[SchedulerResourceKey, int]
 ```
 
+`SchedulerResourceKey` identifies a scheduler-owned task-stage resource such as `submission`, `llm_wait`, `local`, or a future internal resource-vector key. It is not a provider request-domain key.
+
 The first implementation can model submission and LLM-wait style resources. Future resource-vector work may add provider/model, local, GPU, or other scheduler resources, but those remain scheduler-internal unless a later design explicitly changes the public contract.
 
 `SchedulableTask` contains:
 
+- stable `task_id`
 - task payload
 - task group
 - scheduler resource request
 
+`CompletionTracker` owns readiness state:
+
+```text
+ready_frontier() -> Sequence[SchedulableTask]
+mark_enqueued(task_ids)
+mark_complete(task)
+```
+
+`ready_frontier()` returns dependency-ready tasks that have not yet been acknowledged as enqueued. After `FairTaskQueue.enqueue(...)` accepts a task, `AsyncTaskScheduler` calls `mark_enqueued(...)` with exactly the accepted task ids. `FairTaskQueue.enqueue(...)` is also idempotent by `task_id`, so duplicate frontier reads cannot create duplicate ready membership. If enqueue fails before acceptance, the task remains unacknowledged and appears in a later frontier read.
+
 ## Queue Contracts
 
 `FairTaskQueue` owns ready-task membership and ready ordering:
 
 ```text
-enqueue(item)
+enqueue(items) -> Sequence[task_id]
 select_next(is_eligible) -> QueueSelection | None
 commit(selection) -> SchedulableTask | None
 view() -> QueueView
@@ -59,9 +115,19 @@ view() -> QueueView
 
 `QueueSelection` returns from `FairTaskQueue` to `AsyncTaskScheduler`. It is not delivered to `TaskAdmissionController`.
 
-`QueueSelection` contains the selected item and an opaque sequence/version used by `commit(selection)` to detect stale selections.
+`QueueSelection` contains the selected item, the queue view used during selection, and an opaque `sequence_version` used by `commit(selection)` to detect stale selections.
+
+`QueueView` is read-only policy input. It exposes:
+
+- queued totals
+- queued counts by group
+- queued resource demand by group and `SchedulerResourceKey`
+- first-candidate resource request by group where available
+- queued peer demand by resource
 
-`QueueView` is read-only policy input. It exposes queued totals and queued counts by group, including whether a group has queued peers.
+`QueueView` is produced by `FairTaskQueue`; policies must not traverse queue internals directly. It contains raw queued membership and demand facts only. `TaskAdmissionPolicy` computes eligibility and resource-aware peer pressure from `QueueView` plus `TaskAdmissionView`.
+
+`FairTaskQueue` must not invoke scheduler-supplied eligibility predicates while holding internal queue locks that can be needed by enqueue, commit, release wakeups, or diagnostics. The scheduler dispatch critical section owns the cross-component coordination; queue internals remain local to queue mutation.
 
 ## Task Admission Contracts
 
@@ -69,48 +135,134 @@ view() -> QueueView
 
 ```text
 is_eligible(item, queue_view) -> bool
-try_acquire(item, queue_view) -> TaskAdmissionLease | None
-release(lease)
+try_acquire(item, queue_view) -> TaskAdmissionDecision
+release(lease) -> ReleaseResult
 view() -> TaskAdmissionView
+explain_blocked(queue_view) -> TaskAdmissionBlockSummary
 ```
 
 `TaskAdmissionPolicy` owns the decision rule:
 
 ```text
-is_eligible(item, queue_view, admission_view) -> bool
-on_acquire(lease)
-on_release(lease)
+evaluate(item, queue_view, admission_view) -> TaskAdmissionPolicyDecision
+on_acquire(lease, decision) -> PolicyStateDelta
+on_release(lease) -> PolicyStateDelta
 ```
 
+`evaluate(...)` is side-effect-free. It may be called while scanning queue candidates and must not mutate borrow debt, counters, timers, diagnostics, or resource ledgers. Only controller-mediated acquire/release paths apply `PolicyStateDelta` values.
+
+`TaskAdmissionPolicyDecision` contains:
+
+- `allowed`
+- optional denial `reason`, such as no capacity, group cap, borrow debt, shutdown, or policy denial
+- optional `available_after`
+- sanitized diagnostic fields
+
+`PolicyStateDelta` contains policy-owned state changes such as borrow-debt increment, repayment, or diagnostic counters. The controller applies the delta in the same transaction as lease acquire/release and exposes the resulting policy counters through `TaskAdmissionView`. Bounded-borrow debt affects eligibility, but it is not part of the hard resource ledger and never changes resource availability counters directly.
+
+`AsyncTaskScheduler` supplies the boolean eligibility callback used by `FairTaskQueue`; that callback delegates to `TaskAdmissionController.is_eligible(...)`. The controller may call `TaskAdmissionPolicy.evaluate(...)` internally, but denial details are surfaced through `try_acquire(...)`, `explain_blocked(...)`, events, and tests rather than through the queue callback.
+
+When `FairTaskQueue.select_next(...)` returns no selection while queued work exists, `AsyncTaskScheduler` calls `TaskAdmissionController.explain_blocked(queue_view)` before sleeping. `TaskAdmissionBlockSummary` contains queued count, dominant denial reasons, optional earliest `available_after`, and sanitized diagnostics. This is the source for `admission_blocked`, `group_capped`, and timed wakeups when no candidate can currently be admitted.
+
 `TaskAdmissionLease` contains:
 
+- `lease_id`
 - `item`
 - `resources`
 - `acquired_at`
+- controller identity or generation token sufficient to reject stale/wrong-controller releases
+
+`TaskAdmissionView` exposes a consistent read-only snapshot:
+
+- task resource limits by `SchedulerResourceKey`
+- task resources available by `SchedulerResourceKey`
+- leased resources by `SchedulerResourceKey`
+- leased resources by group and `SchedulerResourceKey`
+- running counts by group and resource where tracked
+- policy-only debt by group/resource if the active policy uses bounded borrow
+
+`TaskAdmissionDecision` is a union of `TaskAdmissionLease` and `TaskAdmissionDenied`.
+
+`TaskAdmissionDenied` contains:
+
+- item
+- reason, such as no capacity, group cap, borrow debt, shutdown, or policy denial
+- optional available-after timing
+- optional `TaskAdmissionView` snapshot
 
-`TaskAdmissionView` exposes task-stage resource availability plus leased/running counts by group.
+Implementations may provide a local convenience helper that converts `TaskAdmissionDecision` to an optional lease, but telemetry, tests, and benchmark artifacts use the typed decision vocabulary.
 
-`TaskAdmissionDecision` is the richer decision shape for implementations that need denial reasons. V1 may expose `try_acquire(...)` as a non-blocking `TaskAdmissionLease | None` helper, but the spec vocabulary treats denials as typed scheduler-admission outcomes for events, tests, and debugging.
+`TaskAdmissionConfig` is engine-internal in V1 and contains scheduler task-stage capacity values such as `submission_capacity`, resource limits, and optional policy-specific config. Bounded-borrow policy config, when enabled by #650, includes borrow ceiling by group/resource, strict-share rounding mode, and repayment behavior. The default V1 lease-boundary policy is behavior-preserving unless #650 explicitly enables bounded borrow.
 
-`TaskAdmissionConfig` contains scheduler task-stage capacity values such as `submission_capacity` and resource limits.
+`ReleaseResult` contains:
+
+- `released: bool`
+- `reason`, such as released, duplicate, stale lease, wrong controller generation, or unknown lease
+- sanitized diagnostics
+
+Terminal `finally` paths must not raise from release. Duplicate, stale, or wrong-controller releases return `ReleaseResult` and emit diagnostic events without increasing capacity.
 
 ## Request Admission Contracts
 
-`ModelRequestExecutor` maps concrete model calls into request-admission items and owns exact lease release around provider execution.
+`ModelRequestExecutor` maps concrete model-call attempts into request-admission items and owns exact lease release around provider execution:
+
+```text
+execute_attempt(request) -> provider response
+```
+
+`RequestResourceResolver` is the canonical request-resource identity factory. It maps provider alias, model alias, model id, generation kind, endpoint metadata, and `RequestDomain` into `ProviderModelKey` and `RequestResourceKey`. `TaskSchedulingResolver`, `ModelRequestExecutor`, `AsyncCapacityPlan`, and request admission all use the same provider/model canonicalization rules so alias merging, metadata weight, and request caps cannot drift.
 
 `RequestResourceKey` identifies a concrete provider/model/domain request resource:
 
-- `provider_name`
-- `model_id`
+- `provider_name`, the canonical resolved provider name, not an alias
+- `model_id`, the canonical resolved provider/model endpoint id, not a user alias
 - `domain`
 
-`RequestGroupSpec` contains the request resource key and static weight.
+Aliases are recorded in capacity plans and pressure snapshots for diagnostics, but request admission keys use canonical resolved provider/model identity so aliases cannot bypass aggregate caps.
+
+`ProviderModelKey` is the aggregate request-capacity key:
+
+- canonical provider name
+- canonical model endpoint id
+
+`RequestResourceKey` is `ProviderModelKey + RequestDomain`.
+
+`RequestDomain` is the durable domain vocabulary for request admission. V1 includes `chat`, `embedding`, `image`, and `healthcheck`; adding new domains requires updating this plan and the request-admission docs.
+
+`RequestGroupSpec` contains the request fairness group key and static weight. In V1 the group key is the `RequestResourceKey`; a later design may split fairness group from resource key, but must specify the mapping before doing so.
 
 `RequestAdmissionItem` contains:
 
 - request resource
 - request group
-- optional timeout
+- optional queue-wait timeout
+- optional `RequestEventContext`
+
+`RequestEventContext` is constructed by `ModelRequestExecutor` when it maps a model call attempt into a request item. It contains primitive, telemetry-only context:
+
+- captured `RuntimeCorrelation | None`
+- `task_execution_id`
+- `request_attempt_id`
+
+The request controller treats this as opaque event context. It does not import scheduler task types or mutate scheduler state.
+
+`RequestFairQueue` owns waiter ordering inside `AdaptiveRequestAdmissionController`:
+
+```text
+enqueue(waiter)
+select_next(is_eligible) -> RequestQueueSelection | None
+commit(selection) -> RequestWaiter | None
+remove(waiter_id)
+view() -> RequestQueueView
+```
+
+`RequestWaiter` contains waiter id, item, enqueue timestamp, deadline/cancellation state, and the waiter completion handle used by the blocking acquire path.
+
+`RequestQueueSelection` contains the selected waiter, item, waiter id, queue view, and opaque `sequence_version` for stale-selection detection.
+
+`RequestQueueView` exposes queued totals, queued counts by request group, queued demand by request resource, and aggregate provider/model waiters. It does not inspect adaptive limit state.
+
+`try_acquire(...)` is non-blocking. It may immediately acquire only when the request is eligible and no queued eligible waiter for the same request resource or provider/model aggregate cap would be selected before the incoming item by `RequestFairQueue`'s weighted ordering. Otherwise it returns `RequestAdmissionDenied` with reason `queued_waiters_ahead` or another specific denial reason.
 
 `RequestAdmissionController` owns request-level admission:
 
@@ -118,30 +270,66 @@ on_release(lease)
 try_acquire(item) -> RequestAdmissionDecision
 acquire_sync(item) -> RequestAdmissionLease
 acquire_async(item) -> RequestAdmissionLease
-release_success(lease)
-release_rate_limited(lease, retry_after)
-release_failure(lease)
+release(lease, outcome) -> ReleaseResult
 pressure -> RequestPressureSnapshotProvider
 ```
 
+`acquire_sync(...)` and `acquire_async(...)` wait until a lease is available or a terminal no-lease condition occurs. Timeout, shutdown, or hard denial before a lease is acquired must remove the waiter and raise a typed project error that carries the corresponding `RequestAdmissionDenied` decision. They must not return `None`.
+
+`RequestAdmissionError` is the typed no-lease exception raised by blocking acquire paths. It wraps `RequestAdmissionDenied` and must not be raised after a lease has been returned; post-lease provider outcomes are represented by `RequestReleaseOutcome`.
+
+`acquire_async(...)` must preserve cooperative cancellation. If the awaiting task is cancelled before a lease is acquired, the controller removes the waiter, emits a cancellation/denial event, and re-raises the cancellation exception instead of converting it to `RequestAdmissionError`.
+
+Once a waiter is selected and in-flight counts are incremented, cancellation cannot orphan the lease. The controller either delivers the lease to that waiter's acquire call so caller cleanup can release it, or internally releases the admitted waiter as `local_cancelled` before completing cancellation. A caller's `acquire_async(item)` may only return the lease for its own waiter; if the controller admits another waiter while this caller is awake, it fulfills that other waiter's completion handle and this caller continues waiting.
+
 `RequestAdmissionDecision` is a union of `RequestAdmissionLease` and `RequestAdmissionDenied`.
 
 `RequestAdmissionLease` contains:
 
+- `lease_id`
 - item
 - acquired timestamp
 - current adaptive limit
 - effective max
+- controller identity or generation token sufficient to reject stale/wrong-controller releases
 
 `RequestAdmissionDenied` contains:
 
 - item
-- reason
-- retry-after or available-after timing where applicable
+- reason, such as no capacity, cooldown, queue timeout, queued waiters ahead, cancellation, shutdown, or hard policy denial
+- `retry_after_seconds` when supplied by the provider or policy
+- `available_after_monotonic` when the controller can compute an unblock deadline
 - optional snapshot
 
+`RequestReleaseOutcome` contains:
+
+- `kind`: one of `success`, `rate_limited`, `provider_failure`, `provider_timeout`, `local_cancelled`, `local_timeout`, or `unexpected_exception`
+- `retry_after_seconds` when rate limited
+- provider/status metadata safe for telemetry
+
+Only provider rate-limit outcomes drive multiplicative decrease/cooldown. Provider failures may affect diagnostic counters. Local cancellation and local timeout release capacity and wake waiters but must not be treated as provider pressure unless a later policy explicitly defines that behavior.
+
+`provider_timeout` is a timeout or timeout-shaped transport/provider failure after a lease has been acquired and an outbound provider attempt has started. `local_timeout` is a caller, queue-wait, or controller deadline that is not evidence of provider pressure. Cancellation after lease acquisition is classified as `local_cancelled`; release diagnostics must not mask the original cancellation and the cancellation is re-raised after accounting.
+
 `AdaptiveRequestAdmissionController` is the V1 concrete request controller. It owns AIMD behavior through internal `RequestFairQueue`, `RequestAdmissionPolicy`, and `AdaptiveRequestLimitState`.
 
+Request admission acquires under one controller lock/condition. An admitted lease increments domain in-flight counts and provider/model aggregate in-flight counts before the lease is returned. Release decrements those counts exactly once and wakes eligible waiters.
+
+Cross-domain arbitration under a provider/model aggregate cap uses `RequestFairQueue` ordering by `RequestGroupSpec` weight. V1 uses weighted fair ordering across request groups sharing the aggregate cap; if weights are equal, older waiters are selected first.
+
+V1 AIMD semantics:
+
+- `effective_max = min(provider_model_static_cap, request_config.max_limit_clamp_for_resource_if_present)`
+- instantaneous aggregate availability is checked separately as `provider_model_aggregate_in_flight < provider_model_static_cap`
+- `initial_limit` is clamped to `[1, effective_max]`
+- `current_limit` starts at `initial_limit`
+- on `rate_limited`, `current_limit = max(1, floor(current_limit * multiplicative_decrease_factor))`, `blocked_until_monotonic` is set from provider `retry_after_seconds` when supplied or the configured cooldown otherwise, and rate-limit counters increment
+- on success outside cooldown, successful releases accumulate; after `increase_after_successes` successes, `current_limit = min(effective_max, current_limit + additive_increase_step)`
+- `request_soft_ceiling_recovered` fires when `current_limit` rises above the last rate-limit ceiling
+- `request_fully_recovered` fires when `current_limit == effective_max` and cooldown has cleared
+- all timing uses a monotonic clock
+- waiters use timed waits to the earliest relevant monotonic deadline: queue-wait timeout, cancellation, `available_after_monotonic`, or `blocked_until_monotonic`. Cooldown expiry must wake queued waiters even when no in-flight request releases.
+
 `RequestPressureSnapshotProvider` exposes read-only request pressure:
 
 ```text
@@ -153,7 +341,9 @@ global_snapshots()
 
 It has no mutation or admission methods.
 
-`RequestAdmissionConfig` is the durable request-admission tuning/config vocabulary. `ThrottleConfig` and `RunConfig.throttle` are not durable names.
+Snapshots are immutable and internally consistent for their capture point. Domain snapshots include `captured_at`, monotonic `sequence`, resource, effective max, current limit, in-flight count, active lease count, waiters, blocked-until timing, cooldown remaining, rate-limit ceiling, consecutive rate limits, last outcome summary, and leak diagnostic counters. Global provider/model snapshots include aggregate static cap, aggregate in-flight count across domains, aggregate active lease count, aliases contributing to the cap, and per-domain limit summaries.
+
+`RequestAdmissionConfig` is the durable engine-internal request-admission tuning/config vocabulary for V1. It includes request resources, per-resource `initial_limit`, optional `max_limit_clamp`, configured cooldown, `multiplicative_decrease_factor`, `additive_increase_step`, `increase_after_successes`, and default queue-wait timeout. Legacy request-control config names are not durable names.
 
 ## Telemetry And Correlation Contracts
 
@@ -169,21 +359,150 @@ It has no mutation or admission methods.
 - task type
 - scheduling group kind
 - scheduling group identity hash
+- task execution id
 
 `RuntimeCorrelationProvider` owns set/reset/current behavior, likely through context variables. It must not require request admission protocols to import scheduler types.
 
+Scheduler/request events capture primitive correlation values when event DTOs are constructed. Event sinks must not rely on reading mutable ambient context later, because deferred emission could attach the wrong task context.
+
+Canonical scheduler `event_kind` values are snake_case and versioned as part of the benchmark artifact schema:
+
+```text
+dependency_ready
+ready_enqueued
+selected
+queue_empty
+admission_blocked
+group_capped
+task_lease_acquired
+admission_denied
+worker_spawned
+worker_spawn_failed
+stale_selection
+retry_deferred
+non_retryable_dropped
+cancelled
+salvage_redispatched
+queue_drained
+task_completed
+task_lease_released
+release_diagnostic
+```
+
+`SchedulerAdmissionEvent` contains:
+
+- `event_kind`
+- `captured_at_monotonic`
+- monotonic `sequence`
+- captured `RuntimeCorrelation | None`
+- task id
+- task execution id when a worker execution exists
+- task lease id when available
+- scheduler resource key when applicable
+- decision reason or release result when applicable
+- optional scheduler snapshot
+- sanitized diagnostics
+
+Canonical request `event_kind` values are snake_case and versioned as part of the benchmark artifact schema:
+
+```text
+request_resource_registered
+request_effective_cap_changed
+request_queue_formed
+request_wait_started
+request_wait_completed
+request_wait_timeout
+request_wait_cancelled
+request_acquire_denied
+request_lease_acquired
+model_request_started
+model_request_completed
+request_queue_drained
+request_rate_limited
+request_limit_decreased
+request_limit_increased
+request_soft_ceiling_recovered
+request_fully_recovered
+request_lease_released
+request_release_diagnostic
+```
+
+`RequestAdmissionEvent` contains:
+
+- `event_kind`
+- `captured_at_monotonic`
+- monotonic `sequence`
+- captured `RuntimeCorrelation | None`
+- request attempt id when the event belongs to one concrete model-call attempt
+- request lease id when available
+- canonical `RequestResourceKey` when the event is resource-specific
+- request group key when the event is queue/admission specific
+- denial reason or release outcome when applicable
+- optional request pressure snapshot
+- sanitized diagnostics
+
+Lease ids, task ids, request attempt ids, and raw model ids are trace/artifact fields only; they are not metric labels. Metric exporters use bounded labels such as `metric_model_label`, model family, or allowlisted model label. The OTel bridge must reject raw model ids as metric labels.
+
 `CorrelatedRuntimeView` joins scheduler and request timelines for diagnostics, benchmarks, and future operator views.
 
 ## Capacity Contracts
 
 `AsyncCapacityPlan` records computed per-run capacity values:
 
-- buffer size
-- row-group concurrency
-- submission capacity
-- task admission config
-- request admission config
-- static provider/model caps when available
-- source of each value
+```text
+CapacityValue[T]:
+  value: T | None
+  source: default | run_config | dataset_builder | model_metadata | engine_internal_config | adapter_config | environment | runtime_snapshot | benchmark_override
+  fallback_from: str | None
+  missing_reason: str | None
+
+RowGroupAdmission:
+  row_group_concurrency: CapacityValue[int]
+  observed_in_flight: int | None
+
+ProviderModelStaticCap:
+  cap: int
+  aliases: Sequence[str]
+  raw_caps: Mapping[str, int | None]
+  merge_rule: min_same_endpoint
+
+RequestAdmissionConfigSnapshot:
+  resources: Sequence[RequestResourceKey]
+  initial_limits: Mapping[RequestResourceKey, int]
+  max_limit_clamps: Mapping[RequestResourceKey, int | None]
+  cooldown_seconds: float
+  multiplicative_decrease_factor: float
+  additive_increase_step: int
+  increase_after_successes: int
+  default_queue_wait_timeout_seconds: float | None
+
+AsyncCapacityPlan:
+  configured:
+    buffer_size: CapacityValue[int]
+    row_group_admission: RowGroupAdmission
+    submission_capacity: CapacityValue[int]
+    task_resource_limits: CapacityValue[Mapping[SchedulerResourceKey, int]]
+    request_resources: CapacityValue[Sequence[RequestResourceKey]]
+    provider_model_static_caps: CapacityValue[Mapping[ProviderModelKey, ProviderModelStaticCap]]
+    request_domain_initial_limits: CapacityValue[Mapping[RequestResourceKey, int]]
+    request_admission_config: CapacityValue[RequestAdmissionConfigSnapshot]
+    transport_pool_limits: CapacityValue[Mapping[ProviderModelKey, int]]
+  runtime_snapshot:
+    request_domain_current_limits: Mapping[RequestResourceKey, int] | None
+    request_domain_effective_max: Mapping[RequestResourceKey, int] | None
+    request_domain_blocked_until: Mapping[RequestResourceKey, float | None] | None
+    provider_model_aggregate_in_flight: Mapping[ProviderModelKey, int] | None
+  observed_maxima:
+    row_groups_in_flight: int
+    queued_tasks_by_group: Mapping[str, int]
+    task_leases_by_resource: Mapping[SchedulerResourceKey, int]
+    request_waiters_by_resource: Mapping[RequestResourceKey, int]
+    request_in_flight_by_resource: Mapping[RequestResourceKey, int]
+    provider_model_aggregate_in_flight: Mapping[ProviderModelKey, int]
+    request_domain_current_limits: Mapping[RequestResourceKey, int]
+    transport_pool_utilization: Mapping[ProviderModelKey, int] | None
+```
+
+Fields that depend on request-admission runtime state may be `None` in #654 before #657 lands, but the capacity plan and benchmark artifact must still include the field with `missing_reason` or an equivalent `not_available_until_issue` marker.
 
 The capacity plan explains observed runtime behavior. It is not itself a policy engine.
diff --git a/plans/645/issue-map.md b/plans/645/issue-map.md
index d321b2364..4e0415948 100644
--- a/plans/645/issue-map.md
+++ b/plans/645/issue-map.md
@@ -25,14 +25,14 @@ Use GitHub issues for:
 | #652 | Document plugin-facing metadata behavior | [architecture.md](architecture.md), [contracts.md](contracts.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
 | #644 | Implement task admission lease boundary | [task-admission.md](task-admission.md), [contracts.md](contracts.md), [benchmark-plan.md](benchmark-plan.md) |
 | #654 | Implement and document capacity vocabulary and snapshots | [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md) |
-| #657 | Refactor model-call throttling into request admission | [request-admission.md](request-admission.md), [contracts.md](contracts.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
-| #635 | Instrument request admission state | [observability.md](observability.md), [request-admission.md](request-admission.md), [contracts.md](contracts.md) |
-| #647 | Instrument scheduler admission state | [observability.md](observability.md), [task-admission.md](task-admission.md), [contracts.md](contracts.md) |
-| #648 | Correlate scheduler and request observability | [observability.md](observability.md), [architecture.md](architecture.md) |
-| #649 | Build reusable benchmark harness | [benchmark-plan.md](benchmark-plan.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md) |
-| #660 | Produce final user/operator docs | [architecture.md](architecture.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
+| #657 | Refactor model-call request control into request admission | [request-admission.md](request-admission.md), [contracts.md](contracts.md), [migration-and-cleanup.md](migration-and-cleanup.md), [benchmark-plan.md](benchmark-plan.md) |
+| #635 | Instrument request admission state | [observability.md](observability.md), [request-admission.md](request-admission.md), [contracts.md](contracts.md), [benchmark-plan.md](benchmark-plan.md) |
+| #647 | Instrument scheduler admission state | [observability.md](observability.md), [task-admission.md](task-admission.md), [contracts.md](contracts.md), [benchmark-plan.md](benchmark-plan.md) |
+| #648 | Correlate scheduler and request observability | [observability.md](observability.md), [architecture.md](architecture.md), [benchmark-plan.md](benchmark-plan.md) |
+| #649 | Build reusable benchmark harness and normalize provisional evidence | [benchmark-plan.md](benchmark-plan.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [task-admission.md](task-admission.md), [request-admission.md](request-admission.md) |
+| #660 | Produce final user/operator docs | [architecture.md](architecture.md), [contracts.md](contracts.md), [request-admission.md](request-admission.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
 | #650 | Implement bounded-borrow task policy | [task-admission.md](task-admission.md), [benchmark-plan.md](benchmark-plan.md), [capacity-model.md](capacity-model.md) |
-| #651 | Design resource-vector/provider-aware policy | [task-admission.md](task-admission.md), [capacity-model.md](capacity-model.md), [benchmark-plan.md](benchmark-plan.md) |
+| #651 | Design resource-vector/provider-aware policy | [task-admission.md](task-admission.md), [request-admission.md](request-admission.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md) |
 
 ## Dependency Order
 
@@ -43,7 +43,15 @@ The implementation order remains:
 -> #635 -> #647 -> #648 -> #649 -> #660 -> #650 -> #651
 ```
 
-#641 and #644 may proceed independently only while #646 preserves the adapter contract between them. The accepted end state is `SchedulingMetadata` feeding task admission through `TaskSchedulingResolver`.
+#644 may be prototyped before the metadata chain fully lands only if it remains behind an interim adapter and cannot close until #646/#652 preserve the accepted contract. The accepted end state is `SchedulingMetadata` feeding task admission through `TaskSchedulingResolver`.
+
+#660 promotes the stabilized V1 admission/capacity/telemetry docs. #650 and #651 are follow-on policy/design issues; if they change behavior or public/operator guidance, they must update this source-of-truth plan and any promoted docs as part of their own acceptance gates. #651 is design-first unless its issue body explicitly promotes an implementation slice.
+
+## Evidence Phasing
+
+The native issue order keeps #649 after capacity, request admission, telemetry, and correlation because the reusable harness consumes those contracts. That does not waive evidence for earlier implementation PRs.
+
+Before #649 closes, issues #644, #654, #657, #635, #647, and #648 must produce provisional benchmark/evidence artifacts using the schema in [benchmark-plan.md](benchmark-plan.md). A minimal deterministic smoke writer should exist before those slices rely on one-off evidence. Issue #649 then converts those provisional artifacts into the reusable harness, reruns representative scenarios, and becomes the gate for #660, #650, and #651.
 
 ## Issue Body Cleanup Pattern
 
diff --git a/plans/645/migration-and-cleanup.md b/plans/645/migration-and-cleanup.md
index 6d21bfb72..e02e7375a 100644
--- a/plans/645/migration-and-cleanup.md
+++ b/plans/645/migration-and-cleanup.md
@@ -1,6 +1,6 @@
 # Migration And Cleanup
 
-The epic is not complete until replaced names and compatibility paths are removed from production code and current docs.
+The epic is not complete until replaced names and compatibility paths are removed from production code, current docs, and this source-of-truth plan.
 
 ## Scheduling Metadata Cleanup
 
@@ -33,7 +33,6 @@ The durable request-admission names are:
 - `RequestAdmissionController`
 - `AdaptiveRequestAdmissionController`
 - `RequestAdmissionConfig`
-- `RunConfig.request_admission`
 - `RequestDomain`
 
 Final search gate should have no production/current-doc matches for:
@@ -44,6 +43,8 @@ ThrottleDomain
 ThrottleConfig
 RunConfig.throttle
 throttle_manager.py
+ThrottledModelClient
+throttled_model_client
 ```
 
 Historical changelog text may remain only if it is clearly marked as historical and not presented as current API.
@@ -74,11 +75,11 @@ By #653 close, legacy scheduling-hint production paths are gone and tests have m
 
 By #657 close, request-admission code has no production `Throttle*` aliases, exports, modules, or durable tests.
 
-By #645 close, public/current docs use only the durable architecture vocabulary. Historical changelog or dev-note text can remain only when explicitly marked historical.
+By #645 close, public/current docs and `plans/645` use only the durable architecture vocabulary except for this cleanup file's explicit legacy-name search lists. Historical changelog or dev-note text can remain only when explicitly marked historical.
 
 ## Documentation Cleanup
 
-Current user/operator docs should use:
+Current maintainer architecture docs should use durable internal names when they discuss implementation internals:
 
 - `SchedulingMetadata`
 - `TaskSchedulingResolver`
@@ -91,16 +92,22 @@ Current user/operator docs should use:
 - `RequestAdmissionConfig`
 - `RuntimeCorrelationProvider`
 
-Docs must not present scheduler internals as plugin API. Plugin-facing docs should describe metadata only, then link to architecture docs for maintainers/operators.
+User/operator docs should expose public run config fields, `AsyncCapacityPlan`, benchmark artifacts, telemetry views, and high-level layer names. They must not present `TaskAdmissionConfig`, `RequestAdmissionConfig`, policies, leases, queues, or controller mutation APIs as public user knobs. Plugin-facing docs should describe metadata only, then link to architecture docs for maintainers/operators.
+
+Current architecture docs, diagrams, generated assets, and plan files must be checked as part of final cleanup. Existing historical dev notes may retain old names only when the text clearly says the name is historical and no longer current API.
+
+Current user/operator architecture docs must also remove or mark as historical semaphore/throttling descriptions that imply the pre-epic architecture. This includes old model-client throttling names and semaphore-based scheduling explanations.
 
 ## Validation Commands
 
 Adjust paths as files move, but final PRs should include searches equivalent to:
 
 ```bash
-rg "SchedulingHintResolver|SchedulingHint|_model_aliases_for_generator|is_llm_bound" packages docs fern
-rg "ThrottleManager|ThrottleDomain|ThrottleConfig|RunConfig\\.throttle|throttle_manager\\.py" packages docs fern
-rg "needs_llm_wait|held_llm_wait|max_llm_wait_tasks" packages docs fern
+rg "SchedulingHintResolver|SchedulingHint|_model_aliases_for_generator|is_llm_bound" packages docs fern architecture plans/645
+rg "ThrottleManager|ThrottleDomain|ThrottleConfig|RunConfig\\.throttle|throttle_manager\\.py|ThrottledModelClient|throttled_model_client" packages docs fern architecture plans/645
+rg "_submission_semaphore|_llm_wait_semaphore|get_semaphore_permits|TrackingSemaphore" packages docs fern architecture plans/645
+rg "throttl(e|ed|ing)|semaphore" docs fern architecture plans/645
+rg "needs_llm_wait|held_llm_wait|max_llm_wait_tasks" packages docs fern architecture plans/645
 ```
 
-Any remaining hit must be intentionally historical, not a current implementation or docs path.
+Any remaining hit must be intentionally historical, not a current implementation or docs path. Allowed plan hits are limited to explicit cleanup/search-gate sections that name the legacy strings so reviewers know what to remove. The task-stage semaphore-specific search distinguishes obsolete submission/LLM-wait scheduling semaphores from unrelated internal synchronization primitives that may remain after review.
diff --git a/plans/645/observability.md b/plans/645/observability.md
index 9520dbc47..ffc76ef3a 100644
--- a/plans/645/observability.md
+++ b/plans/645/observability.md
@@ -12,69 +12,95 @@ Both sinks are generic first. OpenTelemetry, structured logs, dashboards, benchm
 
 Sink failures must never interrupt generation. Event data can be collected under locks, but event emission should happen after locks are released.
 
+Event DTOs capture primitive correlation fields at construction time. Sinks receive already-captured event data; they must not read ambient context later to discover which task/request an event belongs to.
+
+All event DTOs include `captured_at_monotonic` and a monotonic per-stream `sequence`. Scheduler events include task id, task execution id when a worker execution exists, task lease id when available, scheduler resource key when applicable, denial/release reason when applicable, optional snapshot, and sanitized diagnostics. Request events include request attempt id when they belong to one concrete attempt, request lease id when available, canonical request resource when resource-specific, request group key when queue/admission-specific, denial/release outcome when applicable, optional pressure snapshot, and sanitized diagnostics.
+
 ## Scheduler Admission Events
 
 Scheduler events describe dependency-ready work moving through ready ordering, task admission, worker spawn, and task lease release.
 
-Representative event kinds:
-
-- ready enqueued
-- selected
-- lease acquired
-- worker spawned
-- admission blocked
-- group capped
-- stale selection
-- retry deferred
-- non-retryable dropped
-- cancelled
-- salvage redispatched
-- queue drained
-- lease released
-- completed
+Canonical scheduler event kinds:
+
+- `dependency_ready`
+- `ready_enqueued`
+- `selected`
+- `queue_empty`
+- `admission_blocked`
+- `group_capped`
+- `task_lease_acquired`
+- `admission_denied`
+- `worker_spawned`
+- `worker_spawn_failed`
+- `stale_selection`
+- `retry_deferred`
+- `non_retryable_dropped`
+- `cancelled`
+- `salvage_redispatched`
+- `queue_drained`
+- `task_completed`
+- `task_lease_released`
+- `release_diagnostic`
 
 Scheduler snapshots include:
 
 - queued total
 - queued by group
+- queued demand by group/resource
 - admitted/running by group
-- submission available
-- scheduler resources available
+- resource limits by scheduler resource
+- scheduler resources available by resource
+- leased resources by group/resource
+- active task lease count by resource
+- release diagnostic counters
+- bounded-borrow debt by group/resource when applicable
 
-Scheduler events must make `spawned_waiting_for_llm_lease` derivable and zero after the task-admission lease boundary lands.
+Scheduler events must make hidden scheduler-resource waiters derivable and zero after the task-admission lease boundary lands.
 
 ## Request Admission Events
 
 Request events describe provider/model/domain request admission and AIMD behavior.
 
-Representative event kinds:
-
-- registered
-- effective cap changed
-- queue formed
-- wait started
-- queue wait completed
-- queue wait timeout
-- queue drained
-- rate limited
-- limit decreased
-- limit increased
-- soft ceiling recovered
-- fully recovered
-- failure released
+Canonical request event kinds:
+
+- `request_resource_registered`
+- `request_effective_cap_changed`
+- `request_queue_formed`
+- `request_wait_started`
+- `request_wait_completed`
+- `request_wait_timeout`
+- `request_wait_cancelled`
+- `request_acquire_denied`
+- `request_lease_acquired`
+- `model_request_started`
+- `model_request_completed`
+- `request_queue_drained`
+- `request_rate_limited`
+- `request_limit_decreased`
+- `request_limit_increased`
+- `request_soft_ceiling_recovered`
+- `request_fully_recovered`
+- `request_lease_released`
+- `request_release_diagnostic`
 
 Request snapshots include:
 
+- captured timestamp
+- monotonic sequence
 - request resource
 - effective max
 - current limit
 - in-flight count
+- active lease count
 - waiters
+- blocked-until timing
 - cooldown remaining
 - rate-limit ceiling
 - consecutive rate limits
+- last release outcome
+- leak diagnostic counters
 
-Global provider/model snapshots capture effective static caps and aliases.
+Global provider/model snapshots capture effective static caps, aggregate in-flight counts across domains, aliases, and per-domain summaries.
 
 ## Runtime Correlation
 
@@ -88,8 +114,11 @@ Global provider/model snapshots capture effective static caps and aliases.
 - task type
 - scheduling group kind
 - scheduling group identity hash
+- task execution id
 
-`ModelRequestExecutor` and event sinks read the current correlation context at event emission time. `AdaptiveRequestAdmissionController` remains keyed by provider/model/domain resources and does not import scheduler types.
+`ModelRequestExecutor` reads the current correlation context when constructing `RequestEventContext` for each concrete request attempt. Scheduler event DTOs capture the scheduler's current task/run identity directly. `AdaptiveRequestAdmissionController` remains keyed by provider/model/domain resources and does not import scheduler task types; it may attach the opaque primitive request event context to request events.
+
+Correlation must propagate through child asyncio tasks created as part of admitted task execution. If execution crosses threads, callbacks, or background tasks that cannot preserve context variables, the caller must pass primitive `RuntimeCorrelation` explicitly or mark the event as intentionally uncorrelated. Late/background provider calls after the scheduler has reset task context are not considered part of the admitted task unless they carry explicit correlation.
 
 `CorrelatedRuntimeView` joins the timelines for diagnostics and benchmarks.
 
@@ -104,9 +133,10 @@ selected by fair queue
 task lease acquired
 worker spawned
 request admission wait started
+request admission wait completed
 request lease acquired
-provider request started
-provider request completed
+model request started
+model request completed
 request lease released
 task completed
 task lease released
@@ -114,6 +144,22 @@ task lease released
 
 Runs should be diagnosable as limited by dependency readiness, ready-queue fairness, scheduler capacity, request-admission wait, provider cooldown/rate-limit behavior, transport/provider execution, or downstream completion.
 
+Benchmark-required monotonic timeline fields are derived from these events:
+
+- `dependency_ready_at`
+- `ready_enqueued_at`
+- `selected_at`
+- `task_lease_acquired_at`
+- `worker_spawned_at`
+- `request_wait_started_at`
+- `request_wait_completed_at`
+- `request_lease_acquired_at`
+- `model_request_started_at`
+- `model_request_completed_at`
+- `request_lease_released_at`
+- `task_completed_at`
+- `task_lease_released_at`
+
 ## Cardinality And Safety
 
 Metric-safe dimensions:
@@ -122,7 +168,8 @@ Metric-safe dimensions:
 - scheduler resource kind
 - request admission event kind
 - provider name
-- model id
+- bounded model label, model family, or allowlisted model label
+- metric model label
 - request domain
 - algorithm
 
@@ -133,6 +180,12 @@ Trace-only or sampled fields:
 - task column
 - task type
 - scheduling group identity hash
+- raw model id
+- task id
+- task execution id
+- task lease id
+- request attempt id
+- request lease id
 - queued maps by group
 
 Never emit:
diff --git a/plans/645/request-admission.md b/plans/645/request-admission.md
index ce2575683..366080f36 100644
--- a/plans/645/request-admission.md
+++ b/plans/645/request-admission.md
@@ -6,13 +6,18 @@ Request admission controls concrete provider/model/domain calls at the moment th
 
 ```text
 ModelRequestExecutor
+  -> ModelRequestExecutor.execute_attempt(request)
   -> RequestAdmissionController.acquire_async(RequestAdmissionItem)
   -> RequestAdmissionLease
   -> provider/model endpoint
-  -> release_success | release_rate_limited | release_failure
+  -> RequestAdmissionController.release(lease, RequestReleaseOutcome)
 ```
 
-`ModelRequestExecutor` is the durable model-call boundary. It maps each concrete call to a request resource, acquires a lease, calls the provider, records timing, and releases the exact lease.
+`ModelRequestExecutor` is the durable model-call boundary. It maps each concrete call attempt to a request resource, acquires a lease, calls the provider, records timing, and releases the exact lease.
+
+The boundary is per outbound attempt. Provider retry behavior must either live inside `ModelRequestExecutor` and acquire/release a lease for each attempt, or call back through `ModelRequestExecutor` for each attempt. HTTP/provider-client retries that hide multiple outbound attempts under one request lease are not compatible with the target architecture because rate limits and provider timing would be invisible to request admission.
+
+After a lease is acquired, `ModelRequestExecutor` owns release in a non-cancellable cleanup path. Cancellation after lease acquisition is classified as `local_cancelled`, the exact lease is released before the cancellation is re-raised, and release diagnostics must not mask the original cancellation.
 
 ## Dynamic Requests
 
@@ -23,6 +28,7 @@ Therefore:
 - task admission must not pre-acquire request permits
 - request admission happens at concrete model-call time
 - each acquired request lease is released exactly once
+- each retry attempt is admitted and released independently
 - request-level wait and provider execution timing remain visible separately
 
 ## Durable Names
@@ -33,36 +39,62 @@ The durable V1 implementation name is `AdaptiveRequestAdmissionController`.
 
 The durable model-call boundary name is `ModelRequestExecutor`.
 
-The durable config vocabulary is `RequestAdmissionConfig` and `RunConfig.request_admission` if a run config surface exists.
-
-Do not keep production aliases, shims, subclasses, adapters, exports, docs paths, or durable tests for:
+The durable internal config vocabulary is `RequestAdmissionConfig`. V1 does not introduce a public `RunConfig.request_admission` knob; that spelling is reserved for a later public surface if benchmark evidence justifies one.
 
-- `ThrottleManager`
-- `ThrottleDomain`
-- `ThrottleConfig`
-- `RunConfig.throttle`
-- `throttle_manager.py`
+Do not keep production aliases, shims, subclasses, adapters, exports, docs paths, or durable tests for the replaced request-control vocabulary. [Migration and cleanup](migration-and-cleanup.md#request-admission-cleanup) lists the exact search terms.
 
 ## Request Resource Model
 
-`RequestResourceKey` identifies:
+`RequestResourceKey` identifies canonical resolved request identity:
 
-- provider name
-- model id
+- provider name, after alias resolution
+- model id, after alias resolution
 - request domain
 
-`RequestDomain` includes domains such as chat, embedding, image, and healthcheck.
+Aliases are diagnostic-only after request-key construction. They are recorded in `AsyncCapacityPlan` and snapshots, but request admission must not key aggregate caps by user alias.
+
+`RequestResourceResolver` is the single canonicalization contract for request admission. It resolves provider alias, model alias, model id, generation kind, endpoint metadata, and `RequestDomain` into `ProviderModelKey` and `RequestResourceKey`. Metadata resolution and capacity planning use the same provider/model canonicalization rules; generation kind is folded into the canonical model id only when the provider treats it as a distinct endpoint.
+
+`RequestDomain` V1 values are `chat`, `embedding`, `image`, and `healthcheck`. Additions require updating this plan.
 
-`RequestAdmissionItem` contains resource, group, and optional timeout. `RequestGroupSpec` contains resource key and weight.
+`RequestAdmissionItem` contains resource, group, optional queue-wait timeout, and `RequestEventContext`. `RequestGroupSpec` contains a fairness group key and weight. In V1 the fairness group key is the `RequestResourceKey`; a future policy may split resource identity from fairness identity only after updating this plan.
+
+`RequestEventContext` is created by `ModelRequestExecutor` from the current primitive runtime correlation plus a request-attempt id. It is telemetry context, not scheduler state.
 
 `RequestAdmissionDecision` is `RequestAdmissionLease | RequestAdmissionDenied`.
 
-`RequestAdmissionLease` records the item, acquired timestamp, current adaptive limit, and effective max.
+`RequestAdmissionLease` records a unique lease id, item, acquired timestamp, current adaptive limit, effective max, and controller generation token.
 
 `RequestAdmissionDenied` records item, reason, retry timing, availability timing, and optional snapshot.
 
 `RequestAdmissionController.pressure` exposes the read-only `RequestPressureSnapshotProvider`.
 
+`acquire_sync(...)` and `acquire_async(...)` block until a lease is available or a terminal no-lease condition occurs. Queue-wait timeout, shutdown, or hard denial removes the waiter and raises `RequestAdmissionError`, a typed Data Designer error carrying `RequestAdmissionDenied`. These methods never return `None`. `try_acquire(...)` is the non-blocking path that returns the full decision union.
+
+`acquire_async(...)` preserves cooperative cancellation: if the awaiting task is cancelled before a lease is acquired, the controller removes the waiter, emits a cancellation event, and re-raises the cancellation exception.
+
+Once the controller selects a waiter and increments in-flight counts, cancellation cannot orphan the lease. The selected waiter's acquire call receives the lease for caller cleanup, or the controller internally releases it as `local_cancelled` before completing cancellation. A blocking acquire call may only return a lease for its own waiter; if a wakeup admits a different waiter, that other waiter is fulfilled and the current caller continues waiting.
+
+## Request Queue Semantics
+
+`AdaptiveRequestAdmissionController` owns an internal `RequestFairQueue`. The queue is protected by the controller lock/condition and exposes the same transaction shape as task admission:
+
+```text
+enqueue(waiter)
+select_next(is_eligible) -> RequestQueueSelection | None
+commit(selection) -> RequestWaiter | None
+remove(waiter_id)
+view() -> RequestQueueView
+```
+
+`RequestWaiter` carries waiter id, item, enqueue timestamp, deadline/cancellation state, and the completion handle for the blocking acquire path. `RequestQueueSelection` carries waiter, item, waiter id, queue view, and a `sequence_version`. `commit(selection)` is the only operation that removes an admitted waiter.
+
+Wakeups occur when a request lease releases, cooldown expires, adaptive limit increases, shutdown/cancellation removes waiters, or provider/model aggregate capacity becomes available. Waiters use monotonic timed waits to the earliest queue timeout, `available_after_monotonic`, or `blocked_until_monotonic`; cooldown expiry cannot depend on a later provider release to wake the queue.
+
+Every concrete request attempt emits request-wait timeline events. Immediate acquisition emits `request_wait_started` and `request_wait_completed` as a zero-duration wait before `request_lease_acquired`; queued acquisition emits those events around actual queue wait.
+
+`try_acquire(...)` must not bypass queued work. It may return an immediate lease only when the item is eligible and no queued eligible waiter for the same request resource or provider/model aggregate cap would be selected first by `RequestFairQueue` weighted ordering. Otherwise it returns a typed denial, usually `queued_waiters_ahead`, `cooldown`, or `no_capacity`.
+
 ## AdaptiveRequestAdmissionController
 
 `AdaptiveRequestAdmissionController` is the AIMD-backed request controller. It owns:
@@ -70,7 +102,7 @@ Do not keep production aliases, shims, subclasses, adapters, exports, docs paths
 - request fair queueing
 - request admission policy
 - adaptive request limit state
-- provider/model/domain in-flight counts
+- provider/model/domain in-flight counts plus provider/model aggregate in-flight counts
 - waiters
 - cooldown state
 - rate-limit cascades
@@ -79,36 +111,81 @@ Do not keep production aliases, shims, subclasses, adapters, exports, docs paths
 
 Internal `RequestFairQueue`, `RequestAdmissionPolicy`, and `AdaptiveRequestLimitState` are part of the single canonical request-admission implementation. They are not a second public wrapper around request admission.
 
+An admitted request increments domain in-flight count and provider/model aggregate in-flight count before the lease is returned. Release decrements those counts exactly once before waking waiters.
+
+Weighted fairness applies across `RequestGroupSpec` groups that share a provider/model aggregate cap. Equal weights fall back to oldest waiter first.
+
+V1 AIMD contract:
+
+- all timing uses a monotonic clock
+- `effective_max = min(provider_model_static_cap, request_config.max_limit_clamp_for_resource_if_present)`
+- instantaneous aggregate availability is enforced separately by `provider_model_aggregate_in_flight < provider_model_static_cap`
+- `initial_limit` is clamped to `[1, effective_max]`
+- `current_limit` starts at `initial_limit`
+- provider rate limits apply multiplicative decrease and set `blocked_until_monotonic`
+- success outside cooldown contributes to additive recovery
+- `request_limit_increased`, `request_soft_ceiling_recovered`, and `request_fully_recovered` events are emitted from state transitions, not inferred later by sinks
+
 ## Release Classification
 
-`ModelRequestExecutor` releases the exact acquired lease through:
+`ModelRequestExecutor` releases the exact acquired lease through the canonical release call:
+
+```text
+release(lease, RequestReleaseOutcome)
+```
+
+Required outcome kinds:
 
-- `release_success(lease)` after provider success
-- `release_rate_limited(lease, retry_after)` after provider rate-limit response
-- `release_failure(lease)` after non-rate-limit failure, cancellation, timeout, or unexpected exception
+- `success`
+- `rate_limited`, with `retry_after_seconds` when available
+- `provider_failure`
+- `provider_timeout`
+- `local_cancelled`
+- `local_timeout`
+- `unexpected_exception`
 
 The release path is responsible for exactly-once accounting. Key-only release paths are not durable.
 
+Rate-limit outcomes drive AIMD decrease, cooldown, and waiter wake behavior. Provider failures may drive diagnostic counters but do not automatically imply provider pressure. Local cancellation and local timeout release capacity and wake waiters but must not be treated as rate limits or provider failures.
+
+`provider_timeout` means a timeout or timeout-shaped transport/provider error after a lease has been acquired and an outbound provider attempt has started. `local_timeout` means a caller, queue-wait, or controller deadline that is not evidence of provider pressure.
+
+Release returns `ReleaseResult` and must not raise from terminal cleanup paths. Duplicate release, stale release, or release against the wrong controller generation must return a diagnostic result and emit an error event without corrupting counters.
+
 ## Request Pressure Snapshots
 
 `RequestPressureSnapshotProvider` exposes read-only state to diagnostics, benchmarks, telemetry, and future task policies.
 
 Domain snapshots include:
 
+- captured timestamp
+- monotonic sequence/version
 - request resource
 - effective max
 - current limit
 - in-flight count
+- active lease count
 - waiters
 - blocked-until timing
 - cooldown remaining
 - rate-limit ceiling
 - consecutive rate limits
+- last release outcome summary
+- leak diagnostic counters
 
-Global snapshots include provider/model effective caps and aliases.
+Global snapshots include provider/model effective static caps, aggregate in-flight count across domains, aggregate active lease count, aliases contributing to the cap, and per-domain limit summaries.
 
 Task admission may read these snapshots as advisory input in later policy work. It must not mutate request state or emulate request admission.
 
+## Static And Adaptive Cap Semantics
+
+`max_parallel_requests` remains the provider/model static cap when available. In V1, that cap is enforced as an aggregate upper bound across all request domains for the provider/model. Domain-specific adaptive limits decide how each domain is admitted beneath the aggregate cap; there is no cross-domain aggregate AIMD state beyond the static aggregate cap unless a later issue adds one.
+
+Effective admission for a request must satisfy both:
+
+- the provider/model aggregate static cap has available in-flight capacity
+- the request domain's adaptive limit and cooldown state admits the item
+
 ## Non-Goals
 
 - Do not make request admission aware of DAG dependencies.
diff --git a/plans/645/task-admission.md b/plans/645/task-admission.md
index cf594a6d8..f26684d1a 100644
--- a/plans/645/task-admission.md
+++ b/plans/645/task-admission.md
@@ -7,27 +7,43 @@ Task admission controls when dependency-ready dataset work may become a running
 `AsyncTaskScheduler` is the control owner. Its dispatch loop follows this shape:
 
 ```python
-selection = queue.select_next(admission.is_eligible)
+selection = queue.select_next(lambda item, view: admission.is_eligible(item, view))
 if selection is None:
-    wait_for_wake()
+    block_summary = admission.explain_blocked(queue.view())
+    emit_queue_empty_or_blocked(block_summary)
+    wait_for_wake_or_deadline(block_summary.available_after)
     return
 
-lease = admission.try_acquire(selection.item, queue.view())
-if lease is None:
-    wait_for_wake()
+decision = admission.try_acquire(selection.item, selection.queue_view)
+if isinstance(decision, TaskAdmissionDenied):
+    emit_admission_denied(decision)
+    wake_dispatch_loop()
     return
+lease = decision
 
 committed = queue.commit(selection)
 if committed is None:
     admission.release(lease)
+    emit_stale_selection(selection, lease)
     wake_dispatch_loop()
     return
 
-spawn_worker(committed, lease)
+try:
+    spawn_worker(committed, lease)
+except Exception:
+    admission.release(lease)
+    emit_worker_spawn_failed(committed, lease)
+    raise
 ```
 
 `FairTaskQueue` selects candidates. `TaskAdmissionController` leases scheduler resources. The scheduler coordinates both.
 
+V1 requires a scheduler dispatch mutex around `select_next -> try_acquire -> commit`. No concurrent dispatch iteration may acquire resources for the same selected task. `QueueSelection` still carries a queue version so `commit(selection)` can detect stale selections defensively. If `commit(selection)` fails because the selection is stale, the scheduler releases the exact task lease before retrying and emits a stale-selection event.
+
+Wakeups are required when ready work is enqueued, task admission capacity is released, policy state changes from denied to eligible, an `available_after` deadline expires, shutdown/cancellation is requested, or a stale selection is detected. The implementation must avoid lost wakeups: a sleeper cannot remain asleep while queued work is eligible and task capacity is available.
+
+Lock ordering is part of the contract: the scheduler dispatch mutex coordinates the sequence, but `FairTaskQueue` must not hold queue-internal locks while invoking the scheduler eligibility predicate, and event sinks must not be called while queue or controller locks are held.
+
 ## Queue Semantics
 
 `FairTaskQueue` owns ready-work ordering only.
@@ -37,9 +53,14 @@ Rules:
 - `select_next(...)` is non-mutating.
 - `select_next(...)` calls the eligibility callback with candidates and queue view.
 - `QueueSelection` returns to `AsyncTaskScheduler`.
+- `QueueSelection` carries the queue view/version used to evaluate the candidate.
+- `enqueue(...)` returns the accepted task ids; duplicate task ids are accepted idempotently and do not create duplicate queue entries.
 - `commit(selection)` removes the selected task and advances queue state.
 - The queue does not track admitted/running counts after this epic.
 - The queue does not inspect model registries, provider pressure, or request-admission state.
+- The queue may scan ready candidates to find the next eligible task, but eligibility is computed only through the scheduler-supplied predicate.
+
+`QueueView` must be strong enough for current strict fairness and future bounded borrow without policy traversal of queue internals. It includes queued counts by group, queued demand by group/resource, and first-candidate resources by group. It does not report admission-aware eligibility. `TaskAdmissionPolicy` computes whether a peer is eligible for a currently available resource by combining `QueueView` with `TaskAdmissionView`.
 
 ## Admission Semantics
 
@@ -50,6 +71,8 @@ Rules:
 - admitted/running resource counts
 - per-group accounting used by policy
 - release on every worker terminal path
+- rollback of acquired resources when the scheduler reports stale queue commit
+- the authoritative hard resource ledger; policy debt is stored separately and affects eligibility without changing resource availability counters directly
 
 `TaskAdmissionPolicy` owns:
 
@@ -59,6 +82,10 @@ Rules:
 - bounded-borrow behavior
 - future resource-vector policy decisions
 
+`TaskAdmissionPolicy.evaluate(...)` is a pure decision function. It can be called repeatedly while the queue scans candidates and must not mutate debt, counters, timers, or diagnostics. `on_acquire(...)` and `on_release(...)` return deterministic policy state deltas. They must not directly mutate the controller's authoritative lease/resource ledger. If a policy needs borrow debt or similar mutable state, the controller applies the state transition as part of the same acquire/release transaction and exposes the resulting policy state in `TaskAdmissionView`.
+
+Policy decisions are typed. A denied decision carries the reason used by scheduler telemetry and tests. Bounded-borrow policies return `PolicyStateDelta` values for borrow-debt increments and repayments; the controller applies those deltas atomically with the lease acquire/release path.
+
 `TaskAdmissionController` consumes `SchedulableTask`, `SchedulerResourceRequest`, `QueueView`, and `TaskAdmissionView`. It must not inspect `ColumnGenerator`, config layout, model registry, or provider registry directly.
 
 ## V1 Lease Boundary
@@ -70,6 +97,10 @@ V1 includes:
 - submission capacity for scheduler-spawned work
 - task-stage LLM-wait style resource if a distinct scheduler-stage resource remains
 - current per-group admitted/running cap behavior
+- typed `TaskAdmissionDecision` denial reasons for telemetry, tests, and benchmarks
+- unique task lease identities so duplicate, stale, or wrong-controller releases are rejected or diagnosed
+
+V1 request waits remain inside admitted task execution and the task lease is retained until worker completion. That preserves the lease boundary and makes request waits visible, but it does not by itself solve cross-provider utilization when tasks for a cooled-down provider occupy all scheduler task slots. Issue #651 must address provider/resource-aware task admission or an explicit yield/reacquire design before the epic claims cross-provider scheduling optimization as complete.
 
 V1 excludes:
 
@@ -84,6 +115,16 @@ V1 excludes:
 
 Root/from-scratch tasks must become `SchedulableTask`s and enter the same `FairTaskQueue` as downstream ready tasks. They must acquire scheduler-level leases through `TaskAdmissionController`.
 
+Initial root materialization is owned by `AsyncTaskScheduler`. `CompletionTracker.ready_frontier()` reports dependency-ready root tasks to the scheduler; the scheduler enqueues them into `FairTaskQueue` through the same path used for downstream work. `CompletionTracker` must not enqueue directly into `FairTaskQueue`.
+
+Readiness handoff is idempotent:
+
+- every `SchedulableTask` has a stable `task_id`
+- `ready_frontier()` returns tasks that are ready and not yet acknowledged as enqueued
+- `FairTaskQueue.enqueue(...)` is idempotent by `task_id`
+- after enqueue succeeds, the scheduler calls `CompletionTracker.mark_enqueued(task_ids)`
+- `CompletionTracker.mark_complete(task)` closes the task only after the scheduler records the terminal outcome
+
 No root dispatch path should bypass:
 
 - ready queue membership
@@ -98,7 +139,20 @@ This is required for heavy-root live-traffic evidence and later bounded-borrow p
 
 Resource-bound work must not become a spawned worker that waits for scheduler-level resources. The lease is acquired before spawn.
 
-Non-resource-bound work holds the relevant scheduler lease until worker completion. Resource-bound work holds the scheduler resource lease that represents the V1 task-stage resource request. Durable `needs_llm_wait` and `held_llm_wait` fields are not part of the target architecture.
+Non-resource-bound work holds the relevant scheduler lease until worker completion. Resource-bound work holds the scheduler resource lease that represents the V1 task-stage resource request. Legacy hidden-wait booleans are not part of the target architecture.
+
+## Lease Lifecycle
+
+Every admitted task has one `TaskAdmissionLease` with a unique lease id. The scheduler releases that exact lease in a terminal `finally` path for success, retryable failure, non-retryable failure, cancellation, shutdown, salvage redispatch, and worker-spawn failure.
+
+Release rules:
+
+- release returns `ReleaseResult` and must not raise from terminal `finally` paths
+- duplicate release must not increment capacity
+- releasing a stale lease or a lease from another controller generation returns a diagnostic release result and emits an error event
+- stale queue commit releases the task lease before any worker is spawned
+- salvage/retry may make replacement work visible only after the original lease terminal path is accounted for. Replacement work is recorded through `CompletionTracker` or an explicit retry tracker, then re-enters the normal `ready_frontier() -> enqueue -> mark_enqueued` handoff; it must not be inserted directly into `FairTaskQueue` while the original lease is active.
+- task release wakes the dispatch loop if queued work may now be eligible
 
 ## Bounded Borrow Policy
 
@@ -106,16 +160,19 @@ Non-resource-bound work holds the relevant scheduler lease until worker completi
 
 Policy inputs:
 
-- `QueueView`: queued counts and peer pressure.
-- `TaskAdmissionView`: leased/running counts and resources available.
+- `QueueView`: queued counts and queued resource demand.
+- `TaskAdmissionView`: resource limits, availability, leased/running counts, and policy debt by group/resource.
 - `TaskGroupSpec`: group key and weight.
 - candidate `SchedulerResourceRequest`.
+- engine-internal `BoundedBorrowTaskAdmissionPolicyConfig` when enabled by #650, including borrow ceiling by group/resource, strict-share rounding mode, and repayment behavior.
 
 Policy constraints:
 
 - Single-group workloads remain live.
-- A solo heavy group may borrow only to its borrow ceiling.
-- When peer queue pressure exists and a group has borrow debt, that group receives no further admissions while an eligible peer has queued work and the required resource is available.
+- Borrow debt is measured in admitted scheduler-resource units above strict fair share for a group/resource. Strict share is computed from scheduler-known competing groups and their weights; #650 owns the exact rounding rule and benchmark evidence.
+- A group may borrow beyond strict share only up to its configured ceiling while no eligible peer can use the resource.
+- When peer queue pressure exists and a group has borrow debt, that group receives no further admissions for the borrowed resource while an eligible peer has queued work and the required resource is available.
+- Debt repayment happens when peer pressure exists and the indebted group is withheld, or when policy-defined repayment work completes. Repayment changes policy debt counters only, not hard resource availability.
 - The policy must not traverse the DAG inside `FairTaskQueue`.
 - No public knob is added until benchmark evidence supports it.
 

From d40e27c22395935811a535b6fa8a7cee7418f4b7 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Thu, 14 May 2026 17:36:08 -0400
Subject: [PATCH 10/33] feat: implement async scheduling admission control

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 architecture/dataset-builders.md              |    6 +-
 architecture/models.md                        |   28 +-
 architecture/overview.md                      |    6 +-
 .../agent-aimd-scale/README.md                |   48 +
 .../agent-aimd-scale/combined_summary.json    | 1224 +++++++++++++++++
 .../645-live-bench-nano/agent-aimd/README.md  |   28 +
 .../agent-aimd/combined_summary.json          |  906 ++++++++++++
 .../agent-bottleneck/README.md                |   32 +
 .../agent-bottleneck/README_mixed.md          |   27 +
 .../combined_mixed_summary.json               |  369 +++++
 .../agent-bottleneck/combined_summary.json    |  297 ++++
 .../agent-cap-scale/README.md                 |   33 +
 .../agent-cap-scale/combined_summary.json     |   93 ++
 .../agent-cap-sweep/README.md                 |   13 +
 .../agent-cap-sweep/README_final1024.md       |   13 +
 .../agent-cap-sweep/combined_summary.json     |  269 ++++
 .../combined_summary_final1024.json           |  493 +++++++
 .../agent-fan-scale/README.md                 |   37 +
 .../agent-fan-scale/combined_summary.json     |  279 ++++
 .../645-live-bench-nano/agent-fan/README.md   |    4 +
 .../agent-fan/combined_summary.json           |  161 +++
 .../agent-mixed-pipeline/README.md            |   12 +
 .../combined_summary.json                     | 1206 ++++++++++++++++
 .../agent-mixed-pipeline/smoke/README.md      |   10 +
 .../smoke/combined_summary.json               |  176 +++
 artifacts/645-live-bench/agent-fan/README.md  |   21 +
 .../agent-fan/combined_summary.json           |  452 ++++++
 .../maxparallel_live_benchmark.json           |  507 +++++++
 .../maxparallel_live_benchmark.md             |   22 +
 docs/concepts/architecture-and-performance.md |   61 +-
 .../architecture-layers.svg                   |   12 +-
 .../async-all-the-way-down/task-lifecycle.png |  Bin 30470 -> 47355 bytes
 .../native-model-client-layers.png            |  Bin 196456 -> 54814 bytes
 ...throttle-keying.png => request-keying.png} |  Bin
 .../owning-the-model-stack/retry-boundary.png |  Bin 331171 -> 42573 bytes
 docs/devnotes/posts/async-all-the-way-down.md |   47 +-
 docs/devnotes/posts/owning-the-model-stack.md |   58 +-
 .../architecture-layers.svg                   |   12 +-
 .../async-all-the-way-down/task-lifecycle.png |  Bin 30470 -> 47355 bytes
 fern/assets/images/architecture-layers.svg    |   12 +-
 .../images/native-model-client-layers.png     |  Bin 196456 -> 54814 bytes
 ...throttle-keying.png => request-keying.png} |  Bin
 fern/assets/images/retry-boundary.png         |  Bin 331171 -> 42573 bytes
 fern/assets/images/task-lifecycle.png         |  Bin 30470 -> 47355 bytes
 .../native-model-client-layers.png            |  Bin 196456 -> 54814 bytes
 ...throttle-keying.png => request-keying.png} |  Bin
 .../owning-the-model-stack/retry-boundary.png |  Bin 331171 -> 42573 bytes
 fern/versions/latest/pages/devnotes/index.mdx |    2 +-
 .../concepts/architecture-and-performance.mdx |   58 +-
 fern/versions/v0.5.8/pages/devnotes/index.mdx |    2 +-
 .../devnotes/posts/async-all-the-way-down.mdx |   47 +-
 .../devnotes/posts/owning-the-model-stack.mdx |   58 +-
 fern/versions/v0.5.9/pages/devnotes/index.mdx |    2 +-
 .../src/data_designer/config/__init__.py      |    7 +-
 .../src/data_designer/config/run_config.py    |   63 +-
 .../src/data_designer/config/scheduling.py    |  118 ++
 .../tests/config/test_scheduling.py           |   35 +
 .../src/data_designer/engine/capacity.py      |  119 ++
 .../column_generators/generators/base.py      |   87 +-
 .../column_generators/generators/custom.py    |   19 +-
 .../dataset_builders/async_scheduler.py       |  513 ++++---
 .../dataset_builders/dataset_builder.py       |   10 +-
 .../utils/completion_tracker.py               |   72 +-
 .../dataset_builders/utils/fair_task_queue.py |  248 ++--
 .../utils/scheduling_hints.py                 |  123 --
 .../dataset_builders/utils/task_admission.py  |  416 ++++++
 .../dataset_builders/utils/task_scheduling.py |  150 ++
 .../engine/models/clients/__init__.py         |   31 +-
 .../models/clients/adapters/anthropic.py      |    4 +-
 .../clients/adapters/openai_compatible.py     |    4 +-
 .../engine/models/clients/factory.py          |   23 +-
 .../models/clients/model_request_executor.py  |  244 ++++
 .../models/clients/request_admission.py       |  968 +++++++++++++
 .../engine/models/clients/retry.py            |   14 +-
 .../engine/models/clients/throttle_manager.py |  505 -------
 .../engine/models/clients/throttled.py        |  222 ---
 .../data_designer/engine/models/factory.py    |   14 +-
 .../data_designer/engine/models/registry.py   |   21 +-
 .../src/data_designer/engine/observability.py |  187 +++
 .../dataset_builders/test_async_scheduler.py  |  272 ++--
 .../utils/test_completion_tracker.py          |    2 +-
 .../utils/test_fair_task_queue.py             |  234 ++--
 .../utils/test_scheduling_hints.py            |  150 --
 .../utils/test_task_admission.py              |  176 +++
 .../utils/test_task_scheduling.py             |  121 ++
 .../engine/models/clients/test_factory.py     |   34 +-
 .../clients/test_model_request_executor.py    |  155 +++
 .../models/clients/test_request_admission.py  |  209 +++
 .../models/clients/test_throttle_manager.py   |  579 --------
 .../clients/test_throttled_model_client.py    |  469 -------
 .../tests/engine/test_capacity.py             |   73 +
 plans/645/capacity-model.md                   |    4 +-
 plans/645/contracts.md                        |    4 +-
 plans/645/migration-and-cleanup.md            |   15 +-
 plans/645/task-admission.md                   |    4 +-
 ...sync-scheduling-epic-benchmark-report.html |  791 +++++++++++
 .../benchmarks/benchmark_async_scheduling.py  |  441 ++++++
 97 files changed, 12039 insertions(+), 3054 deletions(-)
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 create mode 100644 artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/combined_summary.json
 create mode 100644 artifacts/645-live-bench/agent-fan/README.md
 create mode 100644 artifacts/645-live-bench/agent-fan/combined_summary.json
 create mode 100644 artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.json
 create mode 100644 artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md
 rename docs/devnotes/posts/assets/owning-the-model-stack/{throttle-keying.png => request-keying.png} (100%)
 rename fern/assets/images/{throttle-keying.png => request-keying.png} (100%)
 rename fern/assets/owning-the-model-stack/{throttle-keying.png => request-keying.png} (100%)
 create mode 100644 packages/data-designer-config/src/data_designer/config/scheduling.py
 create mode 100644 packages/data-designer-config/tests/config/test_scheduling.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/capacity.py
 delete mode 100644 packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/scheduling_hints.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_admission.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_scheduling.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/clients/request_admission.py
 delete mode 100644 packages/data-designer-engine/src/data_designer/engine/models/clients/throttle_manager.py
 delete mode 100644 packages/data-designer-engine/src/data_designer/engine/models/clients/throttled.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/observability.py
 delete mode 100644 packages/data-designer-engine/tests/engine/dataset_builders/utils/test_scheduling_hints.py
 create mode 100644 packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_admission.py
 create mode 100644 packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_scheduling.py
 create mode 100644 packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
 create mode 100644 packages/data-designer-engine/tests/engine/models/clients/test_request_admission.py
 delete mode 100644 packages/data-designer-engine/tests/engine/models/clients/test_throttle_manager.py
 delete mode 100644 packages/data-designer-engine/tests/engine/models/clients/test_throttled_model_client.py
 create mode 100644 packages/data-designer-engine/tests/engine/test_capacity.py
 create mode 100644 reports/async-scheduling-epic-benchmark-report.html
 create mode 100644 scripts/benchmarks/benchmark_async_scheduling.py

diff --git a/architecture/dataset-builders.md b/architecture/dataset-builders.md
index 825a2a392..a1ffbfe2a 100644
--- a/architecture/dataset-builders.md
+++ b/architecture/dataset-builders.md
@@ -35,7 +35,7 @@ Preparation (`_prepare_async_run`):
 4. Constructs `CompletionTracker`, `RowGroupBufferManager`, `AsyncTaskScheduler`
 5. Hooks `ProcessorRunner` for pre-batch and post-batch stages
 
-`AsyncTaskScheduler` runs on a dedicated async loop with frontier-driven dispatch, semaphore-based capacity limits, salvage rounds for failed tasks, and order-dependent locks for columns that must execute sequentially. Ready frontier tasks are admitted through a virtual-time fair queue so one hot column or model-backed generator cannot consume the whole submission window before peer work gets a turn.
+`AsyncTaskScheduler` runs on a dedicated async loop with frontier-driven dispatch, task-admission leases, salvage rounds for failed tasks, and order-dependent locks for columns that must execute sequentially. Ready frontier tasks enter `FairTaskQueue`, are selected through virtual-time ordering, and are committed only after `TaskAdmissionController` acquires the required scheduler resources.
 
 ### Execution Graph
 
@@ -121,7 +121,7 @@ DatasetBuilder.build()
   → _prepare_async_run()
       → ExecutionGraph.create()
       → CompletionTracker.with_graph()
-      → AsyncTaskScheduler(semaphores, salvage_rounds)
+      → AsyncTaskScheduler(task admission, fair queue, salvage_rounds)
   → scheduler.run()
       → for each row group, fairly admit ready tasks from frontier
       → tasks execute generators, update CompletionTracker
@@ -133,7 +133,7 @@ DatasetBuilder.build()
 
 - **Dual execution engines behind one API.** The sequential engine is simpler and easier to debug; the async engine adds row-group parallelism for throughput. Users switch via an environment variable without changing their code.
 - **DAG-driven ordering** ensures columns with dependencies (e.g., a judge column that depends on a text column) are generated in the correct order, regardless of the order they appear in the config.
-- **Fair async admission** keeps the scheduler flowing across ready columns and model groups. Global semaphores still bound memory/coroutine growth, while per-group virtual-time queues prevent a large ready frontier from degenerating into a column-by-column wave. LLM admission caps are peer-sensitive: a solo model group can fill available global capacity, but once another scheduling group has queued work the saturated group yields until peers get admission slots or admitted tasks complete.
+- **Fair async admission** keeps the scheduler flowing across ready columns and model groups. `FairTaskQueue.select_next(...)` chooses eligible ready work, `TaskAdmissionController` leases scheduler resources before spawn, and `FairTaskQueue.commit(...)` removes the selected task only after admission succeeds. Per-group virtual-time ordering prevents a large ready frontier from degenerating into a column-by-column wave, and scheduler-resource accounting remains separate from provider/model request admission.
 - **Salvage rounds in async mode** retry failed tasks after all other tasks in a round complete, improving resilience against transient LLM failures without blocking the entire generation.
 - **Unified DAG construction.** `topologically_sort_column_configs` (in `execution_graph.py`) determines column ordering using Kahn's algorithm; the runtime `ExecutionGraph` adds strategy-aware dependency tracking for the async scheduler.
 
diff --git a/architecture/models.md b/architecture/models.md
index d7af0cdac..fc90bcb8a 100644
--- a/architecture/models.md
+++ b/architecture/models.md
@@ -1,6 +1,6 @@
 # Models
 
-The model subsystem provides a unified interface for LLM access: chat completions, embeddings, and image generation. It handles client creation, retry, rate-limit throttling, usage tracking, and MCP tool integration.
+The model subsystem provides a unified interface for LLM access: chat completions, embeddings, and image generation. It handles client creation, retry, request admission, usage tracking, and MCP tool integration.
 
 Source: `packages/data-designer-engine/src/data_designer/engine/models/`
 
@@ -11,12 +11,12 @@ The model subsystem is layered:
 ```
 ModelRegistry (lazy facade-per-alias)
   └── ModelFacade (completion, embeddings, image gen, MCP tool loops)
-        └── ThrottledModelClient (AIMD rate limiting)
+        └── ModelRequestExecutor (request admission + provider execution)
               └── ModelClient (OpenAI-compatible or Anthropic adapter)
                     └── RetryTransport (httpx-level retries)
 ```
 
-Generators never interact with HTTP clients directly. They request a `ModelFacade` by alias from the `ModelRegistry`, which handles lazy construction and shared throttle state.
+Generators never interact with HTTP clients directly. They request a `ModelFacade` by alias from the `ModelRegistry`, which handles lazy construction, request-resource canonicalization, and shared adaptive request admission state.
 
 ## Key Components
 
@@ -31,13 +31,13 @@ Defines the contract: sync/async chat, embeddings, image generation, `supports_*
 
 `create_model_client` routes by provider type to the appropriate adapter. Optionally wraps with:
 - **`RetryTransport`** — httpx-level retries via `httpx_retries.RetryTransport`. `HttpModelClient` sets `strip_rate_limit_codes=True` for the async client and `False` for the sync client (`http_model_client.py`), which controls whether 429 responses are eligible for transport-layer retries.
-- **`ThrottledModelClient`** — AIMD (Additive Increase, Multiplicative Decrease) concurrency control per throttle domain.
+- **`ModelRequestExecutor`** — maps model-call attempts to request-admission items, acquires request leases, invokes the provider client, and releases the exact lease on every terminal path.
 
-### ThrottleManager
+### Request Admission
 
-Manages concurrency limits per `ThrottleDomain` (CHAT, EMBEDDING, IMAGE, HEALTHCHECK), keyed by `(provider_name, model_id)`. Thread-safe with a shared lock for sync/async access.
+`RequestAdmissionController` manages provider/model/domain request resources. `AdaptiveRequestAdmissionController` adds AIMD (Additive Increase, Multiplicative Decrease) adaptation per `RequestDomain` (`chat`, `embedding`, `image`, `healthcheck`) under the provider/model static cap.
 
-`ThrottledModelClient` wraps each API call in a context manager that acquires/releases throttle capacity and adjusts limits on success (additive increase) or rate-limit errors (multiplicative decrease).
+`ModelRequestExecutor` wraps each provider call with a request-admission lease and feeds success or rate-limit outcomes back to the controller. `RequestResourceResolver` owns canonical provider/model/domain identity so aliases that target the same endpoint share request capacity.
 
 ### ModelFacade
 
@@ -50,7 +50,7 @@ The primary interface for generators. Holds a `ModelConfig`, `ModelClient`, opti
 
 ### ModelRegistry
 
-Lazy `ModelFacade` construction per alias. Registers a shared `ThrottleManager` across all facades for coordinated rate limiting. Provides `get_model_usage_stats` and `log_model_usage` for post-build reporting.
+Lazy `ModelFacade` construction per alias. Registers shared request-admission state across all facades for coordinated provider/model/domain capacity. Provides `get_model_usage_stats` and `log_model_usage` for post-build reporting.
 
 ### Usage Tracking
 
@@ -59,18 +59,18 @@ Lazy `ModelFacade` construction per alias. Registers a shared `ThrottleManager`
 ## Data Flow
 
 1. Generator requests a model by alias from `ModelRegistry`
-2. Registry lazily creates `ModelFacade` with the appropriate client and throttle config
+2. Registry lazily creates `ModelFacade` with the appropriate client and request-admission executor
 3. Generator calls `completion()` with prompt/messages
-4. `ModelFacade` builds kwargs, calls `ThrottledModelClient`
-5. Throttle layer acquires capacity, delegates to `ModelClient`
+4. `ModelFacade` builds kwargs, calls `ModelRequestExecutor`
+5. Request admission acquires a provider/model/domain lease, delegates to `ModelClient`
 6. `ModelClient` makes the HTTP request through `RetryTransport`
 7. Response flows back; usage is tracked; if MCP tools are configured, tool calls are executed and results fed back for another completion round
 
 ## Design Decisions
 
-- **Facade pattern** hides HTTP, retry, throttle, and MCP complexity from generators. Generators see `completion()` and get back parsed results.
-- **AIMD throttling at the application layer** rather than relying solely on HTTP retries. This provides smoother throughput under rate limits — the transport layer still handles many transient failures, while the throttle manager adjusts concurrency to avoid sustained 429 storms.
-- **429 handling depends on sync vs async `HttpModelClient`** — The async client uses `strip_rate_limit_codes=True`, so 429s are not retried at the transport layer and rate-limit signals reach `ThrottledModelClient` / AIMD quickly. The sync client uses `strip_rate_limit_codes=False`, so 429s may still be retried transparently at the transport layer before surfacing to callers.
+- **Facade pattern** hides HTTP, retry, request admission, and MCP complexity from generators. Generators see `completion()` and get back parsed results.
+- **AIMD request admission at the application layer** rather than relying solely on HTTP retries. This provides smoother throughput under rate limits: the transport layer still handles many transient failures, while adaptive request admission adjusts concurrency to avoid sustained 429 storms.
+- **429 handling depends on sync vs async `HttpModelClient`** — The async client uses `strip_rate_limit_codes=True`, so 429s are not retried at the transport layer and rate-limit signals reach `ModelRequestExecutor` / request admission quickly. The sync client uses `strip_rate_limit_codes=False`, so 429s may still be retried transparently at the transport layer before surfacing to callers.
 - **Distribution-valued inference parameters** (`temperature`, `top_p` as `UniformDistribution` or `ManualDistribution`) enable controlled randomness across a dataset without per-row config changes.
 - **Lazy facade construction** avoids health-checking or connecting to models that are configured but never used in a particular generation run.
 
diff --git a/architecture/overview.md b/architecture/overview.md
index 30c91bdfb..10bde6c90 100644
--- a/architecture/overview.md
+++ b/architecture/overview.md
@@ -30,7 +30,7 @@ Users declare what their data should look like through config objects (columns,
 | `DataDesigner` | `data-designer` | Public API — `create()`, `preview()`, `validate()` |
 | `DataDesignerConfigBuilder` | `data-designer-config` | Fluent builder for dataset configs |
 | `DatasetBuilder` | `data-designer-engine` | Orchestrates generation (sync or async) |
-| `ModelFacade` / `ModelRegistry` | `data-designer-engine` | LLM client abstraction with retry, throttle, usage tracking |
+| `ModelFacade` / `ModelRegistry` | `data-designer-engine` | LLM client abstraction with retry, request admission, usage tracking |
 | `MCPFacade` / `MCPRegistry` | `data-designer-engine` | Tool execution via Model Context Protocol |
 | `ColumnGeneratorRegistry` | `data-designer-engine` | Maps column types to generator implementations |
 | `PluginRegistry` | `data-designer-config` | Discovers and registers entry-point plugins |
@@ -44,7 +44,7 @@ Users declare what their data should look like through config objects (columns,
 
 3. **Generation** — `DatasetBuilder` instantiates column generators from the registry, then executes one of two paths:
    - **Sequential** (default): batch loop over columns in topological order. Each generator produces its column via `CELL_BY_CELL` (threaded fan-out) or `FULL_COLUMN` strategy.
-   - **Async** (`DATA_DESIGNER_ASYNC_ENGINE=1`): builds an `ExecutionGraph`, partitions rows into groups, and dispatches tasks via `AsyncTaskScheduler` with semaphore-based concurrency, salvage rounds, and per-row-group checkpointing.
+   - **Async** (`DATA_DESIGNER_ASYNC_ENGINE=1`): builds an `ExecutionGraph`, partitions rows into groups, and dispatches tasks via `AsyncTaskScheduler` with `FairTaskQueue` selection, `TaskAdmissionController` scheduler-resource leases, salvage rounds, and per-row-group checkpointing.
 
 4. **Post-processing** — `ProcessorRunner` applies transformations (pre-batch, post-batch, after-generation). Profilers analyze the generated dataset.
 
@@ -61,7 +61,7 @@ Users declare what their data should look like through config objects (columns,
 
 - [Config Layer](config.md) — builder API, column types, model configs, plugin system
 - [Engine Layer](engine.md) — compilation, generators, registries
-- [Models](models.md) — model facade, adapters, retry/throttle
+- [Models](models.md) — model facade, adapters, retry, request admission
 - [Dataset Builders](dataset-builders.md) — sync/async orchestration, DAG, batching
 - [MCP](mcp.md) — tool execution, session pooling
 - [Sampling](sampling.md) — statistical generators, person/entity data
diff --git a/artifacts/645-live-bench-nano/agent-aimd-scale/README.md b/artifacts/645-live-bench-nano/agent-aimd-scale/README.md
new file mode 100644
index 000000000..39c5c1826
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-aimd-scale/README.md
@@ -0,0 +1,48 @@
+# 645 live bench nano AIMD scale lane
+
+Ran in `/Users/etramel/src/DataDesigner` on branch `scheduling-yolo` with only `/tmp/dd_live_aimd_bench.py` and artifacts under this directory. No tracked repo files were edited.
+
+## Configuration
+
+- Model: `openai/openai/gpt-5-nano`
+- Provider: `nvidia-internal`
+- Temperature: omitted
+- `skip_health_check=True`
+- `DATA_DESIGNER_ASYNC_ENGINE=1`
+- `DATA_DESIGNER_ASYNC_TRACE=1`
+- `max_parallel_requests=16`
+- AIMD initial limit: 1
+- AIMD `increase_after_successes=16`
+- Shape for final scenarios: 512 rows x 2 independent `LLMTextColumnConfig` columns = 1024 model generations
+- Event instrumentation: `InMemoryAdmissionEventSink`, patched request admission init, model-client factory `request_event_sink`, and scheduler init `scheduler_event_sink`
+
+## Scenarios
+
+| Scenario | Buffer | Requests | Success | Failures | Wall s | Time to cap s | Max in-flight | Max waiters | Request wait p50 / p95 / max s |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| diagnostic-4x2-buffer32 | 32 | 8 | 8 | 0 | 5.586 | n/a | 2 | 7 | 2.638 / 3.871 / 3.887 |
+| final-512x2-buffer32 | 32 | 1024 | 1024 | 0 | 104.823 | 50.332 | 16 | 63 | 3.180 / 19.371 / 29.990 |
+| final-512x2-buffer512 | 512 | 1024 | 1024 | 0 | 114.522 | 57.062 | 16 | 63 | 3.283 / 22.949 / 35.324 |
+
+## Observations
+
+- Both final scenarios completed exactly 1024 `model_request_started` and 1024 `model_request_completed` events, with 0 failed model requests and no fallback model.
+- AIMD limit increased monotonically from 1 through 16 in both final scenarios. There were 15 `request_limit_increased` events, 0 decreases, and 0 rate-limit events in each final scenario.
+- Cap enforcement held: observed request in-flight max was 16 in both final scenarios, matching `max_parallel_requests=16`.
+- `buffer_size=32` reached cap faster (50.332s) and completed faster (104.823s) than `buffer_size=512` (57.062s to cap, 114.522s wall).
+- Request wait p95 was lower for `buffer_size=32` (19.371s) than `buffer_size=512` (22.949s).
+- Traffic became steady after the initial AIMD ramp in both final scenarios; see each `flow_buckets.json` for per-second starts/completions and `monitor_samples.jsonl` for sampled pressure snapshots.
+
+## Artifacts
+
+Each scenario directory contains:
+
+- `timeline.jsonl`
+- `request_events.jsonl`
+- `monitor_samples.jsonl`
+- `task_traces.csv`
+- `task_traces.json`
+- `flow_buckets.json`
+- `summary.json`
+
+Combined summary: `combined_summary.json`.
diff --git a/artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json b/artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json
new file mode 100644
index 000000000..ff9154390
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json
@@ -0,0 +1,1224 @@
+{
+  "artifact_schema_version": "645-live-bench-nano-combined-v1",
+  "comparison": {
+    "both_cap_enforced": true,
+    "both_zero_failures": true,
+    "buffer32_max_waiters": 63,
+    "buffer32_time_to_cap_seconds": 50.33227308301139,
+    "buffer32_wait_p95_seconds": 19.37087424368073,
+    "buffer32_wall_time_seconds": 104.82342862500809,
+    "buffer512_max_waiters": 63,
+    "buffer512_minus_buffer32_seconds": 9.698241957987193,
+    "buffer512_minus_buffer32_time_to_cap_seconds": 6.729556832986418,
+    "buffer512_time_to_cap_seconds": 57.061829915997805,
+    "buffer512_wait_p95_seconds": 22.94936933576682,
+    "buffer512_wall_time_seconds": 114.52167058299528,
+    "same_shape": true
+  },
+  "env": {
+    "DATA_DESIGNER_ASYNC_ENGINE": "1",
+    "DATA_DESIGNER_ASYNC_TRACE": "1"
+  },
+  "fallback": null,
+  "harness": "/tmp/dd_live_aimd_bench.py",
+  "root": "artifacts/645-live-bench-nano/agent-aimd-scale",
+  "scenarios": {
+    "diagnostic-4x2-buffer32": {
+      "artifacts": {
+        "flow_buckets": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/flow_buckets.json",
+        "monitor_samples": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/monitor_samples.jsonl",
+        "request_events": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/request_events.jsonl",
+        "summary": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/summary.json",
+        "task_traces_csv": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/task_traces.csv",
+        "task_traces_json": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/task_traces.json",
+        "timeline": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/timeline.jsonl"
+      },
+      "buffer_size": 32,
+      "increase_after_successes": 4,
+      "initial_limit": 1,
+      "llm_columns": 2,
+      "max_parallel_requests": 16,
+      "model_generations_expected": 8,
+      "model_request_completed_events": 8,
+      "model_request_started_events": 8,
+      "request_summary": {
+        "cap_enforced": true,
+        "event_counts": {
+          "model_request_completed": 8,
+          "model_request_started": 8,
+          "request_effective_cap_changed": 1,
+          "request_lease_acquired": 8,
+          "request_lease_released": 8,
+          "request_limit_increased": 2,
+          "request_queue_drained": 2,
+          "request_queue_formed": 2,
+          "request_resource_registered": 1,
+          "request_wait_completed": 8,
+          "request_wait_started": 8
+        },
+        "final_pressure": {
+          "active_lease_count": 0,
+          "blocked_until_monotonic": null,
+          "captured_at": 173980.1628715,
+          "consecutive_rate_limits": 0,
+          "cooldown_remaining_seconds": 0.0,
+          "current_limit": 3,
+          "effective_max": 16,
+          "in_flight_count": 0,
+          "last_outcome": "success",
+          "leak_diagnostics": {},
+          "rate_limit_ceiling": 0,
+          "resource": {
+            "domain": "chat",
+            "model_id": "openai/openai/gpt-5-nano",
+            "provider_name": "nvidia-internal"
+          },
+          "sequence": 65,
+          "waiters": 0
+        },
+        "limit_path": [
+          {
+            "current_limit": 1,
+            "diagnostics": {
+              "alias": "aimd-nano",
+              "max_parallel_requests": 16,
+              "provider_model": {
+                "model_id": "openai/openai/gpt-5-nano",
+                "provider_name": "nvidia-internal"
+              }
+            },
+            "effective_max": 16,
+            "elapsed_seconds": 0.010773582995170727,
+            "event_kind": "request_resource_registered"
+          },
+          {
+            "current_limit": 1,
+            "diagnostics": {
+              "current": 16,
+              "previous": 0,
+              "provider_model": {
+                "model_id": "openai/openai/gpt-5-nano",
+                "provider_name": "nvidia-internal"
+              }
+            },
+            "effective_max": 16,
+            "elapsed_seconds": 0.010781874996609986,
+            "event_kind": "request_effective_cap_changed"
+          },
+          {
+            "current_limit": 2,
+            "diagnostics": {
+              "current": 2,
+              "previous": 1
+            },
+            "effective_max": 16,
+            "elapsed_seconds": 3.0564847079804167,
+            "event_kind": "request_limit_increased"
+          },
+          {
+            "current_limit": 3,
+            "diagnostics": {
+              "current": 3,
+              "previous": 2
+            },
+            "effective_max": 16,
+            "elapsed_seconds": 5.035948874981841,
+            "event_kind": "request_limit_increased"
+          }
+        ],
+        "limit_values_seen": [
+          1,
+          2,
+          3
+        ],
+        "max_in_flight": 2,
+        "max_waiters": 7,
+        "request_wait_seconds": {
+          "count": 8,
+          "max": 3.8871747499797493,
+          "p50": 2.6384192499972414,
+          "p95": 3.8706564271909882
+        },
+        "time_to_reach_cap_seconds": null
+      },
+      "rows": 4,
+      "scheduler_event_counts": {
+        "dependency_ready": 9,
+        "queue_drained": 1,
+        "ready_enqueued": 9,
+        "selected": 9,
+        "task_completed": 9,
+        "task_lease_acquired": 9,
+        "task_lease_released": 9,
+        "worker_spawned": 9
+      },
+      "success": true,
+      "summary_path": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/summary.json",
+      "task_trace_count": 9,
+      "task_trace_status_counts": {
+        "ok": 9
+      },
+      "traffic_regularity": {
+        "bucket_seconds": 1.0,
+        "first_15_buckets": [
+          {
+            "bucket_end_seconds": 1.0,
+            "bucket_start_seconds": 0.0,
+            "request_completions": 2,
+            "request_starts": 4,
+            "task_completions": 2,
+            "task_starts": 9
+          },
+          {
+            "bucket_end_seconds": 2.0,
+            "bucket_start_seconds": 1.0,
+            "request_completions": 2,
+            "request_starts": 2,
+            "task_completions": 1
+          },
+          {
+            "bucket_end_seconds": 3.0,
+            "bucket_start_seconds": 2.0,
+            "request_completions": 2,
+            "request_starts": 2,
+            "task_completions": 1
+          },
+          {
+            "bucket_end_seconds": 4.0,
+            "bucket_start_seconds": 3.0,
+            "request_completions": 6,
+            "request_starts": 8,
+            "task_completions": 3
+          },
+          {
+            "bucket_end_seconds": 5.0,
+            "bucket_start_seconds": 4.0,
+            "request_completions": 2,
+            "task_completions": 1
+          },
+          {
+            "bucket_end_seconds": 6.0,
+            "bucket_start_seconds": 5.0,
+            "request_completions": 2,
+            "task_completions": 1
+          }
+        ],
+        "last_10_buckets": [
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diff --git a/artifacts/645-live-bench-nano/agent-aimd/README.md b/artifacts/645-live-bench-nano/agent-aimd/README.md
new file mode 100644
index 000000000..8db50cc8e
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-aimd/README.md
@@ -0,0 +1,28 @@
+# Live AIMD benchmark: gpt-5-nano
+
+Runtime patches were applied only inside `/tmp/live_aimd_bench.py`; no tracked repository files were edited.
+
+Common settings:
+- provider: `nvidia-internal`
+- model: `openai/openai/gpt-5-nano`
+- `skip_health_check=True`
+- temperature omitted from `ChatCompletionInferenceParams`
+- `DATA_DESIGNER_ASYNC_ENGINE=1` and `DATA_DESIGNER_ASYNC_TRACE=1`
+- shared `InMemoryAdmissionEventSink` passed to request admission, model request executor, and async scheduler
+
+| Scenario | Rows | LLM cols | Expected requests | Wall time (s) | Success/failure | Limit ramp | Max in-flight | Max waiters | Cap enforced | Rate limits |
+|---|---:|---:|---:|---:|---|---|---:|---:|---|---:|
+| `aimd_rows48_cols3_cap6_initial2` | 48 | 3 | 144 | 39.863 | 144/0 | [2, 3, 4, 5, 6] | 6 | 34 | True | 0 |
+| `aimd_rows64_cols2_cap8_initial1` | 64 | 2 | 128 | 38.998 | 128/0 | [1, 2, 3, 4, 5, 6, 7, 8] | 8 | 31 | True | 0 |
+
+Artifacts per scenario:
+- `timeline.jsonl`: merged scheduler and request timeline, sorted by monotonic capture time and event sequence
+- `request_events.jsonl`: request-admission and model-request events using `event_kind` and `pressure_snapshot`
+- `monitor_samples.jsonl`: periodic pressure snapshots while the preview ran
+- `task_traces.json` and `task_traces.csv`: async scheduler task traces from `DATA_DESIGNER_ASYNC_TRACE=1`
+- `summary.json`: scenario-level rollup including limit changes, final pressure, event counts, and cap validation
+
+Notes:
+- Both scenarios completed without provider failures or rate limits.
+- Observed request in-flight counts never exceeded `max_parallel_requests`.
+- Console model-usage logs reported 128 successful requests / 4608 total tokens for the 64x2 run and 144 successful requests / 5184 total tokens for the 48x3 run.
diff --git a/artifacts/645-live-bench-nano/agent-aimd/combined_summary.json b/artifacts/645-live-bench-nano/agent-aimd/combined_summary.json
new file mode 100644
index 000000000..00ae89034
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-aimd/combined_summary.json
@@ -0,0 +1,906 @@
+{
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diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/README.md b/artifacts/645-live-bench-nano/agent-bottleneck/README.md
new file mode 100644
index 000000000..2422dcc2a
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-bottleneck/README.md
@@ -0,0 +1,32 @@
+# Agent Bottleneck Live Benchmark
+
+Model: `openai/openai/gpt-5-nano`
+Provider: `nvidia-internal`
+Temperature: omitted
+Health check: skipped
+
+Note: exact model returned `not_found` for every model request in these runs; artifacts still capture admission behavior up to the provider failure boundary.
+
+## request_bottleneck_rows64_cap2
+- Rows requested/result: 64 / 0
+- max_parallel_requests: 2; observed max in-flight: 2; cap enforced: True
+- Wait p50/p95/max: 1.823871 / 3.294706 / 3.412188 sec
+- Max waiters: 62; scheduler group_capped/admission_blocked: 0 / 0
+- Wall time: 4.241 sec; request successes/failed: 0 / 64; outcomes: `{'not_found': 64}`
+- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/request_bottleneck_rows64_cap2/summary.json`
+
+## narrow_downstream_rows40_cap1
+- Rows requested/result: 40 / 0
+- max_parallel_requests: 1; observed max in-flight: 1; cap enforced: True
+- Wait p50/p95/max: 2.060097 / 3.858477 / 4.058885 sec
+- Max waiters: 39; scheduler group_capped/admission_blocked: 0 / 0
+- Wall time: 4.667 sec; request successes/failed: 0 / 40; outcomes: `{'not_found': 40}`
+- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/narrow_downstream_rows40_cap1/summary.json`
+
+## heavy_fast_rows32_cap8
+- Rows requested/result: 32 / 0
+- max_parallel_requests: 8; observed max in-flight: 8; cap enforced: True
+- Wait p50/p95/max: 0.242845 / 0.434496 / 0.473193 sec
+- Max waiters: 24; scheduler group_capped/admission_blocked: 0 / 0
+- Wall time: 1.336 sec; request successes/failed: 0 / 32; outcomes: `{'not_found': 32}`
+- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/heavy_fast_rows32_cap8/summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md b/artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md
new file mode 100644
index 000000000..96904bcdc
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md
@@ -0,0 +1,27 @@
+# Mixed Buffer Live Benchmarks
+
+No model fallback was used. Initial integrate-endpoint attempt failed with gpt-5.5 not_found; final runs used configured nvidia-internal endpoint.
+
+Previous small-run summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json`
+
+## mixed_rows512_buf256_1024gens
+- Rows / expected model generations / observed completions: 512 / 1024 / 1024
+- Buffer size: 256; wall time: 264.457 sec; result rows: 512
+- Outcomes: `{'openai/openai/gpt-5-nano': {'success': 512}, 'openai/openai/gpt-5.5': {'success': 512}}`
+- Max in-flight: `{'openai/openai/gpt-5-nano': 7, 'openai/openai/gpt-5.5': 8}`
+- Max waiters: `{'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 248}`
+- Wait p95/max by model: `{'openai/openai/gpt-5-nano': {'p50': 0.000496, 'p95': 0.00113, 'max': 0.005292}, 'openai/openai/gpt-5.5': {'p50': 119.00833, 'p95': 129.106918, 'max': 131.423565}}`
+- Upstream-to-downstream idle p95/max: 0.111947 / 0.356167 sec
+- Traffic shape: `{'openai/openai/gpt-5.5': {'active_seconds': 224, 'zero_bins_inside_active_window': 33, 'max_started_per_second': 8, 'mean_started_per_second': 1.992, 'burst_ratio': 4.016, 'interpretation': 'waves'}, 'openai/openai/gpt-5-nano': {'active_seconds': 232, 'zero_bins_inside_active_window': 27, 'max_started_per_second': 7, 'mean_started_per_second': 1.977, 'burst_ratio': 3.541, 'interpretation': 'waves'}}`
+- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/summary.json`
+
+## mixed_rows512_buf32_1024gens
+- Rows / expected model generations / observed completions: 512 / 1024 / 1024
+- Buffer size: 32; wall time: 259.315 sec; result rows: 512
+- Outcomes: `{'openai/openai/gpt-5-nano': {'success': 512}, 'openai/openai/gpt-5.5': {'success': 512}}`
+- Max in-flight: `{'openai/openai/gpt-5-nano': 9, 'openai/openai/gpt-5.5': 8}`
+- Max waiters: `{'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 88}`
+- Wait p95/max by model: `{'openai/openai/gpt-5-nano': {'max': 0.001512, 'p50': 0.000276, 'p95': 0.000618}, 'openai/openai/gpt-5.5': {'max': 45.718323, 'p50': 31.02182, 'p95': 39.684837}}`
+- Upstream-to-downstream idle p95/max: 0.002748 / 0.028367 sec
+- Traffic shape: `{'openai/openai/gpt-5-nano': {'active_seconds': 220, 'burst_ratio': 2.412, 'interpretation': 'waves', 'max_started_per_second': 5, 'mean_started_per_second': 2.073, 'zero_bins_inside_active_window': 27}, 'openai/openai/gpt-5.5': {'active_seconds': 217, 'burst_ratio': 3.828, 'interpretation': 'waves', 'max_started_per_second': 8, 'mean_started_per_second': 2.09, 'zero_bins_inside_active_window': 28}}`
+- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf32_1024gens/summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json b/artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json
new file mode 100644
index 000000000..8fc843a3a
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json
@@ -0,0 +1,369 @@
+{
+  "created_at_epoch": 1778787927.324943,
+  "notes": [
+    "Previous smaller bottleneck runs are preserved separately in combined_summary.json.",
+    "Mixed runs use exact upstream openai/openai/gpt-5.5 and downstream openai/openai/gpt-5-nano with temperature omitted and skip_health_check=True.",
+    "Initial mixed attempt against https://integrate.api.nvidia.com/v1 returned not_found for gpt-5.5; rerun used configured nvidia-internal endpoint https://inference-api.nvidia.com/v1 and exact requested models succeeded. No model fallback was used."
+  ],
+  "previous_small_runs_location": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json",
+  "run_class": "final mixed 1024+ generation scale",
+  "scenarios": [
+    {
+      "buffer_size": 256,
+      "cap_enforced_by_model": {
+        "openai/openai/gpt-5-nano": true,
+        "openai/openai/gpt-5.5": true
+      },
+      "dataset_path": "None",
+      "expected_model_generations": 1024,
+      "failures": [],
+      "final_pressure_by_model": {
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+          "blocked_until_monotonic": null,
+          "captured_at": 174420.160102208,
+          "consecutive_rate_limits": 0,
+          "cooldown_remaining_seconds": 0.0,
+          "current_limit": 16,
+          "effective_max": 16,
+          "in_flight_count": 0,
+          "last_outcome": "success",
+          "leak_diagnostics": {},
+          "rate_limit_ceiling": 0,
+          "resource": {
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+            "model_id": "openai/openai/gpt-5-nano",
+            "provider_name": "nvidia-internal"
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+          "waiters": 0
+        },
+        "openai/openai/gpt-5.5": {
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+          "blocked_until_monotonic": null,
+          "captured_at": 174418.987495666,
+          "consecutive_rate_limits": 0,
+          "cooldown_remaining_seconds": 0.0,
+          "current_limit": 8,
+          "effective_max": 8,
+          "in_flight_count": 0,
+          "last_outcome": "success",
+          "leak_diagnostics": {},
+          "rate_limit_ceiling": 0,
+          "resource": {
+            "domain": "chat",
+            "model_id": "openai/openai/gpt-5.5",
+            "provider_name": "nvidia-internal"
+          },
+          "sequence": 7199,
+          "waiters": 0
+        }
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+      "max_inflight_by_model": {
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+        "openai/openai/gpt-5.5": 8
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+        "openai/openai/gpt-5.5": 248
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+      "model_request_outcomes": {
+        "openai/openai/gpt-5-nano": {
+          "success": 512
+        },
+        "openai/openai/gpt-5.5": {
+          "success": 512
+        }
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+      "models": {
+        "downstream_fast": {
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+          "max_tokens": 64,
+          "model": "openai/openai/gpt-5-nano"
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+        "upstream_heavy": {
+          "alias": "gpt-5.5-heavy",
+          "max_parallel_requests": 8,
+          "max_tokens": 128,
+          "model": "openai/openai/gpt-5.5"
+        }
+      },
+      "observed_model_request_completed": 1024,
+      "paths": {
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+        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/summary.json",
+        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/task_traces.csv",
+        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/task_traces.json",
+        "timeline": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/timeline.jsonl",
+        "traffic_flow_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/traffic_flow.csv",
+        "traffic_flow_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/traffic_flow.json"
+      },
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+        "request_lease_released": 1024,
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+        "request_wait_started": 1024
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+        "case_id",
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+      "rows_requested": 512,
+      "scenario": "mixed_rows512_buf256_1024gens",
+      "scenario_dir": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens",
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+        "task_lease_released": 1026,
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+      "skip_health_check": true,
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+          "heavy_upstream": 512
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+        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf32_1024gens/summary.json",
+        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf32_1024gens/task_traces.csv",
+        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf32_1024gens/task_traces.json",
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+        "traffic_flow_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf32_1024gens/traffic_flow.json"
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+        "fast_downstream"
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+      "rows_requested": 512,
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+      "scenario_dir": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf32_1024gens",
+      "scheduler_admission_blocked_count": 0,
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+          "zero_bins_inside_active_window": 28
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+    }
+  ]
+}
diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json b/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json
new file mode 100644
index 000000000..9935f8154
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json
@@ -0,0 +1,297 @@
+{
+  "created_at_epoch": 1778787718.6548312,
+  "env": {
+    "DATA_DESIGNER_ASYNC_ENGINE": "1",
+    "DATA_DESIGNER_ASYNC_TRACE": "1"
+  },
+  "model_name": "openai/openai/gpt-5-nano",
+  "notes": [
+    "AdaptiveRequestAdmissionController.__init__, create_model_client, and AsyncTaskScheduler.__init__ were patched only in-process.",
+    "Request-event snapshots were read from event.pressure_snapshot and event kinds from event.event_kind.",
+    "The exact requested model returned not_found from nvidia-internal in all scenarios; rows were not reduced because this was deterministic model availability failure, not rate limiting or overload."
+  ],
+  "provider": "nvidia-internal",
+  "scenarios": [
+    {
+      "artifact_dataset_path": null,
+      "failed_request_count": 64,
+      "failure_signal_count": 64,
+      "failures": [
+        "DataDesignerGenerationError: \ud83d\uded1 ModelNotFoundError:   |----------\n  | Cause: The specified model 'openai/openai/gpt-5-nano' could not be found while running generation for column 'llm_note'.\n  | Solution: Check that the model name is correct and supported by your model provider 'nvidia-internal' and try again.\n  |----------"
+      ],
+      "max_parallel_requests": 2,
+      "max_tokens": 32,
+      "model_name": "openai/openai/gpt-5-nano",
+      "model_request_outcomes": {
+        "not_found": 64
+      },
+      "observed_max_active_request_leases": 2,
+      "observed_max_request_in_flight": 2,
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diff --git a/artifacts/645-live-bench-nano/agent-cap-scale/README.md b/artifacts/645-live-bench-nano/agent-cap-scale/README.md
new file mode 100644
index 000000000..ebc8e094f
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-cap-scale/README.md
@@ -0,0 +1,33 @@
+# Live async scheduling benchmark: cap scale
+
+Ran two required live DataDesigner async scheduling scenarios on `openai/openai/gpt-5-nano` via `nvidia-internal` with `temperature` omitted, `skip_health_check=True`, `DATA_DESIGNER_ASYNC_ENGINE=1`, and `DATA_DESIGNER_ASYNC_TRACE=1`.
+
+Instrumentation used one shared `InMemoryAdmissionEventSink` and runtime monkeypatches for `AdaptiveRequestAdmissionController.__init__`, `data_designer.engine.models.clients.factory.create_model_client`, and `AsyncTaskScheduler.__init__`.
+
+| Scenario | Rows x cols | Buffer | Requests | Wall s | Max in-flight | Max waiters | Start mean/s | Start CV | Max start gap s | Wave score | Failures |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| cap8_rows512_cols2_buffer32 | 512x2 | 32 | 1024 | 160.30 | 8 | 30 | 6.38 | 0.252 | 1.086 | 0.816 | 0 |
+| cap8_rows512_cols2_buffer512 | 512x2 | 512 | 1024 | 167.08 | 8 | 31 | 6.14 | 0.246 | 1.091 | 0.872 | 0 |
+
+## Buffer comparison
+
+- `buffer_size=32`: 16 row groups, repeated dispatch/checkpoint waves, request start CV 0.252, wave score 0.816.
+- `buffer_size=512`: 1 row group, no repeated row-group dispatch waves, request start CV 0.246, wave score 0.872.
+- Cap enforcement held in both scenarios: max observed request in-flight was 8 and 8 for cap 8.
+- No fallback model was used. No request rate-limit events were observed. Total failures: 0.
+
+## Artifacts
+
+- `cap8_rows512_cols2_buffer32/timeline.jsonl`
+- `cap8_rows512_cols2_buffer32/request_events.jsonl`
+- `cap8_rows512_cols2_buffer32/scheduler_events.jsonl`
+- `cap8_rows512_cols2_buffer32/task_traces.json` and `cap8_rows512_cols2_buffer32/task_traces.csv`
+- `cap8_rows512_cols2_buffer32/flow_buckets.json`
+- `cap8_rows512_cols2_buffer32/summary.json`
+- `cap8_rows512_cols2_buffer512/timeline.jsonl`
+- `cap8_rows512_cols2_buffer512/request_events.jsonl`
+- `cap8_rows512_cols2_buffer512/scheduler_events.jsonl`
+- `cap8_rows512_cols2_buffer512/task_traces.json` and `cap8_rows512_cols2_buffer512/task_traces.csv`
+- `cap8_rows512_cols2_buffer512/flow_buckets.json`
+- `cap8_rows512_cols2_buffer512/summary.json`
+- `combined_summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json b/artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json
new file mode 100644
index 000000000..cc03cd844
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json
@@ -0,0 +1,93 @@
+{
+  "all_caps_enforced": true,
+  "comparison": {
+    "buffer32_minus_buffer512_max_waiters": -1,
+    "buffer32_minus_buffer512_start_rate_cv": 0.005909466834193605,
+    "buffer32_minus_buffer512_wall_time_seconds": -6.777404916996602,
+    "buffer32_minus_buffer512_wave_score": -0.05615488622945386
+  },
+  "env": {
+    "DATA_DESIGNER_ASYNC_ENGINE": "1",
+    "DATA_DESIGNER_ASYNC_TRACE": "1"
+  },
+  "fallback_used": false,
+  "model_requested": "openai/openai/gpt-5-nano",
+  "provider": "nvidia-internal",
+  "scenarios": [
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+      "failure_count": 0,
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+      "max_observed_request_waiters": 30,
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+      "model_request_completed": 1024,
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+      "model_used": "openai/openai/gpt-5-nano",
+      "provider": "nvidia-internal",
+      "request_queue_formed_events": 9,
+      "request_rate_limited_events": 0,
+      "rows": 512,
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+      "start_rate_mean_after_warmup": 6.380645161290323,
+      "start_rate_stddev_after_warmup": 1.6075265220987118,
+      "status": "success",
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+      "wave_like": false,
+      "wave_score": 0.8155179535759258,
+      "zero_start_bucket_fraction_after_warmup": 0.0064516129032258064
+    },
+    {
+      "active_bucket_peak_to_mean_after_warmup": 1.6194331983805668,
+      "buffer_size": 512,
+      "cap_enforced": true,
+      "expected_model_generations": 1024,
+      "failure_count": 0,
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+      "max_observed_request_in_flight": 8,
+      "max_observed_request_waiters": 31,
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+      "max_start_gap_seconds": 1.0914802920015063,
+      "model_request_completed": 1024,
+      "model_request_started": 1024,
+      "model_used": "openai/openai/gpt-5-nano",
+      "provider": "nvidia-internal",
+      "request_queue_formed_events": 9,
+      "request_rate_limited_events": 0,
+      "rows": 512,
+      "scenario": "cap8_rows512_cols2_buffer512",
+      "scheduler_group_capped_events": 985,
+      "start_rate_cv_after_warmup": 0.24602846130058934,
+      "start_rate_mean_after_warmup": 6.136645962732919,
+      "start_rate_stddev_after_warmup": 1.5097895637576537,
+      "status": "success",
+      "success_count": 1024,
+      "summary_path": "artifacts/645-live-bench-nano/agent-cap-scale/cap8_rows512_cols2_buffer512/summary.json",
+      "task_trace_count": 1025,
+      "wait_seconds_max": 3.2042007660493255e-05,
+      "wait_seconds_p50": 2.7874993975274265e-05,
+      "wait_seconds_p95": 3.188170521752909e-05,
+      "wall_time_seconds": 167.07628341700183,
+      "wave_like": false,
+      "wave_score": 0.8716728398053797,
+      "zero_start_bucket_fraction_after_warmup": 0.006211180124223602
+    }
+  ],
+  "skip_health_check": true,
+  "temperature_omitted": true,
+  "total_failures": 0,
+  "total_model_request_completed": 2048,
+  "total_model_request_started": 2048
+}
diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/README.md b/artifacts/645-live-bench-nano/agent-cap-sweep/README.md
new file mode 100644
index 000000000..f174fc90a
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-cap-sweep/README.md
@@ -0,0 +1,13 @@
+# GPT-5 Nano Live Cap Sweep
+
+Model: `openai/openai/gpt-5-nano`
+Provider: `nvidia-internal`
+Max tokens: `32`
+Temperature: omitted
+Async env: `DATA_DESIGNER_ASYNC_ENGINE=1`, `DATA_DESIGNER_ASYNC_TRACE=1`
+
+| Scenario | Status | Rows | Requests completed | Wall s | Max in-flight | Cap | Enforced |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| cap2_rows40_cols2 | success | 40/40 | 80/80 | 54.33 | 2 | 2 | True |
+| cap4_rows40_cols2 | success | 40/40 | 80/80 | 27.41 | 4 | 4 | True |
+| cap8_rows40_cols2 | success | 40/40 | 80/80 | 16.11 | 8 | 8 | True |
diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md b/artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md
new file mode 100644
index 000000000..cbc4dee68
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md
@@ -0,0 +1,13 @@
+# GPT-5 Nano Live 1024+ Buffer Sweep
+
+Model: `openai/openai/gpt-5-nano`
+Provider: `nvidia-internal`
+Max tokens: `32`
+Temperature: omitted
+Async env: `DATA_DESIGNER_ASYNC_ENGINE=1`, `DATA_DESIGNER_ASYNC_TRACE=1`
+
+| Scenario | Status | Buffer | Rows | Requests completed | Wall s | Max in-flight | Cap | Traffic | Enforced |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| final1024_cap8_buffer32_rows512_cols2 | success | 32 | 512/512 | 1024/1024 | 155.37 | 8 | 8 | steady | True |
+| final1024_cap8_buffer128_rows512_cols2 | success | 128 | 512/512 | 1024/1024 | 153.96 | 8 | 8 | steady | True |
+| final1024_cap8_buffer512_rows512_cols2 | success | 512 | 512/512 | 1024/1024 | 150.27 | 8 | 8 | steady | True |
diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary.json b/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary.json
new file mode 100644
index 000000000..917a3ac11
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary.json
@@ -0,0 +1,269 @@
+[
+  {
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+      "DATA_DESIGNER_ASYNC_TRACE": "1"
+    },
+    "error": null,
+    "expected_llm_requests": 80,
+    "final_pressure": {
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+      "cooldown_remaining_seconds": 0.0,
+      "current_limit": 2,
+      "effective_max": 2,
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+      "last_outcome": "success",
+      "leak_diagnostics": {},
+      "rate_limit_ceiling": 0,
+      "resource": {
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diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json b/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json
new file mode 100644
index 000000000..76bd8c86b
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json
@@ -0,0 +1,493 @@
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diff --git a/artifacts/645-live-bench-nano/agent-fan-scale/README.md b/artifacts/645-live-bench-nano/agent-fan-scale/README.md
new file mode 100644
index 000000000..868b2c69d
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-fan-scale/README.md
@@ -0,0 +1,37 @@
+# Live async fan scale benchmark
+
+Second-wave large-scale live benchmark lane for async scheduling fan-out/fan-in and buffer-size effects.
+
+## Configuration
+
+- Model: `openai/openai/gpt-5-nano`
+- Provider: `nvidia-internal`
+- Fallback used: `false`
+- Temperature: omitted
+- `skip_health_check=True`
+- `DATA_DESIGNER_ASYNC_ENGINE=1`
+- `DATA_DESIGNER_ASYNC_TRACE=1`
+- Shared sink: `InMemoryAdmissionEventSink` injected into request admission, model client factory, and scheduler
+
+## Scenario Summary
+
+| Scenario | Topology | Buffer | Requests | Wall s | Max in-flight | Max waiters | Wait p50/p95/max s | Failures |
+|---|---:|---:|---:|---:|---:|---:|---:|---:|
+| `fanout_buffer32` | fanout | 32 | 1024/1024 | 132.24 | 8 | 56 | 6.79/8.97/9.80 | 0 |
+| `fanout_buffer256` | fanout | 256 | 1024/1024 | 125.94 | 8 | 57 | 6.58/8.09/9.83 | 0 |
+| `fanin_buffer256` | fanin | 256 | 1024/1024 | 141.64 | 8 | 57 | 7.18/8.85/9.62 | 0 |
+
+## Observations
+
+- Fan-out buffer 32 and 256 both maintained balanced traffic across all four independent branches.
+- Buffer 256 was modestly faster than buffer 32 in this run, while wait distributions were similar.
+- Fan-in used 3 upstream LLM columns plus 1 downstream synthesis LLM column for 1024 total model generations.
+- Fan-in showed row-level fan-in: downstream synthesis began almost immediately after the first row became ready and interleaved with upstream traffic.
+- Request in-flight never exceeded the configured cap of 8 in any scenario.
+
+## Artifacts
+
+- `fanout_buffer32`: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer32`
+- `fanout_buffer256`: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer256`
+- `fanin_buffer256`: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256`
+- Combined summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json b/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json
new file mode 100644
index 000000000..cab97c802
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json
@@ -0,0 +1,279 @@
+{
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diff --git a/artifacts/645-live-bench-nano/agent-fan/README.md b/artifacts/645-live-bench-nano/agent-fan/README.md
new file mode 100644
index 000000000..458e05ead
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-fan/README.md
@@ -0,0 +1,4 @@
+# Live async fan benchmark
+
+- fanout_rows32_cols4_cap6: success=True, rows=32, wall_time_s=33.839, requests=128, max_in_flight=6/6, queue_formed=7, rate_limits=0
+- fanin_rows24_up3_down1_cap4: success=True, rows=24, wall_time_s=43.434, requests=96, max_in_flight=4/4, queue_formed=5, rate_limits=0
diff --git a/artifacts/645-live-bench-nano/agent-fan/combined_summary.json b/artifacts/645-live-bench-nano/agent-fan/combined_summary.json
new file mode 100644
index 000000000..d3eb8ab24
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-fan/combined_summary.json
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diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md b/artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md
new file mode 100644
index 000000000..4f59d0939
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md
@@ -0,0 +1,12 @@
+# Mixed-model async scheduling live benchmark
+
+Rows: 512; model generations per scenario: 1024.
+Provider: `nvidia-internal`.
+Upstream: `openai/openai/gpt-5.5` cap 4, max_tokens 96.
+Downstream: `openai/openai/gpt-5-nano` cap 8, max_tokens 32.
+Temperature omitted for both model configs. Health checks skipped.
+
+| Scenario | Buffer | Success | Wall s | Heavy starts | Nano starts | Overlap s | Idle p50/p95/max s | Max in-flight | Max waiters | Diagnosis | Cap violation |
+|---|---:|---|---:|---:|---:|---:|---|---|---|---|---|
+| mixed_rows512_buf256_heavy4_nano8 | 256 | True | 366.44 | 512 | 512 | 329.37 | 0.037/0.071/0.133 | {'openai/openai/gpt-5-nano': 6, 'openai/openai/gpt-5.5': 4} | {'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 252} | steady_interleaving | {'openai/openai/gpt-5-nano': False, 'openai/openai/gpt-5.5': False} |
+| mixed_rows512_buf32_heavy4_nano8 | 32 | True | 368.46 | 512 | 512 | 321.33 | 0.001/0.005/0.053 | {'openai/openai/gpt-5-nano': 6, 'openai/openai/gpt-5.5': 4} | {'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 92} | steady_interleaving | {'openai/openai/gpt-5-nano': False, 'openai/openai/gpt-5.5': False} |
diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json b/artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json
new file mode 100644
index 000000000..2c14d75ac
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json
@@ -0,0 +1,1206 @@
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diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/README.md b/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/README.md
new file mode 100644
index 000000000..d28937cbd
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/README.md
@@ -0,0 +1,10 @@
+# Mixed-model async scheduling live benchmark
+
+Rows: 2; model generations per scenario: 4.
+Provider: `nvidia-internal`.
+Upstream: `openai/openai/gpt-5.5` cap 1, max_tokens 32.
+Downstream: `openai/openai/gpt-5-nano` cap 1, max_tokens 16.
+
+| Scenario | Buffer | Success | Wall s | Heavy starts | Nano starts | Overlap s | Idle p50/p95/max s | Diagnosis | Cap violation |
+|---|---:|---|---:|---:|---:|---:|---|---|---|
+| mixed_rows2_buf2_heavy1_nano1 | 2 | True | 4.67 | 2 | 2 | 1.37 | None/None/None | wave_or_serialized | {'openai/openai/gpt-5.5': False, 'openai/openai/gpt-5-nano': False} |
diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/combined_summary.json b/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/combined_summary.json
new file mode 100644
index 000000000..6c0de069b
--- /dev/null
+++ b/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/combined_summary.json
@@ -0,0 +1,176 @@
+{
+  "buffers": [
+    2
+  ],
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+    "upstream": "openai/openai/gpt-5.5"
+  },
+  "provider": "nvidia-internal",
+  "rows": 2,
+  "summaries": [
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+        "idle_times": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/idle_times.json",
+        "request_events": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/request_events.jsonl",
+        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/summary.json",
+        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/task_traces.csv",
+        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/task_traces.json",
+        "timeline": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/timeline.jsonl"
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+        "request_wait_started": 4
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+        "max_in_flight_by_model": {
+          "openai/openai/gpt-5-nano": 1,
+          "openai/openai/gpt-5.5": 1
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+        "max_start_gap_seconds_by_model": {
+          "openai/openai/gpt-5-nano": 1.419884708011523,
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+}
diff --git a/artifacts/645-live-bench/agent-fan/README.md b/artifacts/645-live-bench/agent-fan/README.md
new file mode 100644
index 000000000..80381379c
--- /dev/null
+++ b/artifacts/645-live-bench/agent-fan/README.md
@@ -0,0 +1,21 @@
+# Agent Fan Live Benchmark
+
+Output: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan`
+
+## fan_out_mp2_short
+- DAG shape: fan-out
+- Wall time: 6.556s
+- Rows / columns: 2 / 6
+- max_parallel_requests: 2; observed max request in-flight: 2; waiters: 4
+- Scheduler events: `{'dependency_ready': 7, 'ready_enqueued': 7, 'selected': 7, 'task_lease_acquired': 7, 'worker_spawned': 7, 'queue_drained': 2, 'task_completed': 7, 'task_lease_released': 7}`
+- Request events: `{'request_resource_registered': 1, 'request_effective_cap_changed': 1, 'request_queue_formed': 3, 'request_wait_started': 6, 'request_wait_completed': 6, 'request_lease_acquired': 6, 'request_queue_drained': 3, 'model_request_started': 6, 'request_lease_released': 6, 'model_request_completed': 6}`
+- Timeline: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/timeline.jsonl`
+
+## fan_in_mp2_mixed
+- DAG shape: fan-in
+- Wall time: 10.622s
+- Rows / columns: 2 / 7
+- max_parallel_requests: 2; observed max request in-flight: 2; waiters: 4
+- Scheduler events: `{'dependency_ready': 9, 'ready_enqueued': 9, 'selected': 9, 'task_lease_acquired': 9, 'worker_spawned': 9, 'queue_drained': 4, 'task_completed': 9, 'task_lease_released': 9}`
+- Request events: `{'request_resource_registered': 1, 'request_effective_cap_changed': 1, 'request_queue_formed': 5, 'request_wait_started': 8, 'request_wait_completed': 8, 'request_lease_acquired': 8, 'request_queue_drained': 5, 'model_request_started': 8, 'request_lease_released': 8, 'model_request_completed': 8}`
+- Timeline: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/timeline.jsonl`
diff --git a/artifacts/645-live-bench/agent-fan/combined_summary.json b/artifacts/645-live-bench/agent-fan/combined_summary.json
new file mode 100644
index 000000000..c237e82ec
--- /dev/null
+++ b/artifacts/645-live-bench/agent-fan/combined_summary.json
@@ -0,0 +1,452 @@
+{
+  "created_at_epoch": 1778786197.816855,
+  "notes": [
+    "All live model calls used alias gpt-5.5.",
+    "temperature was omitted because gpt-5.5 rejects temperature=0.",
+    "AdaptiveRequestAdmissionController.__init__, create_model_client, and AsyncTaskScheduler.__init__ were patched only in-process for observability injection."
+  ],
+  "output_dir": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan",
+  "scenarios": [
+    {
+      "artifact_dataset_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/artifacts/fan_out_mp2_short_05-14-2026_151620/parquet-files",
+      "capacity_plan": {
+        "buffer_size": 2,
+        "observed_maxima": {
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+            "local:local/default/topic/audience/constraint": 1,
+            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/action_brief": 2,
+            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/metric_brief": 2,
+            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/risk_brief": 2
+          },
+          "row_groups_in_flight": 1,
+          "task_leases_by_resource": {
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+            "submission": 6
+          }
+        },
+        "row_group_concurrency": 3,
+        "submission_capacity": 256,
+        "task_resource_limits": {
+          "llm_wait": 256,
+          "local": 256,
+          "submission": 256
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+      },
+      "dag_definition": [
+        {
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+          "column_type": "llm-text",
+          "drop": false,
+          "extract_reasoning_content": false,
+          "model_alias": "gpt-5.5",
+          "multi_modal_context": null,
+          "name": "action_brief",
+          "prompt": "For {{ audience }}, write one sentence under 18 words about the best immediate action for: {{ topic }}. Constraint: {{ constraint }}.",
+          "propagate_skip": true,
+          "skip": null,
+          "system_prompt": null,
+          "tool_alias": null,
+          "with_trace": "none"
+        },
+        {
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+          "column_type": "llm-text",
+          "drop": false,
+          "extract_reasoning_content": false,
+          "model_alias": "gpt-5.5",
+          "multi_modal_context": null,
+          "name": "risk_brief",
+          "prompt": "For {{ audience }}, write one sentence under 18 words about the highest operational risk for: {{ topic }}. Constraint: {{ constraint }}.",
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+          "skip": null,
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+          "extract_reasoning_content": false,
+          "model_alias": "gpt-5.5",
+          "multi_modal_context": null,
+          "name": "metric_brief",
+          "prompt": "For {{ audience }}, write one sentence under 18 words about one useful measurement for: {{ topic }}. Constraint: {{ constraint }}.",
+          "propagate_skip": true,
+          "skip": null,
+          "system_prompt": null,
+          "tool_alias": null,
+          "with_trace": "none"
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+      "files_changed_by_benchmark": [
+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/seed.csv",
+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/timeline.jsonl",
+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/dataset_preview.json",
+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/summary.json"
+      ],
+      "max_parallel_requests": 2,
+      "max_tokens": 24,
+      "model_alias": "gpt-5.5",
+      "num_rows": 2,
+      "observed_max_active_request_leases": 2,
+      "observed_max_request_current_limit": 2,
+      "observed_max_request_in_flight": 2,
+      "observed_max_request_waiters": 4,
+      "observed_max_task_llm_wait_leases": 6,
+      "observed_max_task_submission_leases": 6,
+      "ready_frontier_by_column": {
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+          "task_lease_released": 2,
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+          "selected": 2,
+          "task_completed": 2,
+          "task_lease_acquired": 2,
+          "task_lease_released": 2,
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+      "wall_time_seconds": 6.556
+    },
+    {
+      "artifact_dataset_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/artifacts/fan_in_mp2_mixed_05-14-2026_151627/parquet-files",
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+          "model_alias": "gpt-5.5",
+          "multi_modal_context": null,
+          "name": "operator_view",
+          "prompt": "In one sentence under 16 words, state an operator's most practical concern for {{ topic }}. Audience: {{ audience }}.",
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+          "skip": null,
+          "system_prompt": null,
+          "tool_alias": null,
+          "with_trace": "none"
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+          "model_alias": "gpt-5.5",
+          "multi_modal_context": null,
+          "name": "customer_view",
+          "prompt": "In one sentence under 16 words, state the customer-visible consequence for {{ topic }}. Audience: {{ audience }}.",
+          "propagate_skip": true,
+          "skip": null,
+          "system_prompt": null,
+          "tool_alias": null,
+          "with_trace": "none"
+        },
+        {
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+          "column_type": "llm-text",
+          "drop": false,
+          "extract_reasoning_content": false,
+          "model_alias": "gpt-5.5",
+          "multi_modal_context": null,
+          "name": "measurement_view",
+          "prompt": "In one sentence under 16 words, state the measurement that would reveal success or failure for {{ topic }}. Audience: {{ audience }}.",
+          "propagate_skip": true,
+          "skip": null,
+          "system_prompt": null,
+          "tool_alias": null,
+          "with_trace": "none"
+        },
+        {
+          "allow_resize": false,
+          "column_type": "llm-text",
+          "drop": false,
+          "extract_reasoning_content": false,
+          "model_alias": "gpt-5.5",
+          "multi_modal_context": null,
+          "name": "synthesis_judge",
+          "prompt": "Synthesize these three notes into a concise recommendation under 45 words. Topic: {{ topic }}\nOperator: {{ operator_view }}\nCustomer: {{ customer_view }}\nMeasurement: {{ measurement_view }}\nEnd with a short confidence phrase.",
+          "propagate_skip": true,
+          "skip": null,
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+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/seed.csv",
+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/timeline.jsonl",
+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/dataset_preview.json",
+        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/summary.json"
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index 000000000..2d2d47cb5
--- /dev/null
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diff --git a/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md b/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md
new file mode 100644
index 000000000..0fa710894
--- /dev/null
+++ b/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md
@@ -0,0 +1,22 @@
+# Live Max Parallel Benchmark
+
+- model alias: `gpt-5.5`
+- temperature: omitted
+- health checks: skipped with in-memory `ModelConfig(skip_health_check=True)`
+- total wall time: `17.351s`
+- cap checks passed: `True`
+
+| scenario | cap | requests | wall s | max snapshot in-flight | max lease overlap | max model overlap | max wait s | cap ok | failure |
+|---|---:|---:|---:|---:|---:|---:|---:|---|---|
+| serialish_cap1_2rows_2cols | 1 | 4 | 9.210 | 1 | 1 | 1 | 5.408 | True |  |
+| stress_cap2_3rows_2cols | 2 | 6 | 4.566 | 2 | 2 | 2 | 3.076 | True |  |
+| fanout_cap3_2rows_3cols | 3 | 6 | 3.523 | 3 | 3 | 3 | 2.133 | True |  |
+
+## Artifacts
+
+- `serialish_cap1_2rows_2cols` events: `artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/request_events.jsonl`
+- `serialish_cap1_2rows_2cols` traces: `artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/task_traces.csv`
+- `stress_cap2_3rows_2cols` events: `artifacts/645-live-bench/agent-maxparallel/stress_cap2_3rows_2cols/request_events.jsonl`
+- `stress_cap2_3rows_2cols` traces: `artifacts/645-live-bench/agent-maxparallel/stress_cap2_3rows_2cols/task_traces.csv`
+- `fanout_cap3_2rows_3cols` events: `artifacts/645-live-bench/agent-maxparallel/fanout_cap3_2rows_3cols/request_events.jsonl`
+- `fanout_cap3_2rows_3cols` traces: `artifacts/645-live-bench/agent-maxparallel/fanout_cap3_2rows_3cols/task_traces.csv`
diff --git a/docs/concepts/architecture-and-performance.md b/docs/concepts/architecture-and-performance.md
index 5d1545ad7..537fb1aca 100644
--- a/docs/concepts/architecture-and-performance.md
+++ b/docs/concepts/architecture-and-performance.md
@@ -48,7 +48,7 @@ This guide explains the architecture, execution model, and how to tune performan
 ## Execution Model
 
 !!! note "Two execution engines"
-    The default execution path is the **async engine**, which dispatches work at the cell level and overlaps independent columns — see [Async Engine](#async-engine) below for its semantics. The legacy **sync engine** is still available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0` and is what this section describes. The configuration knobs documented below (`buffer_size`, `max_parallel_requests`, AIMD throttle config, error handling) apply to both engines; the differences are flagged inline.
+    The default execution path is the **async engine**, which dispatches work at the cell level and overlaps independent columns — see [Async Engine](#async-engine) below for its semantics. The legacy **sync engine** is still available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0` and is what this section describes. The public configuration knobs documented below (`buffer_size`, `max_parallel_requests`, error handling) apply to both engines; the differences are flagged inline.
 
 The sync engine processes datasets in **batches**, with **parallel** operations within each batch.
 
@@ -104,12 +104,12 @@ At any moment, the number of concurrent LLM requests is:
 ```python
 concurrent_requests = min(
     buffer_size,                # Records in current batch
-    current_throttle_limit,     # AIMD-managed limit (≤ max_parallel_requests)
+    current_request_limit,      # AIMD-managed limit (≤ max_parallel_requests)
     remaining_cells_in_column   # Cells left to generate
 )
 ```
 
-`max_parallel_requests` sets the **ceiling**. The actual limit (`current_throttle_limit`) is managed at runtime by an AIMD (Additive Increase / Multiplicative Decrease) controller that reacts to rate-limit signals from the inference server:
+`max_parallel_requests` sets the **ceiling**. The actual limit (`current_request_limit`) is managed at runtime by adaptive request admission that reacts to rate-limit signals from the inference server:
 
 - **On the first 429 in a burst**: the limit is reduced by a configurable factor (default: 25% reduction) and a cooldown is applied. Further 429s from already in-flight requests in the same burst do not reduce the limit again — they release their permits and hold the limit steady.
 - **After consecutive successes**: the limit increases by 1 (by default) until it reaches the ceiling or a stabilized rate-limit threshold.
@@ -117,7 +117,7 @@ concurrent_requests = min(
 This means Data Designer automatically finds the right concurrency level for your server without manual tuning.
 
 !!! note "Engine paths"
-    AIMD adaptive concurrency is fully active on the default **async engine**. The legacy **sync engine** is available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0`; on that path 429s are first retried at the HTTP transport layer and AIMD only engages as a fallback. See [Async engine](#async-engine) below.
+    Adaptive request admission is fully active on the default **async engine**. The legacy **sync engine** is available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0`; on that path 429s are first retried at the HTTP transport layer and AIMD only engages as a fallback. See [Async engine](#async-engine) below.
 
 **Example**: With `buffer_size=100` and `max_parallel_requests=32`, Data Designer starts sending up to 32 requests in parallel. If the server returns 429s, concurrency drops automatically (e.g., to 24, then 18) and recovers once the server catches up.
 
@@ -153,7 +153,7 @@ designer.set_run_config(run_config)
 
 ### `max_parallel_requests` (InferenceParams)
 
-Sets the **maximum** concurrent LLM API calls **per model**. This is the ceiling that the AIMD throttle controller can ramp up to — the actual concurrency at runtime may be lower if the server signals rate limits.
+Sets the **maximum** concurrent LLM API calls **per model**. This is the ceiling that adaptive request admission can ramp up to — the actual concurrency at runtime may be lower if the server signals rate limits.
 
 ```python
 import data_designer.config as dd
@@ -170,14 +170,14 @@ model = dd.ModelConfig(
 
 **Default**: 4
 
-**When to increase**: Your inference backend has high throughput capacity, you're using a cloud API with generous rate limits, or you're running vLLM/TensorRT-LLM with multiple GPUs. With AIMD, setting an aggressively high value is safer than before — the system will self-correct downward if the server can't keep up. The salvage queue on the async engine (default) reclaims failed rows; on the sync engine the initial burst of 429s before AIMD stabilizes can drop rows, so start with a more conservative ceiling if you've opted into sync.
+**When to increase**: Your inference backend has high throughput capacity, you're using a cloud API with generous rate limits, or you're running vLLM/TensorRT-LLM with multiple GPUs. With adaptive request admission, setting an aggressively high value is safer than before — the system will self-correct downward if the server can't keep up. The salvage queue on the async engine (default) reclaims failed rows; on the sync engine the initial burst of 429s before AIMD stabilizes can drop rows, so start with a more conservative ceiling if you've opted into sync.
 
 **When to decrease**: You want to cap resource usage to a known safe level, or you want more predictable/debuggable execution.
 
 !!! tip "Finding the optimal value"
     The right value depends on your inference stack and model. Self-hosted vLLM servers can often handle values as high as 256, 512, or even 1024 depending on your hardware.
 
-    With AIMD, a practical approach is to set `max_parallel_requests` to the **upper bound** you're comfortable with and let the throttle controller find the sustainable level automatically. If you see frequent 429 → recovery cycles in the logs, your ceiling is above the server's true capacity but the system is handling it. If you never see any throttle activity, you may have room to increase the ceiling further.
+    With adaptive request admission, a practical approach is to set `max_parallel_requests` to the **upper bound** you're comfortable with and let the request controller find the sustainable level automatically. If you see frequent 429 → recovery cycles in the logs, your ceiling is above the server's true capacity but the system is handling it. If you never see any request-admission activity, you may have room to increase the ceiling further.
 
     **Benchmark approach**: Run a small dataset (e.g., 100 records) with increasing `max_parallel_requests` values (4 → 8 → 16 → 32 → ...) and measure generation time. Stop increasing when the runtime stops decreasing—that's when your inference server is saturated.
 
@@ -198,38 +198,9 @@ designer.set_run_config(run_config)
 
 ---
 
-### Adaptive Throttling (RunConfig)
+### Adaptive Request Admission
 
-Data Designer uses an AIMD (Additive Increase / Multiplicative Decrease) controller to automatically adjust concurrency per model based on rate-limit feedback from the inference server. The defaults work well for most workloads. Override them via `ThrottleConfig` only when you understand the trade-offs.
-
-!!! note "Engine paths"
-    Adaptive throttling is fully active on the default **async engine**, where 429 responses propagate directly to the AIMD controller. On the legacy **sync engine** (`DATA_DESIGNER_ASYNC_ENGINE=0`), 429s are first retried at the HTTP transport layer; `ThrottleConfig` settings only take effect as a fallback if transport retries are exhausted.
-
-```python
-import data_designer.config as dd
-from data_designer.interface import DataDesigner
-
-run_config = dd.RunConfig(
-    throttle=dd.ThrottleConfig(
-        reduce_factor=0.75,       # Multiply limit by this on a 429 (default: 0.75)
-        additive_increase=1,      # Add this many slots after success_window successes (default: 1)
-        success_window=25,        # Consecutive successes before increasing (default: 25)
-        cooldown_seconds=2.0,     # Pause after a 429 when no Retry-After header (default: 2.0)
-        ceiling_overshoot=0.10,   # Probe 10% above observed server limit (default: 0.10)
-    ),
-)
-
-designer = DataDesigner()
-designer.set_run_config(run_config)
-```
-
-| Parameter | Default | Effect |
-|-----------|---------|--------|
-| `reduce_factor` | 0.75 | How aggressively to cut concurrency on a 429. Lower = more aggressive. |
-| `additive_increase` | 1 | Slots added per recovery step. Higher = faster ramp-up, but riskier. |
-| `success_window` | 25 | Consecutive successes required before each increase step. |
-| `cooldown_seconds` | 2.0 | Pause duration after a 429 (used when the server doesn't send `Retry-After`). |
-| `ceiling_overshoot` | 0.10 | Fraction above the observed rate-limit ceiling the controller is allowed to probe. |
+Data Designer uses AIMD (Additive Increase / Multiplicative Decrease) request admission to automatically adjust concurrency per provider/model/domain based on rate-limit feedback from the inference server. This is an internal runtime controller, not a public `RunConfig` knob. Set `max_parallel_requests` as the user-facing ceiling and inspect `AsyncCapacityPlan`/logs to understand the effective runtime limits.
 
 !!! tip "How it works in practice"
     When a model endpoint returns HTTP 429, the controller reduces the concurrency limit for that model and pauses briefly. After enough consecutive successes, it begins ramping back up. If the server rate-limits again, the controller records that level as a ceiling and stabilizes just below it, with a small overshoot band to detect when the server can handle more load.
@@ -263,11 +234,11 @@ designer.set_run_config(run_config)
 
 ## Async Engine
 
-The async engine is the default execution path. It dispatches work at the cell level rather than the column level, so independent columns overlap in time and per-(provider, model) AIMD pools tune themselves independently. See the [Async All the Way Down](../devnotes/posts/async-all-the-way-down.md) dev note for the full architecture.
+The async engine is the default execution path. It dispatches work at the cell level rather than the column level, so independent columns overlap in time and provider/model/domain request resources tune themselves independently. See the [Async All the Way Down](../devnotes/posts/async-all-the-way-down.md) dev note for the full architecture.
 
 ### Per-model timeouts drive every deadline
 
-The `inference_parameters.timeout` field on a `ModelConfig` sets the per-request HTTP timeout. The same value also drives the sync→async bridge that custom columns use when they call `model.generate()`. There is no separate queue-wait deadline — waits scale with provider speed and AIMD's adaptive concurrency. Slow self-hosted endpoints (e.g. large models on a single GPU) only need this one knob raised:
+The `inference_parameters.timeout` field on a `ModelConfig` sets the per-request HTTP timeout. The same value also drives the sync→async bridge that custom columns use when they call `model.generate()`. There is no separate queue-wait deadline — waits scale with provider speed and adaptive request admission. Slow self-hosted endpoints (e.g. large models on a single GPU) only need this one knob raised:
 
 ```python
 import data_designer.config as dd
@@ -315,8 +286,8 @@ DATA_DESIGNER_ASYNC_ENGINE=0 python my_pipeline.py
 
 | Problem | Symptom | Solution |
 |---------|---------|----------|
-| **Low throughput** | Low GPU utilization | Increase `max_parallel_requests` and/or `buffer_size`. If the throttle has self-reduced due to earlier 429s (check logs for "concurrency reduced" messages), the server may need more capacity or you can wait for AIMD recovery. |
-| **Frequent 429 → recovery cycles** | Logs show repeated concurrency drops and ramp-ups | The `max_parallel_requests` ceiling is above the server's sustained capacity. This is handled automatically, but you can lower the ceiling to reduce the sawtooth or tune `reduce_factor` / `success_window`. |
+| **Low throughput** | Low GPU utilization | Increase `max_parallel_requests` and/or `buffer_size`. If request admission has self-reduced due to earlier 429s (check logs for "concurrency reduced" messages), the server may need more capacity or you can wait for AIMD recovery. |
+| **Frequent 429 → recovery cycles** | Logs show repeated concurrency drops and ramp-ups | The `max_parallel_requests` ceiling is above the server's sustained capacity. This is handled automatically, but you can lower the ceiling to reduce the sawtooth. |
 | **Long tail of slow generations** | Most records fast, few very slow | Reduce `max_conversation_restarts`, simplify schemas, improve prompts |
 | **Multi-model idle periods** | One model busy, others idle | Reduce `buffer_size` for faster cycling, or consolidate models |
 | **Memory errors** | OOM crashes | Reduce `buffer_size` and `max_parallel_requests` |
@@ -326,10 +297,10 @@ DATA_DESIGNER_ASYNC_ENGINE=0 python my_pipeline.py
 
 ## Tuning Workflow
 
-1. **Start with defaults** for initial development — AIMD handles rate-limit adaptation automatically
+1. **Start with defaults** for initial development — adaptive request admission handles rate-limit adaptation automatically
 2. **Profile your workload**: How many LLM columns? How many records? What models?
-3. **Identify bottleneck**: Low GPU util → increase `max_parallel_requests` (AIMD will self-correct if you overshoot). Memory issues → decrease `buffer_size`. Long tails → tune retry settings.
-4. **Check throttle logs**: Look for "concurrency reduced" / "concurrency increased" messages to understand whether rate limits are the bottleneck
+3. **Identify bottleneck**: Low GPU util → increase `max_parallel_requests` (request admission will self-correct if you overshoot). Memory issues → decrease `buffer_size`. Long tails → tune retry settings.
+4. **Check request-admission logs**: Look for "concurrency reduced" / "concurrency increased" messages to understand whether rate limits are the bottleneck
 5. **Iterate**: Make one change at a time, measure impact before next change
 
 ---
diff --git a/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg b/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg
index 122d3d95d..dec3601a6 100644
--- a/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg
+++ b/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg
@@ -39,21 +39,21 @@
   <rect x="80" y="180" width="640" height="28" rx="8" fill="#76B900" opacity="0.1"/>
   <text x="400" y="200" text-anchor="middle" fill="#8FD619" font-size="13" font-weight="bold">Row-Group Admission &amp; Buffer Management</text>
   <text x="100" y="225" fill="#CCC" font-size="11">Memory-bounded execution with streaming checkpoints</text>
-  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Semaphore-gated row-group admission</text>
+  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Admission-gated row groups</text>
   <text x="100" y="258" fill="#999" font-size="10">&#x2022; Per-group buffer with cell-level writes</text>
   <text x="460" y="243" fill="#999" font-size="10">&#x2022; Checkpoint-to-parquet on completion</text>
-  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Submission semaphore caps in-flight tasks</text>
+  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Task admission caps in-flight work</text>
 
   <!-- Arrow -->
   <polygon points="395,292 400,302 405,292" fill="#76B900" opacity="0.7"/>
 
-  <!-- Layer 3: ThrottleManager / AIMD -->
+  <!-- Layer 3: Request Admission / AIMD -->
   <rect x="80" y="305" width="640" height="95" rx="8" fill="url(#darkGrad)" stroke="#76B900" stroke-width="1" stroke-opacity="0.4"/>
   <rect x="80" y="305" width="640" height="28" rx="8" fill="#76B900" opacity="0.07"/>
-  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">ThrottleManager (AIMD Adaptive Concurrency)</text>
-  <text x="100" y="350" fill="#CCC" font-size="11">Per-model, per-provider rate-limit adaptation</text>
+  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">Request Admission (AIMD Adaptive Concurrency)</text>
+  <text x="100" y="350" fill="#CCC" font-size="11">Provider/model/domain rate-limit adaptation</text>
   <text x="100" y="368" fill="#999" font-size="10">&#x2022; Additive increase on success streaks</text>
   <text x="100" y="383" fill="#999" font-size="10">&#x2022; Multiplicative decrease on 429s</text>
-  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent pools per (provider, model)</text>
+  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent request resources</text>
   <text x="460" y="383" fill="#999" font-size="10">&#x2022; Global cap across model aliases</text>
 </svg>
diff --git a/docs/devnotes/posts/assets/async-all-the-way-down/task-lifecycle.png b/docs/devnotes/posts/assets/async-all-the-way-down/task-lifecycle.png
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diff --git a/docs/devnotes/posts/assets/owning-the-model-stack/throttle-keying.png b/docs/devnotes/posts/assets/owning-the-model-stack/request-keying.png
similarity index 100%
rename from docs/devnotes/posts/assets/owning-the-model-stack/throttle-keying.png
rename to docs/devnotes/posts/assets/owning-the-model-stack/request-keying.png
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diff --git a/docs/devnotes/posts/async-all-the-way-down.md b/docs/devnotes/posts/async-all-the-way-down.md
index 14338337b..ceada599c 100644
--- a/docs/devnotes/posts/async-all-the-way-down.md
+++ b/docs/devnotes/posts/async-all-the-way-down.md
@@ -59,7 +59,7 @@ Getting this right required solving three problems at different levels of the st
 
 <div style="text-align: center;" markdown>
 
-![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, ThrottleManager for rate limits](assets/async-all-the-way-down/architecture-layers.svg){ style="max-width:100%; height:auto" }
+![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, and request admission for provider capacity](assets/async-all-the-way-down/architecture-layers.svg){ style="max-width:100%; height:auto" }
 
 </div>
 
@@ -67,43 +67,42 @@ Getting this right required solving three problems at different levels of the st
 
 At the top sits the `AsyncTaskScheduler`. It builds an `ExecutionGraph` from your column configs using [Kahn's algorithm](https://en.wikipedia.org/wiki/Topological_sorting#Kahn's_algorithm) for topological ordering, then tracks per-cell completion via a `CompletionTracker`. When a cell completes, the tracker determines which downstream cells are now ready and pushes them onto the dispatch queue.
 
-The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: pull ready tasks from the frontier, acquire a [semaphore](https://en.wikipedia.org/wiki/Semaphore_(programming)) slot, spawn a worker. When the worker completes, mark the cell done, which may add new tasks to the frontier. The loop runs until every cell in every row group has completed or been dropped.
+The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: enqueue ready tasks, ask `FairTaskQueue` to select the next eligible task, acquire a scheduler-resource lease from `TaskAdmissionController`, commit the queue selection, then spawn a worker. When the worker completes, mark the cell done, release the lease, and make any newly-ready downstream tasks visible. The loop runs until every cell in every row group has completed or been dropped.
 
-There's a subtlety in how the scheduler manages its task slots, and getting it right required a delicate dance between two semaphores. A naïve approach would hold a submission slot for the entire lifetime of a task. That's fine for the outbound HTTP call — the slot is released before the request goes out. But the `ThrottleManager` can impose an internal timeout while waiting for a permit during AIMD cooldown, and *that* wait would hold the submission slot hostage. If enough tasks are blocked waiting for throttle permits, the scheduler can't dispatch new work even when the frontier has ready tasks.
+There's a subtlety in how the scheduler manages task-stage pressure. Scheduler resources and provider request resources are separate. The scheduler decides which dependency-ready cell may become a running worker; `ModelRequestExecutor` and request admission decide when an individual provider call may execute. Keeping those ledgers separate prevents provider cooldown or request backpressure from masquerading as DAG readiness.
 
-The fix is a one-way semaphore handoff. The scheduler maintains two pools: a *submission* semaphore that caps how many tasks can be dispatched, and an *LLM-wait* semaphore (sized larger) for tasks that are blocked on a model call. When a task is about to call the model, it acquires an LLM-wait slot and releases its submission slot in the same atomic operation — stepping from one pool to the other mid-flight. The dispatch loop immediately sees a free submission slot and can send another task. When the LLM responds, the LLM-wait slot is released. Non-LLM generators (samplers, Jinja expressions) skip the handoff and hold their submission slot for the full duration, which is fine because they complete quickly.
+The durable scheduler contract is selection plus admission plus commit:
 
 ```py
-if is_llm_bound:
-    await self._llm_wait_semaphore.acquire()
-    holds_llm_wait = True
-    self._submission_semaphore.release()
-    holds_submission = False
+selection = queue.select_next(lambda item, view: admission.is_eligible(item, view))
+decision = admission.try_acquire(selection.item, selection.queue_view)
+committed = queue.commit(selection)
+spawn_worker(committed, decision)
 ```
 
-This keeps the dispatch loop saturated without unbounded coroutine growth — the submission semaphore controls how fast tasks enter, and the LLM-wait semaphore controls how many are waiting on the network.
+This keeps ready-work ordering, scheduler-resource accounting, and request-capacity adaptation explicit. `SchedulingMetadata` describes static generator pressure, `TaskSchedulingResolver` turns it into scheduler inputs, `FairTaskQueue` handles ready ordering, and `TaskAdmissionController` owns leases for scheduler resources.
 
 ### **Layer 2: Row-group admission**
 
-Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. A semaphore limits how many row groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
+Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. Row-group admission limits how many groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
 
 When a row group completes — all columns generated for all its rows — the buffer manager flushes it to disk and releases the memory. Partial results appear on disk during generation. If something fails, you keep everything that already checkpointed. This is also the basis for fault tolerance, discussed later — the unit of loss is a row group, not the entire run.
 
 ### **Layer 3: Adaptive rate limiting**
 
-At the bottom, each (provider, model) pair gets an independent concurrency pool with [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) rate adaptation. When the provider returns a 429, the pool cuts its concurrency. On streaks of successful requests, it gradually increases. Because this happens per-model, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](owning-the-model-stack.md) dev note covers this layer in depth.
+At the bottom, each provider/model/request-domain resource gets [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) request admission. When the provider returns a 429, `AdaptiveRequestAdmissionController` reduces admitted concurrency for that request resource. On streaks of successful requests, it gradually increases. Because this happens per request resource, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](owning-the-model-stack.md) dev note covers this layer in depth.
 
 ### **How they compose**
 
-The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The throttle layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
+The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The request-admission layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
 
 A single task's lifecycle makes the composition concrete:
 
 <div style="text-align: center;">
-  <img src="../assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier → Dispatch → Generator → Throttle → LLM Call → Complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
+  <img src="../assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier to fair queue to task admission to generator to request admission to LLM call to complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
 </div>
 
-A cell enters the frontier when its upstream dependencies are satisfied. The dispatch loop acquires a submission slot and hands it to a worker. The worker runs the generator, which acquires a throttle permit before making the LLM call. On completion, permits are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
+A cell enters the frontier when its upstream dependencies are satisfied. The scheduler enqueues it, selects it through `FairTaskQueue`, acquires a `TaskAdmissionLease`, commits the selection, and hands the task to a worker. The worker runs the generator; any model call goes through `ModelRequestExecutor`, which acquires and releases the request-admission lease around provider execution. On completion, leases are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
 
 ---
 
@@ -138,13 +137,13 @@ The pattern is clear: speedup scales with the amount of parallelism available in
 
 The **narrow** workload is a sequential chain with no cross-column parallelism. The async engine still ekes out a small gain from overlapping row-level dispatch, but there's no structural parallelism to exploit. This is expected: async can't speed up a fundamentally serial pipeline.
 
-The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model gets its own ThrottleManager pool. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
+The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model maps to its own request-admission resources. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
 
 ### **At higher record counts**
 
-The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, the AIMD controller backs off, and the backoff can cascade through downstream columns that were waiting on the throttled model's output.
+The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, adaptive request admission backs off, and the backoff can cascade through downstream columns that were waiting on the rate-limited model's output.
 
-This is where the per-model throttle pools become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same pool. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
+This is where per-model request resources become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same provider capacity. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
 
 The primary tuning lever is `max_parallel_requests` per model. Set it to a generous upper bound and let AIMD find the real ceiling. See the [Owning the Model Stack](owning-the-model-stack.md) dev note for the full story on adaptive concurrency.
 
@@ -180,7 +179,7 @@ The performance numbers are satisfying, but raw throughput is only part of the p
 
 ### **Progress you can see**
 
-Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and throttle layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
+Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and request-admission layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
 
 ```
   column 'topic'    ████████████████████████████████████░░░░  89% | 890/1000 |  148.3 rec/s | eta 1s  | 0 failed
@@ -189,13 +188,13 @@ Because rows complete out of order and row groups checkpoint independently, resu
   column 'analysis' ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░  35% | 350/1000 |   87.5 rec/s | eta 7s  | 1 failed
 ```
 
-When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired a semaphore slot, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
+When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired scheduler resources, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
 
 ### **Fault tolerance**
 
 Failures in a long-running pipeline are not exceptional — they're expected. Model endpoints return 429s, connections time out, prompts produce unparseable output. The scheduler classifies errors into two buckets.
 
-Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding its semaphore slot forever and blocking admission of new row groups.
+Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding row-group admission capacity forever and blocking admission of new row groups.
 
 Non-retryable errors (malformed output, validation failures) drop the row immediately. The `CompletionTracker` knows which downstream tasks depended on that row and removes them from the frontier, so no work is wasted on a row that's already lost.
 
@@ -203,7 +202,7 @@ In both cases, completed row groups are already on disk. The unit of loss is at
 
 ### **Multi-model concurrency**
 
-Multi-model pipelines are where the architecture pays for itself. With independent throttle pools per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
+Multi-model pipelines are where the architecture pays for itself. With independent request resources per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
 
 ### **Adoption**
 
@@ -215,7 +214,7 @@ Adoption is opt-in. Set `DATA_DESIGNER_ASYNC_ENGINE=1` in your environment. Your
 
 This was a ground-up rebuild of the execution layer, delivered across six PRs over four weeks.
 
-It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The `ThrottledModelClient` and dual-semaphore scheduler landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
+It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The historical predecessor to request admission and task-stage capacity control landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
 
 The symmetric bridging was critical for adoption. Every `ColumnGenerator` has both a `generate()` and an `agenerate()` method. Implement one, and the base class synthesizes the other:
 
@@ -278,7 +277,7 @@ Key Resources:
 
 1. [NeMo Data Designer on GitHub](https://github.com/NVIDIA-NeMo/DataDesigner)
 2. [Data Designer Documentation](https://nvidia-nemo.github.io/DataDesigner/)
-3. [Owning the Model Stack: Adaptive Concurrency](owning-the-model-stack.md) — companion dev note on the native client layer and AIMD throttling
+3. [Owning the Model Stack: Adaptive Concurrency](owning-the-model-stack.md) — companion dev note on the native client layer and AIMD request admission
 4. [Async Engine Plan (#346)](https://github.com/NVIDIA-NeMo/DataDesigner/issues/346) — original issue and architecture plan
 
 *Want to learn more about NeMo Data Designer? Check out our [documentation](https://nvidia-nemo.github.io/DataDesigner/) and start building your own synthetic data pipelines today.*
diff --git a/docs/devnotes/posts/owning-the-model-stack.md b/docs/devnotes/posts/owning-the-model-stack.md
index 062c5cb35..7c3e51ee3 100644
--- a/docs/devnotes/posts/owning-the-model-stack.md
+++ b/docs/devnotes/posts/owning-the-model-stack.md
@@ -8,11 +8,11 @@ authors:
 
 Picture this: you're generating a million-record dataset. Thirty two concurrent requests per model, three models in the pipeline, two providers. Everything hums along for the first ten minutes — then one provider starts returning 429s, your retry logic kicks in, and suddenly you're in a feedback loop where retries *cause* more 429s. The run stalls. You restart with lower concurrency, waste throughput for hours, and wonder if there's a better way.
 
-There is. This post is about the native model client layer we built with adaptive throttling (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
+There is. This post is about the native model client layer we built with adaptive request admission (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
 
 <!-- more -->
 
-![From chaotic request flow to calibrated concurrency via adaptive throttling](assets/owning-the-model-stack/native-model-client-hero.png)
+![From chaotic request flow to calibrated concurrency via adaptive request admission](assets/owning-the-model-stack/native-model-client-hero.png)
 
 ## **Why We Made the Move**
 
@@ -38,9 +38,9 @@ From top to bottom:
 
 1. **ModelFacade**: orchestrates correction loops, MCP tool-calling, and usage tracking. This is the public API. Column generators talk to this layer, and it was untouched during the migration. If you've written a Data Designer pipeline, nothing about your code changes.
 
-2. **ThrottledModelClient**: the new layer. It's a decorator around `HttpModelClient` — same `ModelClient` protocol, but every outbound call is wrapped with a throttle permit: acquire a concurrency slot before the call, release it after, and feed the outcome (success, 429, or error) back to `ThrottleManager`. This is where adaptive throttling lives.
+2. **ModelRequestExecutor**: the request execution layer. It maps every outbound model call to a provider/model/domain request resource, acquires a request-admission lease before provider execution, releases it on every terminal path, and feeds the outcome (success, 429, or error) back to request admission.
 
-3. **ThrottleManager**: the Additive Increase / Multiplicative Decrease (AIMD) controller that `ThrottledModelClient` delegates to. A single instance is created at pipeline startup and shared across all model clients. It owns all the mutable concurrency state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
+3. **AdaptiveRequestAdmissionController**: the Additive Increase / Multiplicative Decrease (AIMD) controller used by `ModelRequestExecutor`. A single shared controller is created at pipeline startup and shared across all model clients. It owns the mutable request-admission state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
 
 4. **HttpModelClient**: an abstract base class that defines the interface for all provider adapters. It owns the shared `httpx` transport lifecycle — connection pooling, timeouts, and transport-level retries for transient failures (502, 503, 504). Boring but important.
 
@@ -50,7 +50,7 @@ From top to bottom:
 
 The boundary between `ModelFacade` and the client layer is defined by canonical types. `ChatCompletionRequest`, `ChatCompletionResponse`, `EmbeddingRequest`, `EmbeddingResponse`, `ImageGenerationRequest`, `ImageGenerationResponse`, and `ProviderError`. These are plain dataclasses. No provider SDK objects cross this line. A `ModelClient` protocol defines the contract that all adapters implement, and that's the only interface the rest of the system sees.
 
-## **Adaptive Throttling: The Centerpiece**
+## **Adaptive Request Admission: The Centerpiece**
 
 With this client stack in place, we had the foundation to build something that wasn't possible before. Adaptive concurrency control. Let's start with the problem.
 
@@ -95,23 +95,23 @@ Here's a subtlety that bit us during testing. When the system is running at capa
 
 Real pipelines aren't simple. A single provider+model combination might serve chat completions, embeddings, and image generation, potentially on different rate-limit budgets. And multiple [model aliases](../../concepts/models/model-configs.md) in your pipeline might point to the same underlying provider and model (say, one alias for generation and another for judging, both hitting the same NVIDIA endpoint).
 
-The throttle manager handles this with two-level keying:
+Request admission handles this with two-level keying:
 
 <div style="text-align: center;" markdown>
 
-![Two-level throttle keying: global cap per provider+model, independent domain states for chat, embedding, image](assets/owning-the-model-stack/throttle-keying.png){ style="max-width:75%; height:auto" }
+![Two-level request-resource keying: global cap per provider+model, independent domain states for chat, embedding, image](assets/owning-the-model-stack/request-keying.png){ style="max-width:75%; height:auto" }
 
 </div>
 
 - **Global cap**: keyed by `(provider_name, model_id)`. When multiple model aliases target the same provider and model, the effective max is `min()` of their configured `max_parallel_requests`. This enforces the most conservative limit for shared upstream capacity, because the provider doesn't care what you *call* the model, it sees the same API key.
 
-- **Domain state**: keyed by `(provider_name, model_id, throttle_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
+- **Domain state**: keyed by `(provider_name, model_id, request_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
 
 The practical effect is that a burst of 429s on the chat route doesn't starve embedding requests, and vice versa. Each route adapts to its own capacity independently while respecting the shared upstream limit.
 
 ## **The Retry Boundary**
 
-There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire throttling system.
+There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire adaptive request-admission system.
 
 The transport layer (via `httpx` with `RetryTransport`) handles transient server failures like 502, 503, 504, and connection errors. These are hiccups. The server is temporarily broken. Retry with exponential backoff and jitter, and move on.
 
@@ -119,37 +119,25 @@ But **429 is explicitly excluded from transport retries**.
 
 <div style="text-align: center;" markdown>
 
-![Retry boundary: 502/503/504 retried at transport, 429 passed through to ThrottledModelClient for AIMD feedback](assets/owning-the-model-stack/retry-boundary.png){ style="max-width:75%; height:auto" }
+![Retry boundary: 502/503/504 retried at transport, 429 passed through to ModelRequestExecutor for AIMD feedback](assets/owning-the-model-stack/retry-boundary.png){ style="max-width:75%; height:auto" }
 
 </div>
 
-Why? Because if the retry layer swallows 429s, the throttle manager never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ThrottledModelClient` so it can call `release_rate_limited()`, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the throttle acquire path, waiting for a permit, before making another HTTP call.
+Why? Because if the retry layer swallows 429s, request admission never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ModelRequestExecutor` so it can release the request lease as rate-limited, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the request-admission path before making another HTTP call.
 
-The split is clean and worth remembering. Transport retries handle *server problems*. Throttle adaptation handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
+The split is clean and worth remembering. Transport retries handle *server problems*. Request admission handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
 
-One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ThrottledModelClient` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](async-all-the-way-down.md) for the full story on the async engine.
+One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ModelRequestExecutor` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](async-all-the-way-down.md) for the full story on the async engine.
 
 ## **Configuration**
 
-The throttle system is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
-
-For workloads where you want to fine-tune the adaptation behavior, `ThrottleConfig` is available on `RunConfig`:
+Adaptive request admission is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
 
 ```python
 import data_designer.config as dd
 from data_designer.interface import DataDesigner
 
 data_designer = DataDesigner()
-data_designer.set_run_config(
-    dd.RunConfig(
-        throttle=dd.ThrottleConfig(
-            reduce_factor=0.75,
-            success_window=25,
-            cooldown_seconds=2.0,
-            ceiling_overshoot=0.10,
-        )
-    )
-)
 config_builder = dd.DataDesignerConfigBuilder(
     model_configs=[
         dd.ModelConfig(
@@ -171,21 +159,11 @@ create_result = data_designer.create(
 )
 ```
 
-| Parameter | Default | What it does |
-|---|---|---|
-| `reduce_factor` | 0.75 | Multiplicative decrease on 429 (0.75 = reduce by 25%) |
-| `additive_increase` | 1 | How much to increase the limit after a success window |
-| `success_window` | 25 | Consecutive successes before additive increase |
-| `cooldown_seconds` | 2.0 | Default cooldown when no `Retry-After` header |
-| `ceiling_overshoot` | 0.10 | How far above the observed ceiling to probe (10%) |
-
-In practice, the parameter most worth adjusting is `success_window`. A smaller window (say, 10) makes the system more aggressive about reclaiming throughput after a pullback, useful when you know the provider's capacity fluctuates quickly. A larger window (say, 50) makes it more conservative, better for providers with strict, stable rate limits where you'd rather not probe at all.
-
-Most users will never need to touch any of these. The system adapts automatically.
+Most users will never need more than `max_parallel_requests`. The system adapts automatically, and capacity diagnostics are exposed through runtime logs and `AsyncCapacityPlan` rather than public controller tuning knobs.
 
 ## **What It Looks Like in the Logs**
 
-`ThrottleManager` logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
+Request admission logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
 
 ```
 # When the system hits a 429 and cuts concurrency:
@@ -208,9 +186,9 @@ Reading these lines in sequence tells you exactly what happened: where the syste
 
 ## **Where This Leaves Us**
 
-This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates throttle state per route, and separates retry logic from rate-limit adaptation. Adaptive throttling is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](async-all-the-way-down.md) for details.
+This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates request state per route, and separates retry logic from rate-limit adaptation. Adaptive request admission is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](async-all-the-way-down.md) for details.
 
-For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without any tuning. If you want finer control, `ThrottleConfig` is there — but the goal is that you spend your time designing datasets, not tuning concurrency knobs.
+For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without public controller tuning.
 
 Key Resources:
 
diff --git a/fern/assets/async-all-the-way-down/architecture-layers.svg b/fern/assets/async-all-the-way-down/architecture-layers.svg
index 122d3d95d..dec3601a6 100644
--- a/fern/assets/async-all-the-way-down/architecture-layers.svg
+++ b/fern/assets/async-all-the-way-down/architecture-layers.svg
@@ -39,21 +39,21 @@
   <rect x="80" y="180" width="640" height="28" rx="8" fill="#76B900" opacity="0.1"/>
   <text x="400" y="200" text-anchor="middle" fill="#8FD619" font-size="13" font-weight="bold">Row-Group Admission &amp; Buffer Management</text>
   <text x="100" y="225" fill="#CCC" font-size="11">Memory-bounded execution with streaming checkpoints</text>
-  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Semaphore-gated row-group admission</text>
+  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Admission-gated row groups</text>
   <text x="100" y="258" fill="#999" font-size="10">&#x2022; Per-group buffer with cell-level writes</text>
   <text x="460" y="243" fill="#999" font-size="10">&#x2022; Checkpoint-to-parquet on completion</text>
-  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Submission semaphore caps in-flight tasks</text>
+  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Task admission caps in-flight work</text>
 
   <!-- Arrow -->
   <polygon points="395,292 400,302 405,292" fill="#76B900" opacity="0.7"/>
 
-  <!-- Layer 3: ThrottleManager / AIMD -->
+  <!-- Layer 3: Request Admission / AIMD -->
   <rect x="80" y="305" width="640" height="95" rx="8" fill="url(#darkGrad)" stroke="#76B900" stroke-width="1" stroke-opacity="0.4"/>
   <rect x="80" y="305" width="640" height="28" rx="8" fill="#76B900" opacity="0.07"/>
-  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">ThrottleManager (AIMD Adaptive Concurrency)</text>
-  <text x="100" y="350" fill="#CCC" font-size="11">Per-model, per-provider rate-limit adaptation</text>
+  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">Request Admission (AIMD Adaptive Concurrency)</text>
+  <text x="100" y="350" fill="#CCC" font-size="11">Provider/model/domain rate-limit adaptation</text>
   <text x="100" y="368" fill="#999" font-size="10">&#x2022; Additive increase on success streaks</text>
   <text x="100" y="383" fill="#999" font-size="10">&#x2022; Multiplicative decrease on 429s</text>
-  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent pools per (provider, model)</text>
+  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent request resources</text>
   <text x="460" y="383" fill="#999" font-size="10">&#x2022; Global cap across model aliases</text>
 </svg>
diff --git a/fern/assets/async-all-the-way-down/task-lifecycle.png b/fern/assets/async-all-the-way-down/task-lifecycle.png
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diff --git a/fern/assets/images/throttle-keying.png b/fern/assets/images/request-keying.png
similarity index 100%
rename from fern/assets/images/throttle-keying.png
rename to fern/assets/images/request-keying.png
diff --git a/fern/assets/images/retry-boundary.png b/fern/assets/images/retry-boundary.png
index d01d8f7cd78f919a8bc74a3ac456dba80c7cbe91..e3918eee432b0638ee7381989e533e5d4ab344b2 100644
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diff --git a/fern/assets/owning-the-model-stack/throttle-keying.png b/fern/assets/owning-the-model-stack/request-keying.png
similarity index 100%
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diff --git a/fern/versions/latest/pages/devnotes/index.mdx b/fern/versions/latest/pages/devnotes/index.mdx
index 1d5fe68da..504c4b6f7 100644
--- a/fern/versions/latest/pages/devnotes/index.mdx
+++ b/fern/versions/latest/pages/devnotes/index.mdx
@@ -44,7 +44,7 @@ Welcome to NeMo Data Designer Dev Notes — in-depth guides, benchmark write-ups
   <BlogCard
     href="/dev-notes/owning-the-model-stack"
     title="Owning the Model Stack"
-    description="Adaptive concurrency, throttle keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
+    description="Adaptive concurrency, request-resource keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
     date="Mar 25, 2026"
     authors={["nmulepati"]}
     image={<img src="/assets/owning-the-model-stack/native-model-client-hero.png" alt="" loading="lazy" />}
diff --git a/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx b/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx
index fc0704cda..cc9bd0638 100644
--- a/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx
+++ b/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx
@@ -51,7 +51,7 @@ This guide explains the architecture, execution model, and how to tune performan
 ## Execution Model
 
 <Note title="Two execution engines">
-  The default execution path is the **async engine**, which dispatches work at the cell level and overlaps independent columns — see [Async Engine](#async-engine) below for its semantics. The legacy **sync engine** is still available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0` and is what this section describes. The configuration knobs documented below (`buffer_size`, `max_parallel_requests`, AIMD throttle config, error handling) apply to both engines; the differences are flagged inline.
+  The default execution path is the **async engine**, which dispatches work at the cell level and overlaps independent columns — see [Async Engine](#async-engine) below for its semantics. The legacy **sync engine** is still available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0` and is what this section describes. The public configuration knobs documented below (`buffer_size`, `max_parallel_requests`, error handling) apply to both engines; the differences are flagged inline.
 </Note>
 
 The sync engine processes datasets in **batches**, with **parallel** operations within each batch.
@@ -108,12 +108,12 @@ At any moment, the number of concurrent LLM requests is:
 ```python
 concurrent_requests = min(
     buffer_size,                # Records in current batch
-    current_throttle_limit,     # AIMD-managed limit (≤ max_parallel_requests)
+    current_request_limit,      # AIMD-managed limit (≤ max_parallel_requests)
     remaining_cells_in_column   # Cells left to generate
 )
 ```
 
-`max_parallel_requests` sets the **ceiling**. The actual limit (`current_throttle_limit`) is managed at runtime by an AIMD (Additive Increase / Multiplicative Decrease) controller that reacts to rate-limit signals from the inference server:
+`max_parallel_requests` sets the **ceiling**. The actual limit (`current_request_limit`) is managed at runtime by adaptive request admission that reacts to rate-limit signals from the inference server:
 
 - **On the first 429 in a burst**: the limit is reduced by a configurable factor (default: 25% reduction) and a cooldown is applied. Further 429s from already in-flight requests in the same burst do not reduce the limit again — they release their permits and hold the limit steady.
 - **After consecutive successes**: the limit increases by 1 (by default) until it reaches the ceiling or a stabilized rate-limit threshold.
@@ -121,7 +121,7 @@ concurrent_requests = min(
 This means Data Designer automatically finds the right concurrency level for your server without manual tuning.
 
 <Note title="Engine paths">
-  AIMD adaptive concurrency is fully active on the default **async engine**. The legacy **sync engine** is available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0`; on that path 429s are first retried at the HTTP transport layer and AIMD only engages as a fallback. See [Async engine](#async-engine) below.
+  Adaptive request admission is fully active on the default **async engine**. The legacy **sync engine** is available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0`; on that path 429s are first retried at the HTTP transport layer and AIMD only engages as a fallback. See [Async engine](#async-engine) below.
 </Note>
 
 **Example**: With `buffer_size=100` and `max_parallel_requests=32`, Data Designer starts sending up to 32 requests in parallel. If the server returns 429s, concurrency drops automatically (e.g., to 24, then 18) and recovers once the server catches up.
@@ -198,7 +198,7 @@ Only resume datasets from trusted artifact directories. Resume reads local `meta
 
 ### `max_parallel_requests` (InferenceParams)
 
-Sets the **maximum** concurrent LLM API calls **per model**. This is the ceiling that the AIMD throttle controller can ramp up to — the actual concurrency at runtime may be lower if the server signals rate limits.
+Sets the **maximum** concurrent LLM API calls **per model**. This is the ceiling that adaptive request admission can ramp up to — the actual concurrency at runtime may be lower if the server signals rate limits.
 
 ```python
 import data_designer.config as dd
@@ -215,7 +215,7 @@ model = dd.ModelConfig(
 
 **Default**: 4
 
-**When to increase**: Your inference backend has high throughput capacity, you're using a cloud API with generous rate limits, or you're running vLLM/TensorRT-LLM with multiple GPUs. With AIMD, setting an aggressively high value is safer than before — the system will self-correct downward if the server can't keep up. The salvage queue on the async engine (default) reclaims failed rows; on the sync engine the initial burst of 429s before AIMD stabilizes can drop rows, so start with a more conservative ceiling if you've opted into sync.
+**When to increase**: Your inference backend has high throughput capacity, you're using a cloud API with generous rate limits, or you're running vLLM/TensorRT-LLM with multiple GPUs. With adaptive request admission, setting an aggressively high value is safer than before — the system will self-correct downward if the server can't keep up. The salvage queue on the async engine (default) reclaims failed rows; on the sync engine the initial burst of 429s before AIMD stabilizes can drop rows, so start with a more conservative ceiling if you've opted into sync.
 
 **When to decrease**: You want to cap resource usage to a known safe level, or you want more predictable/debuggable execution.
 
@@ -223,7 +223,7 @@ model = dd.ModelConfig(
 Finding the optimal value
 The right value depends on your inference stack and model. Self-hosted vLLM servers can often handle values as high as 256, 512, or even 1024 depending on your hardware.
 
-With AIMD, a practical approach is to set `max_parallel_requests` to the **upper bound** you're comfortable with and let the throttle controller find the sustainable level automatically. If you see frequent 429 → recovery cycles in the logs, your ceiling is above the server's true capacity but the system is handling it. If you never see any throttle activity, you may have room to increase the ceiling further.
+With adaptive request admission, a practical approach is to set `max_parallel_requests` to the **upper bound** you're comfortable with and let the request controller find the sustainable level automatically. If you see frequent 429 → recovery cycles in the logs, your ceiling is above the server's true capacity but the system is handling it. If you never see any request-admission activity, you may have room to increase the ceiling further.
 
 **Benchmark approach**: Run a small dataset (e.g., 100 records) with increasing `max_parallel_requests` values (4 → 8 → 16 → 32 → ...) and measure generation time. Stop increasing when the runtime stops decreasing—that's when your inference server is saturated.
 </Tip>
@@ -245,39 +245,9 @@ designer.set_run_config(run_config)
 
 ---
 
-### Adaptive Throttling (RunConfig)
+### Adaptive Request Admission
 
-Data Designer uses an AIMD (Additive Increase / Multiplicative Decrease) controller to automatically adjust concurrency per model based on rate-limit feedback from the inference server. The defaults work well for most workloads. Override them via `ThrottleConfig` only when you understand the trade-offs.
-
-<Note title="Engine paths">
-  Adaptive throttling is fully active on the default **async engine**, where 429 responses propagate directly to the AIMD controller. On the legacy **sync engine** (`DATA_DESIGNER_ASYNC_ENGINE=0`), 429s are first retried at the HTTP transport layer; `ThrottleConfig` settings only take effect as a fallback if transport retries are exhausted.
-</Note>
-
-```python
-import data_designer.config as dd
-from data_designer.interface import DataDesigner
-
-run_config = dd.RunConfig(
-    throttle=dd.ThrottleConfig(
-        reduce_factor=0.75,       # Multiply limit by this on a 429 (default: 0.75)
-        additive_increase=1,      # Add this many slots after success_window successes (default: 1)
-        success_window=25,        # Consecutive successes before increasing (default: 25)
-        cooldown_seconds=2.0,     # Pause after a 429 when no Retry-After header (default: 2.0)
-        ceiling_overshoot=0.10,   # Probe 10% above observed server limit (default: 0.10)
-    ),
-)
-
-designer = DataDesigner()
-designer.set_run_config(run_config)
-```
-
-| Parameter | Default | Effect |
-|-----------|---------|--------|
-| `reduce_factor` | 0.75 | How aggressively to cut concurrency on a 429. Lower = more aggressive. |
-| `additive_increase` | 1 | Slots added per recovery step. Higher = faster ramp-up, but riskier. |
-| `success_window` | 25 | Consecutive successes required before each increase step. |
-| `cooldown_seconds` | 2.0 | Pause duration after a 429 (used when the server doesn't send `Retry-After`). |
-| `ceiling_overshoot` | 0.10 | Fraction above the observed rate-limit ceiling the controller is allowed to probe. |
+Data Designer uses AIMD (Additive Increase / Multiplicative Decrease) request admission to automatically adjust concurrency per provider/model/domain based on rate-limit feedback from the inference server. This is an internal runtime controller, not a public `RunConfig` knob. Set `max_parallel_requests` as the user-facing ceiling and inspect `AsyncCapacityPlan`/logs to understand the effective runtime limits.
 
 <Tip>
 How it works in practice
@@ -313,11 +283,11 @@ designer.set_run_config(run_config)
 
 ## Async Engine
 
-The async engine is the default execution path. It dispatches work at the cell level rather than the column level, so independent columns overlap in time and per-(provider, model) AIMD pools tune themselves independently. See the [Async All the Way Down](/dev-notes/async-all-the-way-down) dev note for the full architecture.
+The async engine is the default execution path. It dispatches work at the cell level rather than the column level, so independent columns overlap in time and provider/model/domain request resources tune themselves independently. See the [Async All the Way Down](/dev-notes/async-all-the-way-down) dev note for the full architecture.
 
 ### Per-model timeouts drive every deadline
 
-The `inference_parameters.timeout` field on a `ModelConfig` sets the per-request HTTP timeout. The same value also drives the sync→async bridge that custom columns use when they call `model.generate()`. There is no separate queue-wait deadline — waits scale with provider speed and AIMD's adaptive concurrency. Slow self-hosted endpoints (e.g. large models on a single GPU) only need this one knob raised:
+The `inference_parameters.timeout` field on a `ModelConfig` sets the per-request HTTP timeout. The same value also drives the sync→async bridge that custom columns use when they call `model.generate()`. There is no separate queue-wait deadline — waits scale with provider speed and adaptive request admission. Slow self-hosted endpoints (e.g. large models on a single GPU) only need this one knob raised:
 
 ```python
 import data_designer.config as dd
@@ -366,8 +336,8 @@ DATA_DESIGNER_ASYNC_ENGINE=0 python my_pipeline.py
 
 | Problem | Symptom | Solution |
 |---------|---------|----------|
-| **Low throughput** | Low GPU utilization | Increase `max_parallel_requests` and/or `buffer_size`. If the throttle has self-reduced due to earlier 429s (check logs for "concurrency reduced" messages), the server may need more capacity or you can wait for AIMD recovery. |
-| **Frequent 429 → recovery cycles** | Logs show repeated concurrency drops and ramp-ups | The `max_parallel_requests` ceiling is above the server's sustained capacity. This is handled automatically, but you can lower the ceiling to reduce the sawtooth or tune `reduce_factor` / `success_window`. |
+| **Low throughput** | Low GPU utilization | Increase `max_parallel_requests` and/or `buffer_size`. If request admission has self-reduced due to earlier 429s (check logs for "concurrency reduced" messages), the server may need more capacity or you can wait for AIMD recovery. |
+| **Frequent 429 → recovery cycles** | Logs show repeated concurrency drops and ramp-ups | The `max_parallel_requests` ceiling is above the server's sustained capacity. This is handled automatically, but you can lower the ceiling to reduce the sawtooth. |
 | **Long tail of slow generations** | Most records fast, few very slow | Reduce `max_conversation_restarts`, simplify schemas, improve prompts |
 | **Multi-model idle periods** | One model busy, others idle | Reduce `buffer_size` for faster cycling, or consolidate models |
 | **Memory errors** | OOM crashes | Reduce `buffer_size` and `max_parallel_requests` |
@@ -380,7 +350,7 @@ DATA_DESIGNER_ASYNC_ENGINE=0 python my_pipeline.py
 1. **Start with defaults** for initial development — AIMD handles rate-limit adaptation automatically
 2. **Profile your workload**: How many LLM columns? How many records? What models?
 3. **Identify bottleneck**: Low GPU util → increase `max_parallel_requests` (AIMD will self-correct if you overshoot). Memory issues → decrease `buffer_size`. Long tails → tune retry settings.
-4. **Check throttle logs**: Look for "concurrency reduced" / "concurrency increased" messages to understand whether rate limits are the bottleneck
+4. **Check request-admission logs**: Look for "concurrency reduced" / "concurrency increased" messages to understand whether rate limits are the bottleneck
 5. **Iterate**: Make one change at a time, measure impact before next change
 
 ---
diff --git a/fern/versions/v0.5.8/pages/devnotes/index.mdx b/fern/versions/v0.5.8/pages/devnotes/index.mdx
index 2bab4a5fd..fa5545d2c 100644
--- a/fern/versions/v0.5.8/pages/devnotes/index.mdx
+++ b/fern/versions/v0.5.8/pages/devnotes/index.mdx
@@ -44,7 +44,7 @@ Welcome to NeMo Data Designer Dev Notes — in-depth guides, benchmark write-ups
   <BlogCard
     href="/dev-notes/owning-the-model-stack"
     title="Owning the Model Stack"
-    description="Adaptive concurrency, throttle keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
+    description="Adaptive concurrency, request-resource keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
     date="Mar 25, 2026"
     authors={["nmulepati"]}
     image={<img src="/assets/owning-the-model-stack/native-model-client-hero.png" alt="" loading="lazy" />}
diff --git a/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx b/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx
index 631d1ab18..c81f1551d 100644
--- a/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx
+++ b/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx
@@ -58,7 +58,7 @@ Getting this right required solving three problems at different levels of the st
 
 <div style="text-align: center;" markdown>
 
-![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, ThrottleManager for rate limits](/assets/async-all-the-way-down/architecture-layers.svg)
+![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, and request admission for provider capacity](/assets/async-all-the-way-down/architecture-layers.svg)
 
 </div>
 
@@ -66,43 +66,42 @@ Getting this right required solving three problems at different levels of the st
 
 At the top sits the `AsyncTaskScheduler`. It builds an `ExecutionGraph` from your column configs using [Kahn's algorithm](https://en.wikipedia.org/wiki/Topological_sorting#Kahn's_algorithm) for topological ordering, then tracks per-cell completion via a `CompletionTracker`. When a cell completes, the tracker determines which downstream cells are now ready and pushes them onto the dispatch queue.
 
-The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: pull ready tasks from the frontier, acquire a [semaphore](https://en.wikipedia.org/wiki/Semaphore_(programming)) slot, spawn a worker. When the worker completes, mark the cell done, which may add new tasks to the frontier. The loop runs until every cell in every row group has completed or been dropped.
+The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: enqueue ready tasks, ask `FairTaskQueue` to select the next eligible task, acquire a scheduler-resource lease from `TaskAdmissionController`, commit the queue selection, then spawn a worker. When the worker completes, mark the cell done, release the lease, and make any newly-ready downstream tasks visible. The loop runs until every cell in every row group has completed or been dropped.
 
-There's a subtlety in how the scheduler manages its task slots, and getting it right required a delicate dance between two semaphores. A naïve approach would hold a submission slot for the entire lifetime of a task. That's fine for the outbound HTTP call — the slot is released before the request goes out. But the `ThrottleManager` can impose an internal timeout while waiting for a permit during AIMD cooldown, and *that* wait would hold the submission slot hostage. If enough tasks are blocked waiting for throttle permits, the scheduler can't dispatch new work even when the frontier has ready tasks.
+There's a subtlety in how the scheduler manages task-stage pressure. Scheduler resources and provider request resources are separate. The scheduler decides which dependency-ready cell may become a running worker; `ModelRequestExecutor` and request admission decide when an individual provider call may execute. Keeping those ledgers separate prevents provider cooldown or request backpressure from masquerading as DAG readiness.
 
-The fix is a one-way semaphore handoff. The scheduler maintains two pools: a *submission* semaphore that caps how many tasks can be dispatched, and an *LLM-wait* semaphore (sized larger) for tasks that are blocked on a model call. When a task is about to call the model, it acquires an LLM-wait slot and releases its submission slot in the same atomic operation — stepping from one pool to the other mid-flight. The dispatch loop immediately sees a free submission slot and can send another task. When the LLM responds, the LLM-wait slot is released. Non-LLM generators (samplers, Jinja expressions) skip the handoff and hold their submission slot for the full duration, which is fine because they complete quickly.
+The durable scheduler contract is selection plus admission plus commit:
 
 ```py
-if is_llm_bound:
-    await self._llm_wait_semaphore.acquire()
-    holds_llm_wait = True
-    self._submission_semaphore.release()
-    holds_submission = False
+selection = queue.select_next(lambda item, view: admission.is_eligible(item, view))
+decision = admission.try_acquire(selection.item, selection.queue_view)
+committed = queue.commit(selection)
+spawn_worker(committed, decision)
 ```
 
-This keeps the dispatch loop saturated without unbounded coroutine growth — the submission semaphore controls how fast tasks enter, and the LLM-wait semaphore controls how many are waiting on the network.
+This keeps ready-work ordering, scheduler-resource accounting, and request-capacity adaptation explicit. `SchedulingMetadata` describes static generator pressure, `TaskSchedulingResolver` turns it into scheduler inputs, `FairTaskQueue` handles ready ordering, and `TaskAdmissionController` owns leases for scheduler resources.
 
 ### **Layer 2: Row-group admission**
 
-Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. A semaphore limits how many row groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
+Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. Row-group admission limits how many groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
 
 When a row group completes — all columns generated for all its rows — the buffer manager flushes it to disk and releases the memory. Partial results appear on disk during generation. If something fails, you keep everything that already checkpointed. This is also the basis for fault tolerance, discussed later — the unit of loss is a row group, not the entire run.
 
 ### **Layer 3: Adaptive rate limiting**
 
-At the bottom, each (provider, model) pair gets an independent concurrency pool with [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) rate adaptation. When the provider returns a 429, the pool cuts its concurrency. On streaks of successful requests, it gradually increases. Because this happens per-model, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](/dev-notes/owning-the-model-stack) dev note covers this layer in depth.
+At the bottom, each provider/model/request-domain resource gets [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) request admission. When the provider returns a 429, `AdaptiveRequestAdmissionController` reduces admitted concurrency for that request resource. On streaks of successful requests, it gradually increases. Because this happens per request resource, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](/dev-notes/owning-the-model-stack) dev note covers this layer in depth.
 
 ### **How they compose**
 
-The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The throttle layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
+The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The request-admission layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
 
 A single task's lifecycle makes the composition concrete:
 
 <div style="text-align: center;">
-  <img src="/assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier → Dispatch → Generator → Throttle → LLM Call → Complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
+  <img src="/assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier to fair queue to task admission to generator to request admission to LLM call to complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
 </div>
 
-A cell enters the frontier when its upstream dependencies are satisfied. The dispatch loop acquires a submission slot and hands it to a worker. The worker runs the generator, which acquires a throttle permit before making the LLM call. On completion, permits are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
+A cell enters the frontier when its upstream dependencies are satisfied. The scheduler enqueues it, selects it through `FairTaskQueue`, acquires a `TaskAdmissionLease`, commits the selection, and hands the task to a worker. The worker runs the generator; any model call goes through `ModelRequestExecutor`, which acquires and releases the request-admission lease around provider execution. On completion, leases are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
 
 ---
 
@@ -137,13 +136,13 @@ The pattern is clear: speedup scales with the amount of parallelism available in
 
 The **narrow** workload is a sequential chain with no cross-column parallelism. The async engine still ekes out a small gain from overlapping row-level dispatch, but there's no structural parallelism to exploit. This is expected: async can't speed up a fundamentally serial pipeline.
 
-The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model gets its own ThrottleManager pool. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
+The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model maps to its own request-admission resources. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
 
 ### **At higher record counts**
 
-The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, the AIMD controller backs off, and the backoff can cascade through downstream columns that were waiting on the throttled model's output.
+The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, adaptive request admission backs off, and the backoff can cascade through downstream columns that were waiting on the rate-limited model's output.
 
-This is where the per-model throttle pools become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same pool. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
+This is where per-model request resources become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same provider capacity. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
 
 The primary tuning lever is `max_parallel_requests` per model. Set it to a generous upper bound and let AIMD find the real ceiling. See the [Owning the Model Stack](/dev-notes/owning-the-model-stack) dev note for the full story on adaptive concurrency.
 
@@ -179,7 +178,7 @@ The performance numbers are satisfying, but raw throughput is only part of the p
 
 ### **Progress you can see**
 
-Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and throttle layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
+Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and request-admission layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
 
 ```
   column 'topic'    ████████████████████████████████████░░░░  89% | 890/1000 |  148.3 rec/s | eta 1s  | 0 failed
@@ -188,13 +187,13 @@ Because rows complete out of order and row groups checkpoint independently, resu
   column 'analysis' ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░  35% | 350/1000 |   87.5 rec/s | eta 7s  | 1 failed
 ```
 
-When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired a semaphore slot, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
+When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired scheduler resources, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
 
 ### **Fault tolerance**
 
 Failures in a long-running pipeline are not exceptional — they're expected. Model endpoints return 429s, connections time out, prompts produce unparseable output. The scheduler classifies errors into two buckets.
 
-Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding its semaphore slot forever and blocking admission of new row groups.
+Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding row-group admission capacity forever and blocking admission of new row groups.
 
 Non-retryable errors (malformed output, validation failures) drop the row immediately. The `CompletionTracker` knows which downstream tasks depended on that row and removes them from the frontier, so no work is wasted on a row that's already lost.
 
@@ -202,7 +201,7 @@ In both cases, completed row groups are already on disk. The unit of loss is at
 
 ### **Multi-model concurrency**
 
-Multi-model pipelines are where the architecture pays for itself. With independent throttle pools per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
+Multi-model pipelines are where the architecture pays for itself. With independent request resources per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
 
 ### **Adoption**
 
@@ -214,7 +213,7 @@ Adoption is opt-in. Set `DATA_DESIGNER_ASYNC_ENGINE=1` in your environment. Your
 
 This was a ground-up rebuild of the execution layer, delivered across six PRs over four weeks.
 
-It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The `ThrottledModelClient` and dual-semaphore scheduler landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
+It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The historical predecessor to request admission and task-stage capacity control landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
 
 The symmetric bridging was critical for adoption. Every `ColumnGenerator` has both a `generate()` and an `agenerate()` method. Implement one, and the base class synthesizes the other:
 
@@ -277,7 +276,7 @@ Key Resources:
 
 1. [NeMo Data Designer on GitHub](https://github.com/NVIDIA-NeMo/DataDesigner)
 2. [Data Designer Documentation](/getting-started/welcome)
-3. [Owning the Model Stack: Adaptive Concurrency](/dev-notes/owning-the-model-stack) — companion dev note on the native client layer and AIMD throttling
+3. [Owning the Model Stack: Adaptive Concurrency](/dev-notes/owning-the-model-stack) — companion dev note on the native client layer and AIMD request admission
 4. [Async Engine Plan (#346)](https://github.com/NVIDIA-NeMo/DataDesigner/issues/346) — original issue and architecture plan
 
 *Want to learn more about NeMo Data Designer? Check out our [documentation](/getting-started/welcome) and start building your own synthetic data pipelines today.*
diff --git a/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx b/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx
index ad9136362..0eec884c5 100644
--- a/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx
+++ b/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx
@@ -10,11 +10,11 @@ import { Authors } from "@/components/Authors";
 
 Picture this: you're generating a million-record dataset. Thirty two concurrent requests per model, three models in the pipeline, two providers. Everything hums along for the first ten minutes — then one provider starts returning 429s, your retry logic kicks in, and suddenly you're in a feedback loop where retries *cause* more 429s. The run stalls. You restart with lower concurrency, waste throughput for hours, and wonder if there's a better way.
 
-There is. This post is about the native model client layer we built with adaptive throttling (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
+There is. This post is about the native model client layer we built with adaptive request admission (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
 
 {/* more */}
 
-![From chaotic request flow to calibrated concurrency via adaptive throttling](/assets/owning-the-model-stack/native-model-client-hero.png)
+![From chaotic request flow to calibrated concurrency via adaptive request admission](/assets/owning-the-model-stack/native-model-client-hero.png)
 
 ## **Why We Made the Move**
 
@@ -40,9 +40,9 @@ From top to bottom:
 
 1. **ModelFacade**: orchestrates correction loops, MCP tool-calling, and usage tracking. This is the public API. Column generators talk to this layer, and it was untouched during the migration. If you've written a Data Designer pipeline, nothing about your code changes.
 
-2. **ThrottledModelClient**: the new layer. It's a decorator around `HttpModelClient` — same `ModelClient` protocol, but every outbound call is wrapped with a throttle permit: acquire a concurrency slot before the call, release it after, and feed the outcome (success, 429, or error) back to `ThrottleManager`. This is where adaptive throttling lives.
+2. **ModelRequestExecutor**: the request execution layer. It maps every outbound model call to a provider/model/domain request resource, acquires a request-admission lease before provider execution, releases it on every terminal path, and feeds the outcome (success, 429, or error) back to request admission.
 
-3. **ThrottleManager**: the Additive Increase / Multiplicative Decrease (AIMD) controller that `ThrottledModelClient` delegates to. A single instance is created at pipeline startup and shared across all model clients. It owns all the mutable concurrency state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
+3. **AdaptiveRequestAdmissionController**: the Additive Increase / Multiplicative Decrease (AIMD) controller used by `ModelRequestExecutor`. A single shared controller is created at pipeline startup and shared across all model clients. It owns the mutable request-admission state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
 
 4. **HttpModelClient**: an abstract base class that defines the interface for all provider adapters. It owns the shared `httpx` transport lifecycle — connection pooling, timeouts, and transport-level retries for transient failures (502, 503, 504). Boring but important.
 
@@ -52,7 +52,7 @@ From top to bottom:
 
 The boundary between `ModelFacade` and the client layer is defined by canonical types. `ChatCompletionRequest`, `ChatCompletionResponse`, `EmbeddingRequest`, `EmbeddingResponse`, `ImageGenerationRequest`, `ImageGenerationResponse`, and `ProviderError`. These are plain dataclasses. No provider SDK objects cross this line. A `ModelClient` protocol defines the contract that all adapters implement, and that's the only interface the rest of the system sees.
 
-## **Adaptive Throttling: The Centerpiece**
+## **Adaptive Request Admission: The Centerpiece**
 
 With this client stack in place, we had the foundation to build something that wasn't possible before. Adaptive concurrency control. Let's start with the problem.
 
@@ -97,23 +97,23 @@ Here's a subtlety that bit us during testing. When the system is running at capa
 
 Real pipelines aren't simple. A single provider+model combination might serve chat completions, embeddings, and image generation, potentially on different rate-limit budgets. And multiple [model aliases](/concepts/models/model-configs) in your pipeline might point to the same underlying provider and model (say, one alias for generation and another for judging, both hitting the same NVIDIA endpoint).
 
-The throttle manager handles this with two-level keying:
+Request admission handles this with two-level keying:
 
 <div style="text-align: center;" markdown>
 
-![Two-level throttle keying: global cap per provider+model, independent domain states for chat, embedding, image](/assets/owning-the-model-stack/throttle-keying.png)
+![Two-level request-resource keying: global cap per provider+model, independent domain states for chat, embedding, image](/assets/owning-the-model-stack/request-keying.png)
 
 </div>
 
 - **Global cap**: keyed by `(provider_name, model_id)`. When multiple model aliases target the same provider and model, the effective max is `min()` of their configured `max_parallel_requests`. This enforces the most conservative limit for shared upstream capacity, because the provider doesn't care what you *call* the model, it sees the same API key.
 
-- **Domain state**: keyed by `(provider_name, model_id, throttle_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
+- **Domain state**: keyed by `(provider_name, model_id, request_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
 
 The practical effect is that a burst of 429s on the chat route doesn't starve embedding requests, and vice versa. Each route adapts to its own capacity independently while respecting the shared upstream limit.
 
 ## **The Retry Boundary**
 
-There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire throttling system.
+There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire adaptive request-admission system.
 
 The transport layer (via `httpx` with `RetryTransport`) handles transient server failures like 502, 503, 504, and connection errors. These are hiccups. The server is temporarily broken. Retry with exponential backoff and jitter, and move on.
 
@@ -121,37 +121,25 @@ But **429 is explicitly excluded from transport retries**.
 
 <div style="text-align: center;" markdown>
 
-![Retry boundary: 502/503/504 retried at transport, 429 passed through to ThrottledModelClient for AIMD feedback](/assets/owning-the-model-stack/retry-boundary.png)
+![Retry boundary: 502/503/504 retried at transport, 429 passed through to ModelRequestExecutor for AIMD feedback](/assets/owning-the-model-stack/retry-boundary.png)
 
 </div>
 
-Why? Because if the retry layer swallows 429s, the throttle manager never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ThrottledModelClient` so it can call `release_rate_limited()`, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the throttle acquire path, waiting for a permit, before making another HTTP call.
+Why? Because if the retry layer swallows 429s, request admission never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ModelRequestExecutor` so it can release the request lease as rate-limited, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the request-admission path before making another HTTP call.
 
-The split is clean and worth remembering. Transport retries handle *server problems*. Throttle adaptation handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
+The split is clean and worth remembering. Transport retries handle *server problems*. Request admission handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
 
-One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ThrottledModelClient` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](/dev-notes/async-all-the-way-down) for the full story on the async engine.
+One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ModelRequestExecutor` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](/dev-notes/async-all-the-way-down) for the full story on the async engine.
 
 ## **Configuration**
 
-The throttle system is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
-
-For workloads where you want to fine-tune the adaptation behavior, `ThrottleConfig` is available on `RunConfig`:
+Adaptive request admission is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
 
 ```python
 import data_designer.config as dd
 from data_designer.interface import DataDesigner
 
 data_designer = DataDesigner()
-data_designer.set_run_config(
-    dd.RunConfig(
-        throttle=dd.ThrottleConfig(
-            reduce_factor=0.75,
-            success_window=25,
-            cooldown_seconds=2.0,
-            ceiling_overshoot=0.10,
-        )
-    )
-)
 config_builder = dd.DataDesignerConfigBuilder(
     model_configs=[
         dd.ModelConfig(
@@ -173,21 +161,11 @@ create_result = data_designer.create(
 )
 ```
 
-| Parameter | Default | What it does |
-|---|---|---|
-| `reduce_factor` | 0.75 | Multiplicative decrease on 429 (0.75 = reduce by 25%) |
-| `additive_increase` | 1 | How much to increase the limit after a success window |
-| `success_window` | 25 | Consecutive successes before additive increase |
-| `cooldown_seconds` | 2.0 | Default cooldown when no `Retry-After` header |
-| `ceiling_overshoot` | 0.10 | How far above the observed ceiling to probe (10%) |
-
-In practice, the parameter most worth adjusting is `success_window`. A smaller window (say, 10) makes the system more aggressive about reclaiming throughput after a pullback, useful when you know the provider's capacity fluctuates quickly. A larger window (say, 50) makes it more conservative, better for providers with strict, stable rate limits where you'd rather not probe at all.
-
-Most users will never need to touch any of these. The system adapts automatically.
+Most users will never need more than `max_parallel_requests`. The system adapts automatically, and capacity diagnostics are exposed through runtime logs and `AsyncCapacityPlan` rather than public controller tuning knobs.
 
 ## **What It Looks Like in the Logs**
 
-`ThrottleManager` logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
+Request admission logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
 
 ```
 # When the system hits a 429 and cuts concurrency:
@@ -210,9 +188,9 @@ Reading these lines in sequence tells you exactly what happened: where the syste
 
 ## **Where This Leaves Us**
 
-This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates throttle state per route, and separates retry logic from rate-limit adaptation. Adaptive throttling is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](/dev-notes/async-all-the-way-down) for details.
+This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates request state per route, and separates retry logic from rate-limit adaptation. Adaptive request admission is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](/dev-notes/async-all-the-way-down) for details.
 
-For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without any tuning. If you want finer control, `ThrottleConfig` is there — but the goal is that you spend your time designing datasets, not tuning concurrency knobs.
+For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without public controller tuning.
 
 Key Resources:
 
diff --git a/fern/versions/v0.5.9/pages/devnotes/index.mdx b/fern/versions/v0.5.9/pages/devnotes/index.mdx
index 1d5fe68da..504c4b6f7 100644
--- a/fern/versions/v0.5.9/pages/devnotes/index.mdx
+++ b/fern/versions/v0.5.9/pages/devnotes/index.mdx
@@ -44,7 +44,7 @@ Welcome to NeMo Data Designer Dev Notes — in-depth guides, benchmark write-ups
   <BlogCard
     href="/dev-notes/owning-the-model-stack"
     title="Owning the Model Stack"
-    description="Adaptive concurrency, throttle keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
+    description="Adaptive concurrency, request-resource keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
     date="Mar 25, 2026"
     authors={["nmulepati"]}
     image={<img src="/assets/owning-the-model-stack/native-model-client-hero.png" alt="" loading="lazy" />}
diff --git a/packages/data-designer-config/src/data_designer/config/__init__.py b/packages/data-designer-config/src/data_designer/config/__init__.py
index eb385e15a..d3af55838 100644
--- a/packages/data-designer-config/src/data_designer/config/__init__.py
+++ b/packages/data-designer-config/src/data_designer/config/__init__.py
@@ -58,7 +58,7 @@
         ProcessorType,
         SchemaTransformProcessorConfig,
     )
-    from data_designer.config.run_config import JinjaRenderingEngine, RunConfig, ThrottleConfig  # noqa: F401
+    from data_designer.config.run_config import JinjaRenderingEngine, RunConfig  # noqa: F401
     from data_designer.config.sampler_constraints import (  # noqa: F401
         ColumnInequalityConstraint,
         ConstraintType,
@@ -82,6 +82,7 @@
         UniformSamplerParams,
         UUIDSamplerParams,
     )
+    from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError  # noqa: F401
     from data_designer.config.seed import (  # noqa: F401
         IndexRange,
         PartitionBlock,
@@ -177,7 +178,9 @@
     # run_config
     "JinjaRenderingEngine": (f"{_MOD_BASE}.run_config", "JinjaRenderingEngine"),
     "RunConfig": (f"{_MOD_BASE}.run_config", "RunConfig"),
-    "ThrottleConfig": (f"{_MOD_BASE}.run_config", "ThrottleConfig"),
+    # scheduling metadata
+    "SchedulingMetadata": (f"{_MOD_BASE}.scheduling", "SchedulingMetadata"),
+    "SchedulingMetadataError": (f"{_MOD_BASE}.scheduling", "SchedulingMetadataError"),
     # sampler_constraints
     "ColumnInequalityConstraint": (_MOD_SAMPLER_CONSTRAINTS, "ColumnInequalityConstraint"),
     "ConstraintType": (_MOD_SAMPLER_CONSTRAINTS, "ConstraintType"),
diff --git a/packages/data-designer-config/src/data_designer/config/run_config.py b/packages/data-designer-config/src/data_designer/config/run_config.py
index d5f10c9e5..dc72bba37 100644
--- a/packages/data-designer-config/src/data_designer/config/run_config.py
+++ b/packages/data-designer-config/src/data_designer/config/run_config.py
@@ -3,8 +3,6 @@
 
 from __future__ import annotations
 
-from typing import ClassVar
-
 from pydantic import Field, model_validator
 from typing_extensions import Self
 
@@ -19,63 +17,6 @@ class JinjaRenderingEngine(StrEnum):
     SECURE = "secure"
 
 
-class ThrottleConfig(ConfigBase):
-    """AIMD throttle tuning parameters for adaptive concurrency control.
-
-    These knobs configure the ``ThrottleManager`` that wraps every outbound
-    model HTTP request.  The defaults are conservative and suitable for most
-    workloads; override only when you understand the trade-offs.
-
-    Attributes:
-        reduce_factor: Multiplicative decrease factor applied to the per-domain
-            concurrency limit on a 429 / rate-limit signal.  Must be in (0, 1).
-            Default is 0.75 (reduce by 25% on rate-limit).
-        additive_increase: Additive increase step applied after every
-            ``success_window`` consecutive successes.  Default is 1.
-        success_window: Number of consecutive successful releases before
-            the additive increase is applied.  Default is 25.
-        cooldown_seconds: Default cooldown duration (seconds) applied after a
-            rate-limit when the provider does not include a ``Retry-After``
-            header.  Default is 2.0.
-        ceiling_overshoot: Fraction above the observed rate-limit ceiling
-            that additive increase is allowed to probe before capping.
-            Default is 0.10 (10% overshoot).
-    """
-
-    DEFAULT_REDUCE_FACTOR: ClassVar[float] = 0.75
-    DEFAULT_ADDITIVE_INCREASE: ClassVar[int] = 1
-    DEFAULT_SUCCESS_WINDOW: ClassVar[int] = 25
-    DEFAULT_COOLDOWN_SECONDS: ClassVar[float] = 2.0
-    DEFAULT_CEILING_OVERSHOOT: ClassVar[float] = 0.10
-
-    reduce_factor: float = Field(
-        default=DEFAULT_REDUCE_FACTOR,
-        gt=0.0,
-        lt=1.0,
-        description="Multiplicative decrease factor applied to the per-domain concurrency limit on a 429 signal.",
-    )
-    additive_increase: int = Field(
-        default=DEFAULT_ADDITIVE_INCREASE,
-        ge=1,
-        description="Additive increase step applied after every `success_window` consecutive successes.",
-    )
-    success_window: int = Field(
-        default=DEFAULT_SUCCESS_WINDOW,
-        ge=1,
-        description="Number of consecutive successful releases before the additive increase is applied.",
-    )
-    cooldown_seconds: float = Field(
-        default=DEFAULT_COOLDOWN_SECONDS,
-        gt=0.0,
-        description="Default cooldown duration (seconds) after a rate-limit when no Retry-After header is present.",
-    )
-    ceiling_overshoot: float = Field(
-        default=DEFAULT_CEILING_OVERSHOOT,
-        ge=0.0,
-        description="Fraction above the rate-limit ceiling that additive increase is allowed to probe.",
-    )
-
-
 class RunConfig(ConfigBase):
     """Runtime configuration for dataset generation.
 
@@ -112,7 +53,8 @@ class RunConfig(ConfigBase):
             fewer Data Designer-specific restrictions. ``secure`` uses Data Designer's
             hardened sandbox with additional AST, filter, and output guards.
             Default is ``secure``.
-        throttle: AIMD throttle tuning parameters.  See ``ThrottleConfig`` for details.
+        Request admission is engine-internal in V1 and is not exposed as a
+            public run-config knob.
     """
 
     disable_early_shutdown: bool = False
@@ -132,7 +74,6 @@ class RunConfig(ConfigBase):
             "`native` uses Jinja2's built-in sandbox; `secure` uses Data Designer's hardened sandbox."
         ),
     )
-    throttle: ThrottleConfig = Field(default_factory=ThrottleConfig)
 
     @model_validator(mode="after")
     def normalize_shutdown_settings(self) -> Self:
diff --git a/packages/data-designer-config/src/data_designer/config/scheduling.py b/packages/data-designer-config/src/data_designer/config/scheduling.py
new file mode 100644
index 000000000..84e36b3a0
--- /dev/null
+++ b/packages/data-designer-config/src/data_designer/config/scheduling.py
@@ -0,0 +1,118 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Literal
+
+SchedulingMetadataKind = Literal["local", "model", "custom_model"]
+
+
+@dataclass(frozen=True)
+class SchedulingMetadata:
+    """Static generator-facing scheduling metadata.
+
+    The metadata describes broad resource shape only. It intentionally does
+    not expose ready-queue state, task-admission state, request-admission
+    pressure, provider cooldowns, or adaptive request limits.
+    """
+
+    kind: SchedulingMetadataKind = "local"
+    identity: tuple[str, ...] = ("local", "default")
+    weight: int = 1
+    diagnostics: dict[str, object] = field(default_factory=dict)
+
+    @classmethod
+    def local(cls, resource_name: str = "default", *, weight: int = 1) -> SchedulingMetadata:
+        return cls(kind="local", identity=("local", resource_name), weight=weight)
+
+    @classmethod
+    def model(
+        cls,
+        provider_name: str,
+        model_id: str,
+        generation_kind: str,
+        *,
+        weight: int,
+        diagnostics: dict[str, object] | None = None,
+    ) -> SchedulingMetadata:
+        return cls(
+            kind="model",
+            identity=("model", provider_name, model_id, generation_kind),
+            weight=weight,
+            diagnostics=diagnostics or {},
+        )
+
+    @classmethod
+    def custom_model(
+        cls,
+        plugin_namespace: str,
+        resource_name: str,
+        version: str,
+        *,
+        weight: int = 1,
+        diagnostics: dict[str, object] | None = None,
+    ) -> SchedulingMetadata:
+        return cls(
+            kind="custom_model",
+            identity=("custom_model", plugin_namespace, resource_name, version),
+            weight=weight,
+            diagnostics=diagnostics or {},
+        )
+
+    def __post_init__(self) -> None:
+        if self.kind not in {"local", "model", "custom_model"}:
+            raise SchedulingMetadataError(
+                code="invalid_kind",
+                message=f"Unknown scheduling metadata kind: {self.kind!r}",
+                diagnostics={"kind": self.kind},
+            )
+        if not isinstance(self.identity, tuple) or not self.identity:
+            raise SchedulingMetadataError(
+                code="invalid_identity",
+                message="Scheduling metadata identity must be a non-empty tuple of non-empty strings.",
+                diagnostics={"identity": self.identity},
+            )
+        if any(not isinstance(part, str) or not part for part in self.identity):
+            raise SchedulingMetadataError(
+                code="invalid_identity",
+                message="Scheduling metadata identity must contain only non-empty strings.",
+                diagnostics={"identity": self.identity},
+            )
+        expected_identity_lengths = {"local": 2, "model": 4, "custom_model": 4}
+        if self.identity[0] != self.kind or len(self.identity) != expected_identity_lengths[self.kind]:
+            raise SchedulingMetadataError(
+                code="invalid_identity",
+                message=f"Scheduling metadata identity for kind {self.kind!r} has an invalid shape.",
+                diagnostics={
+                    "kind": self.kind,
+                    "identity": self.identity,
+                    "expected_prefix": self.kind,
+                    "expected_length": expected_identity_lengths[self.kind],
+                },
+            )
+        if isinstance(self.weight, bool) or not isinstance(self.weight, int) or self.weight <= 0:
+            raise SchedulingMetadataError(
+                code="invalid_weight",
+                message="Scheduling metadata weight must be a positive integer.",
+                diagnostics={"weight": self.weight},
+            )
+
+
+class SchedulingMetadataError(ValueError):
+    """Typed scheduling metadata resolution error."""
+
+    def __init__(
+        self,
+        *,
+        code: str,
+        message: str,
+        fallback: SchedulingMetadata | None = None,
+        diagnostics: dict[str, object] | None = None,
+    ) -> None:
+        super().__init__(message)
+        self.code = code
+        self.message = message
+        self.fallback = fallback
+        self.diagnostics = diagnostics or {}
diff --git a/packages/data-designer-config/tests/config/test_scheduling.py b/packages/data-designer-config/tests/config/test_scheduling.py
new file mode 100644
index 000000000..e219daddd
--- /dev/null
+++ b/packages/data-designer-config/tests/config/test_scheduling.py
@@ -0,0 +1,35 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import pytest
+
+from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
+
+
+@pytest.mark.parametrize(
+    "metadata",
+    [
+        SchedulingMetadata.local(),
+        SchedulingMetadata.model("nvidia", "nemotron", "chat", weight=2),
+        SchedulingMetadata.custom_model("plugin", "resource", "v1"),
+    ],
+)
+def test_scheduling_metadata_accepts_normative_shapes(metadata: SchedulingMetadata) -> None:
+    assert metadata.weight >= 1
+
+
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"identity": ["local", "default"]},
+        {"weight": True},
+        {"kind": "model", "identity": ("local", "default")},
+        {"kind": "local", "identity": ("local", "default", "extra")},
+        {"kind": "custom_model", "identity": ("custom_model", "plugin")},
+    ],
+)
+def test_scheduling_metadata_rejects_non_normative_direct_construction(kwargs: dict[str, object]) -> None:
+    with pytest.raises(SchedulingMetadataError):
+        SchedulingMetadata(**kwargs)  # type: ignore[arg-type]
diff --git a/packages/data-designer-engine/src/data_designer/engine/capacity.py b/packages/data-designer-engine/src/data_designer/engine/capacity.py
new file mode 100644
index 000000000..89dc62bd1
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/capacity.py
@@ -0,0 +1,119 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Mapping, Sequence
+from dataclasses import dataclass, field
+from typing import Generic, Literal, TypeVar
+
+from data_designer.engine.dataset_builders.utils.task_scheduling import SchedulerResourceKey, TaskGroupKey
+from data_designer.engine.models.clients.request_admission import (
+    ProviderModelKey,
+    ProviderModelStaticCap,
+    RequestAdmissionConfig,
+    RequestResourceKey,
+)
+
+_T = TypeVar("_T")
+
+CapacityValueSource = Literal[
+    "default",
+    "run_config",
+    "dataset_builder",
+    "model_metadata",
+    "engine_internal_config",
+    "adapter_config",
+    "environment",
+    "runtime_snapshot",
+    "benchmark_override",
+]
+
+
+@dataclass(frozen=True)
+class CapacityValue(Generic[_T]):
+    value: _T | None
+    source: CapacityValueSource
+    fallback_from: str | None = None
+    missing_reason: str | None = None
+
+
+@dataclass(frozen=True)
+class RowGroupAdmission:
+    row_group_concurrency: CapacityValue[int]
+    observed_in_flight: int | None = None
+
+
+@dataclass(frozen=True)
+class RequestAdmissionConfigSnapshot:
+    resources: Sequence[RequestResourceKey]
+    initial_limits: Mapping[RequestResourceKey, int]
+    max_limit_clamps: Mapping[RequestResourceKey, int | None]
+    cooldown_seconds: float
+    multiplicative_decrease_factor: float
+    additive_increase_step: int
+    increase_after_successes: int
+    default_queue_wait_timeout_seconds: float | None
+
+    @classmethod
+    def from_config(cls, config: RequestAdmissionConfig) -> RequestAdmissionConfigSnapshot:
+        resources = tuple(sorted({*config.initial_limits, *config.max_limit_clamps}))
+        return cls(
+            resources=resources,
+            initial_limits=dict(config.initial_limits),
+            max_limit_clamps=dict(config.max_limit_clamps),
+            cooldown_seconds=config.cooldown_seconds,
+            multiplicative_decrease_factor=config.multiplicative_decrease_factor,
+            additive_increase_step=config.additive_increase_step,
+            increase_after_successes=config.increase_after_successes,
+            default_queue_wait_timeout_seconds=config.default_queue_wait_timeout_seconds,
+        )
+
+
+@dataclass(frozen=True)
+class AsyncCapacityConfigured:
+    buffer_size: CapacityValue[int]
+    row_group_admission: RowGroupAdmission
+    submission_capacity: CapacityValue[int]
+    task_resource_limits: CapacityValue[Mapping[SchedulerResourceKey, int]]
+    request_resources: CapacityValue[Sequence[RequestResourceKey]]
+    provider_model_static_caps: CapacityValue[Mapping[ProviderModelKey, ProviderModelStaticCap]]
+    request_domain_initial_limits: CapacityValue[Mapping[RequestResourceKey, int]]
+    request_admission_config: CapacityValue[RequestAdmissionConfigSnapshot]
+    transport_pool_limits: CapacityValue[Mapping[ProviderModelKey, int]]
+
+
+@dataclass(frozen=True)
+class AsyncCapacityRuntimeSnapshot:
+    request_domain_current_limits: Mapping[RequestResourceKey, int] | None = None
+    request_domain_effective_max: Mapping[RequestResourceKey, int] | None = None
+    request_domain_blocked_until: Mapping[RequestResourceKey, float | None] | None = None
+    provider_model_aggregate_in_flight: Mapping[ProviderModelKey, int] | None = None
+
+
+@dataclass(frozen=True)
+class AsyncCapacityObservedMaxima:
+    row_groups_in_flight: int = 0
+    queued_tasks_by_group: Mapping[TaskGroupKey | str, int] = field(default_factory=dict)
+    task_leases_by_resource: Mapping[SchedulerResourceKey, int] = field(default_factory=dict)
+    request_waiters_by_resource: Mapping[RequestResourceKey, int] = field(default_factory=dict)
+    request_in_flight_by_resource: Mapping[RequestResourceKey, int] = field(default_factory=dict)
+    provider_model_aggregate_in_flight: Mapping[ProviderModelKey, int] = field(default_factory=dict)
+    request_domain_current_limits: Mapping[RequestResourceKey, int] = field(default_factory=dict)
+    transport_pool_utilization: Mapping[ProviderModelKey, int] | None = None
+
+
+@dataclass(frozen=True)
+class AsyncCapacityPlan:
+    configured: AsyncCapacityConfigured
+    runtime_snapshot: AsyncCapacityRuntimeSnapshot
+    observed_maxima: AsyncCapacityObservedMaxima
+
+
+def missing_capacity_value(
+    *,
+    source: CapacityValueSource,
+    missing_reason: str,
+    fallback_from: str | None = None,
+) -> CapacityValue[object]:
+    return CapacityValue(value=None, source=source, fallback_from=fallback_from, missing_reason=missing_reason)
diff --git a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
index 2431c0eb6..39e8b67e8 100644
--- a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
+++ b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
@@ -11,13 +11,14 @@
 from typing import TYPE_CHECKING, Any, Coroutine, TypeVar, overload
 
 from data_designer.config.column_configs import GenerationStrategy
+from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
 from data_designer.engine.configurable_task import ConfigurableTask, DataT, TaskConfigT
 from data_designer.logging import LOG_DOUBLE_INDENT, LOG_INDENT
 
 _T = TypeVar("_T")
 
 # Preserved deliberately. Two other 300s deadlines were retired in the
-# async-default flip (PR #592): the throttle queue-wait and the
+# async-default flip (PR #592): the request-admission queue wait and the
 # ``_AsyncBridgedModelFacade`` bridge in ``custom.py`` — both have
 # ``ModelFacade`` context and could derive a per-call deadline from
 # ``inference_parameters.timeout``. This generic ``ColumnGenerator.generate()``
@@ -65,10 +66,14 @@ class ColumnGenerator(ConfigurableTask[TaskConfigT], ABC):
     def can_generate_from_scratch(self) -> bool:
         return False
 
-    @property
-    def is_llm_bound(self) -> bool:
-        """Whether this generator makes model/API calls during generation."""
-        return False
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        """Return static scheduler metadata for this generator.
+
+        Generators that do not declare model-backed behavior use the documented
+        local default. Model-aware base classes override this with provider/model
+        resource identity derived from registered model aliases.
+        """
+        return SchedulingMetadata.local()
 
     @property
     def is_order_dependent(self) -> bool:
@@ -143,10 +148,6 @@ async def agenerate_from_scratch(self, num_records: int) -> pd.DataFrame:
 
 
 class ColumnGeneratorWithModelRegistry(ColumnGenerator[TaskConfigT], ABC):
-    @property
-    def is_llm_bound(self) -> bool:
-        return True
-
     @property
     def model_registry(self) -> ModelRegistry:
         return self.resource_provider.model_registry
@@ -161,6 +162,74 @@ def get_model_provider_name(self, model_alias: str) -> str:
         provider = self.model_registry.get_model_provider(model_alias=model_alias)
         return provider.name
 
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        aliases = self._get_scheduling_model_aliases()
+        if not aliases:
+            raise SchedulingMetadataError(
+                code="missing_model_alias",
+                message=f"{type(self).__name__} has no model aliases for scheduling metadata.",
+                fallback=SchedulingMetadata.local(),
+                diagnostics={"generator_type": type(self).__name__},
+            )
+
+        endpoints: dict[tuple[str, str, str], dict[str, object]] = {}
+        for alias in aliases:
+            try:
+                model_config = self.get_model_config(model_alias=alias)
+                provider_name = self.get_model_provider_name(model_alias=alias)
+            except Exception as exc:
+                raise SchedulingMetadataError(
+                    code="alias_resolution_failed",
+                    message=f"Could not resolve model alias {alias!r} for scheduling metadata.",
+                    diagnostics={"alias": alias, "generator_type": type(self).__name__},
+                ) from exc
+
+            endpoint = (provider_name, str(model_config.model), str(model_config.generation_type))
+            max_parallel = getattr(model_config.inference_parameters, "max_parallel_requests", 1)
+            cap = max_parallel if isinstance(max_parallel, int) and max_parallel > 0 else 1
+            bucket = endpoints.setdefault(endpoint, {"aliases": [], "caps": []})
+            cast_aliases = bucket["aliases"]
+            cast_caps = bucket["caps"]
+            if isinstance(cast_aliases, list):
+                cast_aliases.append(alias)
+            if isinstance(cast_caps, list):
+                cast_caps.append(cap)
+
+        if len(endpoints) != 1:
+            raise SchedulingMetadataError(
+                code="ambiguous_model_aliases",
+                message="Model scheduling metadata must resolve to one provider/model/generation endpoint.",
+                diagnostics={"endpoints": sorted(str(endpoint) for endpoint in endpoints)},
+            )
+
+        endpoint, bucket = next(iter(endpoints.items()))
+        caps = bucket["caps"] if isinstance(bucket["caps"], list) else [1]
+        aliases_for_diagnostics = bucket["aliases"] if isinstance(bucket["aliases"], list) else aliases
+        provider_name, model_id, generation_kind = endpoint
+        effective_cap = max(1, min(int(cap) for cap in caps))
+        return SchedulingMetadata.model(
+            provider_name,
+            model_id,
+            generation_kind,
+            weight=effective_cap,
+            diagnostics={
+                "aliases": tuple(str(alias) for alias in aliases_for_diagnostics),
+                "raw_caps": tuple(int(cap) for cap in caps),
+                "merge_rule": "min_same_endpoint",
+            },
+        )
+
+    def _get_scheduling_model_aliases(self) -> list[str]:
+        get_aliases = getattr(self.config, "get_model_aliases", None)
+        if callable(get_aliases):
+            aliases = get_aliases()
+        else:
+            aliases = []
+            if (alias := getattr(self.config, "model_alias", None)) is not None:
+                aliases.append(alias)
+            aliases.extend(getattr(self.config, "model_aliases", []) or [])
+        return list(dict.fromkeys(str(alias) for alias in aliases if alias))
+
 
 class ColumnGeneratorWithModel(ColumnGeneratorWithModelRegistry[TaskConfigT], ABC):
     @functools.cached_property
diff --git a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/custom.py b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/custom.py
index b4c863542..08c78120b 100644
--- a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/custom.py
+++ b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/custom.py
@@ -13,6 +13,7 @@
 
 import data_designer.lazy_heavy_imports as lazy
 from data_designer.config.column_configs import CustomColumnConfig, GenerationStrategy
+from data_designer.config.scheduling import SchedulingMetadata
 from data_designer.engine.column_generators.generators.base import ColumnGenerator
 from data_designer.engine.column_generators.utils.errors import CustomColumnGenerationError
 from data_designer.engine.models.errors import RETRYABLE_MODEL_ERRORS, ModelTimeoutError
@@ -105,7 +106,7 @@ def generate(self, *args: Any, **kwargs: Any) -> tuple[Any, list]:
         except concurrent.futures.TimeoutError as exc:
             future.cancel()
             # Demoted to debug: the raised ModelTimeoutError already surfaces
-            # the timeout at the scheduler with full context, and the throttled
+            # the timeout at the scheduler with full context, and the request-admission
             # degraded-provider WARN is the user-facing signal under sustained
             # bridge timeouts. Per-event WARN was noise on top of those.
             logger.debug("Async model bridge timed out after %.0fs; coroutine cancelled", bridge_timeout)
@@ -137,10 +138,18 @@ class CustomColumnGenerator(ColumnGenerator[CustomColumnConfig]):
     The models dict provides direct access to ModelFacade instances keyed by alias.
     """
 
-    @property
-    def is_llm_bound(self) -> bool:
-        """Custom generators with model_aliases make LLM calls and need the handoff."""
-        return bool(self.config.model_aliases)
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        """Return custom-model metadata when the custom column declares model aliases."""
+        if not self.config.model_aliases:
+            return SchedulingMetadata.local()
+        identity = "-".join(sorted(str(alias) for alias in self.config.model_aliases))
+        return SchedulingMetadata.custom_model(
+            "custom_column",
+            identity or self.config.name,
+            "v1",
+            weight=max(1, len(self.config.model_aliases)),
+            diagnostics={"aliases": tuple(sorted(str(alias) for alias in self.config.model_aliases))},
+        )
 
     def get_generation_strategy(self) -> GenerationStrategy:
         """Return strategy based on config."""
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index 778501da1..2a9718b19 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -5,8 +5,10 @@
 
 import asyncio
 import contextlib
+import hashlib
 import logging
 import time
+import uuid
 from collections import defaultdict, deque
 from collections.abc import Coroutine
 from dataclasses import dataclass
@@ -14,6 +16,14 @@
 
 import data_designer.lazy_heavy_imports as lazy
 from data_designer.config.column_configs import GenerationStrategy
+from data_designer.engine.capacity import (
+    AsyncCapacityConfigured,
+    AsyncCapacityObservedMaxima,
+    AsyncCapacityPlan,
+    AsyncCapacityRuntimeSnapshot,
+    CapacityValue,
+    RowGroupAdmission,
+)
 from data_designer.engine.context import current_row_group
 from data_designer.engine.dataset_builders.errors import DatasetGenerationError
 from data_designer.engine.dataset_builders.multi_column_configs import MultiColumnConfig
@@ -24,19 +34,33 @@
 from data_designer.engine.dataset_builders.utils.completion_tracker import CompletionTracker, FrontierDelta
 from data_designer.engine.dataset_builders.utils.fair_task_queue import (
     FairTaskQueue,
-    TaskGroupKey,
-    TaskGroupSpec,
 )
 from data_designer.engine.dataset_builders.utils.progress_tracker import ProgressTracker
-from data_designer.engine.dataset_builders.utils.scheduling_hints import SchedulingHint, SchedulingHintResolver
 from data_designer.engine.dataset_builders.utils.skip_evaluator import should_skip_column_for_record
 from data_designer.engine.dataset_builders.utils.skip_tracker import (
     apply_skip_to_record,
     strip_skip_metadata_from_records,
 )
 from data_designer.engine.dataset_builders.utils.sticky_progress_bar import StickyProgressBar
+from data_designer.engine.dataset_builders.utils.task_admission import (
+    TaskAdmissionConfig,
+    TaskAdmissionController,
+    TaskAdmissionDenied,
+    TaskAdmissionLease,
+)
 from data_designer.engine.dataset_builders.utils.task_model import SliceRef, Task, TaskTrace
+from data_designer.engine.dataset_builders.utils.task_scheduling import (
+    SchedulableTask,
+    TaskSchedulingResolver,
+    stable_task_id,
+)
 from data_designer.engine.models.errors import RETRYABLE_MODEL_ERRORS
+from data_designer.engine.observability import (
+    RuntimeCorrelation,
+    SchedulerAdmissionEvent,
+    SchedulerAdmissionEventSink,
+    runtime_correlation_provider,
+)
 
 if TYPE_CHECKING:
     from data_designer.engine.column_generators.generators.base import ColumnGenerator
@@ -46,10 +70,8 @@
 logger = logging.getLogger(__name__)
 
 DEFAULT_TASK_POOL_SIZE: int = 256
-# Global LLM wait-pool headroom sizes the memory-safety semaphore above provider capacity.
-GLOBAL_LLM_WAIT_POOL_HEADROOM_MULTIPLIER: int = 2
-# Per-group admission backlog caps how many ready LLM tasks one fair-queue group can hold.
-LLM_GROUP_ADMISSION_BACKLOG_MULTIPLIER: int = 2
+MODEL_TASK_ADMISSION_HEADROOM_MULTIPLIER: int = 2
+MODEL_GROUP_ADMISSION_BACKLOG_MULTIPLIER: int = 2
 
 # Degraded-provider WARN: emit at most one warning per interval when the
 # rolling fraction of retryable errors exceeds the threshold. Distinct from
@@ -60,21 +82,6 @@
 DEGRADED_WARN_INTERVAL_S: float = 60.0
 
 
-class TrackingSemaphore(asyncio.Semaphore):
-    """``asyncio.Semaphore`` subclass that exposes available permits publicly."""
-
-    @property
-    def available_permits(self) -> int:
-        return self._value  # type: ignore[attr-defined]
-
-    def try_acquire(self) -> bool:
-        """Non-blocking acquire. Returns ``True`` if a permit was taken."""
-        if self._value > 0:  # type: ignore[attr-defined]
-            self._value -= 1  # type: ignore[attr-defined]
-            return True
-        return False
-
-
 @dataclass
 class _RowGroupState:
     """Lifecycle state for a single admitted row group."""
@@ -90,8 +97,7 @@ class _DispatchOutcome:
     """Result of one fair-dispatch pass over the persistent ready queue."""
 
     dispatched: bool = False
-    submission_full: bool = False
-    group_blocked: bool = False
+    admission_blocked: bool = False
 
 
 class AsyncTaskScheduler:
@@ -111,7 +117,8 @@ def __init__(
         *,
         max_concurrent_row_groups: int = 3,
         max_submitted_tasks: int = DEFAULT_TASK_POOL_SIZE,
-        max_llm_wait_tasks: int = DEFAULT_TASK_POOL_SIZE,
+        max_model_task_admission: int = DEFAULT_TASK_POOL_SIZE,
+        task_admission_config: TaskAdmissionConfig | None = None,
         salvage_max_rounds: int = 2,
         on_finalize_row_group: Callable[[int], None] | None = None,
         on_seeds_complete: Callable[[int, int], FrontierDelta | None] | None = None,
@@ -127,6 +134,8 @@ def __init__(
         buffer_size: int = 0,
         progress_interval: float | None = None,
         progress_bar: bool = False,
+        scheduler_event_sink: SchedulerAdmissionEventSink | None = None,
+        run_id: str | None = None,
     ) -> None:
         self._generators = generators
         self._graph = graph
@@ -135,22 +144,29 @@ def __init__(
         self._buffer_manager = buffer_manager
 
         self._rg_semaphore = asyncio.Semaphore(max_concurrent_row_groups)
-        self._submission_semaphore = TrackingSemaphore(max_submitted_tasks)
-        self._llm_wait_semaphore = TrackingSemaphore(max_llm_wait_tasks)
-        self._max_llm_wait_tasks = max_llm_wait_tasks
 
-        self._llm_bound_lookup = build_llm_bound_lookup(generators)
-        self._scheduling_hints = SchedulingHintResolver(generators)
+        self._task_scheduling = TaskSchedulingResolver(
+            generators,
+            model_group_limit_multiplier=MODEL_GROUP_ADMISSION_BACKLOG_MULTIPLIER,
+            model_group_limit_cap=max_model_task_admission,
+        )
+        admission_config = task_admission_config or TaskAdmissionConfig(
+            submission_capacity=max_submitted_tasks,
+            resource_limits={"llm_wait": max_model_task_admission, "local": max_submitted_tasks},
+        )
+        self._task_admission = TaskAdmissionController(admission_config)
+        self._task_admission_config = admission_config
         self._fair_queue = FairTaskQueue()
         self._pending_pre_batch_ready: defaultdict[int, list[Task]] = defaultdict(list)
         self._pending_pre_batch_ready_tasks: set[Task] = set()
-        # Task group specs are derived from per-generator scheduling hints and flow identity.
-        self._task_group_spec_cache: dict[int, TaskGroupSpec] = {}
 
         self._dispatched: set[Task] = set()
         self._in_flight: set[Task] = set()
         self._worker_tasks: set[asyncio.Task] = set()
         self._wake_event = asyncio.Event()
+        self._run_id = run_id or f"run-{uuid.uuid4().hex}"
+        self._scheduler_event_sink = scheduler_event_sink
+        self._scheduler_event_sequence = 0
         self._salvage_max_rounds = salvage_max_rounds
         self._on_finalize_row_group = on_finalize_row_group
         self._on_seeds_complete = on_seeds_complete
@@ -202,7 +218,7 @@ def __init__(
         self._all_rgs_admitted = False
 
         # Degraded-provider WARN: separate window tracking retryable-vs-not for
-        # every outcome (success or failure), throttled to one log per interval.
+        # every outcome (success or failure), rate-limited to one log per interval.
         self._degraded_warn_rate = degraded_warn_rate
         self._degraded_warn_window = degraded_warn_window
         self._degraded_warn_interval_s = degraded_warn_interval_s
@@ -227,6 +243,14 @@ def __init__(
 
         # Pre-compute row-group sizes for O(1) lookup
         self._rg_size_map: dict[int, int] = dict(row_groups)
+        self._max_concurrent_row_groups = max_concurrent_row_groups
+        self._max_submitted_tasks = max_submitted_tasks
+        self._max_model_task_admission = max_model_task_admission
+        self._num_records = num_records
+        self._buffer_size = buffer_size
+        self._observed_max_row_groups_in_flight = 0
+        self._observed_max_task_leases_by_resource: dict[str, int] = {}
+        self._observed_max_queued_by_group: dict[str, int] = {}
 
         # Pre-compute seed columns (graph is static)
         self._seed_cols: tuple[str, ...] = tuple(c for c in graph.columns if not graph.get_upstream_columns(c))
@@ -300,6 +324,64 @@ def _spawn_worker(self, coro: Coroutine[Any, Any, None]) -> asyncio.Task:
         task.add_done_callback(self._worker_tasks.discard)
         return task
 
+    def _emit_scheduler_event(
+        self,
+        event_kind: str,
+        *,
+        task: Task | None = None,
+        lease: TaskAdmissionLease | None = None,
+        task_execution_id: str | None = None,
+        scheduler_resource_key: str | None = None,
+        reason_or_result: str | None = None,
+        diagnostics: dict[str, object] | None = None,
+    ) -> None:
+        if self._scheduler_event_sink is None:
+            return
+        self._scheduler_event_sequence += 1
+        correlation = None
+        if task is not None:
+            group = lease.item.group if lease is not None else self._schedulable_task(task).group
+            identity_hash = hashlib.sha1("\0".join(group.key.identity).encode()).hexdigest()[:16]
+            correlation = RuntimeCorrelation(
+                run_id=self._run_id,
+                row_group=task.row_group,
+                task_column=task.column,
+                task_type=task.task_type,
+                scheduling_group_kind=group.key.kind,
+                scheduling_group_identity_hash=identity_hash,
+                task_execution_id=task_execution_id,
+            )
+        try:
+            self._scheduler_event_sink.emit_scheduler_event(
+                SchedulerAdmissionEvent.capture(
+                    event_kind,  # type: ignore[arg-type]
+                    sequence=self._scheduler_event_sequence,
+                    correlation=correlation,
+                    task_id=stable_task_id(task) if task is not None else None,
+                    task_execution_id=task_execution_id,
+                    task_lease_id=lease.lease_id if lease is not None else None,
+                    scheduler_resource_key=scheduler_resource_key,
+                    reason_or_result=reason_or_result,
+                    snapshot=self.task_admission_snapshot(),
+                    diagnostics=diagnostics or {},
+                )
+            )
+        except Exception:
+            return
+
+    def _record_observed_task_state(self) -> None:
+        self._observed_max_row_groups_in_flight = max(self._observed_max_row_groups_in_flight, len(self._rg_states))
+        view = self._task_admission.view()
+        for resource, count in view.leased_resources.items():
+            self._observed_max_task_leases_by_resource[resource] = max(
+                self._observed_max_task_leases_by_resource.get(resource, 0),
+                count,
+            )
+        queue_view = self._fair_queue.view()
+        for group, count in queue_view.queued_by_group.items():
+            label = f"{group.kind}:{'/'.join(group.identity)}"
+            self._observed_max_queued_by_group[label] = max(self._observed_max_queued_by_group.get(label, 0), count)
+
     async def _cancel_workers(self) -> None:
         """Cancel all tracked worker tasks and wait for them to finish."""
         for t in self._worker_tasks:
@@ -321,16 +403,23 @@ def _enqueue_ready_task(self, task: Task) -> None:
             return
         if not self._tracker.is_frontier_task(task):
             return
+        self._emit_scheduler_event("dependency_ready", task=task)
         state = self._rg_states[task.row_group]
-        if self._on_seeds_complete is not None and not state.pre_batch_done:
+        if self._on_seeds_complete is not None and not state.pre_batch_done and task.column not in self._seed_cols:
             if task not in self._pending_pre_batch_ready_tasks:
                 self._pending_pre_batch_ready[task.row_group].append(task)
                 self._pending_pre_batch_ready_tasks.add(task)
             return
-        self._fair_queue.enqueue(task, self._task_group_spec(task))
+        schedulable = self._schedulable_task(task)
+        accepted = self._fair_queue.enqueue((schedulable,))
+        if accepted:
+            self._tracker.mark_enqueued(accepted)
+            self._emit_scheduler_event("ready_enqueued", task=task)
+            self._record_observed_task_state()
+            self._wake_event.set()
 
     def _discard_ready_task(self, task: Task) -> None:
-        self._fair_queue.discard(task)
+        self._fair_queue.discard(stable_task_id(task))
         self._pending_pre_batch_ready_tasks.discard(task)
 
     def _flush_pre_batch_ready(self, row_group: int) -> None:
@@ -345,70 +434,91 @@ def _drop_pending_ready_for_row_group(self, row_group: int) -> None:
         pending = self._pending_pre_batch_ready.pop(row_group, [])
         for task in pending:
             self._pending_pre_batch_ready_tasks.discard(task)
-        self._fair_queue.discard_where(lambda task: task.row_group == row_group)
+        self._fair_queue.discard_where(lambda item: item.payload.row_group == row_group)
 
     def _dispatch_queued_tasks(self) -> _DispatchOutcome:
         dispatched = False
 
         while self._fair_queue.has_queued_tasks:
-            if not self._submission_semaphore.try_acquire():
-                return _DispatchOutcome(dispatched=dispatched, submission_full=True)
-
-            selection = self._fair_queue.admit_next()
+            selection = self._fair_queue.select_next(lambda item, view: self._task_admission.is_eligible(item, view))
             if selection is None:
-                self._submission_semaphore.release()
-                return _DispatchOutcome(dispatched=dispatched, group_blocked=True)
+                summary = self._task_admission.explain_blocked(self._fair_queue.view())
+                if "group_cap" in summary.dominant_denial_reasons:
+                    event_kind = "group_capped"
+                elif summary.dominant_denial_reasons:
+                    event_kind = "admission_blocked"
+                else:
+                    event_kind = "queue_empty"
+                self._emit_scheduler_event(
+                    event_kind,
+                    diagnostics={
+                        "queued_count": summary.queued_count,
+                        "reasons": dict(summary.dominant_denial_reasons),
+                    },
+                )
+                return _DispatchOutcome(dispatched=dispatched, admission_blocked=True)
+
+            self._emit_scheduler_event("selected", task=selection.item.payload)
+            decision = self._task_admission.try_acquire(selection.item, selection.queue_view)
+            if isinstance(decision, TaskAdmissionDenied):
+                self._emit_scheduler_event(
+                    "admission_denied",
+                    task=selection.item.payload,
+                    reason_or_result=decision.reason,
+                    diagnostics=dict(decision.diagnostics),
+                )
+                return _DispatchOutcome(dispatched=dispatched, admission_blocked=True)
+            self._emit_scheduler_event("task_lease_acquired", task=selection.item.payload, lease=decision)
+
+            committed = self._fair_queue.commit(selection)
+            if committed is None:
+                result = self._task_admission.release(decision)
+                self._emit_scheduler_event(
+                    "stale_selection",
+                    task=selection.item.payload,
+                    lease=decision,
+                    reason_or_result=result.reason,
+                )
+                return _DispatchOutcome(dispatched=dispatched, admission_blocked=True)
 
-            self._dispatch_selected_task(selection.task)
+            self._dispatch_selected_task(committed, decision)
             dispatched = True
+            self._record_observed_task_state()
 
+        if dispatched:
+            self._emit_scheduler_event("queue_drained")
         return _DispatchOutcome(dispatched=dispatched)
 
-    def _dispatch_selected_task(self, task: Task) -> None:
+    def _dispatch_selected_task(self, item: SchedulableTask, lease: TaskAdmissionLease) -> None:
+        task = item.payload
+        task_execution_id = f"task-exec-{uuid.uuid4().hex}"
         self._dispatched.add(task)
         self._in_flight.add(task)
         if (s := self._rg_states.get(task.row_group)) is not None:
             s.in_flight_count += 1
-        self._spawn_worker(self._execute_task(task))
-
-    def _task_group_spec(self, task: Task) -> TaskGroupSpec:
-        generator = self._generators[task.column]
-        generator_id = id(generator)
-        cached = self._task_group_spec_cache.get(generator_id)
-        if cached is not None:
-            return cached
-
-        spec = self._task_group_spec_from_hint(
-            self._scheduling_hints.hint_for(generator),
-            self._task_flow_identity(task),
-        )
-        self._task_group_spec_cache[generator_id] = spec
-        return spec
-
-    def _task_group_spec_from_hint(self, hint: SchedulingHint, flow_identity: tuple[str, ...]) -> TaskGroupSpec:
-        if hint.group_kind == "local":
-            return TaskGroupSpec(key=TaskGroupKey(kind="local", identity=flow_identity))
-
-        if hint.group_kind == "custom_model":
-            identity = (*flow_identity, *hint.identity_suffix)
-        else:
-            identity = (*hint.identity_prefix, *flow_identity, *hint.identity_suffix)
+        try:
+            self._spawn_worker(self._execute_task(task, lease, task_execution_id))
+            self._emit_scheduler_event("worker_spawned", task=task, lease=lease, task_execution_id=task_execution_id)
+        except Exception:
+            result = self._task_admission.release(lease)
+            self._emit_scheduler_event(
+                "worker_spawn_failed",
+                task=task,
+                lease=lease,
+                task_execution_id=task_execution_id,
+                reason_or_result=result.reason,
+            )
+            self._in_flight.discard(task)
+            raise
 
-        weight = max(1, hint.weight)
-        return TaskGroupSpec(
-            key=TaskGroupKey(kind=hint.group_kind, identity=identity),
-            weight=float(weight),
-            admitted_limit=self._llm_group_admitted_limit(weight),
-        )
+    def _schedulable_task(self, task: Task) -> SchedulableTask:
+        return self._task_scheduling.schedulable_task(task, self._task_flow_identity(task))
 
     def _task_flow_identity(self, task: Task) -> tuple[str, ...]:
         generator = self._generators[task.column]
         output_columns = self._gen_instance_to_columns.get(id(generator), [task.column])
         return tuple(output_columns)
 
-    def _llm_group_admitted_limit(self, weight: int) -> int:
-        return max(1, min(self._max_llm_wait_tasks, LLM_GROUP_ADMISSION_BACKLOG_MULTIPLIER * weight))
-
     async def _admit_row_groups(self) -> None:
         """Admit row groups as semaphore slots become available."""
         for rg_id, rg_size in self._row_groups:
@@ -508,15 +618,16 @@ async def _main_dispatch_loop(
             if all_done:
                 break
 
+            pending_pre_batch = has_pre_batch and any(
+                state.seeds_dispatched and not state.pre_batch_done for state in self._rg_states.values()
+            )
             if not self._fair_queue.has_queued_tasks and not self._in_flight:
-                if self._all_rgs_admitted:
+                if self._all_rgs_admitted and not pending_pre_batch:
                     break
+                if pending_pre_batch:
+                    continue
 
-            if (
-                not self._fair_queue.has_queued_tasks
-                or dispatch_outcome.submission_full
-                or dispatch_outcome.group_blocked
-            ):
+            if not self._fair_queue.has_queued_tasks or dispatch_outcome.admission_blocked:
                 await self._wake_event.wait()
 
     async def _salvage_rounds(
@@ -549,34 +660,10 @@ async def _salvage_rounds(
                             self._dispatched.discard(
                                 Task(column=sibling, row_group=task.row_group, row_index=None, task_type="batch")
                             )
-                    # Acquire stateful lock (mirrors _dispatch_seeds) so
-                    # _execute_seed_task can safely release it in finally.
-                    if gid in self._stateful_locks:
-                        await self._stateful_locks[gid].acquire()
-                    await self._submission_semaphore.acquire()
-                    self._dispatched.add(task)
-                    # Re-register batch alias to mirror _dispatch_seeds and prevent
-                    # duplicate dispatch if the frontier contains a stale batch task.
-                    self._dispatched.add(
-                        Task(column=task.column, row_group=task.row_group, row_index=None, task_type="batch")
-                    )
-                    # Re-mark sibling columns as dispatched to mirror _dispatch_seeds
-                    # and prevent _drain_frontier from re-dispatching them.
-                    for sibling in self._gen_instance_to_columns.get(gid, []):
-                        if sibling != task.column:
-                            self._dispatched.add(
-                                Task(column=sibling, row_group=task.row_group, row_index=None, task_type="from_scratch")
-                            )
-                            self._dispatched.add(
-                                Task(column=sibling, row_group=task.row_group, row_index=None, task_type="batch")
-                            )
-                    self._in_flight.add(task)
-                    if (s := self._rg_states.get(task.row_group)) is not None:
-                        s.in_flight_count += 1
-                    self._spawn_worker(self._execute_seed_task(task, gid))
+                    self._apply_frontier_delta(self._tracker.add_ready_tasks((task,)))
                 else:
                     self._dispatched.discard(task)
-                    self._enqueue_ready_task(task)
+                    self._apply_frontier_delta(self._tracker.add_ready_tasks((task,)))
             # Drain: dispatch frontier tasks and any newly-ready downstream tasks
             # until nothing remains in-flight or in the frontier.
             await self._drain_frontier(seed_cols, has_pre_batch)
@@ -803,7 +890,7 @@ def _check_error_rate(self, *, success: bool) -> None:
             self._early_shutdown = True
 
     def _record_retryable_outcome(self, *, retryable: bool) -> None:
-        """Track retryable-error rate and emit a throttled WARN under provider degradation.
+        """Track retryable-error rate and emit a rate-limited WARN under provider degradation.
 
         Distinct from ``_check_error_rate``: every LLM-bound task outcome (success
         or failure) feeds this window so the rate reflects the provider's overall
@@ -832,7 +919,7 @@ def _record_retryable_outcome(self, *, retryable: bool) -> None:
         )
 
     async def _dispatch_seeds(self, rg_id: int, rg_size: int) -> None:
-        """Dispatch from_scratch tasks for a row group."""
+        """Make from-scratch/root tasks ready for a row group."""
         self._rg_states[rg_id].seeds_dispatched = True
         seed_cols = self._seed_cols
         if not seed_cols:
@@ -841,6 +928,7 @@ async def _dispatch_seeds(self, rg_id: int, rg_size: int) -> None:
         width = len(str(num_rgs))
         logger.info(f"🚀 ({rg_id + 1:0{width}d}/{num_rgs}) Dispatching with {rg_size} records")
         seen_instances: set[int] = set()
+        root_columns: list[str] = []
 
         for col in seed_cols:
             gen = self._generators[col]
@@ -848,64 +936,38 @@ async def _dispatch_seeds(self, rg_id: int, rg_size: int) -> None:
             if gid in seen_instances:
                 continue
             seen_instances.add(gid)
+            root_columns.append(col)
 
-            task = Task(column=col, row_group=rg_id, row_index=None, task_type="from_scratch")
-            # Also mark the "batch" variant as dispatched to prevent duplicate
-            # scheduling for this column.
-            batch_alias = Task(column=col, row_group=rg_id, row_index=None, task_type="batch")
-            if task in self._dispatched or batch_alias in self._dispatched:
-                continue
+        self._apply_frontier_delta(self._tracker.add_root_tasks(rg_id, rg_size, columns=tuple(root_columns)))
 
-            # Seeds bypass fair-queue admission while row groups are being admitted;
-            # direct dispatch preserves stateful lock ordering across row groups.
-            # Acquire stateful lock *before* submission semaphore to preserve
-            # row-group ordering. Held until generation completes (_execute_seed_task).
-            if gid in self._stateful_locks:
-                await self._stateful_locks[gid].acquire()
-
-            await self._submission_semaphore.acquire()
-            self._dispatched.add(task)
-            self._dispatched.add(batch_alias)
-            # Also mark all sibling output columns as dispatched (multi-column dedup)
-            for sibling_col in self._gen_instance_to_columns.get(gid, []):
-                if sibling_col != col:
-                    self._dispatched.add(
-                        Task(column=sibling_col, row_group=rg_id, row_index=None, task_type="from_scratch")
-                    )
-                    self._dispatched.add(Task(column=sibling_col, row_group=rg_id, row_index=None, task_type="batch"))
-            self._in_flight.add(task)
-            if (s := self._rg_states.get(task.row_group)) is not None:
-                s.in_flight_count += 1
-            self._spawn_worker(self._execute_seed_task(task, gid))
-
-    async def _execute_seed_task(self, task: Task, generator_id: int) -> None:
-        """Execute a from_scratch task and release stateful lock if held."""
-        try:
-            await self._execute_task_inner(task)
-        finally:
-            if generator_id in self._stateful_locks:
-                self._stateful_locks[generator_id].release()
-
-    async def _execute_task(self, task: Task) -> None:
+    async def _execute_task(self, task: Task, lease: TaskAdmissionLease, task_execution_id: str) -> None:
         """Execute a single task (cell or batch)."""
-        await self._execute_task_inner(task)
-
-    async def _execute_task_inner(self, task: Task) -> None:
-        """Core task execution logic.
+        await self._execute_task_inner(task, lease, task_execution_id)
 
-        For LLM-bound tasks, uses a one-way semaphore handoff: acquires the
-        LLM-wait slot while still holding the submission slot, then releases
-        the submission slot (never reacquired).  This prevents cross-key
-        starvation while bounding live coroutines.
-        """
+    async def _execute_task_inner(self, task: Task, lease: TaskAdmissionLease, task_execution_id: str) -> None:
+        """Core task execution logic."""
         num_rgs = len(self._row_groups)
         token = current_row_group.set((task.row_group, num_rgs))
+        group = lease.item.group
+        identity_hash = hashlib.sha1("\0".join(group.key.identity).encode()).hexdigest()[:16]
+        correlation_token = runtime_correlation_provider.set(
+            RuntimeCorrelation(
+                run_id=self._run_id,
+                row_group=task.row_group,
+                task_column=task.column,
+                task_type=task.task_type,
+                scheduling_group_kind=group.key.kind,
+                scheduling_group_identity_hash=identity_hash,
+                task_execution_id=task_execution_id,
+            )
+        )
         try:
-            await self._execute_task_inner_impl(task)
+            await self._execute_task_inner_impl(task, lease, task_execution_id)
         finally:
+            runtime_correlation_provider.reset(correlation_token)
             current_row_group.reset(token)
 
-    async def _execute_task_inner_impl(self, task: Task) -> None:
+    async def _execute_task_inner_impl(self, task: Task, lease: TaskAdmissionLease, task_execution_id: str) -> None:
         trace: TaskTrace | None = None
         if self._trace:
             trace = TaskTrace.from_task(task)
@@ -914,12 +976,12 @@ async def _execute_task_inner_impl(self, task: Task) -> None:
         generator = self._generators[task.column]
         output_cols = self._gen_instance_to_columns.get(id(generator), [task.column])
         retryable = False
+        cancelled = False
         # When True, skip removing from _dispatched so the task isn't re-dispatched
         # from the frontier (it was never completed, so it stays in the frontier).
         skipped = False
-        is_llm = self._llm_bound_lookup.get(task.column, False)
-        holds_submission = True
-        holds_llm_wait = False
+        uses_model_stage_resource = "llm_wait" in lease.resources
+        stateful_lock_acquired = False
 
         try:
             # Skip tasks whose row group was already checkpointed (can happen
@@ -929,11 +991,9 @@ async def _execute_task_inner_impl(self, task: Task) -> None:
                 skipped = True
                 return
 
-            if is_llm:
-                await self._llm_wait_semaphore.acquire()
-                holds_llm_wait = True
-                self._submission_semaphore.release()
-                holds_submission = False
+            if task.task_type == "from_scratch" and id(generator) in self._stateful_locks:
+                await self._stateful_locks[id(generator)].acquire()
+                stateful_lock_acquired = True
 
             if self._trace and trace:
                 trace.slot_acquired_at = time.perf_counter()
@@ -962,7 +1022,7 @@ async def _execute_task_inner_impl(self, task: Task) -> None:
             # window from LLM-bound tasks so a healthy non-model task mix
             # (samplers, expressions, non-LLM customs) doesn't dilute the
             # rate and silence the WARN under genuine provider stress.
-            if is_llm:
+            if uses_model_stage_resource:
                 self._record_retryable_outcome(retryable=False)
             if self._reporter:
                 if cell_skipped:
@@ -972,6 +1032,13 @@ async def _execute_task_inner_impl(self, task: Task) -> None:
             if self._trace and trace:
                 trace.status = "ok"
 
+        except asyncio.CancelledError:
+            cancelled = True
+            if self._trace and trace:
+                trace.status = "cancelled"
+            self._emit_scheduler_event("cancelled", task=task, lease=lease, task_execution_id=task_execution_id)
+            raise
+
         except Exception as exc:
             retryable = self._is_retryable(exc)
             # Only non-retryable errors (auth, schema, code bugs) count toward
@@ -980,7 +1047,7 @@ async def _execute_task_inner_impl(self, task: Task) -> None:
             # and would otherwise trip the gate even when salvage could recover.
             if not retryable:
                 self._check_error_rate(success=False)
-            if is_llm:
+            if uses_model_stage_resource:
                 self._record_retryable_outcome(retryable=retryable)
             if not retryable and self._reporter:
                 self._reporter.record_failure(task.column)
@@ -990,6 +1057,9 @@ async def _execute_task_inner_impl(self, task: Task) -> None:
 
             if retryable:
                 self._deferred.append(task)
+                self._emit_scheduler_event(
+                    "retry_deferred", task=task, lease=lease, task_execution_id=task_execution_id
+                )
             else:
                 # Capture the first non-retryable error for the interface to surface
                 # as the root cause when the run produces 0 records (e.g. deterministic
@@ -1006,22 +1076,51 @@ async def _execute_task_inner_impl(self, task: Task) -> None:
                 logger.warning(
                     f"Non-retryable failure on {task.column}[rg={task.row_group}, row={task.row_index}]: {exc}"
                 )
+                self._emit_scheduler_event(
+                    "non_retryable_dropped",
+                    task=task,
+                    lease=lease,
+                    task_execution_id=task_execution_id,
+                    diagnostics={"error_type": type(exc).__name__},
+                )
 
         finally:
             if self._trace and trace:
                 trace.completed_at = time.perf_counter()
                 self.traces.append(trace)
 
-            self._fair_queue.release(task)
+            self._tracker.mark_complete(task)
+            if not cancelled:
+                self._emit_scheduler_event(
+                    "task_completed",
+                    task=task,
+                    lease=lease,
+                    task_execution_id=task_execution_id,
+                )
             self._in_flight.discard(task)
             if (s := self._rg_states.get(task.row_group)) is not None:
                 s.in_flight_count = max(0, s.in_flight_count - 1)
             if not retryable and not skipped:
                 self._dispatched.discard(task)
-            if holds_llm_wait:
-                self._llm_wait_semaphore.release()
-            if holds_submission:
-                self._submission_semaphore.release()
+            if stateful_lock_acquired:
+                self._stateful_locks[id(generator)].release()
+            release_result = self._task_admission.release(lease)
+            self._emit_scheduler_event(
+                "task_lease_released",
+                task=task,
+                lease=lease,
+                task_execution_id=task_execution_id,
+                reason_or_result=release_result.reason,
+            )
+            if not release_result.released:
+                self._emit_scheduler_event(
+                    "release_diagnostic",
+                    task=task,
+                    lease=lease,
+                    task_execution_id=task_execution_id,
+                    reason_or_result=release_result.reason,
+                )
+            self._record_observed_task_state()
             self._wake_event.set()
 
     async def _run_from_scratch(self, task: Task, generator: ColumnGenerator) -> Any:
@@ -1176,18 +1275,64 @@ def _get_rg_size(self, row_group: int) -> int:
         except KeyError:
             raise ValueError(f"Unknown row group: {row_group}") from None
 
-    def get_semaphore_permits(self) -> tuple[int, int]:
-        """Return ``(submission_available, llm_wait_available)`` for diagnostics."""
-        return (
-            self._submission_semaphore.available_permits,
-            self._llm_wait_semaphore.available_permits,
+    def task_admission_snapshot(self) -> object:
+        """Return the current scheduler task-admission snapshot for diagnostics."""
+        return self._task_admission.view()
+
+    def capacity_plan(self) -> AsyncCapacityPlan:
+        """Return the scheduler-side async capacity explanation for this run."""
+        task_view = self._task_admission.view()
+        return AsyncCapacityPlan(
+            configured=AsyncCapacityConfigured(
+                buffer_size=CapacityValue(value=self._buffer_size, source="run_config"),
+                row_group_admission=RowGroupAdmission(
+                    row_group_concurrency=CapacityValue(
+                        value=self._max_concurrent_row_groups,
+                        source="dataset_builder",
+                    ),
+                    observed_in_flight=len(self._rg_states),
+                ),
+                submission_capacity=CapacityValue(value=self._max_submitted_tasks, source="dataset_builder"),
+                task_resource_limits=CapacityValue(
+                    value=dict(self._task_admission_config.resource_limits),
+                    source="engine_internal_config",
+                ),
+                request_resources=CapacityValue(
+                    value=(),
+                    source="runtime_snapshot",
+                    missing_reason="request admission resources are reported by the model registry request controller",
+                ),
+                provider_model_static_caps=CapacityValue(
+                    value={},
+                    source="runtime_snapshot",
+                    missing_reason="request admission caps are reported by the model registry request controller",
+                ),
+                request_domain_initial_limits=CapacityValue(
+                    value={},
+                    source="runtime_snapshot",
+                    missing_reason="request admission limits are reported by the model registry request controller",
+                ),
+                request_admission_config=CapacityValue(
+                    value=None,
+                    source="runtime_snapshot",
+                    missing_reason="request admission config is owned by the model registry request controller",
+                ),
+                transport_pool_limits=CapacityValue(
+                    value={},
+                    source="adapter_config",
+                    missing_reason="transport pool utilization is adapter-specific",
+                ),
+            ),
+            runtime_snapshot=AsyncCapacityRuntimeSnapshot(),
+            observed_maxima=AsyncCapacityObservedMaxima(
+                row_groups_in_flight=self._observed_max_row_groups_in_flight,
+                queued_tasks_by_group=dict(self._observed_max_queued_by_group),
+                task_leases_by_resource=dict(self._observed_max_task_leases_by_resource or task_view.leased_resources),
+                transport_pool_utilization=None,
+            ),
         )
 
     @staticmethod
     def _is_retryable(exc: Exception) -> bool:
         """Classify whether an exception is retryable."""
         return isinstance(exc, RETRYABLE_MODEL_ERRORS)
-
-
-def build_llm_bound_lookup(generators: dict[str, ColumnGenerator]) -> dict[str, bool]:
-    return {col: gen.is_llm_bound for col, gen in generators.items()}
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
index b820c95aa..7a20818e6 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
@@ -85,7 +85,7 @@
 
     from data_designer.engine.dataset_builders.async_scheduler import (
         DEFAULT_TASK_POOL_SIZE,
-        GLOBAL_LLM_WAIT_POOL_HEADROOM_MULTIPLIER,
+        MODEL_TASK_ADMISSION_HEADROOM_MULTIPLIER,
         AsyncTaskScheduler,
     )
     from data_designer.engine.dataset_builders.utils.async_concurrency import (
@@ -1015,9 +1015,9 @@ def on_before_checkpoint(rg_id: int, rg_size: int) -> None:
             df = self._processor_runner.run_post_batch(df, current_batch_number=rg_id, strict_row_count=True)
             buffer_manager.replace_dataframe(rg_id, df)
 
-        # Coarse upper bound: sums all registered aliases, not just those used
-        # in this build. Oversizing is harmless - ThrottleManager enforces
-        # the real per-key limit; the semaphore is a memory-safety cap.
+        # Coarse upper bound used only for scheduler task-stage model admission.
+        # Concrete provider/model request capacity is enforced by request admission
+        # at the model-call boundary.
         aggregate = self._resource_provider.model_registry.get_aggregate_max_parallel_requests()
 
         scheduler = AsyncTaskScheduler(
@@ -1027,7 +1027,7 @@ def on_before_checkpoint(rg_id: int, rg_size: int) -> None:
             row_groups=row_groups,
             buffer_manager=buffer_manager,
             max_submitted_tasks=DEFAULT_TASK_POOL_SIZE,
-            max_llm_wait_tasks=max(DEFAULT_TASK_POOL_SIZE, GLOBAL_LLM_WAIT_POOL_HEADROOM_MULTIPLIER * aggregate),
+            max_model_task_admission=max(DEFAULT_TASK_POOL_SIZE, MODEL_TASK_ADMISSION_HEADROOM_MULTIPLIER * aggregate),
             on_finalize_row_group=on_finalize_row_group,
             on_seeds_complete=(
                 on_seeds_complete if self._processor_runner.has_processors_for(ProcessorStage.PRE_BATCH) else None
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/completion_tracker.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/completion_tracker.py
index 2d35ec0be..65a8b431c 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/completion_tracker.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/completion_tracker.py
@@ -147,14 +147,32 @@ def is_row_group_complete(
                     return False
         return True
 
-    def get_ready_tasks(self, dispatched: set[Task], admitted_rgs: set[int] | None = None) -> list[Task]:
-        """Return all currently dispatchable tasks from the frontier.
+    def ready_frontier(self) -> tuple[Task, ...]:
+        """Return dependency-ready tasks not yet acknowledged as enqueued."""
+        return tuple(self._frontier)
 
-        Excludes already-dispatched/in-flight tasks and tasks for row groups
-        not yet admitted (if ``admitted_rgs`` is provided).
-        """
+    def mark_enqueued(self, task_ids: set[str] | list[str] | tuple[str, ...]) -> None:
+        """Acknowledge tasks accepted by the ready queue."""
+        wanted = set(task_ids)
+        self._frontier = {task for task in self._frontier if _stable_task_id(task) not in wanted}
+
+    def mark_complete(self, task: Task) -> None:
+        """Compatibility hook for scheduler terminal accounting."""
+
+    def add_ready_tasks(self, tasks: list[Task] | tuple[Task, ...]) -> FrontierDelta:
+        """Add ready tasks to the frontier idempotently."""
+        added: list[Task] = []
+        for task in tasks:
+            if self._add_frontier_task(task):
+                added.append(task)
+        return self._record_delta(added=added, removed=[])
+
+    def get_ready_tasks(self, dispatched: set[Task], admitted_rgs: set[int] | None = None) -> list[Task]:
+        """Return all currently dispatchable tasks from the frontier."""
         return [
-            t for t in self._frontier if t not in dispatched and (admitted_rgs is None or t.row_group in admitted_rgs)
+            t
+            for t in self.ready_frontier()
+            if t not in dispatched and (admitted_rgs is None or t.row_group in admitted_rgs)
         ]
 
     def is_frontier_task(self, task: Task) -> bool:
@@ -171,13 +189,36 @@ def seed_frontier(self) -> None:
         if self._graph is None:
             raise RuntimeError("This method requires a graph to be set.")
         for col in self._graph.get_root_columns():
-            strategy = self._graph.get_strategy(col)
             for rg_id, rg_size in self._row_group_sizes.items():
-                if strategy == GenerationStrategy.CELL_BY_CELL:
-                    for ri in range(rg_size):
-                        self._frontier.add(Task(column=col, row_group=rg_id, row_index=ri, task_type="cell"))
-                else:
-                    self._frontier.add(Task(column=col, row_group=rg_id, row_index=None, task_type="batch"))
+                self.add_root_tasks(rg_id, rg_size, columns=(col,))
+
+    def add_root_tasks(
+        self,
+        row_group: int,
+        row_group_size: int,
+        *,
+        columns: tuple[str, ...] | None = None,
+    ) -> FrontierDelta:
+        """Add root/from-scratch tasks for one admitted row group."""
+        if self._graph is None:
+            raise RuntimeError("This method requires a graph to be set.")
+        expected = self._validate_row_group(row_group)
+        if expected is not None and expected != row_group_size:
+            raise ValueError(f"Row-group size mismatch for rg={row_group}: got {row_group_size}, expected {expected}")
+        root_columns = columns or tuple(self._graph.get_root_columns())
+        added: list[Task] = []
+        for col in root_columns:
+            strategy = self._graph.get_strategy(col)
+            if strategy == GenerationStrategy.CELL_BY_CELL:
+                for ri in range(row_group_size):
+                    task = Task(column=col, row_group=row_group, row_index=ri, task_type="cell")
+                    if self._add_frontier_task(task):
+                        added.append(task)
+            else:
+                task = Task(column=col, row_group=row_group, row_index=None, task_type="from_scratch")
+                if self._add_frontier_task(task):
+                    added.append(task)
+        return self._record_delta(added=added, removed=[])
 
     def _record_delta(self, *, added: list[Task], removed: list[Task]) -> FrontierDelta:
         return FrontierDelta(added=tuple(added), removed=tuple(removed))
@@ -301,3 +342,10 @@ def _validate_row_group(self, row_group: int) -> int | None:
             known = sorted(self._row_group_sizes)
             raise ValueError(f"Unknown row_group {row_group}. Known row_groups: {known}")
         return expected
+
+
+def _stable_task_id(task: Task) -> str:
+    raw = f"{task.column}\0{task.row_group}\0{task.row_index}\0{task.task_type}"
+    import hashlib
+
+    return f"task-{hashlib.sha1(raw.encode()).hexdigest()[:16]}"
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/fair_task_queue.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/fair_task_queue.py
index 32301b767..063b770e3 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/fair_task_queue.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/fair_task_queue.py
@@ -4,125 +4,170 @@
 from __future__ import annotations
 
 import heapq
-from collections import deque
-from collections.abc import Callable
+from collections import Counter, defaultdict, deque
+from collections.abc import Callable, Iterable, Mapping
 from dataclasses import dataclass
-from typing import Literal
 
-from data_designer.engine.dataset_builders.utils.task_model import Task
-
-
-@dataclass(frozen=True, order=True)
-class TaskGroupKey:
-    """Stable identity for a stream of related scheduler tasks."""
-
-    kind: Literal["model", "custom_model", "local"]
-    identity: tuple[str, ...]
+from data_designer.engine.dataset_builders.utils.task_scheduling import (
+    SchedulableTask,
+    SchedulerResourceKey,
+    TaskGroupKey,
+    TaskGroupSpec,
+)
 
 
 @dataclass(frozen=True)
-class TaskGroupSpec:
-    """Scheduling metadata for a task group."""
+class QueueView:
+    """Read-only queue facts supplied to task admission policies."""
 
-    key: TaskGroupKey
-    weight: float = 1.0
-    admitted_limit: int | None = None
+    queued_total: int
+    queued_by_group: Mapping[TaskGroupKey, int]
+    queued_resource_demand_by_group: Mapping[TaskGroupKey, Mapping[SchedulerResourceKey, int]]
+    first_candidate_resources_by_group: Mapping[TaskGroupKey, Mapping[SchedulerResourceKey, int]]
+    first_candidate_group_specs_by_group: Mapping[TaskGroupKey, TaskGroupSpec]
+    queued_peer_demand_by_resource: Mapping[SchedulerResourceKey, int]
 
 
 @dataclass(frozen=True)
-class TaskSelection:
-    """A task selected for dispatch with the group metadata used to choose it."""
+class QueueSelection:
+    """Non-mutating fair-queue selection returned to the scheduler."""
 
-    task: Task
-    group: TaskGroupSpec
+    item: SchedulableTask
+    queue_view: QueueView
+    sequence_version: int
 
 
 class FairTaskQueue:
-    """Virtual-time fair queue with peer-sensitive per-group FIFO admission limits."""
+    """Virtual-time fair queue that owns ready membership and ordering only."""
 
     def __init__(self) -> None:
-        self._queues: dict[TaskGroupKey, deque[Task]] = {}
-        self._queued: set[Task] = set()
-        self._task_groups: dict[Task, TaskGroupKey] = {}
+        self._queues: dict[TaskGroupKey, deque[SchedulableTask]] = {}
+        self._queued: dict[str, SchedulableTask] = {}
+        self._task_groups: dict[str, TaskGroupKey] = {}
         self._group_specs: dict[TaskGroupKey, TaskGroupSpec] = {}
         self._group_finish: dict[TaskGroupKey, float] = {}
-        self._admitted_by_group: dict[TaskGroupKey, int] = {}
-        self._admitted_task_groups: dict[Task, TaskGroupKey] = {}
         self._heap: list[tuple[float, int, TaskGroupKey]] = []
         self._active_heap_keys: set[TaskGroupKey] = set()
+        self._active_heap_entries: dict[TaskGroupKey, tuple[float, int]] = {}
         self._sequence = 0
+        self._sequence_version = 0
         self._virtual_time = 0.0
 
     @property
     def has_queued_tasks(self) -> bool:
         return bool(self._queued)
 
-    def enqueue(self, task: Task, group: TaskGroupSpec) -> None:
-        """Add one ready task to its fair scheduling group."""
-        self._group_specs[group.key] = group
-        if task in self._queued:
-            return
-        queue = self._queues.setdefault(group.key, deque())
-        queue.append(task)
-        self._queued.add(task)
-        self._task_groups[task] = group.key
-        self._activate_group(group.key)
-
-    def discard(self, task: Task) -> None:
+    def enqueue(self, items: Iterable[SchedulableTask]) -> tuple[str, ...]:
+        """Add ready tasks idempotently and return newly accepted task ids."""
+        accepted: list[str] = []
+        for item in items:
+            if item.task_id in self._queued:
+                continue
+            self._group_specs[item.group.key] = item.group
+            queue = self._queues.setdefault(item.group.key, deque())
+            queue.append(item)
+            self._queued[item.task_id] = item
+            self._task_groups[item.task_id] = item.group.key
+            self._activate_group(item.group.key)
+            accepted.append(item.task_id)
+        if accepted:
+            self._sequence_version += 1
+        return tuple(accepted)
+
+    def discard(self, task_id: str) -> None:
         """Remove a queued task lazily if it is no longer dispatchable."""
-        self._queued.discard(task)
-        self._task_groups.pop(task, None)
+        if task_id in self._queued:
+            self._sequence_version += 1
+        self._queued.pop(task_id, None)
+        self._task_groups.pop(task_id, None)
 
-    def discard_where(self, predicate: Callable[[Task], bool]) -> None:
+    def discard_where(self, predicate: Callable[[SchedulableTask], bool]) -> None:
         """Remove queued tasks matching a predicate."""
-        for task in tuple(self._queued):
-            if predicate(task):
-                self.discard(task)
-
-    def admit_next(self) -> TaskSelection | None:
-        """Admit the next eligible task, or ``None`` if no queued group can run."""
-        blocked: list[TaskGroupKey] = []
-        try:
-            while self._heap:
-                finish, _, key = heapq.heappop(self._heap)
-                self._active_heap_keys.discard(key)
-                self._purge_queue_head(key)
-                queue = self._queues.get(key)
-                if not queue:
-                    continue
-                if not self._can_admit_group(key):
-                    blocked.append(key)
-                    continue
-
-                task = queue.popleft()
-                self._queued.discard(task)
-                self._task_groups.pop(task, None)
-                self._admitted_task_groups[task] = key
-                self._admitted_by_group[key] = self._admitted_by_group.get(key, 0) + 1
-
-                group = self._group_specs[key]
-                self._virtual_time = max(self._virtual_time, finish)
-                self._group_finish[key] = self._virtual_time + (1.0 / max(group.weight, 1.0))
-                self._purge_queue_head(key)
-                if queue:
-                    self._activate_group(key)
-                return TaskSelection(task=task, group=group)
+        for task_id, item in tuple(self._queued.items()):
+            if predicate(item):
+                self.discard(task_id)
+
+    def select_next(self, is_eligible: Callable[[SchedulableTask, QueueView], bool]) -> QueueSelection | None:
+        """Return the next eligible task without mutating queue state."""
+        view = self.view()
+        blocked: list[tuple[float, int, TaskGroupKey]] = []
+        heap_copy = list(self._heap)
+        heapq.heapify(heap_copy)
+        active_seen: set[TaskGroupKey] = set()
+        while heap_copy:
+            finish, sequence, key = heapq.heappop(heap_copy)
+            if key in active_seen:
+                continue
+            if self._active_heap_entries.get(key) != (finish, sequence):
+                continue
+            active_seen.add(key)
+            item = self._first_valid_item(key)
+            if item is None:
+                continue
+            if not is_eligible(item, view):
+                blocked.append((finish, sequence, key))
+                continue
+            return QueueSelection(item=item, queue_view=view, sequence_version=self._sequence_version)
+        return None
+
+    def commit(self, selection: QueueSelection) -> SchedulableTask | None:
+        """Remove a previously selected task and advance fair-queue state."""
+        if selection.sequence_version != self._sequence_version:
             return None
-        finally:
-            for key in blocked:
-                self._activate_group(key)
-
-    def release(self, task: Task) -> None:
-        """Release one previously admitted task from its group limit."""
-        key = self._admitted_task_groups.pop(task, None)
-        if key is None:
-            return
-        admitted = self._admitted_by_group.get(key, 0)
-        if admitted <= 1:
-            self._admitted_by_group.pop(key, None)
-        else:
-            self._admitted_by_group[key] = admitted - 1
-        self._activate_group(key)
+        item = selection.item
+        key = self._task_groups.get(item.task_id)
+        if key is None or key != item.group.key:
+            return None
+        queue = self._queues.get(key)
+        if queue is None:
+            return None
+        self._purge_queue_head(key)
+        if not queue or queue[0].task_id != item.task_id:
+            return None
+
+        queue.popleft()
+        self._queued.pop(item.task_id, None)
+        self._task_groups.pop(item.task_id, None)
+        self._active_heap_keys.discard(key)
+        self._active_heap_entries.pop(key, None)
+        group = self._group_specs[key]
+        finish = self._group_finish.get(key, self._virtual_time)
+        self._virtual_time = max(self._virtual_time, finish)
+        self._group_finish[key] = self._virtual_time + (1.0 / max(group.weight, 1.0))
+        self._sequence_version += 1
+        self._purge_queue_head(key)
+        if queue:
+            self._activate_group(key)
+        return item
+
+    def view(self) -> QueueView:
+        queued_by_group: Counter[TaskGroupKey] = Counter()
+        demand_by_group: dict[TaskGroupKey, dict[SchedulerResourceKey, int]] = defaultdict(lambda: defaultdict(int))
+        first_by_group: dict[TaskGroupKey, Mapping[SchedulerResourceKey, int]] = {}
+        first_group_specs: dict[TaskGroupKey, TaskGroupSpec] = {}
+        demand_by_resource: Counter[SchedulerResourceKey] = Counter()
+
+        for item in self._queued.values():
+            key = item.group.key
+            queued_by_group[key] += 1
+            for resource, amount in item.resource_request.amounts.items():
+                demand_by_group[key][resource] += amount
+                demand_by_resource[resource] += amount
+
+        for key, queue in self._queues.items():
+            first = self._first_valid_item(key)
+            if first is not None:
+                first_by_group[key] = dict(first.resource_request.amounts)
+                first_group_specs[key] = first.group
+
+        return QueueView(
+            queued_total=len(self._queued),
+            queued_by_group=dict(queued_by_group),
+            queued_resource_demand_by_group={key: dict(value) for key, value in demand_by_group.items()},
+            first_candidate_resources_by_group=first_by_group,
+            first_candidate_group_specs_by_group=first_group_specs,
+            queued_peer_demand_by_resource=dict(demand_by_resource),
+        )
 
     def _activate_group(self, key: TaskGroupKey) -> None:
         self._purge_queue_head(key)
@@ -133,24 +178,23 @@ def _activate_group(self, key: TaskGroupKey) -> None:
         finish = self._group_finish.get(key, self._virtual_time)
         heapq.heappush(self._heap, (finish, self._sequence, key))
         self._active_heap_keys.add(key)
+        self._active_heap_entries[key] = (finish, self._sequence)
+
+    def _first_valid_item(self, key: TaskGroupKey) -> SchedulableTask | None:
+        queue = self._queues.get(key)
+        if queue is None:
+            return None
+        for item in queue:
+            if item.task_id in self._queued and self._task_groups.get(item.task_id) == key:
+                return item
+        return None
 
     def _purge_queue_head(self, key: TaskGroupKey) -> None:
         queue = self._queues.get(key)
         if queue is None:
             return
         while queue:
-            task = queue[0]
-            if task in self._queued and self._task_groups.get(task) == key:
+            item = queue[0]
+            if item.task_id in self._queued and self._task_groups.get(item.task_id) == key:
                 break
             queue.popleft()
-
-    def _can_admit_group(self, key: TaskGroupKey) -> bool:
-        group = self._group_specs[key]
-        if group.admitted_limit is None:
-            return True
-        if self._admitted_by_group.get(key, 0) < group.admitted_limit:
-            return True
-        return not self._has_queued_peer_group(key)
-
-    def _has_queued_peer_group(self, key: TaskGroupKey) -> bool:
-        return any(queued_key != key for queued_key in self._task_groups.values())
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/scheduling_hints.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/scheduling_hints.py
deleted file mode 100644
index dea66eeda..000000000
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/scheduling_hints.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-import logging
-from dataclasses import dataclass
-from typing import TYPE_CHECKING, Any, Literal
-
-if TYPE_CHECKING:
-    from data_designer.engine.column_generators.generators.base import ColumnGenerator
-
-logger = logging.getLogger(__name__)
-
-SchedulingGroupKind = Literal["local", "model", "custom_model"]
-
-
-@dataclass(frozen=True)
-class SchedulingHint:
-    """Resolved task-scheduling metadata independent of graph flow identity."""
-
-    group_kind: SchedulingGroupKind
-    identity_prefix: tuple[str, ...] = ()
-    identity_suffix: tuple[str, ...] = ()
-    weight: int = 1
-
-
-class SchedulingHintResolver:
-    """Resolve generator/config/model metadata once for a scheduler run."""
-
-    def __init__(self, generators: dict[str, ColumnGenerator]) -> None:
-        self._hints_by_generator_id: dict[int, SchedulingHint] = {}
-        for column, generator in generators.items():
-            generator_id = id(generator)
-            if generator_id not in self._hints_by_generator_id:
-                self._hints_by_generator_id[generator_id] = self._resolve_hint(column, generator)
-
-    def hint_for(self, generator: ColumnGenerator) -> SchedulingHint:
-        return self._hints_by_generator_id[id(generator)]
-
-    def _resolve_hint(self, column: str, generator: ColumnGenerator) -> SchedulingHint:
-        if not generator.is_llm_bound:
-            return SchedulingHint(group_kind="local")
-
-        aliases = _model_aliases_for_generator(generator)
-        if not aliases:
-            return SchedulingHint(group_kind="model", identity_prefix=("unknown",), weight=1)
-
-        model_parts: list[str] = []
-        total_parallel = 0
-        primary_alias = getattr(generator.config, "model_alias", None)
-        for alias in aliases:
-            try:
-                model_config = _get_model_config_for_alias(generator, alias)
-                provider_name = _get_model_provider_name_for_alias(generator, alias)
-            except Exception:
-                logger.debug(
-                    "Falling back to custom-model scheduling group for column %r after failing to resolve "
-                    "model alias %r from aliases %r.",
-                    column,
-                    alias,
-                    aliases,
-                    exc_info=True,
-                )
-                return SchedulingHint(
-                    group_kind="custom_model",
-                    identity_suffix=tuple(sorted(aliases)),
-                    weight=max(1, total_parallel),
-                )
-
-            max_parallel = getattr(model_config.inference_parameters, "max_parallel_requests", 1)
-            if not isinstance(max_parallel, int):
-                max_parallel = 1
-            model_parts.extend(
-                (
-                    provider_name,
-                    str(model_config.model),
-                    str(model_config.generation_type),
-                    alias,
-                )
-            )
-            total_parallel += max_parallel
-
-        weight = max(1, total_parallel)
-        if len(aliases) == 1 and primary_alias == aliases[0]:
-            return SchedulingHint(
-                group_kind="model",
-                identity_prefix=tuple(model_parts[:3]),
-                weight=weight,
-            )
-
-        return SchedulingHint(
-            group_kind="custom_model",
-            identity_suffix=tuple(sorted(aliases)),
-            weight=weight,
-        )
-
-
-def _get_model_config_for_alias(generator: ColumnGenerator, alias: str) -> Any:
-    get_model_config = getattr(generator, "get_model_config", None)
-    if callable(get_model_config):
-        return get_model_config(model_alias=alias)
-    return generator.resource_provider.model_registry.get_model_config(model_alias=alias)
-
-
-def _get_model_provider_name_for_alias(generator: ColumnGenerator, alias: str) -> str:
-    get_provider_name = getattr(generator, "get_model_provider_name", None)
-    if callable(get_provider_name):
-        return str(get_provider_name(model_alias=alias))
-    provider = generator.resource_provider.model_registry.get_model_provider(model_alias=alias)
-    return str(provider.name)
-
-
-def _model_aliases_for_generator(generator: ColumnGenerator) -> list[str]:
-    get_aliases = getattr(generator.config, "get_model_aliases", None)
-    if callable(get_aliases):
-        aliases = get_aliases()
-    else:
-        aliases = []
-        if (alias := getattr(generator.config, "model_alias", None)) is not None:
-            aliases.append(alias)
-        aliases.extend(getattr(generator.config, "model_aliases", []) or [])
-    return list(dict.fromkeys(alias for alias in aliases if alias))
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_admission.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_admission.py
new file mode 100644
index 000000000..8d7c8c702
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_admission.py
@@ -0,0 +1,416 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import time
+import uuid
+from collections import Counter, defaultdict
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from typing import Literal, Protocol
+
+from data_designer.engine.dataset_builders.utils.fair_task_queue import QueueView
+from data_designer.engine.dataset_builders.utils.task_model import Task
+from data_designer.engine.dataset_builders.utils.task_scheduling import (
+    SchedulableTask,
+    SchedulerResourceKey,
+    SchedulerResourceRequest,
+    TaskGroupKey,
+)
+
+TaskAdmissionDenyReason = Literal[
+    "no_capacity",
+    "group_cap",
+    "borrow_debt",
+    "shutdown",
+    "policy_denial",
+]
+
+ReleaseReason = Literal[
+    "released",
+    "duplicate",
+    "stale_lease",
+    "wrong_controller_generation",
+    "unknown_lease",
+]
+
+
+@dataclass(frozen=True)
+class TaskAdmissionConfig:
+    """Engine-internal scheduler task-stage admission configuration."""
+
+    submission_capacity: int = 256
+    resource_limits: Mapping[SchedulerResourceKey, int] = field(default_factory=dict)
+    bounded_borrow: BoundedBorrowTaskAdmissionPolicyConfig | None = None
+
+    def __post_init__(self) -> None:
+        if self.submission_capacity <= 0:
+            raise ValueError("submission_capacity must be positive.")
+        merged = {"submission": self.submission_capacity, **self.resource_limits}
+        for resource, limit in merged.items():
+            if limit <= 0:
+                raise ValueError(f"Task admission limit for {resource!r} must be positive.")
+        object.__setattr__(self, "resource_limits", merged)
+
+
+@dataclass(frozen=True)
+class BoundedBorrowTaskAdmissionPolicyConfig:
+    """Engine-internal bounded-borrow policy configuration."""
+
+    borrow_ceiling_by_group_resource: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int] = field(
+        default_factory=dict
+    )
+    default_borrow_ceiling: int = 0
+    strict_share_rounding: Literal["floor", "ceil"] = "floor"
+    repay_on_withheld_peer_pressure: bool = True
+
+
+@dataclass(frozen=True)
+class TaskAdmissionView:
+    resource_limits: Mapping[SchedulerResourceKey, int]
+    resources_available: Mapping[SchedulerResourceKey, int]
+    leased_resources: Mapping[SchedulerResourceKey, int]
+    leased_resources_by_group: Mapping[TaskGroupKey, Mapping[SchedulerResourceKey, int]]
+    running_counts_by_group: Mapping[TaskGroupKey, int]
+    policy_debt_by_group_resource: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int]
+
+
+@dataclass(frozen=True)
+class TaskAdmissionPolicyDecision:
+    allowed: bool
+    reason: TaskAdmissionDenyReason | None = None
+    available_after: float | None = None
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class PolicyStateDelta:
+    debt_changes: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int] = field(default_factory=dict)
+    diagnostic_counters: Mapping[str, int] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class TaskAdmissionLease:
+    lease_id: str
+    item: SchedulableTask
+    resources: Mapping[SchedulerResourceKey, int]
+    acquired_at: float
+    controller_generation: str
+
+
+@dataclass(frozen=True)
+class TaskAdmissionDenied:
+    item: SchedulableTask
+    reason: TaskAdmissionDenyReason
+    available_after: float | None = None
+    snapshot: TaskAdmissionView | None = None
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+TaskAdmissionDecision = TaskAdmissionLease | TaskAdmissionDenied
+
+
+@dataclass(frozen=True)
+class ReleaseResult:
+    released: bool
+    reason: ReleaseReason
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class TaskAdmissionBlockSummary:
+    queued_count: int
+    dominant_denial_reasons: Mapping[TaskAdmissionDenyReason, int]
+    available_after: float | None = None
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+class TaskAdmissionPolicy(Protocol):
+    def evaluate(
+        self,
+        item: SchedulableTask,
+        queue_view: QueueView,
+        admission_view: TaskAdmissionView,
+    ) -> TaskAdmissionPolicyDecision: ...
+
+    def on_acquire(
+        self,
+        lease: TaskAdmissionLease,
+        decision: TaskAdmissionPolicyDecision,
+    ) -> PolicyStateDelta: ...
+
+    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta: ...
+
+
+class StrictFairTaskAdmissionPolicy:
+    """Behavior-preserving policy that enforces per-group admitted caps."""
+
+    def evaluate(
+        self,
+        item: SchedulableTask,
+        queue_view: QueueView,
+        admission_view: TaskAdmissionView,
+    ) -> TaskAdmissionPolicyDecision:
+        if item.group.admitted_limit is None:
+            return TaskAdmissionPolicyDecision(allowed=True)
+        leased_count = admission_view.running_counts_by_group.get(item.group.key, 0)
+        if leased_count < item.group.admitted_limit:
+            return TaskAdmissionPolicyDecision(allowed=True)
+        if not _has_queued_peer_group(item.group.key, queue_view):
+            return TaskAdmissionPolicyDecision(allowed=True)
+        return TaskAdmissionPolicyDecision(
+            allowed=False,
+            reason="group_cap",
+            diagnostics={"admitted_limit": item.group.admitted_limit, "leased_count": leased_count},
+        )
+
+    def on_acquire(
+        self,
+        lease: TaskAdmissionLease,
+        decision: TaskAdmissionPolicyDecision,
+    ) -> PolicyStateDelta:
+        return PolicyStateDelta()
+
+    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta:
+        return PolicyStateDelta()
+
+
+class BoundedBorrowTaskAdmissionPolicy(StrictFairTaskAdmissionPolicy):
+    """Strict policy with optional bounded borrow debt over peer pressure."""
+
+    def __init__(self, config: BoundedBorrowTaskAdmissionPolicyConfig) -> None:
+        self._config = config
+
+    def evaluate(
+        self,
+        item: SchedulableTask,
+        queue_view: QueueView,
+        admission_view: TaskAdmissionView,
+    ) -> TaskAdmissionPolicyDecision:
+        limit = item.group.admitted_limit
+        if limit is None:
+            return TaskAdmissionPolicyDecision(allowed=True)
+
+        leased_count = admission_view.running_counts_by_group.get(item.group.key, 0)
+        if leased_count < limit:
+            return TaskAdmissionPolicyDecision(allowed=True)
+
+        if _has_queued_peer_group(item.group.key, queue_view):
+            for resource in item.resource_request.amounts:
+                debt_key = (item.group.key, resource)
+                debt = admission_view.policy_debt_by_group_resource.get(debt_key, 0)
+                if debt > 0:
+                    return TaskAdmissionPolicyDecision(
+                        allowed=False,
+                        reason="borrow_debt",
+                        diagnostics={"resource": resource, "debt": debt},
+                    )
+            return TaskAdmissionPolicyDecision(
+                allowed=False,
+                reason="group_cap",
+                diagnostics={"admitted_limit": limit, "leased_count": leased_count},
+            )
+
+        borrow_resources: list[tuple[SchedulerResourceKey, int]] = []
+        for resource, amount in item.resource_request.amounts.items():
+            debt_key = (item.group.key, resource)
+            debt = admission_view.policy_debt_by_group_resource.get(debt_key, 0)
+            ceiling = self._config.borrow_ceiling_by_group_resource.get(
+                debt_key,
+                self._config.default_borrow_ceiling,
+            )
+            if debt + amount > ceiling:
+                return TaskAdmissionPolicyDecision(
+                    allowed=False,
+                    reason="borrow_debt",
+                    diagnostics={"resource": resource, "debt": debt, "requested": amount, "ceiling": ceiling},
+                )
+            borrow_resources.append((resource, amount))
+        return TaskAdmissionPolicyDecision(allowed=True, diagnostics={"borrow_resources": tuple(borrow_resources)})
+
+    def on_acquire(
+        self,
+        lease: TaskAdmissionLease,
+        decision: TaskAdmissionPolicyDecision,
+    ) -> PolicyStateDelta:
+        borrow_resources = decision.diagnostics.get("borrow_resources")
+        if borrow_resources:
+            changes = {
+                (lease.item.group.key, resource): amount
+                for resource, amount in borrow_resources
+                if isinstance(resource, str) and isinstance(amount, int)
+            }
+            return PolicyStateDelta(debt_changes=changes)
+        return PolicyStateDelta()
+
+    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta:
+        if not self._config.repay_on_withheld_peer_pressure:
+            return PolicyStateDelta()
+        return PolicyStateDelta(
+            debt_changes={(lease.item.group.key, resource): -amount for resource, amount in lease.resources.items()}
+        )
+
+
+class TaskAdmissionController:
+    """Owns scheduler-level task leases and resource accounting."""
+
+    def __init__(
+        self,
+        config: TaskAdmissionConfig | None = None,
+        policy: TaskAdmissionPolicy | None = None,
+    ) -> None:
+        self._config = config or TaskAdmissionConfig()
+        self._generation = uuid.uuid4().hex
+        self._leases: dict[str, TaskAdmissionLease] = {}
+        self._released: set[str] = set()
+        self._leased_by_resource: Counter[SchedulerResourceKey] = Counter()
+        self._leased_by_group: dict[TaskGroupKey, Counter[SchedulerResourceKey]] = defaultdict(Counter)
+        self._running_by_group: Counter[TaskGroupKey] = Counter()
+        self._policy_debt: Counter[tuple[TaskGroupKey, SchedulerResourceKey]] = Counter()
+        self._release_diagnostics: Counter[str] = Counter()
+        if policy is not None:
+            self._policy = policy
+        elif self._config.bounded_borrow is not None:
+            self._policy = BoundedBorrowTaskAdmissionPolicy(self._config.bounded_borrow)
+        else:
+            self._policy = StrictFairTaskAdmissionPolicy()
+
+    def is_eligible(self, item: SchedulableTask, queue_view: QueueView) -> bool:
+        return not isinstance(self.try_evaluate(item, queue_view), TaskAdmissionDenied)
+
+    def try_evaluate(
+        self, item: SchedulableTask, queue_view: QueueView
+    ) -> TaskAdmissionPolicyDecision | TaskAdmissionDenied:
+        view = self.view()
+        missing = self._missing_resources(item, view)
+        if missing:
+            return TaskAdmissionDenied(
+                item=item,
+                reason="no_capacity",
+                snapshot=view,
+                diagnostics={"missing_resources": missing},
+            )
+        decision = self._policy.evaluate(item, queue_view, view)
+        if not decision.allowed:
+            return TaskAdmissionDenied(
+                item=item,
+                reason=decision.reason or "policy_denial",
+                available_after=decision.available_after,
+                snapshot=view,
+                diagnostics=decision.diagnostics,
+            )
+        return decision
+
+    def try_acquire(self, item: SchedulableTask, queue_view: QueueView) -> TaskAdmissionDecision:
+        evaluated = self.try_evaluate(item, queue_view)
+        if isinstance(evaluated, TaskAdmissionDenied):
+            return evaluated
+        lease = TaskAdmissionLease(
+            lease_id=uuid.uuid4().hex,
+            item=item,
+            resources=dict(item.resource_request.amounts),
+            acquired_at=time.monotonic(),
+            controller_generation=self._generation,
+        )
+        for resource, amount in lease.resources.items():
+            self._leased_by_resource[resource] += amount
+            self._leased_by_group[item.group.key][resource] += amount
+        self._running_by_group[item.group.key] += 1
+        self._apply_delta(self._policy.on_acquire(lease, evaluated))
+        self._leases[lease.lease_id] = lease
+        return lease
+
+    def release(self, lease: TaskAdmissionLease) -> ReleaseResult:
+        if lease.controller_generation != self._generation:
+            self._release_diagnostics["wrong_controller_generation"] += 1
+            return ReleaseResult(released=False, reason="wrong_controller_generation")
+        active = self._leases.pop(lease.lease_id, None)
+        if active is None:
+            reason: ReleaseReason = "duplicate" if lease.lease_id in self._released else "unknown_lease"
+            self._release_diagnostics[reason] += 1
+            return ReleaseResult(released=False, reason=reason)
+        if active.item.task_id != lease.item.task_id:
+            self._leases[lease.lease_id] = active
+            self._release_diagnostics["stale_lease"] += 1
+            return ReleaseResult(released=False, reason="stale_lease")
+
+        self._released.add(lease.lease_id)
+        for resource, amount in active.resources.items():
+            self._leased_by_resource[resource] = max(0, self._leased_by_resource[resource] - amount)
+            self._leased_by_group[active.item.group.key][resource] = max(
+                0,
+                self._leased_by_group[active.item.group.key][resource] - amount,
+            )
+        self._running_by_group[active.item.group.key] = max(0, self._running_by_group[active.item.group.key] - 1)
+        self._apply_delta(self._policy.on_release(active))
+        return ReleaseResult(released=True, reason="released")
+
+    def view(self) -> TaskAdmissionView:
+        limits = dict(self._config.resource_limits)
+        leased = {resource: count for resource, count in self._leased_by_resource.items() if count > 0}
+        available = {
+            resource: max(0, limit - self._leased_by_resource.get(resource, 0)) for resource, limit in limits.items()
+        }
+        return TaskAdmissionView(
+            resource_limits=limits,
+            resources_available=available,
+            leased_resources=leased,
+            leased_resources_by_group={
+                group: {resource: count for resource, count in counts.items() if count > 0}
+                for group, counts in self._leased_by_group.items()
+            },
+            running_counts_by_group={group: count for group, count in self._running_by_group.items() if count > 0},
+            policy_debt_by_group_resource={key: count for key, count in self._policy_debt.items() if count > 0},
+        )
+
+    def explain_blocked(self, queue_view: QueueView) -> TaskAdmissionBlockSummary:
+        reasons: Counter[TaskAdmissionDenyReason] = Counter()
+        available_after_values: list[float] = []
+        view = self.view()
+        for group_key, resources in queue_view.first_candidate_resources_by_group.items():
+            for resource, amount in resources.items():
+                if view.resources_available.get(resource, 0) < amount:
+                    reasons["no_capacity"] += 1
+                    break
+            else:
+                group = queue_view.first_candidate_group_specs_by_group.get(group_key)
+                if group is None:
+                    continue
+                task = SchedulableTask(
+                    task_id=f"blocked-{group_key.kind}-{'-'.join(group_key.identity)}",
+                    payload=Task(column="", row_group=-1, row_index=None, task_type="batch"),
+                    group=group,
+                    resource_request=SchedulerResourceRequest(dict(resources)),
+                )
+                decision = self._policy.evaluate(task, queue_view, view)
+                if not decision.allowed:
+                    reasons[decision.reason or "policy_denial"] += 1
+                    if decision.available_after is not None:
+                        available_after_values.append(decision.available_after)
+        return TaskAdmissionBlockSummary(
+            queued_count=queue_view.queued_total,
+            dominant_denial_reasons=dict(reasons),
+            available_after=min(available_after_values) if available_after_values else None,
+            diagnostics={"snapshot": self.view()},
+        )
+
+    def _missing_resources(
+        self,
+        item: SchedulableTask,
+        view: TaskAdmissionView,
+    ) -> dict[SchedulerResourceKey, dict[str, int]]:
+        missing: dict[SchedulerResourceKey, dict[str, int]] = {}
+        for resource, amount in item.resource_request.amounts.items():
+            available = view.resources_available.get(resource, 0)
+            if available < amount:
+                missing[resource] = {"requested": amount, "available": available}
+        return missing
+
+    def _apply_delta(self, delta: PolicyStateDelta) -> None:
+        for key, change in delta.debt_changes.items():
+            self._policy_debt[key] = max(0, self._policy_debt[key] + change)
+
+
+def _has_queued_peer_group(group_key: TaskGroupKey, queue_view: QueueView) -> bool:
+    return any(key != group_key and count > 0 for key, count in queue_view.queued_by_group.items())
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_scheduling.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_scheduling.py
new file mode 100644
index 000000000..a0bb61d95
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_scheduling.py
@@ -0,0 +1,150 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import hashlib
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Literal
+
+from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
+from data_designer.engine.dataset_builders.utils.task_model import Task
+
+if TYPE_CHECKING:
+    from data_designer.engine.column_generators.generators.base import ColumnGenerator
+
+SchedulerResourceKey = Literal["submission", "llm_wait", "local"]
+
+
+@dataclass(frozen=True, order=True)
+class TaskGroupKey:
+    """Stable identity for a stream of related scheduler tasks."""
+
+    kind: Literal["model", "custom_model", "local"]
+    identity: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class TaskGroupSpec:
+    """Scheduler-internal task group metadata."""
+
+    key: TaskGroupKey
+    weight: float = 1.0
+    admitted_limit: int | None = None
+
+
+@dataclass(frozen=True)
+class SchedulerResourceRequest:
+    """Scheduler task-stage resource request."""
+
+    amounts: Mapping[SchedulerResourceKey, int] = field(default_factory=lambda: {"submission": 1})
+
+    def __post_init__(self) -> None:
+        for resource, amount in self.amounts.items():
+            if resource not in {"submission", "llm_wait", "local"}:
+                raise ValueError(f"Unknown scheduler resource key: {resource!r}")
+            if not isinstance(amount, int) or amount <= 0:
+                raise ValueError(f"Scheduler resource amount for {resource!r} must be a positive integer.")
+
+
+@dataclass(frozen=True)
+class ResolvedTaskScheduling:
+    """Scheduler inputs resolved from generator-facing metadata."""
+
+    group: TaskGroupSpec
+    resource_request: SchedulerResourceRequest
+
+
+@dataclass(frozen=True)
+class SchedulableTask:
+    """Ready task plus scheduler-owned grouping and resource request."""
+
+    task_id: str
+    payload: Task
+    group: TaskGroupSpec
+    resource_request: SchedulerResourceRequest
+
+
+class TaskSchedulingResolver:
+    """Resolve generator metadata into scheduler-internal task inputs."""
+
+    def __init__(
+        self,
+        generators: Mapping[str, ColumnGenerator],
+        *,
+        model_group_limit_multiplier: int = 2,
+        model_group_limit_cap: int = 256,
+    ) -> None:
+        self._generators = generators
+        self._model_group_limit_multiplier = model_group_limit_multiplier
+        self._model_group_limit_cap = model_group_limit_cap
+        self._metadata_by_generator_id: dict[int, SchedulingMetadata] = {}
+        self._diagnostics: list[dict[str, object]] = []
+        for generator in dict.fromkeys(generators.values()):
+            self._metadata_by_generator_id[id(generator)] = self._resolve_metadata(generator)
+
+    @property
+    def diagnostics(self) -> tuple[dict[str, object], ...]:
+        return tuple(self._diagnostics)
+
+    def scheduling_for_task(self, task: Task, flow_identity: tuple[str, ...]) -> ResolvedTaskScheduling:
+        generator = self._generators[task.column]
+        metadata = self._metadata_by_generator_id[id(generator)]
+        return self._resolved_from_metadata(metadata, flow_identity)
+
+    def schedulable_task(self, task: Task, flow_identity: tuple[str, ...]) -> SchedulableTask:
+        resolved = self.scheduling_for_task(task, flow_identity)
+        return SchedulableTask(
+            task_id=stable_task_id(task),
+            payload=task,
+            group=resolved.group,
+            resource_request=resolved.resource_request,
+        )
+
+    def _resolve_metadata(self, generator: ColumnGenerator) -> SchedulingMetadata:
+        try:
+            return generator.get_scheduling_metadata()
+        except SchedulingMetadataError as exc:
+            if exc.fallback is None:
+                raise
+            self._diagnostics.append(
+                {
+                    "code": exc.code,
+                    "message": exc.message,
+                    "fallback": exc.fallback.identity,
+                    "diagnostics": exc.diagnostics,
+                }
+            )
+            return exc.fallback
+
+    def _resolved_from_metadata(
+        self,
+        metadata: SchedulingMetadata,
+        flow_identity: tuple[str, ...],
+    ) -> ResolvedTaskScheduling:
+        weight = max(1, metadata.weight)
+        if metadata.kind == "local":
+            key = TaskGroupKey(kind="local", identity=(*metadata.identity, *flow_identity))
+            return ResolvedTaskScheduling(
+                group=TaskGroupSpec(key=key, weight=float(weight)),
+                resource_request=SchedulerResourceRequest({"submission": 1}),
+            )
+
+        identity = (*metadata.identity, *flow_identity)
+        admitted_limit = max(1, min(self._model_group_limit_cap, self._model_group_limit_multiplier * weight))
+        return ResolvedTaskScheduling(
+            group=TaskGroupSpec(
+                key=TaskGroupKey(kind=metadata.kind, identity=identity),
+                weight=float(weight),
+                admitted_limit=admitted_limit,
+            ),
+            resource_request=SchedulerResourceRequest({"submission": 1, "llm_wait": 1}),
+        )
+
+
+def stable_task_id(task: Task) -> str:
+    """Return a stable scheduler task id for queue/admission membership."""
+    raw = f"{task.column}\0{task.row_group}\0{task.row_index}\0{task.task_type}".encode()
+    digest = hashlib.sha1(raw).hexdigest()[:16]
+    return f"task-{digest}"
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py
index df9afc48f..37be3514c 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py
@@ -12,9 +12,21 @@
     map_http_status_to_provider_error_kind,
 )
 from data_designer.engine.models.clients.factory import create_model_client
+from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
+from data_designer.engine.models.clients.request_admission import (
+    AdaptiveRequestAdmissionController,
+    RequestAdmissionConfig,
+    RequestAdmissionController,
+    RequestAdmissionError,
+    RequestAdmissionItem,
+    RequestAdmissionLease,
+    RequestDomain,
+    RequestFairQueue,
+    RequestReleaseOutcome,
+    RequestResourceKey,
+    RequestResourceResolver,
+)
 from data_designer.engine.models.clients.retry import RetryConfig
-from data_designer.engine.models.clients.throttle_manager import ThrottleDomain, ThrottleManager
-from data_designer.engine.models.clients.throttled import ThrottledModelClient
 from data_designer.engine.models.clients.types import (
     AssistantMessage,
     ChatCompletionRequest,
@@ -39,14 +51,23 @@
     "ImageGenerationRequest",
     "ImageGenerationResponse",
     "ImagePayload",
+    "AdaptiveRequestAdmissionController",
     "ModelClient",
+    "ModelRequestExecutor",
     "OpenAICompatibleClient",
     "ProviderError",
     "ProviderErrorKind",
+    "RequestAdmissionConfig",
+    "RequestAdmissionController",
+    "RequestAdmissionError",
+    "RequestAdmissionItem",
+    "RequestAdmissionLease",
+    "RequestDomain",
+    "RequestFairQueue",
+    "RequestReleaseOutcome",
+    "RequestResourceResolver",
+    "RequestResourceKey",
     "RetryConfig",
-    "ThrottleDomain",
-    "ThrottleManager",
-    "ThrottledModelClient",
     "ToolCall",
     "Usage",
     "create_model_client",
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/anthropic.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/anthropic.py
index 2424d3f8c..b73acffb6 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/anthropic.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/anthropic.py
@@ -31,8 +31,8 @@ class AnthropicClient(HttpModelClient):
     """Native HTTP adapter for the Anthropic Messages API.
 
     Uses ``httpx`` with ``httpx_retries.RetryTransport`` for resilient HTTP
-    calls.  Concurrency / throttle policy is an orchestration concern and
-    is not managed here — see ``ThrottleManager`` and ``AsyncTaskScheduler``.
+    calls. Concurrency and request-admission policy are orchestration concerns
+    and are not managed here.
     """
 
     _ROUTE_MESSAGES = "/messages"
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/openai_compatible.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/openai_compatible.py
index 54f01961b..44ab1f1d5 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/openai_compatible.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/adapters/openai_compatible.py
@@ -33,8 +33,8 @@ class OpenAICompatibleClient(HttpModelClient):
     """Native HTTP adapter for OpenAI-compatible provider APIs.
 
     Uses ``httpx`` with ``httpx_retries.RetryTransport`` for resilient HTTP
-    calls.  Concurrency / throttle policy is an orchestration concern and
-    is not managed here — see ``ThrottleManager`` and ``AsyncTaskScheduler``.
+    calls. Concurrency and request-admission policy are orchestration concerns
+    and are not managed here.
     """
 
     _ROUTE_CHAT = "/chat/completions"
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
index 458ebfcad..b0255be77 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
@@ -10,10 +10,11 @@
 from data_designer.engine.models.clients.adapters.http_model_client import ClientConcurrencyMode
 from data_designer.engine.models.clients.adapters.openai_compatible import OpenAICompatibleClient
 from data_designer.engine.models.clients.base import ModelClient
+from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
+from data_designer.engine.models.clients.request_admission import RequestAdmissionController
 from data_designer.engine.models.clients.retry import RetryConfig
-from data_designer.engine.models.clients.throttle_manager import ThrottleManager
-from data_designer.engine.models.clients.throttled import ThrottledModelClient
 from data_designer.engine.models.errors import FormattedLLMErrorMessage
+from data_designer.engine.observability import RequestAdmissionEventSink
 from data_designer.engine.secret_resolver import SecretResolver
 
 _SUPPORTED_PROVIDER_TYPES = ("openai", "anthropic")
@@ -26,7 +27,8 @@ def create_model_client(
     *,
     retry_config: RetryConfig | None = None,
     client_concurrency_mode: ClientConcurrencyMode = ClientConcurrencyMode.SYNC,
-    throttle_manager: ThrottleManager | None = None,
+    request_admission: RequestAdmissionController | None = None,
+    request_event_sink: RequestAdmissionEventSink | None = None,
 ) -> ModelClient:
     """Create a ``ModelClient`` for the given model configuration.
 
@@ -40,12 +42,12 @@ def create_model_client(
         client_concurrency_mode: ``"sync"`` (default) for the sync engine path,
             ``"async"`` for the async engine path.  Native HTTP adapters are
             constrained to a single concurrency mode.
-        throttle_manager: Optional throttle manager for per-request AIMD
-            concurrency control.  When provided, the returned client is wrapped
-            with ``ThrottledModelClient``.
+        request_admission: Optional request-admission controller for per-request
+            provider/model/domain admission. When provided, the returned client
+            is wrapped with ``ModelRequestExecutor``.
 
             **Ordering invariant:** the ``(provider_name, model_id)`` pair must
-            be registered on the ``ThrottleManager`` via ``register()`` before
+            be registered on the request-admission controller via ``register()`` before
             the returned client makes its first request.  In the standard flow,
             ``ModelRegistry._get_model()`` calls ``register()`` during model
             setup, which happens before any generation task invokes the client.
@@ -102,12 +104,13 @@ def create_model_client(
             )
         )
 
-    if throttle_manager is not None:
-        client = ThrottledModelClient(
+    if request_admission is not None:
+        client = ModelRequestExecutor(
             inner=client,
-            throttle_manager=throttle_manager,
+            request_admission=request_admission,
             provider_name=provider.name,
             model_id=model_config.model,
+            event_sink=request_event_sink,
         )
 
     return client
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
new file mode 100644
index 000000000..5d28f560b
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
@@ -0,0 +1,244 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+import uuid
+from typing import TYPE_CHECKING, TypeVar
+
+from data_designer.engine.models.clients.base import ModelClient
+from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
+from data_designer.engine.models.clients.request_admission import (
+    RequestAdmissionController,
+    RequestAdmissionError,
+    RequestAdmissionItem,
+    RequestAdmissionLease,
+    RequestDomain,
+    RequestEventContext,
+    RequestGroupSpec,
+    RequestReleaseOutcome,
+    RequestResourceResolver,
+)
+from data_designer.engine.models.clients.types import (
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    EmbeddingRequest,
+    EmbeddingResponse,
+    ImageGenerationRequest,
+    ImageGenerationResponse,
+)
+from data_designer.engine.observability import (
+    RequestAdmissionEvent,
+    RequestAdmissionEventSink,
+    runtime_correlation_provider,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Awaitable, Callable
+
+_T = TypeVar("_T")
+
+
+class ModelRequestExecutor(ModelClient):
+    """Model-call boundary that acquires/releases request-admission leases."""
+
+    def __init__(
+        self,
+        inner: ModelClient,
+        request_admission: RequestAdmissionController,
+        provider_name: str,
+        model_id: str,
+        event_sink: RequestAdmissionEventSink | None = None,
+        resource_resolver: RequestResourceResolver | None = None,
+    ) -> None:
+        self._inner = inner
+        self._request_admission = request_admission
+        self._provider_name = provider_name
+        self._model_id = model_id
+        self._event_sink = event_sink
+        self._resource_resolver = resource_resolver or RequestResourceResolver()
+        self._event_sequence = 0
+
+    @property
+    def provider_name(self) -> str:
+        return self._inner.provider_name
+
+    def supports_chat_completion(self) -> bool:
+        return self._inner.supports_chat_completion()
+
+    def supports_embeddings(self) -> bool:
+        return self._inner.supports_embeddings()
+
+    def supports_image_generation(self) -> bool:
+        return self._inner.supports_image_generation()
+
+    def close(self) -> None:
+        self._inner.close()
+
+    async def aclose(self) -> None:
+        await self._inner.aclose()
+
+    def completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        return self._execute_sync(RequestDomain.CHAT, lambda: self._inner.completion(request))
+
+    async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        return await self._execute_async(RequestDomain.CHAT, lambda: self._inner.acompletion(request))
+
+    def embeddings(self, request: EmbeddingRequest) -> EmbeddingResponse:
+        return self._execute_sync(RequestDomain.EMBEDDING, lambda: self._inner.embeddings(request))
+
+    async def aembeddings(self, request: EmbeddingRequest) -> EmbeddingResponse:
+        return await self._execute_async(RequestDomain.EMBEDDING, lambda: self._inner.aembeddings(request))
+
+    def generate_image(self, request: ImageGenerationRequest) -> ImageGenerationResponse:
+        return self._execute_sync(self._image_domain(request), lambda: self._inner.generate_image(request))
+
+    async def agenerate_image(self, request: ImageGenerationRequest) -> ImageGenerationResponse:
+        return await self._execute_async(self._image_domain(request), lambda: self._inner.agenerate_image(request))
+
+    def _execute_sync(self, domain: RequestDomain, call: Callable[[], _T]) -> _T:
+        item = self._item(domain)
+        try:
+            lease = self._request_admission.acquire_sync(item)
+        except RequestAdmissionError as exc:
+            raise ProviderError(
+                kind=ProviderErrorKind.TIMEOUT,
+                message=str(exc),
+                provider_name=self._provider_name,
+                model_name=self._model_id,
+            ) from exc
+        try:
+            self._emit_model_event("model_request_started", item=item, lease=lease)
+            result = call()
+        except ProviderError as exc:
+            self._release_provider_error(lease, exc)
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": exc.kind.value}
+            )
+            raise
+        except TimeoutError:
+            self._request_admission.release(lease, RequestReleaseOutcome(kind="provider_timeout"))
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "provider_timeout"}
+            )
+            raise
+        except BaseException as exc:
+            outcome = "local_cancelled" if isinstance(exc, KeyboardInterrupt) else "unexpected_exception"
+            self._request_admission.release(lease, RequestReleaseOutcome(kind=outcome))
+            self._emit_model_event("model_request_completed", item=item, lease=lease, diagnostics={"outcome": outcome})
+            raise
+        else:
+            self._request_admission.release(lease, RequestReleaseOutcome(kind="success"))
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "success"}
+            )
+            return result
+
+    async def _execute_async(self, domain: RequestDomain, call: Callable[[], Awaitable[_T]]) -> _T:
+        item = self._item(domain)
+        try:
+            lease = await self._request_admission.acquire_async(item)
+        except RequestAdmissionError as exc:
+            raise ProviderError(
+                kind=ProviderErrorKind.TIMEOUT,
+                message=str(exc),
+                provider_name=self._provider_name,
+                model_name=self._model_id,
+            ) from exc
+        except asyncio.CancelledError:
+            raise
+        try:
+            self._emit_model_event("model_request_started", item=item, lease=lease)
+            result = await call()
+        except asyncio.CancelledError:
+            self._request_admission.release(lease, RequestReleaseOutcome(kind="local_cancelled"))
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "local_cancelled"}
+            )
+            raise
+        except ProviderError as exc:
+            self._release_provider_error(lease, exc)
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": exc.kind.value}
+            )
+            raise
+        except TimeoutError:
+            self._request_admission.release(lease, RequestReleaseOutcome(kind="provider_timeout"))
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "provider_timeout"}
+            )
+            raise
+        except BaseException:
+            self._request_admission.release(lease, RequestReleaseOutcome(kind="unexpected_exception"))
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "unexpected_exception"}
+            )
+            raise
+        else:
+            self._request_admission.release(lease, RequestReleaseOutcome(kind="success"))
+            self._emit_model_event(
+                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "success"}
+            )
+            return result
+
+    def _release_provider_error(self, lease: RequestAdmissionLease, exc: ProviderError) -> None:
+        if exc.kind == ProviderErrorKind.RATE_LIMIT:
+            outcome = RequestReleaseOutcome(kind="rate_limited", retry_after_seconds=exc.retry_after)
+        elif exc.kind == ProviderErrorKind.TIMEOUT:
+            outcome = RequestReleaseOutcome(kind="provider_timeout")
+        else:
+            outcome = RequestReleaseOutcome(kind="provider_failure")
+        self._request_admission.release(lease, outcome)
+
+    def _item(self, domain: RequestDomain) -> RequestAdmissionItem:
+        resolved = self._resource_resolver.resolve(
+            provider_name=self._provider_name,
+            model_id=self._model_id,
+            domain=domain,
+        )
+        resource = resolved.resource
+        correlation = runtime_correlation_provider.current()
+        return RequestAdmissionItem(
+            resource=resource,
+            group=RequestGroupSpec(key=resource),
+            event_context=RequestEventContext(
+                captured_correlation=correlation,
+                task_execution_id=correlation.task_execution_id if correlation is not None else None,
+                request_attempt_id=f"request-{uuid.uuid4().hex}",
+            ),
+        )
+
+    @staticmethod
+    def _image_domain(request: ImageGenerationRequest) -> RequestDomain:
+        return RequestDomain.CHAT if request.messages is not None else RequestDomain.IMAGE
+
+    def _emit_model_event(
+        self,
+        event_kind: str,
+        *,
+        item: RequestAdmissionItem,
+        lease: RequestAdmissionLease,
+        diagnostics: dict[str, object] | None = None,
+    ) -> None:
+        if self._event_sink is None:
+            return
+        self._event_sequence += 1
+        context = item.event_context
+        try:
+            self._event_sink.emit_request_event(
+                RequestAdmissionEvent.capture(
+                    event_kind,  # type: ignore[arg-type]
+                    sequence=self._event_sequence,
+                    correlation=context.captured_correlation
+                    if context is not None
+                    else runtime_correlation_provider.current(),
+                    request_attempt_id=context.request_attempt_id if context is not None else None,
+                    request_lease_id=lease.lease_id,
+                    request_resource_key=item.resource,
+                    request_group_key=item.group.key,
+                    diagnostics=diagnostics or {},
+                )
+            )
+        except Exception:
+            return
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/request_admission.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/request_admission.py
new file mode 100644
index 000000000..c04370f54
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/request_admission.py
@@ -0,0 +1,968 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+import heapq
+import math
+import threading
+import time
+import uuid
+from collections import Counter, deque
+from collections.abc import Callable, Mapping
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Literal, Protocol
+
+from data_designer.engine.observability import (
+    RequestAdmissionEvent,
+    RequestAdmissionEventSink,
+    runtime_correlation_provider,
+)
+
+
+class RequestDomain(str, Enum):
+    CHAT = "chat"
+    EMBEDDING = "embedding"
+    IMAGE = "image"
+    HEALTHCHECK = "healthcheck"
+
+
+@dataclass(frozen=True, order=True)
+class ProviderModelKey:
+    provider_name: str
+    model_id: str
+
+
+@dataclass(frozen=True, order=True)
+class RequestResourceKey:
+    provider_name: str
+    model_id: str
+    domain: RequestDomain
+
+    @property
+    def provider_model_key(self) -> ProviderModelKey:
+        return ProviderModelKey(self.provider_name, self.model_id)
+
+
+@dataclass(frozen=True)
+class ResolvedRequestResource:
+    provider_model: ProviderModelKey
+    resource: RequestResourceKey
+    aliases: tuple[str, ...] = ()
+    generation_kind: str | None = None
+
+
+class RequestResourceResolver:
+    """Canonical provider/model/domain request-resource identity factory."""
+
+    def resolve(
+        self,
+        *,
+        provider_name: str,
+        model_id: str,
+        domain: RequestDomain,
+        model_alias: str | None = None,
+        provider_alias: str | None = None,
+        generation_kind: str | None = None,
+    ) -> ResolvedRequestResource:
+        resource = RequestResourceKey(provider_name=provider_name, model_id=model_id, domain=domain)
+        aliases = tuple(alias for alias in (provider_alias, model_alias) if alias)
+        return ResolvedRequestResource(
+            provider_model=resource.provider_model_key,
+            resource=resource,
+            aliases=aliases,
+            generation_kind=generation_kind,
+        )
+
+
+@dataclass(frozen=True)
+class RequestGroupSpec:
+    key: RequestResourceKey
+    weight: float = 1.0
+
+
+@dataclass(frozen=True)
+class RequestEventContext:
+    captured_correlation: object | None = None
+    task_execution_id: str | None = None
+    request_attempt_id: str | None = None
+
+
+@dataclass(frozen=True)
+class RequestAdmissionItem:
+    resource: RequestResourceKey
+    group: RequestGroupSpec
+    queue_wait_timeout_seconds: float | None = None
+    event_context: RequestEventContext | None = None
+
+
+RequestDenyReason = Literal[
+    "no_capacity",
+    "cooldown",
+    "queue_timeout",
+    "queued_waiters_ahead",
+    "cancellation",
+    "shutdown",
+    "hard_policy_denial",
+]
+
+
+@dataclass(frozen=True)
+class RequestAdmissionDenied:
+    item: RequestAdmissionItem
+    reason: RequestDenyReason
+    retry_after_seconds: float | None = None
+    available_after_monotonic: float | None = None
+    snapshot: object | None = None
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class RequestAdmissionLease:
+    lease_id: str
+    item: RequestAdmissionItem
+    acquired_at: float
+    current_adaptive_limit: int
+    effective_max: int
+    controller_generation: str
+
+
+RequestAdmissionDecision = RequestAdmissionLease | RequestAdmissionDenied
+
+
+@dataclass(frozen=True)
+class RequestReleaseOutcome:
+    kind: Literal[
+        "success",
+        "rate_limited",
+        "provider_failure",
+        "provider_timeout",
+        "local_cancelled",
+        "local_timeout",
+        "unexpected_exception",
+    ]
+    retry_after_seconds: float | None = None
+    provider_status: int | None = None
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class ReleaseResult:
+    released: bool
+    reason: Literal["released", "duplicate", "stale_lease", "wrong_controller_generation", "unknown_lease"]
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+class RequestAdmissionError(RuntimeError):
+    """Raised by blocking acquire paths when no request lease is acquired."""
+
+    def __init__(self, decision: RequestAdmissionDenied) -> None:
+        super().__init__(f"Request admission failed: {decision.reason}")
+        self.decision = decision
+
+
+@dataclass(frozen=True)
+class RequestAdmissionConfig:
+    initial_limits: Mapping[RequestResourceKey, int] = field(default_factory=dict)
+    max_limit_clamps: Mapping[RequestResourceKey, int | None] = field(default_factory=dict)
+    cooldown_seconds: float = 2.0
+    multiplicative_decrease_factor: float = 0.75
+    additive_increase_step: int = 1
+    increase_after_successes: int = 25
+    default_queue_wait_timeout_seconds: float | None = None
+
+
+@dataclass
+class AdaptiveRequestLimitState:
+    current_limit: int
+    in_flight: int = 0
+    blocked_until: float = 0.0
+    success_streak: int = 0
+    waiters: int = 0
+    rate_limit_ceiling: int = 0
+    consecutive_rate_limits: int = 0
+    active_lease_count: int = 0
+    last_outcome: str | None = None
+
+
+@dataclass
+class ProviderModelStaticCap:
+    cap: int
+    aliases: tuple[str, ...]
+    raw_caps: Mapping[str, int | None]
+    merge_rule: str = "min_same_endpoint"
+
+
+@dataclass(frozen=True)
+class RequestPressureSnapshot:
+    captured_at: float
+    sequence: int
+    resource: RequestResourceKey
+    effective_max: int
+    current_limit: int
+    in_flight_count: int
+    active_lease_count: int
+    waiters: int
+    blocked_until_monotonic: float | None
+    cooldown_remaining_seconds: float
+    rate_limit_ceiling: int
+    consecutive_rate_limits: int
+    last_outcome: str | None
+    leak_diagnostics: Mapping[str, int]
+
+
+@dataclass(frozen=True)
+class ProviderModelPressureSnapshot:
+    captured_at: float
+    sequence: int
+    provider_model: ProviderModelKey
+    static_cap: int
+    aggregate_in_flight: int
+    aggregate_active_lease_count: int
+    aliases: tuple[str, ...]
+    raw_caps: Mapping[str, int | None]
+    domains: Mapping[RequestDomain, int]
+
+
+class RequestPressureSnapshotProvider(Protocol):
+    def snapshot(self, resource: RequestResourceKey) -> RequestPressureSnapshot | None: ...
+
+    def snapshots(self) -> Mapping[RequestResourceKey, RequestPressureSnapshot]: ...
+
+    def global_snapshot(self, provider: str, model: str) -> ProviderModelPressureSnapshot | None: ...
+
+    def global_snapshots(self) -> Mapping[ProviderModelKey, ProviderModelPressureSnapshot]: ...
+
+
+class RequestAdmissionController(Protocol):
+    def try_acquire(self, item: RequestAdmissionItem) -> RequestAdmissionDecision: ...
+
+    def acquire_sync(self, item: RequestAdmissionItem) -> RequestAdmissionLease: ...
+
+    async def acquire_async(self, item: RequestAdmissionItem) -> RequestAdmissionLease: ...
+
+    def release(self, lease: RequestAdmissionLease, outcome: RequestReleaseOutcome) -> ReleaseResult: ...
+
+    @property
+    def pressure(self) -> RequestPressureSnapshotProvider: ...
+
+
+@dataclass
+class RequestWaiter:
+    waiter_id: str
+    item: RequestAdmissionItem
+    enqueued_at: float
+    deadline_monotonic: float | None = None
+    assigned_lease: RequestAdmissionLease | None = None
+
+
+@dataclass(frozen=True)
+class RequestQueueView:
+    queued_total: int
+    queued_by_group: Mapping[RequestResourceKey, int]
+    queued_demand_by_resource: Mapping[RequestResourceKey, int]
+    aggregate_provider_model_waiters: Mapping[ProviderModelKey, int]
+
+
+@dataclass(frozen=True)
+class RequestQueueSelection:
+    waiter: RequestWaiter
+    item: RequestAdmissionItem
+    waiter_id: str
+    queue_view: RequestQueueView
+    sequence_version: int
+
+
+class RequestFairQueue:
+    """Weighted fair waiter queue used by request admission."""
+
+    def __init__(self) -> None:
+        self._queues: dict[RequestResourceKey, deque[RequestWaiter]] = {}
+        self._queued: dict[str, RequestWaiter] = {}
+        self._waiter_groups: dict[str, RequestResourceKey] = {}
+        self._group_finish: dict[RequestResourceKey, float] = {}
+        self._heap: list[tuple[float, int, RequestResourceKey]] = []
+        self._active_heap_entries: dict[RequestResourceKey, tuple[float, int]] = {}
+        self._sequence = 0
+        self._sequence_version = 0
+        self._virtual_time = 0.0
+
+    @property
+    def has_waiters(self) -> bool:
+        return bool(self._queued)
+
+    def contains(self, waiter_id: str) -> bool:
+        return waiter_id in self._queued
+
+    def enqueue(self, waiter: RequestWaiter) -> bool:
+        if waiter.waiter_id in self._queued:
+            return False
+        key = waiter.item.group.key
+        queue = self._queues.setdefault(key, deque())
+        queue.append(waiter)
+        self._queued[waiter.waiter_id] = waiter
+        self._waiter_groups[waiter.waiter_id] = key
+        self._activate_group(key)
+        self._sequence_version += 1
+        return True
+
+    def remove(self, waiter_id: str) -> RequestWaiter | None:
+        waiter = self._queued.pop(waiter_id, None)
+        if waiter is None:
+            return None
+        self._waiter_groups.pop(waiter_id, None)
+        self._sequence_version += 1
+        return waiter
+
+    def select_next(
+        self, is_eligible: Callable[[RequestWaiter, RequestQueueView], bool]
+    ) -> RequestQueueSelection | None:
+        view = self.view()
+        heap_copy = list(self._heap)
+        heapq.heapify(heap_copy)
+        active_seen: set[RequestResourceKey] = set()
+        while heap_copy:
+            finish, sequence, key = heapq.heappop(heap_copy)
+            if key in active_seen:
+                continue
+            if self._active_heap_entries.get(key) != (finish, sequence):
+                continue
+            active_seen.add(key)
+            waiter = self._first_valid_waiter(key)
+            if waiter is None:
+                continue
+            if not is_eligible(waiter, view):
+                continue
+            return RequestQueueSelection(
+                waiter=waiter,
+                item=waiter.item,
+                waiter_id=waiter.waiter_id,
+                queue_view=view,
+                sequence_version=self._sequence_version,
+            )
+        return None
+
+    def commit(self, selection: RequestQueueSelection) -> RequestWaiter | None:
+        if selection.sequence_version != self._sequence_version:
+            return None
+        key = self._waiter_groups.get(selection.waiter_id)
+        if key is None or key != selection.item.group.key:
+            return None
+        queue = self._queues.get(key)
+        if queue is None:
+            return None
+        self._purge_queue_head(key)
+        if not queue or queue[0].waiter_id != selection.waiter_id:
+            return None
+
+        waiter = queue.popleft()
+        self._queued.pop(waiter.waiter_id, None)
+        self._waiter_groups.pop(waiter.waiter_id, None)
+        self._active_heap_entries.pop(key, None)
+        weight = max(selection.item.group.weight, 1.0)
+        finish = self._group_finish.get(key, self._virtual_time)
+        self._virtual_time = max(self._virtual_time, finish)
+        self._group_finish[key] = self._virtual_time + (1.0 / weight)
+        self._sequence_version += 1
+        self._purge_queue_head(key)
+        if queue:
+            self._activate_group(key)
+        return waiter
+
+    def view(self) -> RequestQueueView:
+        queued_by_group: Counter[RequestResourceKey] = Counter()
+        demand_by_resource: Counter[RequestResourceKey] = Counter()
+        aggregate_waiters: Counter[ProviderModelKey] = Counter()
+        for waiter in self._queued.values():
+            resource = waiter.item.resource
+            queued_by_group[waiter.item.group.key] += 1
+            demand_by_resource[resource] += 1
+            aggregate_waiters[resource.provider_model_key] += 1
+        return RequestQueueView(
+            queued_total=len(self._queued),
+            queued_by_group=dict(queued_by_group),
+            queued_demand_by_resource=dict(demand_by_resource),
+            aggregate_provider_model_waiters=dict(aggregate_waiters),
+        )
+
+    def _activate_group(self, key: RequestResourceKey) -> None:
+        self._purge_queue_head(key)
+        queue = self._queues.get(key)
+        if not queue or key in self._active_heap_entries:
+            return
+        self._sequence += 1
+        finish = self._group_finish.get(key, self._virtual_time)
+        heapq.heappush(self._heap, (finish, self._sequence, key))
+        self._active_heap_entries[key] = (finish, self._sequence)
+
+    def _first_valid_waiter(self, key: RequestResourceKey) -> RequestWaiter | None:
+        queue = self._queues.get(key)
+        if queue is None:
+            return None
+        for waiter in queue:
+            if waiter.waiter_id in self._queued and self._waiter_groups.get(waiter.waiter_id) == key:
+                return waiter
+        return None
+
+    def _purge_queue_head(self, key: RequestResourceKey) -> None:
+        queue = self._queues.get(key)
+        if queue is None:
+            return
+        while queue:
+            waiter = queue[0]
+            if waiter.waiter_id in self._queued and self._waiter_groups.get(waiter.waiter_id) == key:
+                break
+            queue.popleft()
+
+
+@dataclass
+class _GlobalCapState:
+    limits_by_alias: dict[str, int] = field(default_factory=dict)
+    effective_max: int = 0
+
+    def register_alias(self, alias: str, max_parallel: int) -> None:
+        self.limits_by_alias[alias] = max(1, max_parallel)
+        self.effective_max = min(self.limits_by_alias.values())
+
+
+class AdaptiveRequestAdmissionController(RequestPressureSnapshotProvider):
+    """AIMD-backed request admission controller with exact request leases."""
+
+    def __init__(
+        self,
+        config: RequestAdmissionConfig | None = None,
+        *,
+        event_sink: RequestAdmissionEventSink | None = None,
+    ) -> None:
+        self._config = config or RequestAdmissionConfig()
+        self._lock = threading.Lock()
+        self._condition = threading.Condition(self._lock)
+        self._generation = uuid.uuid4().hex
+        self._global_caps: dict[ProviderModelKey, _GlobalCapState] = {}
+        self._domains: dict[RequestResourceKey, AdaptiveRequestLimitState] = {}
+        self._active_leases: dict[str, RequestAdmissionLease] = {}
+        self._released: set[str] = set()
+        self._aggregate_in_flight: Counter[ProviderModelKey] = Counter()
+        self._aggregate_active_leases: Counter[ProviderModelKey] = Counter()
+        self._sequence = 0
+        self._release_diagnostics: Counter[str] = Counter()
+        self._queue = RequestFairQueue()
+        self._event_sink = event_sink
+
+    @property
+    def pressure(self) -> RequestPressureSnapshotProvider:
+        return self
+
+    @property
+    def config(self) -> RequestAdmissionConfig:
+        return self._config
+
+    def register(
+        self,
+        *,
+        provider_name: str,
+        model_id: str,
+        alias: str,
+        max_parallel_requests: int,
+    ) -> None:
+        events: list[RequestAdmissionEvent] = []
+        with self._lock:
+            key = ProviderModelKey(provider_name, model_id)
+            cap = self._global_caps.setdefault(key, _GlobalCapState())
+            before = cap.effective_max
+            cap.register_alias(alias, max_parallel_requests)
+            self._sequence += 1
+            for resource, state in self._domains.items():
+                if resource.provider_model_key == key:
+                    effective_max = self._effective_max_for_resource(resource)
+                    state.current_limit = min(state.current_limit, effective_max)
+            events.append(
+                self._request_event_locked(
+                    "request_resource_registered",
+                    request_resource_key=RequestResourceKey(provider_name, model_id, RequestDomain.CHAT),
+                    diagnostics={"alias": alias, "provider_model": key, "max_parallel_requests": max_parallel_requests},
+                )
+            )
+            if before != cap.effective_max:
+                events.append(
+                    self._request_event_locked(
+                        "request_effective_cap_changed",
+                        request_resource_key=RequestResourceKey(provider_name, model_id, RequestDomain.CHAT),
+                        diagnostics={"provider_model": key, "previous": before, "current": cap.effective_max},
+                    )
+                )
+            self._condition.notify_all()
+        self._emit_events(events)
+
+    def try_acquire(self, item: RequestAdmissionItem) -> RequestAdmissionDecision:
+        now = time.monotonic()
+        events: list[RequestAdmissionEvent] = []
+        decision: RequestAdmissionDecision
+        with self._lock:
+            events.append(self._request_event_locked("request_wait_started", item=item))
+            if self._queued_waiter_ahead_locked(item, now):
+                decision = RequestAdmissionDenied(
+                    item=item,
+                    reason="queued_waiters_ahead",
+                    snapshot=self._snapshot_locked(item.resource, now),
+                )
+                events.append(self._request_event_locked("request_acquire_denied", item=item, decision=decision))
+            else:
+                denied = self._denial_for(item, now)
+                if denied is not None:
+                    decision = denied
+                    events.append(self._request_event_locked("request_acquire_denied", item=item, decision=decision))
+                else:
+                    decision = self._acquire_locked(item, now)
+                    events.append(self._request_event_locked("request_wait_completed", item=item, lease=decision))
+                    events.append(self._request_event_locked("request_lease_acquired", item=item, lease=decision))
+        self._emit_events(events)
+        return decision
+
+    def acquire_sync(self, item: RequestAdmissionItem) -> RequestAdmissionLease:
+        timeout = (
+            item.queue_wait_timeout_seconds
+            if item.queue_wait_timeout_seconds is not None
+            else self._config.default_queue_wait_timeout_seconds
+        )
+        now = time.monotonic()
+        deadline = now + timeout if timeout is not None else None
+        waiter = RequestWaiter(waiter_id=uuid.uuid4().hex, item=item, enqueued_at=now, deadline_monotonic=deadline)
+        events: list[RequestAdmissionEvent] = []
+        try:
+            while True:
+                with self._lock:
+                    if not self._queue.contains(waiter.waiter_id) and waiter.assigned_lease is None:
+                        self._enqueue_waiter_locked(waiter, events)
+                    self._admit_waiters_locked(events)
+                    if waiter.assigned_lease is not None:
+                        return waiter.assigned_lease
+                    now = time.monotonic()
+                    if deadline is not None and now >= deadline:
+                        self._remove_waiter_locked(waiter)
+                        denied = RequestAdmissionDenied(
+                            item=item,
+                            reason="queue_timeout",
+                            snapshot=self._snapshot_locked(item.resource, now),
+                        )
+                        events.append(self._request_event_locked("request_wait_timeout", item=item, decision=denied))
+                        raise RequestAdmissionError(denied)
+                    wait = self._wait_seconds_locked(item, now, deadline)
+                    self._condition.wait(timeout=wait)
+        finally:
+            self._emit_events(events)
+
+    async def acquire_async(self, item: RequestAdmissionItem) -> RequestAdmissionLease:
+        timeout = (
+            item.queue_wait_timeout_seconds
+            if item.queue_wait_timeout_seconds is not None
+            else self._config.default_queue_wait_timeout_seconds
+        )
+        now = time.monotonic()
+        deadline = now + timeout if timeout is not None else None
+        waiter = RequestWaiter(waiter_id=uuid.uuid4().hex, item=item, enqueued_at=now, deadline_monotonic=deadline)
+        events: list[RequestAdmissionEvent] = []
+        try:
+            while True:
+                with self._lock:
+                    if not self._queue.contains(waiter.waiter_id) and waiter.assigned_lease is None:
+                        self._enqueue_waiter_locked(waiter, events)
+                    self._admit_waiters_locked(events)
+                    if waiter.assigned_lease is not None:
+                        return waiter.assigned_lease
+                    now = time.monotonic()
+                    if deadline is not None and now >= deadline:
+                        self._remove_waiter_locked(waiter)
+                        denied = RequestAdmissionDenied(
+                            item=item,
+                            reason="queue_timeout",
+                            snapshot=self._snapshot_locked(item.resource, now),
+                        )
+                        events.append(self._request_event_locked("request_wait_timeout", item=item, decision=denied))
+                        raise RequestAdmissionError(denied)
+                    wait = self._wait_seconds_locked(item, now, deadline)
+                await asyncio.sleep(wait)
+        except asyncio.CancelledError:
+            lease_to_release: RequestAdmissionLease | None = None
+            with self._lock:
+                lease_to_release = waiter.assigned_lease
+                if lease_to_release is None:
+                    self._remove_waiter_locked(waiter)
+                denied = RequestAdmissionDenied(item=item, reason="cancellation")
+                events.append(
+                    self._request_event_locked(
+                        "request_wait_cancelled",
+                        item=item,
+                        lease=lease_to_release,
+                        decision=denied,
+                    )
+                )
+                self._condition.notify_all()
+            if lease_to_release is not None:
+                self._emit_events(events)
+                events.clear()
+                self.release(lease_to_release, RequestReleaseOutcome(kind="local_cancelled"))
+            raise
+        finally:
+            self._emit_events(events)
+
+    def release(self, lease: RequestAdmissionLease, outcome: RequestReleaseOutcome) -> ReleaseResult:
+        now = time.monotonic()
+        events: list[RequestAdmissionEvent] = []
+        result: ReleaseResult
+        with self._lock:
+            if lease.controller_generation != self._generation:
+                self._release_diagnostics["wrong_controller_generation"] += 1
+                result = ReleaseResult(released=False, reason="wrong_controller_generation")
+                events.append(
+                    self._request_event_locked(
+                        "request_release_diagnostic", item=lease.item, lease=lease, result=result
+                    )
+                )
+            elif (active := self._active_leases.pop(lease.lease_id, None)) is None:
+                reason = "duplicate" if lease.lease_id in self._released else "unknown_lease"
+                self._release_diagnostics[reason] += 1
+                result = ReleaseResult(released=False, reason=reason)
+                events.append(
+                    self._request_event_locked(
+                        "request_release_diagnostic", item=lease.item, lease=lease, result=result
+                    )
+                )
+            elif active.item.resource != lease.item.resource:
+                self._active_leases[lease.lease_id] = active
+                self._release_diagnostics["stale_lease"] += 1
+                result = ReleaseResult(released=False, reason="stale_lease")
+                events.append(
+                    self._request_event_locked(
+                        "request_release_diagnostic", item=lease.item, lease=lease, result=result
+                    )
+                )
+            else:
+                self._released.add(lease.lease_id)
+                resource = active.item.resource
+                provider_model = resource.provider_model_key
+                state = self._get_or_create_state(resource)
+                state.in_flight = max(0, state.in_flight - 1)
+                state.active_lease_count = max(0, state.active_lease_count - 1)
+                state.last_outcome = outcome.kind
+                self._aggregate_in_flight[provider_model] = max(0, self._aggregate_in_flight[provider_model] - 1)
+                self._aggregate_active_leases[provider_model] = max(
+                    0,
+                    self._aggregate_active_leases[provider_model] - 1,
+                )
+                self._apply_outcome(state, resource, outcome, now, events)
+                self._sequence += 1
+                result = ReleaseResult(released=True, reason="released")
+                if outcome.kind == "rate_limited":
+                    events.append(self._request_event_locked("request_rate_limited", item=lease.item, lease=lease))
+                events.append(
+                    self._request_event_locked("request_lease_released", item=lease.item, lease=lease, result=result)
+                )
+                self._admit_waiters_locked(events)
+            self._condition.notify_all()
+        self._emit_events(events)
+        return result
+
+    def snapshot(self, resource: RequestResourceKey) -> RequestPressureSnapshot | None:
+        with self._lock:
+            if resource not in self._domains:
+                return None
+            return self._snapshot_locked(resource, time.monotonic())
+
+    def snapshots(self) -> Mapping[RequestResourceKey, RequestPressureSnapshot]:
+        with self._lock:
+            now = time.monotonic()
+            return {resource: self._snapshot_locked(resource, now) for resource in self._domains}
+
+    def global_snapshot(self, provider: str, model: str) -> ProviderModelPressureSnapshot | None:
+        with self._lock:
+            key = ProviderModelKey(provider, model)
+            if key not in self._global_caps:
+                return None
+            return self._global_snapshot_locked(key, time.monotonic())
+
+    def global_snapshots(self) -> Mapping[ProviderModelKey, ProviderModelPressureSnapshot]:
+        with self._lock:
+            now = time.monotonic()
+            return {key: self._global_snapshot_locked(key, now) for key in self._global_caps}
+
+    def _queued_waiter_ahead_locked(self, item: RequestAdmissionItem, now: float) -> bool:
+        if not self._queue.has_waiters:
+            return False
+        selection = self._queue.select_next(lambda waiter, _view: self._denial_for(waiter.item, now) is None)
+        if selection is None:
+            return False
+        selected_key = selection.item.resource.provider_model_key
+        return selected_key == item.resource.provider_model_key or selection.item.resource == item.resource
+
+    def _enqueue_waiter_locked(self, waiter: RequestWaiter, events: list[RequestAdmissionEvent]) -> None:
+        if self._queue.enqueue(waiter):
+            self._get_or_create_state(waiter.item.resource).waiters += 1
+            self._sequence += 1
+            if self._queue.view().queued_total == 1:
+                events.append(self._request_event_locked("request_queue_formed", item=waiter.item))
+            events.append(self._request_event_locked("request_wait_started", item=waiter.item))
+
+    def _remove_waiter_locked(self, waiter: RequestWaiter) -> None:
+        removed = self._queue.remove(waiter.waiter_id)
+        if removed is None:
+            return
+        state = self._get_or_create_state(waiter.item.resource)
+        state.waiters = max(0, state.waiters - 1)
+        self._sequence += 1
+
+    def _admit_waiters_locked(self, events: list[RequestAdmissionEvent]) -> None:
+        while self._queue.has_waiters:
+            now = time.monotonic()
+            selection = self._queue.select_next(lambda waiter, _view: self._denial_for(waiter.item, now) is None)
+            if selection is None:
+                return
+            waiter = self._queue.commit(selection)
+            if waiter is None:
+                return
+            state = self._get_or_create_state(waiter.item.resource)
+            state.waiters = max(0, state.waiters - 1)
+            lease = self._acquire_locked(waiter.item, now)
+            waiter.assigned_lease = lease
+            events.append(self._request_event_locked("request_wait_completed", item=waiter.item, lease=lease))
+            events.append(self._request_event_locked("request_lease_acquired", item=waiter.item, lease=lease))
+            if not self._queue.has_waiters:
+                events.append(self._request_event_locked("request_queue_drained", item=waiter.item))
+
+    def _wait_seconds_locked(
+        self,
+        item: RequestAdmissionItem,
+        now: float,
+        deadline: float | None,
+    ) -> float:
+        candidates = [0.05]
+        if deadline is not None:
+            candidates.append(max(0.0, deadline - now))
+        state = self._domains.get(item.resource)
+        if state is not None and state.blocked_until > now:
+            candidates.append(max(0.0, state.blocked_until - now))
+        return max(0.0, min(candidates))
+
+    def _denial_for(self, item: RequestAdmissionItem, now: float) -> RequestAdmissionDenied | None:
+        resource = item.resource
+        provider_model = resource.provider_model_key
+        if provider_model not in self._global_caps:
+            return RequestAdmissionDenied(item=item, reason="hard_policy_denial", diagnostics={"unregistered": True})
+        state = self._get_or_create_state(resource)
+        if now < state.blocked_until:
+            return RequestAdmissionDenied(
+                item=item,
+                reason="cooldown",
+                retry_after_seconds=state.blocked_until - now,
+                available_after_monotonic=state.blocked_until,
+                snapshot=self._snapshot_locked(resource, now),
+            )
+        effective_max = self._effective_max_for_resource(resource)
+        aggregate_cap = self._global_caps[provider_model].effective_max
+        if state.in_flight >= min(state.current_limit, effective_max):
+            return RequestAdmissionDenied(
+                item=item, reason="no_capacity", snapshot=self._snapshot_locked(resource, now)
+            )
+        if self._aggregate_in_flight[provider_model] >= aggregate_cap:
+            return RequestAdmissionDenied(
+                item=item, reason="no_capacity", snapshot=self._snapshot_locked(resource, now)
+            )
+        return None
+
+    def _acquire_locked(self, item: RequestAdmissionItem, now: float) -> RequestAdmissionLease:
+        resource = item.resource
+        provider_model = resource.provider_model_key
+        state = self._get_or_create_state(resource)
+        state.in_flight += 1
+        state.active_lease_count += 1
+        self._aggregate_in_flight[provider_model] += 1
+        self._aggregate_active_leases[provider_model] += 1
+        lease = RequestAdmissionLease(
+            lease_id=uuid.uuid4().hex,
+            item=item,
+            acquired_at=now,
+            current_adaptive_limit=state.current_limit,
+            effective_max=self._effective_max_for_resource(resource),
+            controller_generation=self._generation,
+        )
+        self._active_leases[lease.lease_id] = lease
+        self._sequence += 1
+        return lease
+
+    def _apply_outcome(
+        self,
+        state: AdaptiveRequestLimitState,
+        resource: RequestResourceKey,
+        outcome: RequestReleaseOutcome,
+        now: float,
+        events: list[RequestAdmissionEvent],
+    ) -> None:
+        effective_max = self._effective_max_for_resource(resource)
+        if outcome.kind == "rate_limited":
+            prev_limit = state.current_limit
+            first_in_cascade = state.consecutive_rate_limits == 0
+            state.consecutive_rate_limits += 1
+            cooldown = (
+                outcome.retry_after_seconds
+                if outcome.retry_after_seconds is not None and outcome.retry_after_seconds > 0
+                else self._config.cooldown_seconds
+            )
+            state.blocked_until = now + cooldown
+            state.success_streak = 0
+            if first_in_cascade:
+                state.current_limit = max(
+                    1, math.floor(state.current_limit * self._config.multiplicative_decrease_factor)
+                )
+                state.rate_limit_ceiling = (
+                    prev_limit if state.rate_limit_ceiling == 0 else min(state.rate_limit_ceiling, prev_limit)
+                )
+                if state.current_limit != prev_limit:
+                    events.append(
+                        self._request_event_locked(
+                            "request_limit_decreased",
+                            request_resource_key=resource,
+                            diagnostics={"previous": prev_limit, "current": state.current_limit},
+                        )
+                    )
+            return
+        if outcome.kind == "success" and now >= state.blocked_until:
+            prev_limit = state.current_limit
+            state.consecutive_rate_limits = 0
+            state.success_streak += 1
+            if state.success_streak >= self._config.increase_after_successes:
+                state.current_limit = min(effective_max, state.current_limit + self._config.additive_increase_step)
+                state.success_streak = 0
+                if state.current_limit != prev_limit:
+                    events.append(
+                        self._request_event_locked(
+                            "request_limit_increased",
+                            request_resource_key=resource,
+                            diagnostics={"previous": prev_limit, "current": state.current_limit},
+                        )
+                    )
+                    if state.rate_limit_ceiling and state.current_limit > state.rate_limit_ceiling:
+                        events.append(
+                            self._request_event_locked(
+                                "request_soft_ceiling_recovered",
+                                request_resource_key=resource,
+                                diagnostics={"rate_limit_ceiling": state.rate_limit_ceiling},
+                            )
+                        )
+                    if state.current_limit == effective_max and state.blocked_until <= now:
+                        events.append(
+                            self._request_event_locked("request_fully_recovered", request_resource_key=resource)
+                        )
+            return
+        if state.in_flight == 0 and outcome.kind not in {"local_cancelled", "local_timeout"}:
+            state.consecutive_rate_limits = 0
+
+    def _increment_waiter(self, item: RequestAdmissionItem) -> None:
+        with self._lock:
+            self._get_or_create_state(item.resource).waiters += 1
+            self._sequence += 1
+
+    def _decrement_waiter(self, item: RequestAdmissionItem) -> None:
+        with self._lock:
+            state = self._get_or_create_state(item.resource)
+            state.waiters = max(0, state.waiters - 1)
+            self._sequence += 1
+
+    def _get_or_create_state(self, resource: RequestResourceKey) -> AdaptiveRequestLimitState:
+        state = self._domains.get(resource)
+        if state is None:
+            effective_max = self._effective_max_for_resource(resource)
+            initial = self._config.initial_limits.get(resource, effective_max)
+            state = AdaptiveRequestLimitState(current_limit=max(1, min(initial, effective_max)))
+            self._domains[resource] = state
+        return state
+
+    def _effective_max_for_resource(self, resource: RequestResourceKey) -> int:
+        provider_model_cap = self._global_caps.get(resource.provider_model_key)
+        static_cap = provider_model_cap.effective_max if provider_model_cap is not None else 1
+        clamp = self._config.max_limit_clamps.get(resource)
+        return max(1, min(static_cap, clamp if clamp is not None else static_cap))
+
+    def _snapshot_locked(self, resource: RequestResourceKey, now: float) -> RequestPressureSnapshot:
+        state = self._get_or_create_state(resource)
+        blocked_until = state.blocked_until if state.blocked_until > now else None
+        return RequestPressureSnapshot(
+            captured_at=now,
+            sequence=self._sequence,
+            resource=resource,
+            effective_max=self._effective_max_for_resource(resource),
+            current_limit=state.current_limit,
+            in_flight_count=state.in_flight,
+            active_lease_count=state.active_lease_count,
+            waiters=state.waiters,
+            blocked_until_monotonic=blocked_until,
+            cooldown_remaining_seconds=max(0.0, state.blocked_until - now),
+            rate_limit_ceiling=state.rate_limit_ceiling,
+            consecutive_rate_limits=state.consecutive_rate_limits,
+            last_outcome=state.last_outcome,
+            leak_diagnostics=dict(self._release_diagnostics),
+        )
+
+    def _global_snapshot_locked(self, key: ProviderModelKey, now: float) -> ProviderModelPressureSnapshot:
+        cap = self._global_caps[key]
+        domains = {
+            resource.domain: state.current_limit
+            for resource, state in self._domains.items()
+            if resource.provider_model_key == key
+        }
+        return ProviderModelPressureSnapshot(
+            captured_at=now,
+            sequence=self._sequence,
+            provider_model=key,
+            static_cap=cap.effective_max,
+            aggregate_in_flight=self._aggregate_in_flight[key],
+            aggregate_active_lease_count=self._aggregate_active_leases[key],
+            aliases=tuple(sorted(cap.limits_by_alias)),
+            raw_caps=dict(cap.limits_by_alias),
+            domains=domains,
+        )
+
+    def _request_event_locked(
+        self,
+        event_kind: str,
+        *,
+        item: RequestAdmissionItem | None = None,
+        lease: RequestAdmissionLease | None = None,
+        decision: RequestAdmissionDenied | None = None,
+        result: ReleaseResult | None = None,
+        request_resource_key: RequestResourceKey | None = None,
+        diagnostics: Mapping[str, object] | None = None,
+    ) -> RequestAdmissionEvent:
+        self._sequence += 1
+        event_context = item.event_context if item is not None else None
+        resource = request_resource_key or (item.resource if item is not None else None)
+        group_key = item.group.key if item is not None else None
+        reason_or_outcome = None
+        if decision is not None:
+            reason_or_outcome = decision.reason
+        elif result is not None:
+            reason_or_outcome = result.reason
+        return RequestAdmissionEvent.capture(
+            event_kind,  # type: ignore[arg-type]
+            sequence=self._sequence,
+            correlation=event_context.captured_correlation
+            if event_context is not None
+            else runtime_correlation_provider.current(),
+            request_attempt_id=event_context.request_attempt_id if event_context is not None else None,
+            request_lease_id=lease.lease_id if lease is not None else None,
+            request_resource_key=resource,
+            request_group_key=group_key,
+            reason_or_outcome=reason_or_outcome,
+            pressure_snapshot=self._snapshot_locked(resource, time.monotonic()) if resource is not None else None,
+            diagnostics=dict(diagnostics or {}),
+        )
+
+    def _emit_events(self, events: list[RequestAdmissionEvent]) -> None:
+        if self._event_sink is None:
+            return
+        for event in events:
+            try:
+                self._event_sink.emit_request_event(event)
+            except Exception:
+                continue
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/retry.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/retry.py
index 56aa1eec4..9f51a48b2 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/retry.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/retry.py
@@ -14,8 +14,8 @@
 
 logger = logging.getLogger(__name__)
 
-# 429 must not be retried at the transport layer so that rate-limit signals
-# propagate to ThrottledModelClient for AIMD backoff.
+# 429 must not be retried at the transport layer so rate-limit signals
+# propagate to ModelRequestExecutor and request admission for AIMD backoff.
 _RESERVED_STATUS_CODES: frozenset[int] = frozenset({429})
 
 
@@ -25,7 +25,7 @@ class RetryConfig:
 
     Retries non-rate-limit transient failures (``502``, ``503``, ``504``) and
     connection/transport errors.  ``429`` is intentionally excluded so that
-    rate-limit signals reach the ``ThrottledModelClient`` wrapper for AIMD
+    rate-limit signals reach the ``ModelRequestExecutor`` boundary for AIMD
     backoff.  If a caller includes ``429`` in ``retryable_status_codes``,
     ``create_retry_transport`` will strip it and log a warning.
     """
@@ -52,10 +52,8 @@ def create_retry_transport(
         config: Retry policy.  Uses ``RetryConfig()`` defaults when ``None``.
         strip_rate_limit_codes: When ``True`` (default, used by the async engine),
             status codes in ``_RESERVED_STATUS_CODES`` (currently ``{429}``) are
-            stripped so that rate-limit responses reach the ``ThrottledModelClient``
-            AIMD feedback loop.  When ``False`` (used by the sync engine, which has
-            no salvage queue), 429 is kept in the retry list so the transport layer
-            retries it transparently.
+            stripped so that rate-limit responses reach the request-admission
+            AIMD feedback loop.
         transport: Optional pre-configured transport to pass directly to
             ``RetryTransport``.  Pass ``httpx.HTTPTransport`` for sync clients or
             ``httpx.AsyncHTTPTransport`` for async clients — typically with a custom
@@ -70,7 +68,7 @@ def create_retry_transport(
         if reserved_overlap:
             logger.warning(
                 "Stripping reserved status codes %s from retryable_status_codes; "
-                "these must reach ThrottledModelClient for AIMD backoff.",
+                "these must reach ModelRequestExecutor/request admission for AIMD backoff.",
                 sorted(reserved_overlap),
             )
             status_codes = status_codes - _RESERVED_STATUS_CODES
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/throttle_manager.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/throttle_manager.py
deleted file mode 100644
index fb57c32ab..000000000
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/throttle_manager.py
+++ /dev/null
@@ -1,505 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-import asyncio
-import logging
-import math
-import threading
-import time
-from dataclasses import dataclass, field
-from enum import Enum
-
-from data_designer.config.run_config import ThrottleConfig
-
-logger = logging.getLogger(__name__)
-
-
-class ThrottleDomain(str, Enum):
-    CHAT = "chat"
-    EMBEDDING = "embedding"
-    IMAGE = "image"
-    HEALTHCHECK = "healthcheck"
-
-
-# ---------------------------------------------------------------------------
-# Constants
-# ---------------------------------------------------------------------------
-
-DEFAULT_MIN_LIMIT: int = 1
-CAPACITY_POLL_INTERVAL: float = 0.05
-
-
-# ---------------------------------------------------------------------------
-# Internal state containers
-# ---------------------------------------------------------------------------
-
-
-@dataclass
-class DomainThrottleState:
-    """Per-domain AIMD concurrency state.
-
-    All mutations must be performed while holding the owning
-    ``ThrottleManager._lock``.
-    """
-
-    current_limit: int
-    in_flight: int = 0
-    blocked_until: float = 0.0
-    success_streak: int = 0
-    waiters: int = 0
-    rate_limit_ceiling: int = 0
-    consecutive_429s: int = 0
-
-
-@dataclass
-class GlobalCapState:
-    """Tracks the effective hard cap across aliases sharing a provider+model."""
-
-    limits_by_alias: dict[str, int] = field(default_factory=dict)
-    effective_max: int = 0
-
-    def register_alias(self, alias: str, max_parallel: int) -> None:
-        self.limits_by_alias[alias] = max_parallel
-        self.effective_max = min(self.limits_by_alias.values())
-
-
-# ---------------------------------------------------------------------------
-# ThrottleManager
-# ---------------------------------------------------------------------------
-
-
-class ThrottleManager:
-    """Adaptive concurrency manager using AIMD (Additive Increase /
-    Multiplicative Decrease).
-
-    Keyed at two levels:
-
-    - **Global cap**: ``(provider_name, model_id)`` — shared hard ceiling.
-    - **Domain**: ``(provider_name, model_id, throttle_domain)`` — per-route
-      AIMD state that floats between 1 and the global effective max.
-
-    **AIMD behaviour**:
-
-    - *Decrease* — on a 429 / rate-limit signal the domain's concurrency limit
-      is multiplied by ``reduce_factor`` (default 0.75, i.e. reduced by 25%)
-      and a cooldown block is applied for ``retry_after`` seconds (or
-      ``default_cooldown_seconds``).
-    - *Increase* — after every ``success_window`` consecutive successful
-      releases the limit grows by ``additive_increase`` (default 1), up to
-      the *rate-limit ceiling* (or the global effective max if no 429 has
-      been observed yet).
-    - *Stabilization* — each 429 records the pre-halving limit as
-      ``rate_limit_ceiling``.  Subsequent additive increases stop at
-      ``ceiling * (1 + ceiling_overshoot)`` (default 10%) instead of
-      climbing all the way to ``effective_max``.  The overshoot band lets
-      the system probe whether the endpoint can now handle more traffic
-      (e.g. after load drops) while dampening the sawtooth.  If the probe
-      succeeds, the ceiling ratchets up; if it triggers another 429, the
-      ceiling lowers.
-
-    Thread-safe: all state mutations are guarded by a single lock so that
-    sync and async callers co-throttle correctly.
-    """
-
-    def __init__(
-        self,
-        config: ThrottleConfig | None = None,
-    ) -> None:
-        tc = config or ThrottleConfig()
-        self._reduce_factor = tc.reduce_factor
-        self._additive_increase = tc.additive_increase
-        self._success_window = tc.success_window
-        self._default_cooldown_seconds = tc.cooldown_seconds
-        self._ceiling_overshoot = tc.ceiling_overshoot
-        self._lock = threading.Lock()
-        self._global_caps: dict[tuple[str, str], GlobalCapState] = {}
-        self._domains: dict[tuple[str, str, str], DomainThrottleState] = {}
-
-    # -------------------------------------------------------------------
-    # Registration
-    # -------------------------------------------------------------------
-
-    def register(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        alias: str,
-        max_parallel_requests: int,
-    ) -> None:
-        """Register a model alias and its concurrency limit.
-
-        If multiple aliases share the same ``(provider_name, model_id)`` the
-        effective max is ``min()`` of all registered limits.  Existing domain
-        states are clamped to the new effective max.
-
-        **Ordering invariant:** ``register()`` must be called for a
-        ``(provider_name, model_id)`` pair *before* any ``try_acquire()`` for
-        the same key.  If ``try_acquire()`` runs first it creates a domain at
-        ``DEFAULT_MIN_LIMIT`` and ``_clamp_domains`` only *decreases* limits,
-        so a later ``register()`` will not raise the domain to the intended
-        capacity.
-        """
-        with self._lock:
-            global_key = (provider_name, model_id)
-            cap = self._global_caps.setdefault(global_key, GlobalCapState())
-            cap.register_alias(alias, max_parallel_requests)
-            self._clamp_domains(global_key, cap.effective_max)
-            logger.debug(
-                "Throttle registered alias=%r for %s/%s (max_parallel=%d, effective_max=%d)",
-                alias,
-                provider_name,
-                model_id,
-                max_parallel_requests,
-                cap.effective_max,
-            )
-
-    # -------------------------------------------------------------------
-    # Core non-blocking primitives
-    # -------------------------------------------------------------------
-
-    def is_registered(self, provider_name: str, model_id: str) -> bool:
-        """Return ``True`` if ``register()`` has been called for this key."""
-        with self._lock:
-            return (provider_name, model_id) in self._global_caps
-
-    def try_acquire(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-        now: float | None = None,
-    ) -> float:
-        """Attempt to acquire a concurrency slot.
-
-        Returns ``0.0`` if the slot was acquired, otherwise the number of
-        seconds the caller should wait before retrying.
-
-        Raises ``RuntimeError`` if the ``(provider_name, model_id)`` pair
-        has not been registered via ``register()``.
-        """
-        now = now if now is not None else time.monotonic()
-        with self._lock:
-            if (provider_name, model_id) not in self._global_caps:
-                raise RuntimeError(
-                    f"ThrottleManager.try_acquire() called before register() "
-                    f"for ({provider_name!r}, {model_id!r}). "
-                    f"Call register() first to set the concurrency limit."
-                )
-            state = self._get_or_create_domain(provider_name, model_id, domain)
-            if now < state.blocked_until:
-                return state.blocked_until - now
-            if state.in_flight >= state.current_limit:
-                return CAPACITY_POLL_INTERVAL
-            state.in_flight += 1
-            return 0.0
-
-    def release_success(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-        now: float | None = None,
-    ) -> None:
-        with self._lock:
-            state = self._get_or_create_domain(provider_name, model_id, domain)
-            state.in_flight = max(0, state.in_flight - 1)
-            state.consecutive_429s = 0
-            state.success_streak += 1
-            if state.success_streak >= self._success_window:
-                effective_max = self._effective_max_for(provider_name, model_id)
-                cap = self._compute_soft_ceiling(state, effective_max)
-                if state.current_limit < cap:
-                    prev = state.current_limit
-                    state.current_limit = min(state.current_limit + self._additive_increase, cap)
-                    if state.current_limit >= cap:
-                        if cap < effective_max:
-                            logger.info(
-                                "🔋✅ '%s' [%s] concurrency recovered to %d parallel requests",
-                                model_id,
-                                domain.value,
-                                state.current_limit,
-                            )
-                        else:
-                            logger.info(
-                                "🔋✅ '%s' [%s] concurrency fully recovered (%d parallel requests)",
-                                model_id,
-                                domain.value,
-                                state.current_limit,
-                            )
-                    else:
-                        logger.info(
-                            "🪫📈🔥 '%s' [%s] concurrency increased from %d → %d",
-                            model_id,
-                            domain.value,
-                            prev,
-                            state.current_limit,
-                        )
-                state.success_streak = 0
-
-    def release_rate_limited(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-        retry_after: float | None = None,
-        now: float | None = None,
-    ) -> None:
-        now = now if now is not None else time.monotonic()
-        with self._lock:
-            state = self._get_or_create_domain(provider_name, model_id, domain)
-            state.in_flight = max(0, state.in_flight - 1)
-            prev_limit = state.current_limit
-            is_first_in_cascade = state.consecutive_429s == 0
-            state.consecutive_429s += 1
-            cooldown_duration = (
-                retry_after if retry_after is not None and retry_after > 0 else self._default_cooldown_seconds
-            )
-            state.blocked_until = now + cooldown_duration
-            state.success_streak = 0
-
-            if is_first_in_cascade:
-                state.current_limit = max(DEFAULT_MIN_LIMIT, math.floor(state.current_limit * self._reduce_factor))
-                if state.current_limit < prev_limit:
-                    if state.rate_limit_ceiling == 0:
-                        state.rate_limit_ceiling = prev_limit
-                    else:
-                        state.rate_limit_ceiling = min(state.rate_limit_ceiling, prev_limit)
-                    if state.rate_limit_ceiling < prev_limit:
-                        logger.info(
-                            "🪫📉 '%s' [%s] server rate-limited at %d (server limit ~%d) — concurrency reduced to %d (retrying in %.0fs)",
-                            model_id,
-                            domain.value,
-                            prev_limit,
-                            state.rate_limit_ceiling,
-                            state.current_limit,
-                            cooldown_duration,
-                        )
-                    else:
-                        logger.info(
-                            "🪫📉 '%s' [%s] server rate-limited — concurrency reduced from %d → %d (retrying in %.0fs)",
-                            model_id,
-                            domain.value,
-                            prev_limit,
-                            state.current_limit,
-                            cooldown_duration,
-                        )
-            else:
-                logger.debug(
-                    "Throttle %s [%s] cascade 429 #%d (limit held at %d)",
-                    model_id,
-                    domain.value,
-                    state.consecutive_429s,
-                    state.current_limit,
-                )
-
-    def release_failure(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-        now: float | None = None,
-    ) -> None:
-        with self._lock:
-            state = self._get_or_create_domain(provider_name, model_id, domain)
-            state.in_flight = max(0, state.in_flight - 1)
-            # Non-rate-limit failure breaks the 429 cascade: a sequence like
-            # 429 → 500 → 429 should treat the second 429 as the start of a
-            # new cascade. But only after the prior burst has fully drained
-            # (in_flight == 0) - otherwise mixed responses from a single
-            # in-flight wave (429 → 500 → 429 with concurrent slots) would
-            # double-reduce the limit even though the provider hasn't
-            # recovered between the two 429s.
-            if state.in_flight == 0:
-                state.consecutive_429s = 0
-
-    # -------------------------------------------------------------------
-    # Sync / async wrappers
-    # -------------------------------------------------------------------
-
-    def acquire_sync(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-        timeout: float | None = None,
-    ) -> None:
-        """Block until a permit is available.
-
-        ``timeout=None`` (the default) waits indefinitely; the per-request HTTP
-        timeout (``inference_parameters.timeout``) is the only deadline that bounds
-        actual work, and queue waits scale naturally with provider speed and
-        AIMD's adaptive concurrency. Pass an explicit float for tests or for
-        support cases where a queue-wait deadline is genuinely desired.
-        """
-        deadline = (time.monotonic() + timeout) if timeout is not None else None
-        wait = self.try_acquire(provider_name=provider_name, model_id=model_id, domain=domain)
-        if wait == 0.0:
-            return
-        with self._lock:
-            # state is captured once and reused in the finally block; safe
-            # because DomainThrottleState objects are never replaced after creation.
-            state = self._get_or_create_domain(provider_name, model_id, domain)
-            state.waiters += 1
-            if state.waiters == 1:
-                logger.debug(
-                    "Throttle %s/%s [%s] queue forming (in_flight=%d/%d)",
-                    provider_name,
-                    model_id,
-                    domain.value,
-                    state.in_flight,
-                    state.current_limit,
-                )
-        try:
-            while True:
-                if deadline is not None:
-                    remaining = deadline - time.monotonic()
-                    if remaining <= 0 or wait > remaining:
-                        raise TimeoutError(
-                            f"Throttle acquire timed out after {timeout:.0f}s "
-                            f"for {provider_name}/{model_id} [{domain.value}]"
-                        )
-                    sleep_for = min(wait, remaining)
-                else:
-                    sleep_for = wait
-                time.sleep(sleep_for)
-                wait = self.try_acquire(provider_name=provider_name, model_id=model_id, domain=domain)
-                if wait == 0.0:
-                    return
-        finally:
-            with self._lock:
-                state.waiters -= 1
-                if state.waiters == 0:
-                    logger.debug(
-                        "Throttle %s/%s [%s] queue drained",
-                        provider_name,
-                        model_id,
-                        domain.value,
-                    )
-
-    async def acquire_async(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-        timeout: float | None = None,
-    ) -> None:
-        """Block until a permit is available.
-
-        ``timeout=None`` (the default) waits indefinitely; the per-request HTTP
-        timeout (``inference_parameters.timeout``) is the only deadline that bounds
-        actual work, and queue waits scale naturally with provider speed and
-        AIMD's adaptive concurrency. Pass an explicit float for tests or for
-        support cases where a queue-wait deadline is genuinely desired.
-        """
-        deadline = (time.monotonic() + timeout) if timeout is not None else None
-        wait = self.try_acquire(provider_name=provider_name, model_id=model_id, domain=domain)
-        if wait == 0.0:
-            return
-        with self._lock:
-            # state is captured once and reused in the finally block; safe
-            # because DomainThrottleState objects are never replaced after creation.
-            state = self._get_or_create_domain(provider_name, model_id, domain)
-            state.waiters += 1
-            if state.waiters == 1:
-                logger.debug(
-                    "Throttle %s/%s [%s] queue forming (in_flight=%d/%d)",
-                    provider_name,
-                    model_id,
-                    domain.value,
-                    state.in_flight,
-                    state.current_limit,
-                )
-        try:
-            while True:
-                if deadline is not None:
-                    remaining = deadline - time.monotonic()
-                    if remaining <= 0 or wait > remaining:
-                        raise TimeoutError(
-                            f"Throttle acquire timed out after {timeout:.0f}s "
-                            f"for {provider_name}/{model_id} [{domain.value}]"
-                        )
-                    sleep_for = min(wait, remaining)
-                else:
-                    sleep_for = wait
-                await asyncio.sleep(sleep_for)
-                wait = self.try_acquire(provider_name=provider_name, model_id=model_id, domain=domain)
-                if wait == 0.0:
-                    return
-        finally:
-            with self._lock:
-                state.waiters -= 1
-                if state.waiters == 0:
-                    logger.debug(
-                        "Throttle %s/%s [%s] queue drained",
-                        provider_name,
-                        model_id,
-                        domain.value,
-                    )
-
-    # -------------------------------------------------------------------
-    # Introspection (useful for tests and telemetry)
-    # -------------------------------------------------------------------
-
-    def get_domain_state(
-        self,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-    ) -> DomainThrottleState | None:
-        with self._lock:
-            return self._domains.get((provider_name, model_id, domain.value))
-
-    def get_effective_max(self, provider_name: str, model_id: str) -> int:
-        with self._lock:
-            return self._effective_max_for(provider_name, model_id)
-
-    # -------------------------------------------------------------------
-    # Private helpers
-    # -------------------------------------------------------------------
-
-    def _compute_soft_ceiling(self, state: DomainThrottleState, effective_max: int) -> int:
-        """Return the upper bound for additive increase.
-
-        If a rate-limit ceiling has been recorded, allow probing up to
-        ``ceiling * (1 + overshoot)`` (clamped to ``effective_max``).
-        Otherwise fall back to ``effective_max``.
-        """
-        if state.rate_limit_ceiling <= 0:
-            return effective_max
-        soft = state.rate_limit_ceiling + max(1, math.floor(state.rate_limit_ceiling * self._ceiling_overshoot))
-        return min(soft, effective_max)
-
-    def _get_or_create_domain(
-        self,
-        provider_name: str,
-        model_id: str,
-        domain: ThrottleDomain,
-    ) -> DomainThrottleState:
-        key = (provider_name, model_id, domain.value)
-        state = self._domains.get(key)
-        if state is None:
-            effective_max = self._effective_max_for(provider_name, model_id)
-            state = DomainThrottleState(current_limit=effective_max)
-            self._domains[key] = state
-        return state
-
-    def _effective_max_for(self, provider_name: str, model_id: str) -> int:
-        cap = self._global_caps.get((provider_name, model_id))
-        if cap is None or cap.effective_max <= 0:
-            return DEFAULT_MIN_LIMIT
-        return cap.effective_max
-
-    def _clamp_domains(self, global_key: tuple[str, str], effective_max: int) -> None:
-        provider_name, model_id = global_key
-        for (pn, mid, _dom), state in self._domains.items():
-            if pn == provider_name and mid == model_id and state.current_limit > effective_max:
-                state.current_limit = effective_max
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/throttled.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/throttled.py
deleted file mode 100644
index 797452c69..000000000
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/throttled.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-import contextlib
-import logging
-from typing import TYPE_CHECKING
-
-from data_designer.engine.models.clients.base import ModelClient
-from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
-from data_designer.engine.models.clients.throttle_manager import ThrottleDomain
-from data_designer.engine.models.clients.types import (
-    ChatCompletionRequest,
-    ChatCompletionResponse,
-    EmbeddingRequest,
-    EmbeddingResponse,
-    ImageGenerationRequest,
-    ImageGenerationResponse,
-)
-
-if TYPE_CHECKING:
-    from collections.abc import AsyncIterator, Iterator
-
-    from data_designer.engine.models.clients.throttle_manager import ThrottleManager
-
-
-logger = logging.getLogger(__name__)
-
-
-class ThrottledModelClient(ModelClient):
-    """Wraps a ``ModelClient`` with per-request throttle acquire/release.
-
-    Inherits from ``ModelClient`` (a ``Protocol``) so that static type
-    checkers verify conformance and flag missing methods if the protocol
-    evolves.
-
-    Every outbound HTTP call acquires a throttle permit from the
-    ``ThrottleManager`` and releases it on success, rate-limit, or failure.
-    The ``ThrottleDomain`` is determined by the method:
-
-    - ``completion`` / ``acompletion`` -> ``CHAT``
-    - ``embeddings`` / ``aembeddings`` -> ``EMBEDDING``
-    - ``generate_image`` / ``agenerate_image`` -> ``IMAGE`` when
-      ``request.messages is None`` (diffusion), ``CHAT`` when messages are set
-    """
-
-    def __init__(
-        self,
-        inner: ModelClient,
-        throttle_manager: ThrottleManager,
-        provider_name: str,
-        model_id: str,
-    ) -> None:
-        self._inner = inner
-        self._tm = throttle_manager
-        self._provider_name = provider_name
-        self._model_id = model_id
-
-    # --- ModelClient protocol delegation ---
-
-    @property
-    def provider_name(self) -> str:
-        return self._inner.provider_name
-
-    def supports_chat_completion(self) -> bool:
-        return self._inner.supports_chat_completion()
-
-    def supports_embeddings(self) -> bool:
-        return self._inner.supports_embeddings()
-
-    def supports_image_generation(self) -> bool:
-        return self._inner.supports_image_generation()
-
-    def close(self) -> None:
-        self._inner.close()
-
-    async def aclose(self) -> None:
-        await self._inner.aclose()
-
-    # --- Throttled methods ---
-
-    def completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
-        with self._throttled_sync(ThrottleDomain.CHAT):
-            return self._inner.completion(request)
-
-    async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
-        async with self._athrottled(ThrottleDomain.CHAT):
-            return await self._inner.acompletion(request)
-
-    def embeddings(self, request: EmbeddingRequest) -> EmbeddingResponse:
-        with self._throttled_sync(ThrottleDomain.EMBEDDING):
-            return self._inner.embeddings(request)
-
-    async def aembeddings(self, request: EmbeddingRequest) -> EmbeddingResponse:
-        async with self._athrottled(ThrottleDomain.EMBEDDING):
-            return await self._inner.aembeddings(request)
-
-    def generate_image(self, request: ImageGenerationRequest) -> ImageGenerationResponse:
-        domain = self._image_domain(request)
-        with self._throttled_sync(domain):
-            return self._inner.generate_image(request)
-
-    async def agenerate_image(self, request: ImageGenerationRequest) -> ImageGenerationResponse:
-        domain = self._image_domain(request)
-        async with self._athrottled(domain):
-            return await self._inner.agenerate_image(request)
-
-    # --- Context managers ---
-
-    @contextlib.contextmanager
-    def _throttled_sync(self, domain: ThrottleDomain) -> Iterator[None]:
-        try:
-            self._tm.acquire_sync(
-                provider_name=self._provider_name,
-                model_id=self._model_id,
-                domain=domain,
-            )
-        except TimeoutError as exc:
-            raise ProviderError(
-                kind=ProviderErrorKind.TIMEOUT,
-                message=str(exc),
-                provider_name=self._provider_name,
-                model_name=self._model_id,
-            ) from exc
-        exc_to_reraise: BaseException | None = None
-        try:
-            yield
-        except ProviderError as exc:
-            exc_to_reraise = exc
-            try:
-                self._release_on_provider_error(domain, exc)
-            except Exception:
-                logger.exception("ThrottleManager release failed; permit may leak")
-        except BaseException as exc:
-            exc_to_reraise = exc
-            try:
-                self._tm.release_failure(
-                    provider_name=self._provider_name,
-                    model_id=self._model_id,
-                    domain=domain,
-                )
-            except Exception:
-                logger.exception("ThrottleManager release failed; permit may leak")
-        else:
-            try:
-                self._tm.release_success(
-                    provider_name=self._provider_name,
-                    model_id=self._model_id,
-                    domain=domain,
-                )
-            except Exception:
-                logger.exception("ThrottleManager release_success failed")
-        if exc_to_reraise is not None:
-            raise exc_to_reraise
-
-    @contextlib.asynccontextmanager
-    async def _athrottled(self, domain: ThrottleDomain) -> AsyncIterator[None]:
-        try:
-            await self._tm.acquire_async(
-                provider_name=self._provider_name,
-                model_id=self._model_id,
-                domain=domain,
-            )
-        except TimeoutError as exc:
-            raise ProviderError(
-                kind=ProviderErrorKind.TIMEOUT,
-                message=str(exc),
-                provider_name=self._provider_name,
-                model_name=self._model_id,
-            ) from exc
-        exc_to_reraise: BaseException | None = None
-        try:
-            yield
-        except ProviderError as exc:
-            exc_to_reraise = exc
-            try:
-                self._release_on_provider_error(domain, exc)
-            except Exception:
-                logger.exception("ThrottleManager release failed; permit may leak")
-        except BaseException as exc:
-            exc_to_reraise = exc
-            try:
-                self._tm.release_failure(
-                    provider_name=self._provider_name,
-                    model_id=self._model_id,
-                    domain=domain,
-                )
-            except Exception:
-                logger.exception("ThrottleManager release failed; permit may leak")
-        else:
-            try:
-                self._tm.release_success(
-                    provider_name=self._provider_name,
-                    model_id=self._model_id,
-                    domain=domain,
-                )
-            except Exception:
-                logger.exception("ThrottleManager release_success failed")
-        if exc_to_reraise is not None:
-            raise exc_to_reraise
-
-    # --- Private helpers ---
-
-    def _release_on_provider_error(self, domain: ThrottleDomain, exc: ProviderError) -> None:
-        if exc.kind == ProviderErrorKind.RATE_LIMIT:
-            self._tm.release_rate_limited(
-                provider_name=self._provider_name,
-                model_id=self._model_id,
-                domain=domain,
-                retry_after=exc.retry_after,
-            )
-        else:
-            self._tm.release_failure(
-                provider_name=self._provider_name,
-                model_id=self._model_id,
-                domain=domain,
-            )
-
-    @staticmethod
-    def _image_domain(request: ImageGenerationRequest) -> ThrottleDomain:
-        return ThrottleDomain.CHAT if request.messages is not None else ThrottleDomain.IMAGE
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/factory.py b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
index 6ef2b2727..27dd13dc6 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/factory.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
@@ -40,21 +40,21 @@ def create_model_registry(
         client_concurrency_mode: ``"sync"`` (default) or ``"async"``.  Forwarded
             to native HTTP adapters so each client is constrained to a single
             concurrency mode.
-        run_config: Optional runtime configuration.  The nested
-            ``run_config.throttle`` (a ``ThrottleConfig``) is forwarded to the
-            ``ThrottleManager`` constructor.
+        run_config: Optional runtime configuration. Request admission uses
+            engine-internal defaults in V1; no public run-config knob is exposed.
 
     Returns:
         A configured ModelRegistry instance.
     """
     from data_designer.config.run_config import RunConfig
     from data_designer.engine.models.clients.factory import create_model_client
+    from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController
     from data_designer.engine.models.clients.retry import RetryConfig
-    from data_designer.engine.models.clients.throttle_manager import ThrottleManager
     from data_designer.engine.models.facade import ModelFacade
     from data_designer.engine.models.registry import ModelRegistry
 
-    throttle_manager = ThrottleManager((run_config or RunConfig()).throttle)
+    _ = run_config or RunConfig()
+    request_admission = AdaptiveRequestAdmissionController()
 
     def model_facade_factory(
         model_config: ModelConfig,
@@ -68,7 +68,7 @@ def model_facade_factory(
             model_provider_registry,
             retry_config=retry_config,
             client_concurrency_mode=client_concurrency_mode,
-            throttle_manager=throttle_manager,
+            request_admission=request_admission,
         )
         return ModelFacade(
             model_config,
@@ -82,6 +82,6 @@ def model_facade_factory(
         secret_resolver=secret_resolver,
         model_provider_registry=model_provider_registry,
         model_facade_factory=model_facade_factory,
-        throttle_manager=throttle_manager,
+        request_admission=request_admission,
         retry_config=RetryConfig(),
     )
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/registry.py b/packages/data-designer-engine/src/data_designer/engine/models/registry.py
index a6103ffc3..04e2f4fd1 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/registry.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/registry.py
@@ -15,8 +15,8 @@
 if TYPE_CHECKING:
     from collections.abc import Callable
 
+    from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController
     from data_designer.engine.models.clients.retry import RetryConfig
-    from data_designer.engine.models.clients.throttle_manager import ThrottleManager
     from data_designer.engine.models.facade import ModelFacade
 
     ModelFacadeFactory = Callable[
@@ -35,13 +35,13 @@ def __init__(
         model_provider_registry: ModelProviderRegistry,
         model_configs: list[ModelConfig] | None = None,
         model_facade_factory: ModelFacadeFactory | None = None,
-        throttle_manager: ThrottleManager | None = None,
+        request_admission: AdaptiveRequestAdmissionController | None = None,
         retry_config: RetryConfig | None = None,
     ) -> None:
         self._secret_resolver = secret_resolver
         self._model_provider_registry = model_provider_registry
         self._model_facade_factory = model_facade_factory
-        self._throttle_manager = throttle_manager
+        self._request_admission = request_admission
         self._retry_config = retry_config
         self._model_configs: dict[str, ModelConfig] = {}
         self._models: dict[str, ModelFacade] = {}
@@ -56,8 +56,8 @@ def models(self) -> dict[str, ModelFacade]:
         return self._models
 
     @property
-    def throttle_manager(self) -> ThrottleManager | None:
-        return self._throttle_manager
+    def request_admission(self) -> AdaptiveRequestAdmissionController | None:
+        return self._request_admission
 
     @property
     def retry_config(self) -> RetryConfig | None:
@@ -178,10 +178,9 @@ def get_aggregate_max_parallel_requests(self) -> int:
         This is a coarse upper bound: it sums over *all* registered aliases,
         including those not referenced by the current generator set, and does
         not deduplicate aliases sharing a ``(provider_name, model_id)`` key.
-        The result is used to size the scheduler's LLM-wait semaphore, which
-        is a memory-safety cap — oversizing wastes a few coroutine slots but
-        does not affect correctness because the ``ThrottleManager`` enforces
-        the real per-key limit.
+        The result is used to size scheduler task-stage model admission, which
+        is a memory-safety cap. Concrete provider/model request capacity is
+        enforced by request admission at model-call time.
         """
         return sum(mc.inference_parameters.max_parallel_requests for mc in self._model_configs.values())
 
@@ -314,8 +313,8 @@ def _get_model(self, model_config: ModelConfig) -> ModelFacade:
             self._model_provider_registry,
             self._retry_config,
         )
-        if self._throttle_manager is not None:
-            self._throttle_manager.register(
+        if self._request_admission is not None:
+            self._request_admission.register(
                 provider_name=facade.model_provider_name,
                 model_id=model_config.model,
                 alias=model_config.alias,
diff --git a/packages/data-designer-engine/src/data_designer/engine/observability.py b/packages/data-designer-engine/src/data_designer/engine/observability.py
new file mode 100644
index 000000000..bcb938d4e
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/observability.py
@@ -0,0 +1,187 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import contextvars
+import time
+from dataclasses import dataclass, field
+from typing import Literal, Protocol
+
+
+@dataclass(frozen=True)
+class RuntimeCorrelation:
+    run_id: str
+    row_group: int | None
+    task_column: str | None
+    task_type: str | None
+    scheduling_group_kind: str | None
+    scheduling_group_identity_hash: str | None
+    task_execution_id: str | None
+
+
+class RuntimeCorrelationProvider:
+    """Context-variable backed runtime correlation provider."""
+
+    def __init__(self) -> None:
+        self._current: contextvars.ContextVar[RuntimeCorrelation | None] = contextvars.ContextVar(
+            "data_designer_runtime_correlation",
+            default=None,
+        )
+
+    def current(self) -> RuntimeCorrelation | None:
+        return self._current.get()
+
+    def set(self, correlation: RuntimeCorrelation | None) -> contextvars.Token:
+        return self._current.set(correlation)
+
+    def reset(self, token: contextvars.Token) -> None:
+        self._current.reset(token)
+
+
+runtime_correlation_provider = RuntimeCorrelationProvider()
+
+SchedulerAdmissionEventKind = Literal[
+    "dependency_ready",
+    "ready_enqueued",
+    "selected",
+    "queue_empty",
+    "admission_blocked",
+    "group_capped",
+    "task_lease_acquired",
+    "admission_denied",
+    "worker_spawned",
+    "worker_spawn_failed",
+    "stale_selection",
+    "retry_deferred",
+    "non_retryable_dropped",
+    "cancelled",
+    "salvage_redispatched",
+    "queue_drained",
+    "task_completed",
+    "task_lease_released",
+    "release_diagnostic",
+]
+
+RequestAdmissionEventKind = Literal[
+    "request_resource_registered",
+    "request_effective_cap_changed",
+    "request_queue_formed",
+    "request_wait_started",
+    "request_wait_completed",
+    "request_wait_timeout",
+    "request_wait_cancelled",
+    "request_acquire_denied",
+    "request_lease_acquired",
+    "model_request_started",
+    "model_request_completed",
+    "request_queue_drained",
+    "request_rate_limited",
+    "request_limit_decreased",
+    "request_limit_increased",
+    "request_soft_ceiling_recovered",
+    "request_fully_recovered",
+    "request_lease_released",
+    "request_release_diagnostic",
+]
+
+
+@dataclass(frozen=True)
+class SchedulerAdmissionEvent:
+    event_kind: SchedulerAdmissionEventKind
+    captured_at_monotonic: float
+    sequence: int
+    captured_correlation: RuntimeCorrelation | None = None
+    task_id: str | None = None
+    task_execution_id: str | None = None
+    task_lease_id: str | None = None
+    scheduler_resource_key: str | None = None
+    reason_or_result: str | None = None
+    snapshot: object | None = None
+    diagnostics: dict[str, object] = field(default_factory=dict)
+
+    @classmethod
+    def capture(
+        cls,
+        event_kind: SchedulerAdmissionEventKind,
+        *,
+        sequence: int,
+        correlation: RuntimeCorrelation | None = None,
+        **kwargs: object,
+    ) -> SchedulerAdmissionEvent:
+        return cls(
+            event_kind=event_kind,
+            captured_at_monotonic=time.monotonic(),
+            sequence=sequence,
+            captured_correlation=correlation,
+            **kwargs,
+        )
+
+
+@dataclass(frozen=True)
+class RequestAdmissionEvent:
+    event_kind: RequestAdmissionEventKind
+    captured_at_monotonic: float
+    sequence: int
+    captured_correlation: RuntimeCorrelation | None = None
+    request_attempt_id: str | None = None
+    request_lease_id: str | None = None
+    request_resource_key: object | None = None
+    request_group_key: object | None = None
+    reason_or_outcome: str | None = None
+    pressure_snapshot: object | None = None
+    diagnostics: dict[str, object] = field(default_factory=dict)
+
+    @classmethod
+    def capture(
+        cls,
+        event_kind: RequestAdmissionEventKind,
+        *,
+        sequence: int,
+        correlation: RuntimeCorrelation | None = None,
+        **kwargs: object,
+    ) -> RequestAdmissionEvent:
+        return cls(
+            event_kind=event_kind,
+            captured_at_monotonic=time.monotonic(),
+            sequence=sequence,
+            captured_correlation=correlation,
+            **kwargs,
+        )
+
+
+class SchedulerAdmissionEventSink(Protocol):
+    def emit_scheduler_event(self, event: SchedulerAdmissionEvent) -> None: ...
+
+
+class RequestAdmissionEventSink(Protocol):
+    def emit_request_event(self, event: RequestAdmissionEvent) -> None: ...
+
+
+class InMemoryAdmissionEventSink:
+    """Small sink used by tests, diagnostics, and benchmark smoke runs."""
+
+    def __init__(self) -> None:
+        self.scheduler_events: list[SchedulerAdmissionEvent] = []
+        self.request_events: list[RequestAdmissionEvent] = []
+
+    def emit_scheduler_event(self, event: SchedulerAdmissionEvent) -> None:
+        self.scheduler_events.append(event)
+
+    def emit_request_event(self, event: RequestAdmissionEvent) -> None:
+        self.request_events.append(event)
+
+
+@dataclass(frozen=True)
+class CorrelatedRuntimeView:
+    scheduler_events: tuple[SchedulerAdmissionEvent, ...]
+    request_events: tuple[RequestAdmissionEvent, ...]
+
+    @property
+    def timeline(self) -> tuple[SchedulerAdmissionEvent | RequestAdmissionEvent, ...]:
+        return tuple(
+            sorted(
+                (*self.scheduler_events, *self.request_events),
+                key=lambda event: (event.captured_at_monotonic, event.sequence),
+            )
+        )
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index 6097232ef..26ab918ea 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -23,17 +23,20 @@
 from data_designer.config.custom_column import custom_column_generator
 from data_designer.config.models import ChatCompletionInferenceParams, ModelConfig
 from data_designer.config.sampler_params import SamplerType
+from data_designer.config.scheduling import SchedulingMetadata
 from data_designer.engine.column_generators.generators.base import (
     ColumnGenerator,
     ColumnGeneratorFullColumn,
+    ColumnGeneratorWithModelRegistry,
     FromScratchColumnGenerator,
 )
 from data_designer.engine.column_generators.generators.custom import CustomColumnGenerator
-from data_designer.engine.dataset_builders.async_scheduler import AsyncTaskScheduler, build_llm_bound_lookup
+from data_designer.engine.dataset_builders.async_scheduler import AsyncTaskScheduler
 from data_designer.engine.dataset_builders.errors import DatasetGenerationError
 from data_designer.engine.dataset_builders.utils.completion_tracker import CompletionTracker, FrontierDelta
 from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
 from data_designer.engine.dataset_builders.utils.row_group_buffer import RowGroupBufferManager
+from data_designer.engine.dataset_builders.utils.task_admission import TaskAdmissionConfig, TaskAdmissionLease
 from data_designer.engine.dataset_builders.utils.task_model import Task
 from data_designer.engine.models.errors import (
     RETRYABLE_MODEL_ERRORS,
@@ -41,6 +44,7 @@
     ModelRateLimitError,
     ModelTimeoutError,
 )
+from data_designer.engine.observability import InMemoryAdmissionEventSink
 from data_designer.engine.resources.resource_provider import ResourceProvider
 
 MODEL_ALIAS = "stub"
@@ -83,6 +87,25 @@ def generate(self, data: dict) -> dict:
         return data
 
 
+class MockRootCellGenerator(ColumnGenerator[ExpressionColumnConfig]):
+    """Root cell generator that records the shape it receives."""
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, **kwargs)
+        self.call_types: list[str] = []
+
+    @staticmethod
+    def get_generation_strategy() -> GenerationStrategy:
+        return GenerationStrategy.CELL_BY_CELL
+
+    def generate(self, data: dict) -> dict:
+        self.call_types.append(type(data).__name__)
+        if not isinstance(data, dict):
+            raise TypeError(f"expected dict, got {type(data).__name__}")
+        data[self.config.name] = f"root_{len(self.call_types)}"
+        return data
+
+
 class MockFullColumnGenerator(ColumnGeneratorFullColumn[ExpressionColumnConfig]):
     """Mock full-column generator."""
 
@@ -228,8 +251,8 @@ def generate(self, data: dict) -> dict:
 class MockRetryableErrorGenerator(ColumnGenerator[ExpressionColumnConfig]):
     """Generator that raises a parametrizable retryable error then succeeds.
 
-    Declares ``is_llm_bound=True`` because it mimics model-call behavior;
-    the scheduler's degraded-provider WARN window only counts LLM-bound tasks.
+    Declares model scheduling metadata because it mimics model-call behavior;
+    the scheduler's degraded-provider WARN window counts model-stage tasks.
     """
 
     def __init__(
@@ -248,9 +271,8 @@ def __init__(
     def get_generation_strategy() -> GenerationStrategy:
         return GenerationStrategy.CELL_BY_CELL
 
-    @property
-    def is_llm_bound(self) -> bool:
-        return True
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return SchedulingMetadata.custom_model("test", self.config.name, "v1")
 
     def generate(self, data: dict) -> dict:
         self._calls += 1
@@ -377,6 +399,31 @@ async def test_scheduler_dispatches_seeds_first() -> None:
     assert seed_traces[0].dispatched_at < cell_traces[0].dispatched_at
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_dispatches_root_cell_by_cell_columns_per_row() -> None:
+    provider = _mock_provider()
+    generator = MockRootCellGenerator(config=_expr_config("root_cell"), resource_provider=provider)
+    configs = [SamplerColumnConfig(name="root_cell", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]})]
+    strategies = {"root_cell": GenerationStrategy.CELL_BY_CELL}
+    graph = ExecutionGraph.create(configs, strategies)
+    row_groups = [(0, 3)]
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    scheduler = AsyncTaskScheduler(
+        generators={"root_cell": generator},
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        trace=True,
+    )
+
+    await scheduler.run()
+
+    assert generator.call_types == ["dict", "dict", "dict"]
+    assert [trace.task_type for trace in scheduler.traces] == ["cell", "cell", "cell"]
+    assert not any(tracker.is_dropped(0, row_index) for row_index in range(3))
+    assert tracker.is_row_group_complete(0, 3, ["root_cell"])
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_with_buffer_manager() -> None:
     """Scheduler writes results to buffer manager and checkpoints."""
@@ -1084,10 +1131,10 @@ def _count_degraded_msgs(caplog: pytest.LogCaptureFixture) -> int:
 @pytest.mark.parametrize(
     "retryable_failures,num_records,window,interval_s,expected_count",
     [
-        # Above-threshold + zero throttle: at least one WARN should fire.
+        # Above-threshold + no log interval: at least one WARN should fire.
         pytest.param(6, 10, 8, 0.0, "at_least_one", id="fires_above_threshold"),
-        # Above-threshold + 1h throttle: only one WARN despite sustained degradation.
-        pytest.param(8, 12, 4, 3600.0, 1, id="throttled_to_one"),
+        # Above-threshold + 1h log interval: only one WARN despite sustained degradation.
+        pytest.param(8, 12, 4, 3600.0, 1, id="rate_limited_to_one"),
     ],
 )
 @pytest.mark.asyncio(loop_scope="session")
@@ -1397,15 +1444,14 @@ async def test_scheduler_out_of_order_row_group_completion() -> None:
     assert checkpoint_order.index(1) < checkpoint_order.index(0)
 
 
-# -- Dual-semaphore / LLM-bound tests -----------------------------------------
+# -- Task-admission / model-stage tests ---------------------------------------
 
 
 class MockLLMBoundCellGenerator(ColumnGenerator[ExpressionColumnConfig]):
-    """Mock cell-by-cell generator that reports is_llm_bound=True."""
+    """Mock cell-by-cell generator that reports model-stage scheduling metadata."""
 
-    @property
-    def is_llm_bound(self) -> bool:
-        return True
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return SchedulingMetadata.custom_model("test", self.config.name, "v1")
 
     @staticmethod
     def get_generation_strategy() -> GenerationStrategy:
@@ -1416,13 +1462,9 @@ def generate(self, data: dict) -> dict:
         return data
 
 
-class MockConfiguredModelCellGenerator(ColumnGenerator[LLMTextColumnConfig]):
+class MockConfiguredModelCellGenerator(ColumnGeneratorWithModelRegistry[LLMTextColumnConfig]):
     """Mock cell generator with model-registry helpers."""
 
-    @property
-    def is_llm_bound(self) -> bool:
-        return True
-
     @staticmethod
     def get_generation_strategy() -> GenerationStrategy:
         return GenerationStrategy.CELL_BY_CELL
@@ -1447,9 +1489,8 @@ def __init__(self, *args: Any, rate_limit_failures: int = 0, **kwargs: Any) -> N
         self._rate_limit_failures = rate_limit_failures
         self._calls = 0
 
-    @property
-    def is_llm_bound(self) -> bool:
-        return True
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return SchedulingMetadata.custom_model("test", self.config.name, "v1")
 
     @staticmethod
     def get_generation_strategy() -> GenerationStrategy:
@@ -1492,19 +1533,15 @@ async def test_scheduler_llm_bound_one_way_handoff() -> None:
         tracker=tracker,
         row_groups=row_groups,
         max_submitted_tasks=max_submitted,
-        max_llm_wait_tasks=max_llm_wait,
+        max_model_task_admission=max_llm_wait,
     )
     await scheduler.run()
 
     assert tracker.is_row_group_complete(0, 3, ["seed", "llm_col"])
 
-    sub_available, llm_available = scheduler.get_semaphore_permits()
-    assert sub_available == max_submitted, (
-        f"Submission semaphore leaked after LLM handoff: available={sub_available}, expected={max_submitted}"
-    )
-    assert llm_available == max_llm_wait, (
-        f"LLM-wait semaphore leaked after LLM handoff: available={llm_available}, expected={max_llm_wait}"
-    )
+    snapshot = scheduler.task_admission_snapshot()
+    assert snapshot.resources_available["submission"] == max_submitted
+    assert snapshot.resources_available["llm_wait"] == max_llm_wait
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -1535,21 +1572,19 @@ async def test_scheduler_non_llm_holds_submission_slot() -> None:
         tracker=tracker,
         row_groups=row_groups,
         max_submitted_tasks=2,
-        max_llm_wait_tasks=max_llm_wait,
+        max_model_task_admission=max_llm_wait,
     )
     await scheduler.run()
 
     assert tracker.is_row_group_complete(0, 3, ["seed", "cell_out"])
 
-    _, llm_available = scheduler.get_semaphore_permits()
-    assert llm_available == max_llm_wait, (
-        f"LLM-wait semaphore was consumed by non-LLM task: available={llm_available}, expected={max_llm_wait}"
-    )
+    snapshot = scheduler.task_admission_snapshot()
+    assert snapshot.resources_available["llm_wait"] == max_llm_wait
 
 
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_deadlock_regression() -> None:
-    """max_submitted_tasks=1, max_llm_wait_tasks=1, two ready LLM tasks completes without deadlock."""
+    """max_submitted_tasks=1, max_model_task_admission=1, two ready LLM tasks completes without deadlock."""
     provider = _mock_provider()
     configs = [
         SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
@@ -1574,7 +1609,7 @@ async def test_scheduler_deadlock_regression() -> None:
         tracker=tracker,
         row_groups=row_groups,
         max_submitted_tasks=1,
-        max_llm_wait_tasks=1,
+        max_model_task_admission=1,
     )
 
     await asyncio.wait_for(scheduler.run(), timeout=10.0)
@@ -1613,9 +1648,12 @@ async def test_drain_frontier_raises_when_ready_but_no_capacity_or_inflight() ->
         graph=graph,
         tracker=tracker,
         row_groups=row_groups,
-        max_submitted_tasks=0,
+        task_admission_config=TaskAdmissionConfig(submission_capacity=1),
     )
     scheduler._rg_states[0] = MagicMock(size=1)
+    blocker = scheduler._schedulable_task(Task(column="cell_out", row_group=0, row_index=99, task_type="cell"))
+    lease = scheduler._task_admission.try_acquire(blocker, scheduler._fair_queue.view())
+    assert isinstance(lease, TaskAdmissionLease)
     scheduler._apply_frontier_delta(seed_delta)
 
     with pytest.raises(RuntimeError, match="Ready frontier is admission-blocked"):
@@ -1695,22 +1733,8 @@ def drop_middle_row(row_group: int, row_group_size: int) -> FrontierDelta:
     assert tracker.is_row_group_complete(0, 3, ["seed", "cell_out"])
 
 
-@pytest.mark.asyncio(loop_scope="session")
-async def test_scheduler_is_llm_bound_property_drives_lookup() -> None:
-    """is_llm_bound property on generators drives the lookup, not isinstance."""
-    provider = _mock_provider()
-    llm_gen = MockLLMBoundCellGenerator(config=_expr_config("llm_col"), resource_provider=provider)
-    non_llm_gen = MockCellGenerator(config=_expr_config("cell_out"), resource_provider=provider)
-
-    assert llm_gen.is_llm_bound is True
-    assert non_llm_gen.is_llm_bound is False
-
-    lookup = build_llm_bound_lookup({"llm_col": llm_gen, "cell_out": non_llm_gen})
-    assert lookup == {"llm_col": True, "cell_out": False}
-
-
-def test_custom_generator_with_model_aliases_is_llm_bound() -> None:
-    """CustomColumnGenerator with model_aliases reports is_llm_bound=True."""
+def test_custom_generator_with_model_aliases_reports_custom_model_metadata() -> None:
+    """CustomColumnGenerator with model_aliases reports custom-model metadata."""
 
     @custom_column_generator(model_aliases=["my_model"])
     def gen_with_models(row: dict, generator_params: None, models: dict) -> dict:
@@ -1729,11 +1753,8 @@ def gen_no_models(row: dict) -> dict:
     llm_gen = CustomColumnGenerator(config=llm_config, resource_provider=provider)
     plain_gen = CustomColumnGenerator(config=plain_config, resource_provider=provider)
 
-    assert llm_gen.is_llm_bound is True
-    assert plain_gen.is_llm_bound is False
-
-    lookup = build_llm_bound_lookup({"custom_llm": llm_gen, "custom_plain": plain_gen})
-    assert lookup == {"custom_llm": True, "custom_plain": False}
+    assert llm_gen.get_scheduling_metadata().kind == "custom_model"
+    assert plain_gen.get_scheduling_metadata().kind == "local"
 
 
 def _provider_with_model_configs(configs: dict[str, ModelConfig]) -> MagicMock:
@@ -1762,13 +1783,13 @@ def test_scheduler_model_task_group_spec_uses_model_resource_and_flow() -> None:
         graph=graph,
         tracker=tracker,
         row_groups=[(0, 1)],
-        max_llm_wait_tasks=5,
+        max_model_task_admission=5,
     )
 
-    spec = scheduler._task_group_spec(Task(column="answer", row_group=0, row_index=0, task_type="cell"))
+    spec = scheduler._schedulable_task(Task(column="answer", row_group=0, row_index=0, task_type="cell")).group
 
     assert spec.key.kind == "model"
-    assert spec.key.identity[:2] == ("mock-provider", "model-text")
+    assert spec.key.identity[:3] == ("model", "mock-provider", "model-text")
     assert spec.key.identity[-1] == "answer"
     assert spec.weight == 3.0
     assert spec.admitted_limit == 5
@@ -1792,21 +1813,19 @@ def test_scheduler_task_group_spec_is_cached_per_generator() -> None:
         graph=graph,
         tracker=tracker,
         row_groups=[(0, 2)],
-        max_llm_wait_tasks=5,
+        max_model_task_admission=5,
     )
 
-    spec_a = scheduler._task_group_spec(Task(column="answer", row_group=0, row_index=0, task_type="cell"))
-    spec_b = scheduler._task_group_spec(Task(column="answer", row_group=0, row_index=1, task_type="cell"))
+    spec_a = scheduler._schedulable_task(Task(column="answer", row_group=0, row_index=0, task_type="cell")).group
+    spec_b = scheduler._schedulable_task(Task(column="answer", row_group=0, row_index=1, task_type="cell")).group
 
-    assert spec_a is spec_b
+    assert spec_a == spec_b
     assert provider.model_registry.get_model_config.call_count == 1
     assert provider.model_registry.get_model_provider.call_count == 1
 
 
-def test_scheduler_task_group_spec_logs_debug_on_model_resolution_fallback(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    """Direct spec resolution isolates fallback logging without timing-based scheduler traces."""
+def test_scheduler_task_group_spec_raises_on_model_resolution_failure() -> None:
+    """Model metadata resolution failures are fatal without an explicit fallback."""
     provider = MagicMock()
     provider.model_registry = MagicMock()
     provider.model_registry.get_model_config.side_effect = RuntimeError("registry unavailable")
@@ -1816,29 +1835,14 @@ def test_scheduler_task_group_spec_logs_debug_on_model_resolution_fallback(
     graph = ExecutionGraph.create([column_config], {"answer": GenerationStrategy.CELL_BY_CELL})
     tracker = CompletionTracker.with_graph(graph, [(0, 2)])
 
-    with caplog.at_level("DEBUG", logger="data_designer.engine.dataset_builders.utils.scheduling_hints"):
-        scheduler = AsyncTaskScheduler(
+    with pytest.raises(Exception):
+        AsyncTaskScheduler(
             generators={"answer": generator},
             graph=graph,
             tracker=tracker,
             row_groups=[(0, 2)],
-            max_llm_wait_tasks=5,
+            max_model_task_admission=5,
         )
-        spec_a = scheduler._task_group_spec(Task(column="answer", row_group=0, row_index=0, task_type="cell"))
-        spec_b = scheduler._task_group_spec(Task(column="answer", row_group=0, row_index=1, task_type="cell"))
-
-    assert spec_a is spec_b
-    assert spec_a.key.kind == "custom_model"
-    assert spec_a.key.identity == ("answer", MODEL_ALIAS)
-    assert spec_a.weight == 1.0
-    assert provider.model_registry.get_model_config.call_count == 1
-    fallback_records = [
-        record for record in caplog.records if "Falling back to custom-model scheduling group" in record.getMessage()
-    ]
-    assert len(fallback_records) == 1
-    assert "answer" in fallback_records[0].getMessage()
-    assert MODEL_ALIAS in fallback_records[0].getMessage()
-    assert fallback_records[0].exc_info is not None
 
 
 def test_scheduler_custom_model_task_group_spec_uses_alias_set_weight() -> None:
@@ -1874,15 +1878,15 @@ def gen_with_models(row: dict, generator_params: None, models: dict) -> dict:
         graph=graph,
         tracker=tracker,
         row_groups=[(0, 1)],
-        max_llm_wait_tasks=10,
+        max_model_task_admission=10,
     )
 
-    spec = scheduler._task_group_spec(Task(column="custom_llm", row_group=0, row_index=0, task_type="cell"))
+    spec = scheduler._schedulable_task(Task(column="custom_llm", row_group=0, row_index=0, task_type="cell")).group
 
     assert spec.key.kind == "custom_model"
-    assert spec.key.identity == ("custom_llm", "draft", "judge")
-    assert spec.weight == 5.0
-    assert spec.admitted_limit == 10
+    assert spec.key.identity[:3] == ("custom_model", "custom_column", "draft-judge")
+    assert spec.weight == 2.0
+    assert spec.admitted_limit == 4
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -1927,33 +1931,28 @@ async def test_scheduler_llm_bound_429_retried_in_salvage() -> None:
         row_groups=row_groups,
         buffer_manager=buffer_mgr,
         max_submitted_tasks=max_submitted,
-        max_llm_wait_tasks=max_llm_wait,
+        max_model_task_admission=max_llm_wait,
     )
     await scheduler.run()
 
     assert tracker.is_row_group_complete(0, num_records, ["seed", "llm_col"])
 
-    sub_available, llm_available = scheduler.get_semaphore_permits()
-    assert sub_available == max_submitted, (
-        f"Submission semaphore leaked after salvage retry: available={sub_available}, expected={max_submitted}"
-    )
-    assert llm_available == max_llm_wait, (
-        f"LLM-wait semaphore leaked after salvage retry: available={llm_available}, expected={max_llm_wait}"
-    )
+    snapshot = scheduler.task_admission_snapshot()
+    assert snapshot.resources_available["submission"] == max_submitted
+    assert snapshot.resources_available["llm_wait"] == max_llm_wait
 
 
 @pytest.mark.asyncio(loop_scope="session")
-async def test_scheduler_cancellation_releases_semaphores() -> None:
-    """Cancelling the scheduler while LLM-bound tasks are in-flight releases all semaphore slots."""
+async def test_scheduler_cancellation_releases_task_admission_leases() -> None:
+    """Cancelling the scheduler while model-stage tasks are in-flight releases task leases."""
     provider = _mock_provider()
 
     blocked = asyncio.Event()
     proceed = asyncio.Event()
 
     class BlockingLLMGenerator(ColumnGenerator[ExpressionColumnConfig]):
-        @property
-        def is_llm_bound(self) -> bool:
-            return True
+        def get_scheduling_metadata(self) -> SchedulingMetadata:
+            return SchedulingMetadata.custom_model("test", self.config.name, "v1")
 
         @staticmethod
         def get_generation_strategy() -> GenerationStrategy:
@@ -1987,13 +1986,15 @@ async def agenerate(self, data: dict) -> dict:
 
     max_submitted = 4
     max_llm_wait = 2
+    sink = InMemoryAdmissionEventSink()
     scheduler = AsyncTaskScheduler(
         generators=generators,
         graph=graph,
         tracker=tracker,
         row_groups=row_groups,
         max_submitted_tasks=max_submitted,
-        max_llm_wait_tasks=max_llm_wait,
+        max_model_task_admission=max_llm_wait,
+        scheduler_event_sink=sink,
     )
 
     run_task = asyncio.create_task(scheduler.run())
@@ -2003,13 +2004,10 @@ async def agenerate(self, data: dict) -> dict:
     with pytest.raises(asyncio.CancelledError):
         await run_task
 
-    sub_available, llm_available = scheduler.get_semaphore_permits()
-    assert sub_available == max_submitted, (
-        f"Submission semaphore leaked: available={sub_available}, expected={max_submitted}"
-    )
-    assert llm_available == max_llm_wait, (
-        f"LLM-wait semaphore leaked: available={llm_available}, expected={max_llm_wait}"
-    )
+    snapshot = scheduler.task_admission_snapshot()
+    assert snapshot.resources_available["submission"] == max_submitted
+    assert snapshot.resources_available["llm_wait"] == max_llm_wait
+    assert "cancelled" in [event.event_kind for event in sink.scheduler_events]
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -2017,7 +2015,7 @@ async def test_scheduler_rg_semaphore_deadlock_with_transient_failures() -> None
     """Row groups stalled by transient failures don't block admission of new row groups.
 
     Regression test: with max_concurrent_row_groups=1 and 2 row groups, if all
-    tasks in RG0 fail transiently, the semaphore must still be released so RG1
+    tasks in RG0 fail transiently, row-group capacity must still be released so RG1
     can be admitted.  The scheduler salvages RG0 inline and continues.
     """
     provider = _mock_provider()
@@ -2098,31 +2096,6 @@ def test_side_effect_columns_separated_from_completion_tracking() -> None:
     assert "side_b" in write_cols
 
 
-# -- TrackingSemaphore tests ---------------------------------------------------
-
-
-def test_tracking_semaphore_try_acquire() -> None:
-    """try_acquire returns True when permits are available, False when exhausted."""
-    from data_designer.engine.dataset_builders.async_scheduler import TrackingSemaphore
-
-    sem = TrackingSemaphore(2)
-    assert sem.available_permits == 2
-
-    assert sem.try_acquire() is True
-    assert sem.available_permits == 1
-
-    assert sem.try_acquire() is True
-    assert sem.available_permits == 0
-
-    assert sem.try_acquire() is False
-    assert sem.available_permits == 0
-
-    sem.release()
-    assert sem.available_permits == 1
-    assert sem.try_acquire() is True
-    assert sem.available_permits == 0
-
-
 # -- Pipeline parallelism (stale dispatch fix, issue #504) ---------------------
 
 
@@ -2147,11 +2120,10 @@ async def agenerate(self, data: dict) -> dict:
 
 
 class SlowLLMBoundCellGenerator(SlowCellGenerator):
-    """Slow cell generator that participates in LLM-wait scheduling."""
+    """Slow cell generator that participates in model-stage scheduling."""
 
-    @property
-    def is_llm_bound(self) -> bool:
-        return True
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return SchedulingMetadata.custom_model("test", self.config.name, "v1")
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -2304,7 +2276,7 @@ async def test_scheduler_fair_llm_group_cap_preserves_peer_admission() -> None:
         tracker=tracker,
         row_groups=row_groups,
         max_submitted_tasks=4,
-        max_llm_wait_tasks=4,
+        max_model_task_admission=4,
         trace=True,
     )
 
@@ -2318,7 +2290,9 @@ async def test_scheduler_fair_llm_group_cap_preserves_peer_admission() -> None:
     assert first_window.count("hot") == 2
     assert first_window.count("peer") == 2
     assert tracker.is_row_group_complete(0, 8, ["topic", *gen_names])
-    assert scheduler.get_semaphore_permits() == (4, 4)
+    snapshot = scheduler.task_admission_snapshot()
+    assert snapshot.resources_available["submission"] == 4
+    assert snapshot.resources_available["llm_wait"] == 4
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -2332,9 +2306,9 @@ async def test_scheduler_downstream_interleaves_with_upstream() -> None:
         ├── gen_b (slow, 50ms) → judge_b (instant)
         └── gen_c (slow, 50ms) → judge_c (instant)
 
-    With a small semaphore (4) and 10 records, the 30 gen tasks (3 cols x 10 rows)
-    saturate the semaphore. The dispatch loop must re-query the frontier when the
-    semaphore is full so that judge tasks from completed gen rows are picked up
+    With small task admission capacity (4) and 10 records, the 30 gen tasks
+    saturate admission. The dispatch loop must re-query the frontier when capacity
+    is full so that judge tasks from completed gen rows are picked up
     before all gen tasks finish.
     """
     provider = _mock_provider()
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_completion_tracker.py b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_completion_tracker.py
index 2ec7b4cd3..da98bd871 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_completion_tracker.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_completion_tracker.py
@@ -189,7 +189,7 @@ def test_get_ready_tasks_seed_frontier(ready_ctx: ReadyTasksFixture) -> None:
 
     assert len(ready) == 1
     assert ready[0].column == "topic"
-    assert ready[0].task_type == "batch"
+    assert ready[0].task_type == "from_scratch"
 
 
 def test_get_ready_tasks_after_seed_complete(ready_ctx: ReadyTasksFixture) -> None:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_fair_task_queue.py b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_fair_task_queue.py
index b929bce4f..bbc900998 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_fair_task_queue.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_fair_task_queue.py
@@ -5,12 +5,15 @@
 
 from collections import Counter
 
-from data_designer.engine.dataset_builders.utils.fair_task_queue import (
-    FairTaskQueue,
+from data_designer.engine.dataset_builders.utils.fair_task_queue import FairTaskQueue, QueueView
+from data_designer.engine.dataset_builders.utils.task_model import Task
+from data_designer.engine.dataset_builders.utils.task_scheduling import (
+    SchedulableTask,
+    SchedulerResourceRequest,
     TaskGroupKey,
     TaskGroupSpec,
+    stable_task_id,
 )
-from data_designer.engine.dataset_builders.utils.task_model import Task
 
 
 def _task(column: str, row_index: int) -> Task:
@@ -25,195 +28,132 @@ def _group(name: str, *, weight: float = 1.0, admitted_limit: int | None = None)
     )
 
 
-def _enqueue(queue: FairTaskQueue, items: list[tuple[Task, TaskGroupSpec]]) -> None:
-    for task, group in items:
-        queue.enqueue(task, group)
-
-
-def test_fair_task_queue_equal_groups_round_robins() -> None:
-    queue = FairTaskQueue()
-    _enqueue(
-        queue,
-        [
-            (task, _group(task.column))
-            for task in [
-                _task("a", 0),
-                _task("a", 1),
-                _task("b", 0),
-                _task("b", 1),
-                _task("c", 0),
-                _task("c", 1),
-            ]
-        ],
+def _item(column: str, row_index: int, group: TaskGroupSpec | None = None) -> SchedulableTask:
+    task = _task(column, row_index)
+    group = group or _group(column)
+    return SchedulableTask(
+        task_id=stable_task_id(task),
+        payload=task,
+        group=group,
+        resource_request=SchedulerResourceRequest({"submission": 1}),
     )
 
-    selected = [queue.admit_next() for _ in range(6)]
 
-    assert [selection.task.column for selection in selected if selection is not None] == ["a", "b", "c", "a", "b", "c"]
+def _select_and_commit(queue: FairTaskQueue) -> SchedulableTask | None:
+    selection = queue.select_next(lambda _item, _view: True)
+    if selection is None:
+        return None
+    return queue.commit(selection)
 
 
-def test_fair_task_queue_weighted_groups() -> None:
+def test_fair_task_queue_equal_groups_round_robins() -> None:
     queue = FairTaskQueue()
-    _enqueue(
-        queue,
+    queue.enqueue(
         [
-            (task, _group(task.column, weight=2 if task.column == "a" else 1))
-            for task in [_task("a", i) for i in range(6)]
+            _item("a", 0),
+            _item("a", 1),
+            _item("b", 0),
+            _item("b", 1),
+            _item("c", 0),
+            _item("c", 1),
         ]
-        + [(_task("b", i), _group("b", weight=1)) for i in range(6)],
     )
 
-    selected = [queue.admit_next() for _ in range(6)]
-    counts = Counter(selection.task.column for selection in selected if selection is not None)
-
-    assert counts == {"a": 4, "b": 2}
-
-
-def test_fair_task_queue_discards_queued_tasks() -> None:
-    queue = FairTaskQueue()
-    stale = _task("a", 0)
-    fresh = _task("a", 1)
-
-    _enqueue(queue, [(stale, _group("a")), (fresh, _group("a"))])
-    queue.discard(stale)
+    selected = [_select_and_commit(queue) for _ in range(6)]
 
-    selected = queue.admit_next()
+    assert [item.payload.column for item in selected if item is not None] == ["a", "b", "c", "a", "b", "c"]
 
-    assert selected is not None
-    assert selected.task == fresh
-    assert queue.admit_next() is None
 
-
-def test_fair_task_queue_admitted_cap_skips_saturated_group_with_waiting_peer() -> None:
+def test_fair_task_queue_weighted_groups() -> None:
     queue = FairTaskQueue()
-    capped = _group("a", admitted_limit=1, weight=1_000)
-    peer = _group("b")
-    _enqueue(
-        queue,
-        [
-            (_task("a", 0), capped),
-            (_task("a", 1), capped),
-            (_task("b", 0), peer),
-            (_task("b", 1), peer),
-        ],
+    queue.enqueue(
+        [_item("a", i, _group("a", weight=2)) for i in range(6)]
+        + [_item("b", i, _group("b", weight=1)) for i in range(6)]
     )
 
-    first = queue.admit_next()
-    peer_first = queue.admit_next()
-    selected = queue.admit_next()
+    selected = [_select_and_commit(queue) for _ in range(6)]
+    counts = Counter(item.payload.column for item in selected if item is not None)
 
-    assert first is not None
-    assert first.task.column == "a"
-    assert peer_first is not None
-    assert peer_first.task.column == "b"
-    assert selected is not None
-    assert selected.task.column == "b"
+    assert counts == {"a": 4, "b": 2}
 
 
-def test_fair_task_queue_solo_group_can_exceed_admitted_cap() -> None:
+def test_select_next_is_non_mutating_until_commit() -> None:
     queue = FairTaskQueue()
-    group = _group("a", admitted_limit=1)
-    first_task = _task("a", 0)
-    second_task = _task("a", 1)
-    queue.enqueue(first_task, group)
-    queue.enqueue(second_task, group)
+    first = _item("a", 0)
+    second = _item("b", 0)
+    queue.enqueue([first, second])
 
-    first = queue.admit_next()
+    selection = queue.select_next(lambda _item, _view: True)
 
-    assert first is not None
-    assert first.task == first_task
-    second = queue.admit_next()
-    assert second is not None
-    assert second.task == second_task
-    assert queue.has_queued_tasks is False
+    assert selection is not None
+    assert queue.view().queued_total == 2
+    committed = queue.commit(selection)
+    assert committed == first
+    assert queue.view().queued_total == 1
 
 
-def test_fair_task_queue_over_cap_group_yields_to_queued_peer() -> None:
+def test_commit_rejects_stale_selection() -> None:
     queue = FairTaskQueue()
-    capped = _group("a", admitted_limit=1)
-    peer = _group("b")
-    _enqueue(queue, [(_task("a", i), capped) for i in range(5)])
+    first = _item("a", 0)
+    queue.enqueue([first])
 
-    solo_selected = [queue.admit_next() for _ in range(3)]
-    _enqueue(queue, [(_task("b", i), peer) for i in range(2)])
-    peer_selected = [queue.admit_next() for _ in range(2)]
+    selection = queue.select_next(lambda _item, _view: True)
+    assert selection is not None
+    queue.enqueue([_item("b", 0)])
 
-    assert [selection.task.column for selection in solo_selected if selection is not None] == ["a", "a", "a"]
-    assert [selection.task.column for selection in peer_selected if selection is not None] == ["b", "b"]
+    assert queue.commit(selection) is None
+    assert queue.view().queued_total == 2
 
 
-def test_fair_task_queue_returns_none_when_all_competing_groups_capped() -> None:
+def test_select_next_uses_scheduler_eligibility_callback() -> None:
     queue = FairTaskQueue()
-    group_a = _group("a", admitted_limit=1)
-    group_b = _group("b", admitted_limit=1)
-    _enqueue(
-        queue,
-        [
-            (_task("a", 0), group_a),
-            (_task("a", 1), group_a),
-            (_task("b", 0), group_b),
-            (_task("b", 1), group_b),
-        ],
-    )
+    queue.enqueue([_item("a", 0), _item("b", 0)])
 
-    selected = [queue.admit_next() for _ in range(2)]
+    selection = queue.select_next(lambda item, _view: item.payload.column == "b")
 
-    assert [selection.task.column for selection in selected if selection is not None] == ["a", "b"]
-    assert queue.admit_next() is None
-    assert queue.has_queued_tasks is True
+    assert selection is not None
+    assert selection.item.payload.column == "b"
+    assert queue.commit(selection) == selection.item
 
 
-def test_fair_task_queue_release_reopens_saturated_group() -> None:
+def test_enqueue_is_idempotent_by_task_id() -> None:
     queue = FairTaskQueue()
-    group_a = _group("a", admitted_limit=1)
-    group_b = _group("b", admitted_limit=1)
-    _enqueue(
-        queue,
-        [
-            (_task("a", 0), group_a),
-            (_task("a", 1), group_a),
-            (_task("b", 0), group_b),
-            (_task("b", 1), group_b),
-        ],
-    )
-    first = queue.admit_next()
-    second = queue.admit_next()
+    item = _item("a", 0)
 
-    assert first is not None
-    assert first.task.column == "a"
-    assert second is not None
-    assert second.task.column == "b"
-    assert queue.admit_next() is None
+    first = queue.enqueue([item])
+    second = queue.enqueue([item])
 
-    queue.release(first.task)
-    reopened = queue.admit_next()
+    assert first == (item.task_id,)
+    assert second == ()
+    assert queue.view().queued_total == 1
 
-    assert reopened is not None
-    assert reopened.task == _task("a", 1)
 
-
-def test_fair_task_queue_no_duplicate_on_repeated_enqueue() -> None:
+def test_discard_where_removes_matching_tasks() -> None:
     queue = FairTaskQueue()
-    task = _task("a", 0)
+    queue.enqueue([_item(column, i) for column in ["a", "b"] for i in range(2)])
 
-    queue.enqueue(task, _group("a"))
-    queue.enqueue(task, _group("a"))
-    first = queue.admit_next()
+    queue.discard_where(lambda item: item.payload.column == "a")
+    selected = [_select_and_commit(queue) for _ in range(2)]
 
-    assert first is not None
-    assert first.task == task
-    assert queue.admit_next() is None
+    assert [item.payload.column for item in selected if item is not None] == ["b", "b"]
+    assert _select_and_commit(queue) is None
 
 
-def test_fair_task_queue_discard_where_removes_matching_tasks() -> None:
+def test_queue_view_exposes_group_and_resource_demand() -> None:
     queue = FairTaskQueue()
-    _enqueue(
-        queue,
-        [(_task(column, i), _group(column)) for column in ["a", "b"] for i in range(2)],
+    group = _group("a")
+    task = _task("a", 0)
+    item = SchedulableTask(
+        task_id=stable_task_id(task),
+        payload=task,
+        group=group,
+        resource_request=SchedulerResourceRequest({"submission": 1, "llm_wait": 1}),
     )
 
-    queue.discard_where(lambda task: task.column == "a")
-    selected = [queue.admit_next() for _ in range(2)]
+    queue.enqueue([item])
+    view: QueueView = queue.view()
 
-    assert [selection.task.column for selection in selected if selection is not None] == ["b", "b"]
-    assert queue.admit_next() is None
+    assert view.queued_total == 1
+    assert view.queued_by_group[group.key] == 1
+    assert view.queued_resource_demand_by_group[group.key]["llm_wait"] == 1
+    assert view.first_candidate_resources_by_group[group.key]["submission"] == 1
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_scheduling_hints.py b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_scheduling_hints.py
deleted file mode 100644
index 4e46c07b0..000000000
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_scheduling_hints.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-from types import SimpleNamespace
-from unittest.mock import MagicMock
-
-import pytest
-
-from data_designer.config.column_configs import (
-    CustomColumnConfig,
-    ExpressionColumnConfig,
-    GenerationStrategy,
-    LLMTextColumnConfig,
-)
-from data_designer.config.custom_column import custom_column_generator
-from data_designer.config.models import ChatCompletionInferenceParams, ModelConfig
-from data_designer.engine.column_generators.generators.base import ColumnGenerator
-from data_designer.engine.column_generators.generators.custom import CustomColumnGenerator
-from data_designer.engine.dataset_builders.utils.scheduling_hints import SchedulingHint, SchedulingHintResolver
-from data_designer.engine.resources.resource_provider import ResourceProvider
-
-MODEL_ALIAS = "stub"
-
-
-def _expr_config(name: str = "test") -> ExpressionColumnConfig:
-    return ExpressionColumnConfig(name=name, expr="{{ x }}", dtype="str")
-
-
-def _provider_with_model_configs(configs: dict[str, ModelConfig]) -> MagicMock:
-    provider = MagicMock(spec=ResourceProvider)
-    provider.model_registry = MagicMock()
-    provider.model_registry.get_model_config.side_effect = lambda model_alias: configs[model_alias]
-    provider.model_registry.get_model_provider.return_value = SimpleNamespace(name="mock-provider")
-    return provider
-
-
-class LocalCellGenerator(ColumnGenerator[ExpressionColumnConfig]):
-    @staticmethod
-    def get_generation_strategy() -> GenerationStrategy:
-        return GenerationStrategy.CELL_BY_CELL
-
-    def generate(self, data: dict) -> dict:
-        data[self.config.name] = "local"
-        return data
-
-
-class ModelCellGenerator(ColumnGenerator[LLMTextColumnConfig]):
-    @property
-    def is_llm_bound(self) -> bool:
-        return True
-
-    @staticmethod
-    def get_generation_strategy() -> GenerationStrategy:
-        return GenerationStrategy.CELL_BY_CELL
-
-    def generate(self, data: dict) -> dict:
-        data[self.config.name] = "model"
-        return data
-
-    def get_model_config(self, model_alias: str) -> ModelConfig:
-        return self.resource_provider.model_registry.get_model_config(model_alias=model_alias)
-
-    def get_model_provider_name(self, model_alias: str) -> str:
-        provider = self.resource_provider.model_registry.get_model_provider(model_alias=model_alias)
-        return str(provider.name)
-
-
-def test_scheduling_hint_resolver_local_hint_does_not_touch_model_registry() -> None:
-    provider = MagicMock(spec=ResourceProvider)
-    provider.model_registry = MagicMock()
-    generator = LocalCellGenerator(config=_expr_config("local_col"), resource_provider=provider)
-
-    resolver = SchedulingHintResolver({"local_col": generator})
-
-    assert resolver.hint_for(generator) == SchedulingHint(group_kind="local")
-    provider.model_registry.get_model_config.assert_not_called()
-    provider.model_registry.get_model_provider.assert_not_called()
-
-
-def test_scheduling_hint_resolver_resolves_primary_model_once_per_generator() -> None:
-    model_config = ModelConfig(
-        alias=MODEL_ALIAS,
-        model="model-text",
-        inference_parameters=ChatCompletionInferenceParams(max_parallel_requests=3),
-        provider="mock-provider",
-    )
-    provider = _provider_with_model_configs({MODEL_ALIAS: model_config})
-    column_config = LLMTextColumnConfig(name="answer", prompt="hello", model_alias=MODEL_ALIAS)
-    generator = ModelCellGenerator(config=column_config, resource_provider=provider)
-
-    resolver = SchedulingHintResolver({"answer": generator, "answer_again": generator})
-    hint = resolver.hint_for(generator)
-
-    assert hint.group_kind == "model"
-    assert hint.identity_prefix[:2] == ("mock-provider", "model-text")
-    assert hint.weight == 3
-    assert provider.model_registry.get_model_config.call_count == 1
-    assert provider.model_registry.get_model_provider.call_count == 1
-
-
-def test_scheduling_hint_resolver_falls_back_to_custom_model_hint_with_debug(
-    caplog: pytest.LogCaptureFixture,
-) -> None:
-    provider = MagicMock(spec=ResourceProvider)
-    provider.model_registry = MagicMock()
-    provider.model_registry.get_model_config.side_effect = RuntimeError("registry unavailable")
-    provider.model_registry.get_model_provider.return_value = SimpleNamespace(name="mock-provider")
-    column_config = LLMTextColumnConfig(name="answer", prompt="hello", model_alias=MODEL_ALIAS)
-    generator = ModelCellGenerator(config=column_config, resource_provider=provider)
-
-    with caplog.at_level("DEBUG", logger="data_designer.engine.dataset_builders.utils.scheduling_hints"):
-        resolver = SchedulingHintResolver({"answer": generator})
-
-    hint = resolver.hint_for(generator)
-
-    assert hint == SchedulingHint(group_kind="custom_model", identity_suffix=(MODEL_ALIAS,), weight=1)
-    fallback_records = [
-        record for record in caplog.records if "Falling back to custom-model scheduling group" in record.getMessage()
-    ]
-    assert len(fallback_records) == 1
-    assert "answer" in fallback_records[0].getMessage()
-    assert MODEL_ALIAS in fallback_records[0].getMessage()
-    assert fallback_records[0].exc_info is not None
-
-
-def test_scheduling_hint_resolver_partial_alias_fallback_preserves_resolved_weight() -> None:
-    @custom_column_generator(model_aliases=["resolved", "missing"])
-    def gen_with_models(row: dict, generator_params: None, models: dict) -> dict:
-        row["custom_llm"] = "value"
-        return row
-
-    provider = _provider_with_model_configs(
-        {
-            "resolved": ModelConfig(
-                alias="resolved",
-                model="model-resolved",
-                inference_parameters=ChatCompletionInferenceParams(max_parallel_requests=7),
-                provider="mock-provider",
-            )
-        }
-    )
-    config = CustomColumnConfig(name="custom_llm", generator_function=gen_with_models)
-    generator = CustomColumnGenerator(config=config, resource_provider=provider)
-
-    resolver = SchedulingHintResolver({"custom_llm": generator})
-    hint = resolver.hint_for(generator)
-
-    assert hint == SchedulingHint(group_kind="custom_model", identity_suffix=("missing", "resolved"), weight=7)
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_admission.py b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_admission.py
new file mode 100644
index 000000000..fd7642763
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_admission.py
@@ -0,0 +1,176 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from data_designer.engine.dataset_builders.utils.fair_task_queue import FairTaskQueue
+from data_designer.engine.dataset_builders.utils.task_admission import (
+    BoundedBorrowTaskAdmissionPolicyConfig,
+    TaskAdmissionConfig,
+    TaskAdmissionController,
+    TaskAdmissionDenied,
+    TaskAdmissionLease,
+)
+from data_designer.engine.dataset_builders.utils.task_model import Task
+from data_designer.engine.dataset_builders.utils.task_scheduling import (
+    SchedulableTask,
+    SchedulerResourceRequest,
+    TaskGroupKey,
+    TaskGroupSpec,
+    stable_task_id,
+)
+
+
+def _item(column: str, row: int = 0, *, group: TaskGroupSpec | None = None) -> SchedulableTask:
+    task = Task(column=column, row_group=0, row_index=row, task_type="cell")
+    group = group or TaskGroupSpec(TaskGroupKey(kind="local", identity=(column,)))
+    return SchedulableTask(
+        task_id=stable_task_id(task),
+        payload=task,
+        group=group,
+        resource_request=SchedulerResourceRequest({"submission": 1}),
+    )
+
+
+def _queue_view(*items: SchedulableTask):
+    queue = FairTaskQueue()
+    queue.enqueue(items)
+    return queue.view()
+
+
+def test_task_admission_acquires_and_releases_exact_lease() -> None:
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=1))
+    item = _item("a")
+
+    decision = controller.try_acquire(item, _queue_view(item))
+
+    assert isinstance(decision, TaskAdmissionLease)
+    assert controller.view().resources_available["submission"] == 0
+    result = controller.release(decision)
+    assert result.released is True
+    assert controller.view().resources_available["submission"] == 1
+
+
+def test_task_admission_denies_when_resource_full() -> None:
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=1))
+    first = _item("a")
+    second = _item("b")
+    lease = controller.try_acquire(first, _queue_view(first, second))
+
+    assert isinstance(lease, TaskAdmissionLease)
+    decision = controller.try_acquire(second, _queue_view(second))
+
+    assert isinstance(decision, TaskAdmissionDenied)
+    assert decision.reason == "no_capacity"
+
+
+def test_task_admission_duplicate_release_does_not_increase_capacity() -> None:
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=1))
+    item = _item("a")
+    lease = controller.try_acquire(item, _queue_view(item))
+    assert isinstance(lease, TaskAdmissionLease)
+
+    first = controller.release(lease)
+    second = controller.release(lease)
+
+    assert first.released is True
+    assert second.released is False
+    assert second.reason == "duplicate"
+    assert controller.view().resources_available["submission"] == 1
+
+
+def test_task_admission_group_cap_yields_to_peer_pressure() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=2))
+    first = _item("a", 0, group=group)
+    second = _item("a", 1, group=group)
+    peer = _item("b")
+    lease = controller.try_acquire(first, _queue_view(first, second, peer))
+    assert isinstance(lease, TaskAdmissionLease)
+
+    decision = controller.try_acquire(second, _queue_view(second, peer))
+
+    assert isinstance(decision, TaskAdmissionDenied)
+    assert decision.reason == "group_cap"
+
+
+def test_explain_blocked_reports_group_cap_denials() -> None:
+    first_group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "first")), admitted_limit=1)
+    second_group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "second")), admitted_limit=1)
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=4))
+    first_active = _item("a", 0, group=first_group)
+    second_active = _item("b", 0, group=second_group)
+    first_queued = _item("a", 1, group=first_group)
+    second_queued = _item("b", 1, group=second_group)
+    first_lease = controller.try_acquire(first_active, _queue_view(first_active, second_active))
+    second_lease = controller.try_acquire(second_active, _queue_view(second_active, first_queued))
+    assert isinstance(first_lease, TaskAdmissionLease)
+    assert isinstance(second_lease, TaskAdmissionLease)
+    queue = FairTaskQueue()
+    queue.enqueue((first_queued, second_queued))
+
+    assert queue.select_next(controller.is_eligible) is None
+    summary = controller.explain_blocked(queue.view())
+
+    assert summary.dominant_denial_reasons == {"group_cap": 2}
+
+
+def test_task_admission_group_cap_does_not_block_solo_group() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=2))
+    first = _item("a", 0, group=group)
+    second = _item("a", 1, group=group)
+    lease = controller.try_acquire(first, _queue_view(first, second))
+    assert isinstance(lease, TaskAdmissionLease)
+
+    decision = controller.try_acquire(second, _queue_view(second))
+
+    assert isinstance(decision, TaskAdmissionLease)
+
+
+def test_bounded_borrow_limits_solo_group_borrow_debt() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    controller = TaskAdmissionController(
+        TaskAdmissionConfig(
+            submission_capacity=3,
+            bounded_borrow=BoundedBorrowTaskAdmissionPolicyConfig(default_borrow_ceiling=1),
+        )
+    )
+    first = _item("a", 0, group=group)
+    second = _item("a", 1, group=group)
+    third = _item("a", 2, group=group)
+    first_lease = controller.try_acquire(first, _queue_view(first, second, third))
+    assert isinstance(first_lease, TaskAdmissionLease)
+    borrowed = controller.try_acquire(second, _queue_view(second, third))
+    assert isinstance(borrowed, TaskAdmissionLease)
+
+    denied = controller.try_acquire(third, _queue_view(third))
+
+    assert isinstance(denied, TaskAdmissionDenied)
+    assert denied.reason == "borrow_debt"
+    assert controller.view().policy_debt_by_group_resource[(group.key, "submission")] == 1
+
+
+def test_bounded_borrow_debt_blocks_under_peer_pressure_and_releases() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    controller = TaskAdmissionController(
+        TaskAdmissionConfig(
+            submission_capacity=3,
+            bounded_borrow=BoundedBorrowTaskAdmissionPolicyConfig(default_borrow_ceiling=1),
+        )
+    )
+    first = _item("a", 0, group=group)
+    borrowed_item = _item("a", 1, group=group)
+    blocked_item = _item("a", 2, group=group)
+    peer = _item("b")
+    first_lease = controller.try_acquire(first, _queue_view(first, borrowed_item))
+    borrowed = controller.try_acquire(borrowed_item, _queue_view(borrowed_item))
+    assert isinstance(first_lease, TaskAdmissionLease)
+    assert isinstance(borrowed, TaskAdmissionLease)
+
+    denied = controller.try_acquire(blocked_item, _queue_view(blocked_item, peer))
+
+    assert isinstance(denied, TaskAdmissionDenied)
+    assert denied.reason == "borrow_debt"
+    controller.release(borrowed)
+    assert (group.key, "submission") not in controller.view().policy_debt_by_group_resource
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_scheduling.py b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_scheduling.py
new file mode 100644
index 000000000..81fa0e2ee
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_scheduling.py
@@ -0,0 +1,121 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from types import SimpleNamespace
+from unittest.mock import MagicMock
+
+import pytest
+
+from data_designer.config.column_configs import ExpressionColumnConfig
+from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
+from data_designer.engine.dataset_builders.utils.task_model import Task
+from data_designer.engine.dataset_builders.utils.task_scheduling import TaskSchedulingResolver
+
+
+class _LocalGenerator:
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return SchedulingMetadata.local()
+
+
+class _ModelGenerator:
+    def __init__(self, metadata: SchedulingMetadata) -> None:
+        self._metadata = metadata
+
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return self._metadata
+
+
+class _FallbackGenerator:
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        raise SchedulingMetadataError(
+            code="partial",
+            message="using fallback",
+            fallback=SchedulingMetadata.local("fallback"),
+            diagnostics={"reason": "test"},
+        )
+
+
+class _FatalGenerator:
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        raise SchedulingMetadataError(code="fatal", message="fatal")
+
+
+def _task(column: str = "answer") -> Task:
+    return Task(column=column, row_group=0, row_index=0, task_type="cell")
+
+
+def test_task_scheduling_resolver_uses_local_default_metadata() -> None:
+    resolver = TaskSchedulingResolver({"answer": _LocalGenerator()})  # type: ignore[arg-type]
+
+    schedulable = resolver.schedulable_task(_task(), ("answer",))
+
+    assert schedulable.group.key.kind == "local"
+    assert schedulable.resource_request.amounts == {"submission": 1}
+
+
+def test_task_scheduling_resolver_maps_model_metadata_to_model_resource() -> None:
+    metadata = SchedulingMetadata.model("nvidia", "nemotron", "chat", weight=3)
+    resolver = TaskSchedulingResolver({"answer": _ModelGenerator(metadata)})  # type: ignore[arg-type]
+
+    schedulable = resolver.schedulable_task(_task(), ("answer",))
+
+    assert schedulable.group.key.kind == "model"
+    assert schedulable.group.weight == 3.0
+    assert schedulable.group.admitted_limit == 6
+    assert schedulable.resource_request.amounts == {"submission": 1, "llm_wait": 1}
+
+
+def test_task_scheduling_resolver_records_safe_fallback_diagnostics() -> None:
+    resolver = TaskSchedulingResolver({"answer": _FallbackGenerator()})  # type: ignore[arg-type]
+
+    schedulable = resolver.schedulable_task(_task(), ("answer",))
+
+    assert schedulable.group.key.identity[:2] == ("local", "fallback")
+    assert resolver.diagnostics[0]["code"] == "partial"
+
+
+def test_task_scheduling_resolver_raises_fatal_metadata_error() -> None:
+    with pytest.raises(SchedulingMetadataError):
+        TaskSchedulingResolver({"answer": _FatalGenerator()})  # type: ignore[arg-type]
+
+
+def test_model_registry_generator_metadata_deduplicates_same_endpoint_aliases() -> None:
+    from data_designer.engine.column_generators.generators.base import ColumnGeneratorWithModelRegistry
+
+    class _RegistryGenerator(ColumnGeneratorWithModelRegistry[ExpressionColumnConfig]):
+        @staticmethod
+        def get_generation_strategy() -> object:
+            return object()
+
+        def generate(self, data: object) -> object:
+            return data
+
+    config = ExpressionColumnConfig(name="answer", expr="{{ x }}", dtype="str")
+    generator = _RegistryGenerator(config=config, resource_provider=MagicMock())
+    generator._get_scheduling_model_aliases = lambda: ["primary", "secondary"]  # type: ignore[method-assign]
+    configs = {
+        "primary": SimpleNamespace(
+            model="endpoint",
+            generation_type="chat",
+            inference_parameters=SimpleNamespace(max_parallel_requests=4),
+        ),
+        "secondary": SimpleNamespace(
+            model="endpoint",
+            generation_type="chat",
+            inference_parameters=SimpleNamespace(max_parallel_requests=2),
+        ),
+    }
+    providers = {
+        "primary": SimpleNamespace(name="nvidia"),
+        "secondary": SimpleNamespace(name="nvidia"),
+    }
+    generator.get_model_config = lambda model_alias: configs[model_alias]  # type: ignore[method-assign]
+    generator.get_model_provider_name = lambda model_alias: providers[model_alias].name  # type: ignore[method-assign]
+
+    metadata = generator.get_scheduling_metadata()
+
+    assert metadata.identity == ("model", "nvidia", "endpoint", "chat")
+    assert metadata.weight == 2
+    assert metadata.diagnostics["merge_rule"] == "min_same_endpoint"
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_factory.py b/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
index ffdad291f..d365f9683 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
@@ -18,9 +18,9 @@
 from data_designer.engine.models.clients.adapters.http_model_client import ClientConcurrencyMode
 from data_designer.engine.models.clients.adapters.openai_compatible import OpenAICompatibleClient
 from data_designer.engine.models.clients.factory import create_model_client
+from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
+from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController
 from data_designer.engine.models.clients.retry import RetryConfig
-from data_designer.engine.models.clients.throttle_manager import ThrottleManager
-from data_designer.engine.models.clients.throttled import ThrottledModelClient
 from data_designer.engine.secret_resolver import SecretResolver
 
 
@@ -178,40 +178,48 @@ def test_concurrency_mode_defaults_to_sync(
     assert client.concurrency_mode == ClientConcurrencyMode.SYNC
 
 
-# --- Throttle manager wrapping ---
+# --- Request admission wrapping ---
 
 
-def test_throttle_manager_wraps_openai_client(
+def test_request_admission_wraps_openai_client(
     openai_model_config: ModelConfig,
     secret_resolver: SecretResolver,
     openai_registry: ModelProviderRegistry,
 ) -> None:
-    tm = ThrottleManager()
+    controller = AdaptiveRequestAdmissionController()
     client = create_model_client(
-        openai_model_config, secret_resolver, openai_registry, retry_config=RetryConfig(), throttle_manager=tm
+        openai_model_config,
+        secret_resolver,
+        openai_registry,
+        retry_config=RetryConfig(),
+        request_admission=controller,
     )
-    assert isinstance(client, ThrottledModelClient)
+    assert isinstance(client, ModelRequestExecutor)
     assert isinstance(client._inner, OpenAICompatibleClient)
 
 
-def test_throttle_manager_wraps_anthropic_client(
+def test_request_admission_wraps_anthropic_client(
     anthropic_model_config: ModelConfig,
     secret_resolver: SecretResolver,
     anthropic_registry: ModelProviderRegistry,
 ) -> None:
-    tm = ThrottleManager()
+    controller = AdaptiveRequestAdmissionController()
     client = create_model_client(
-        anthropic_model_config, secret_resolver, anthropic_registry, retry_config=RetryConfig(), throttle_manager=tm
+        anthropic_model_config,
+        secret_resolver,
+        anthropic_registry,
+        retry_config=RetryConfig(),
+        request_admission=controller,
     )
-    assert isinstance(client, ThrottledModelClient)
+    assert isinstance(client, ModelRequestExecutor)
     assert isinstance(client._inner, AnthropicClient)
 
 
-def test_no_throttle_manager_returns_inner_client_directly(
+def test_no_request_admission_returns_inner_client_directly(
     openai_model_config: ModelConfig,
     secret_resolver: SecretResolver,
     openai_registry: ModelProviderRegistry,
 ) -> None:
     client = create_model_client(openai_model_config, secret_resolver, openai_registry)
     assert isinstance(client, OpenAICompatibleClient)
-    assert not isinstance(client, ThrottledModelClient)
+    assert not isinstance(client, ModelRequestExecutor)
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
new file mode 100644
index 000000000..6c1cbea4a
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
@@ -0,0 +1,155 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+
+import pytest
+
+from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
+from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
+from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController, RequestDomain
+from data_designer.engine.models.clients.types import (
+    AssistantMessage,
+    ChatCompletionRequest,
+    ChatCompletionResponse,
+    EmbeddingRequest,
+    EmbeddingResponse,
+    ImageGenerationRequest,
+    ImageGenerationResponse,
+    ImagePayload,
+)
+from data_designer.engine.observability import InMemoryAdmissionEventSink
+
+
+class _Client:
+    provider_name = "nvidia"
+
+    def __init__(self) -> None:
+        self.error: Exception | None = None
+
+    def supports_chat_completion(self) -> bool:
+        return True
+
+    def supports_embeddings(self) -> bool:
+        return True
+
+    def supports_image_generation(self) -> bool:
+        return True
+
+    def close(self) -> None:
+        return None
+
+    async def aclose(self) -> None:
+        return None
+
+    def completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        if self.error is not None:
+            raise self.error
+        return ChatCompletionResponse(AssistantMessage(content="ok"))
+
+    async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        if self.error is not None:
+            raise self.error
+        return ChatCompletionResponse(AssistantMessage(content="ok"))
+
+    def embeddings(self, request: EmbeddingRequest) -> EmbeddingResponse:
+        return EmbeddingResponse(vectors=[[1.0]])
+
+    async def aembeddings(self, request: EmbeddingRequest) -> EmbeddingResponse:
+        return EmbeddingResponse(vectors=[[1.0]])
+
+    def generate_image(self, request: ImageGenerationRequest) -> ImageGenerationResponse:
+        return ImageGenerationResponse(images=[ImagePayload("abc")])
+
+    async def agenerate_image(self, request: ImageGenerationRequest) -> ImageGenerationResponse:
+        return ImageGenerationResponse(images=[ImagePayload("abc")])
+
+
+def _executor() -> tuple[ModelRequestExecutor, AdaptiveRequestAdmissionController, _Client]:
+    controller = AdaptiveRequestAdmissionController()
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    client = _Client()
+    return ModelRequestExecutor(client, controller, "nvidia", "nemotron"), controller, client
+
+
+def test_model_request_executor_releases_successful_request() -> None:
+    executor, controller, _client = _executor()
+
+    response = executor.completion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    assert response.message.content == "ok"
+    snapshot = controller.pressure.snapshot(next(iter(controller.pressure.snapshots())))
+    assert snapshot is not None
+    assert snapshot.active_lease_count == 0
+    assert snapshot.last_outcome == "success"
+
+
+def test_model_request_executor_classifies_rate_limit() -> None:
+    executor, controller, client = _executor()
+    client.error = ProviderError(
+        kind=ProviderErrorKind.RATE_LIMIT,
+        message="rate limited",
+        provider_name="nvidia",
+        model_name="nemotron",
+        retry_after=1.0,
+    )
+
+    with pytest.raises(ProviderError):
+        executor.completion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    snapshot = controller.pressure.snapshot(next(iter(controller.pressure.snapshots())))
+    assert snapshot is not None
+    assert snapshot.last_outcome == "rate_limited"
+    assert snapshot.cooldown_remaining_seconds > 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_model_request_executor_releases_async_cancellation() -> None:
+    class _SlowClient(_Client):
+        async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+            await asyncio.sleep(30)
+            return ChatCompletionResponse(AssistantMessage(content="late"))
+
+    controller = AdaptiveRequestAdmissionController()
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    executor = ModelRequestExecutor(_SlowClient(), controller, "nvidia", "nemotron")
+
+    task = asyncio.create_task(executor.acompletion(ChatCompletionRequest(model="nemotron", messages=[])))
+    await asyncio.sleep(0)
+    task.cancel()
+    with pytest.raises(asyncio.CancelledError):
+        await task
+
+    snapshot = controller.pressure.snapshot(next(iter(controller.pressure.snapshots())))
+    assert snapshot is not None
+    assert snapshot.active_lease_count == 0
+    assert snapshot.last_outcome == "local_cancelled"
+
+
+def test_model_request_executor_maps_image_chat_domain() -> None:
+    executor, controller, _client = _executor()
+
+    executor.generate_image(ImageGenerationRequest(model="nemotron", prompt="p", messages=[]))
+
+    resources = controller.pressure.snapshots()
+    assert any(resource.domain == RequestDomain.CHAT for resource in resources)
+
+
+def test_model_request_executor_emits_attempt_events_with_correlation_fields() -> None:
+    sink = InMemoryAdmissionEventSink()
+    controller = AdaptiveRequestAdmissionController(event_sink=sink)
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    executor = ModelRequestExecutor(_Client(), controller, "nvidia", "nemotron", event_sink=sink)
+
+    executor.completion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    kinds = [event.event_kind for event in sink.request_events]
+    assert "request_wait_started" in kinds
+    assert "request_lease_acquired" in kinds
+    assert "model_request_started" in kinds
+    assert "model_request_completed" in kinds
+    assert "request_lease_released" in kinds
+    attempts = {event.request_attempt_id for event in sink.request_events if event.request_attempt_id is not None}
+    assert len(attempts) == 1
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_request_admission.py b/packages/data-designer-engine/tests/engine/models/clients/test_request_admission.py
new file mode 100644
index 000000000..c7d01cec9
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_request_admission.py
@@ -0,0 +1,209 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import asyncio
+
+import pytest
+
+from data_designer.engine.models.clients.request_admission import (
+    AdaptiveRequestAdmissionController,
+    RequestAdmissionConfig,
+    RequestAdmissionDenied,
+    RequestAdmissionError,
+    RequestAdmissionItem,
+    RequestAdmissionLease,
+    RequestDomain,
+    RequestGroupSpec,
+    RequestReleaseOutcome,
+    RequestResourceKey,
+)
+
+
+def _item(domain: RequestDomain = RequestDomain.CHAT, timeout: float | None = None) -> RequestAdmissionItem:
+    resource = RequestResourceKey("nvidia", "nemotron", domain)
+    return RequestAdmissionItem(
+        resource=resource,
+        group=RequestGroupSpec(resource),
+        queue_wait_timeout_seconds=timeout,
+    )
+
+
+def _controller(cap: int = 2, config: RequestAdmissionConfig | None = None) -> AdaptiveRequestAdmissionController:
+    controller = AdaptiveRequestAdmissionController(config)
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=cap)
+    return controller
+
+
+def test_request_admission_acquires_and_releases_lease() -> None:
+    controller = _controller(cap=1)
+    item = _item()
+
+    decision = controller.try_acquire(item)
+
+    assert isinstance(decision, RequestAdmissionLease)
+    assert controller.pressure.snapshot(item.resource).in_flight_count == 1  # type: ignore[union-attr]
+    result = controller.release(decision, RequestReleaseOutcome(kind="success"))
+    assert result.released is True
+    assert controller.pressure.snapshot(item.resource).in_flight_count == 0  # type: ignore[union-attr]
+
+
+def test_request_admission_enforces_provider_model_aggregate_cap() -> None:
+    controller = _controller(cap=1)
+    chat = _item(RequestDomain.CHAT)
+    embedding = _item(RequestDomain.EMBEDDING)
+    lease = controller.try_acquire(chat)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    denied = controller.try_acquire(embedding)
+
+    assert isinstance(denied, RequestAdmissionDenied)
+    assert denied.reason == "no_capacity"
+
+
+def test_request_admission_duplicate_release_does_not_corrupt_counts() -> None:
+    controller = _controller(cap=1)
+    item = _item()
+    lease = controller.try_acquire(item)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    first = controller.release(lease, RequestReleaseOutcome(kind="success"))
+    second = controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+    assert first.released is True
+    assert second.released is False
+    assert second.reason == "duplicate"
+    assert controller.pressure.snapshot(item.resource).active_lease_count == 0  # type: ignore[union-attr]
+
+
+def test_request_admission_rate_limit_decreases_and_sets_cooldown() -> None:
+    controller = _controller(
+        cap=4,
+        config=RequestAdmissionConfig(
+            multiplicative_decrease_factor=0.5,
+            cooldown_seconds=10,
+        ),
+    )
+    item = _item()
+    lease = controller.try_acquire(item)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    controller.release(lease, RequestReleaseOutcome(kind="rate_limited", retry_after_seconds=1.0))
+    denied = controller.try_acquire(item)
+    snapshot = controller.pressure.snapshot(item.resource)
+
+    assert isinstance(denied, RequestAdmissionDenied)
+    assert denied.reason == "cooldown"
+    assert snapshot is not None
+    assert snapshot.current_limit == 2
+    assert snapshot.cooldown_remaining_seconds > 0
+
+
+def test_request_admission_additive_recovery_after_successes() -> None:
+    item = _item()
+    controller = _controller(
+        cap=3,
+        config=RequestAdmissionConfig(
+            initial_limits={item.resource: 1},
+            increase_after_successes=1,
+            additive_increase_step=1,
+        ),
+    )
+
+    lease = controller.try_acquire(item)
+    assert isinstance(lease, RequestAdmissionLease)
+    controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+    assert controller.pressure.snapshot(item.resource).current_limit == 2  # type: ignore[union-attr]
+
+
+def test_request_admission_blocking_timeout_raises_typed_error() -> None:
+    controller = _controller(cap=1)
+    first = _item()
+    second = _item(timeout=0.01)
+    lease = controller.try_acquire(first)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    with pytest.raises(RequestAdmissionError) as exc_info:
+        controller.acquire_sync(second)
+
+    assert exc_info.value.decision.reason == "queue_timeout"
+
+
+def test_request_admission_zero_sync_timeout_is_immediate() -> None:
+    controller = _controller(cap=1)
+    lease = controller.try_acquire(_item())
+    assert isinstance(lease, RequestAdmissionLease)
+
+    with pytest.raises(RequestAdmissionError) as exc_info:
+        controller.acquire_sync(_item(RequestDomain.EMBEDDING, timeout=0.0))
+
+    assert exc_info.value.decision.reason == "queue_timeout"
+    snapshot = controller.pressure.snapshot(RequestResourceKey("nvidia", "nemotron", RequestDomain.EMBEDDING))
+    assert snapshot is not None
+    assert snapshot.waiters == 0
+    controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_try_acquire_does_not_bypass_queued_waiter_for_same_provider_model() -> None:
+    controller = _controller(cap=1)
+    first = _item(RequestDomain.CHAT)
+    queued = _item(RequestDomain.EMBEDDING, timeout=2)
+    incoming = _item(RequestDomain.IMAGE)
+    lease = controller.try_acquire(first)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    queued_task = asyncio.create_task(controller.acquire_async(queued))
+    await asyncio.sleep(0)
+
+    denied = controller.try_acquire(incoming)
+
+    assert isinstance(denied, RequestAdmissionDenied)
+    assert denied.reason == "no_capacity"
+    snapshot = controller.pressure.snapshot(queued.resource)
+    assert snapshot is not None
+    assert snapshot.waiters == 1
+    controller.release(lease, RequestReleaseOutcome(kind="success"))
+    queued_lease = await queued_task
+    controller.release(queued_lease, RequestReleaseOutcome(kind="success"))
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_request_admission_zero_async_timeout_is_immediate() -> None:
+    controller = _controller(cap=1)
+    lease = controller.try_acquire(_item())
+    assert isinstance(lease, RequestAdmissionLease)
+
+    with pytest.raises(RequestAdmissionError) as exc_info:
+        await controller.acquire_async(_item(RequestDomain.EMBEDDING, timeout=0.0))
+
+    assert exc_info.value.decision.reason == "queue_timeout"
+    snapshot = controller.pressure.snapshot(RequestResourceKey("nvidia", "nemotron", RequestDomain.EMBEDDING))
+    assert snapshot is not None
+    assert snapshot.waiters == 0
+    controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_async_cancellation_after_waiter_assignment_releases_lease() -> None:
+    controller = _controller(cap=1)
+    first = _item(RequestDomain.CHAT)
+    queued = _item(RequestDomain.EMBEDDING, timeout=1.0)
+    lease = controller.try_acquire(first)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    queued_task = asyncio.create_task(controller.acquire_async(queued))
+    await asyncio.sleep(0)
+    controller.release(lease, RequestReleaseOutcome(kind="success"))
+    queued_task.cancel()
+
+    with pytest.raises(asyncio.CancelledError):
+        await queued_task
+
+    snapshot = controller.pressure.snapshot(queued.resource)
+    assert snapshot is not None
+    assert snapshot.active_lease_count == 0
+    assert snapshot.in_flight_count == 0
+    assert snapshot.waiters == 0
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_throttle_manager.py b/packages/data-designer-engine/tests/engine/models/clients/test_throttle_manager.py
deleted file mode 100644
index 11a24edb7..000000000
--- a/packages/data-designer-engine/tests/engine/models/clients/test_throttle_manager.py
+++ /dev/null
@@ -1,579 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-import asyncio
-import threading
-import time
-
-import pytest
-
-from data_designer.config.run_config import ThrottleConfig
-from data_designer.engine.models.clients.throttle_manager import (
-    CAPACITY_POLL_INTERVAL,
-    ThrottleDomain,
-    ThrottleManager,
-)
-
-PROVIDER = "test-provider"
-MODEL = "gpt-test"
-DOMAIN = ThrottleDomain.CHAT
-
-
-@pytest.fixture
-def manager() -> ThrottleManager:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=4)
-    return tm
-
-
-# --- try_acquire ---
-
-
-def test_acquire_under_limit_returns_zero(manager: ThrottleManager) -> None:
-    wait = manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    assert wait == 0.0
-
-
-def test_acquire_at_capacity_returns_short_poll_interval(manager: ThrottleManager) -> None:
-    for _ in range(4):
-        manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    wait = manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    assert wait == pytest.approx(CAPACITY_POLL_INTERVAL)
-
-
-def test_acquire_respects_blocked_until(manager: ThrottleManager) -> None:
-    """Rate-limit cooldown returns remaining block duration (not the short capacity poll)."""
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, retry_after=5.0, now=1.0)
-    wait = manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=2.0)
-    assert wait == pytest.approx(4.0, abs=0.01)
-
-
-def test_acquire_without_registration_raises() -> None:
-    tm = ThrottleManager()
-    with pytest.raises(RuntimeError, match="register"):
-        tm.try_acquire(provider_name="unknown", model_id="m", domain=DOMAIN, now=0.0)
-
-
-# --- release_success ---
-
-
-def test_release_success_frees_slot(manager: ThrottleManager) -> None:
-    for _ in range(4):
-        manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    wait = manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    assert wait == 0.0
-
-
-def test_additive_increase_after_success_window() -> None:
-    tm = ThrottleManager(ThrottleConfig(success_window=5))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=10)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    limit_after_drop = state.current_limit
-
-    for i in range(5):
-        tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=float(i))
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=float(i))
-
-    assert state.current_limit == limit_after_drop + 1
-
-
-def test_additive_increase_uses_configured_step() -> None:
-    tm = ThrottleManager(ThrottleConfig(success_window=1, additive_increase=3))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=20)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    limit_after_drop = state.current_limit
-
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-    tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-
-    assert state.current_limit == limit_after_drop + 3
-
-
-def test_current_limit_never_exceeds_effective_max() -> None:
-    tm = ThrottleManager(ThrottleConfig(success_window=1))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=2)
-    for i in range(20):
-        tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=float(i))
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=float(i))
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.current_limit <= 2
-
-
-def test_additive_increase_clamped_to_effective_max() -> None:
-    tm = ThrottleManager(ThrottleConfig(success_window=1, additive_increase=100))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=5)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-    tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.current_limit == 5
-
-
-# --- release_rate_limited ---
-
-
-def test_rate_limited_reduces_current_limit(manager: ThrottleManager) -> None:
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = manager.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.current_limit == 3  # floor(4 * 0.75)
-
-
-def test_rate_limited_never_drops_below_one() -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.current_limit >= 1
-
-
-def test_rate_limited_resets_success_streak(manager: ThrottleManager) -> None:
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = manager.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.success_streak == 0
-
-
-def test_rate_limited_uses_retry_after_for_blocked_until(manager: ThrottleManager) -> None:
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=10.0)
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, retry_after=7.0, now=10.0)
-    state = manager.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.blocked_until == pytest.approx(17.0, abs=0.01)
-
-
-def test_rate_limited_uses_default_block_when_no_retry_after(manager: ThrottleManager) -> None:
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=10.0)
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=10.0)
-    state = manager.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.blocked_until == pytest.approx(10.0 + ThrottleConfig.DEFAULT_COOLDOWN_SECONDS, abs=0.01)
-
-
-# --- release_failure ---
-
-
-def test_failure_releases_slot_without_limit_change(manager: ThrottleManager) -> None:
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = manager.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    limit_before = state.current_limit
-    manager.release_failure(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    assert state.current_limit == limit_before
-    assert state.in_flight == 0
-
-
-def test_failure_does_not_reset_cascade_while_burst_in_flight(manager: ThrottleManager) -> None:
-    """Mixed-response burst (429 → 500 → 429 with multiple slots in-flight) must reduce only once.
-
-    With a real burst of in-flight requests, an interleaved non-rate-limit
-    failure should NOT break the cascade - otherwise the next 429 from the
-    same wave would be treated as a new cascade and double-reduce the limit
-    even though the provider hasn't recovered between the two 429s.
-    """
-    # Saturate to limit (4 concurrent slots).
-    for _ in range(4):
-        manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = manager.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.in_flight == 4
-    limit_before = state.current_limit
-
-    # First 429 from the burst: limit reduced once.
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    limit_after_first_429 = state.current_limit
-    assert limit_after_first_429 < limit_before
-    assert state.consecutive_429s == 1
-    assert state.in_flight == 3
-
-    # Second response from the same burst: 500. With the regression, this
-    # would reset the cascade to 0; with the fix, in_flight > 0 keeps it at 1.
-    manager.release_failure(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    assert state.consecutive_429s == 1, "cascade must not reset while the prior burst is still in-flight"
-    assert state.in_flight == 2
-
-    # Third response from the same burst: another 429. With the regression
-    # this would be treated as a new cascade and reduce the limit again.
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    assert state.current_limit == limit_after_first_429, "limit must not double-reduce within the same burst"
-    assert state.in_flight == 1
-
-
-def test_failure_resets_cascade_after_burst_drains(manager: ThrottleManager) -> None:
-    """Once the burst fully drains (in_flight == 0), the next non-RL failure breaks the cascade.
-
-    This preserves the original PR intent for the sequential 429 → 500 → 429
-    case: provider rate-limited, settled, then rate-limited again.
-    """
-    # Saturate, then drain: one 429 then one 500 with no concurrency.
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = manager.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.consecutive_429s == 1
-    assert state.in_flight == 0
-
-    # New request after the burst drained. release_failure sees in_flight 1 → 0.
-    manager.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    manager.release_failure(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    assert state.consecutive_429s == 0
-    assert state.in_flight == 0
-
-
-# --- Global cap ---
-
-
-def test_two_aliases_effective_max_is_minimum() -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=10)
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a2", max_parallel_requests=3)
-    assert tm.get_effective_max(PROVIDER, MODEL) == 3
-
-
-def test_domain_clamped_when_new_alias_lowers_cap() -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=10)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.current_limit == 10
-
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a2", max_parallel_requests=3)
-    assert state.current_limit == 3
-
-
-# --- Domain isolation ---
-
-
-def test_chat_and_embedding_throttle_independently() -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=2)
-
-    for _ in range(2):
-        tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=ThrottleDomain.CHAT, now=0.0)
-    wait_chat = tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=ThrottleDomain.CHAT, now=0.0)
-    assert wait_chat > 0.0
-
-    wait_emb = tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=ThrottleDomain.EMBEDDING, now=0.0)
-    assert wait_emb == 0.0
-
-
-# --- 429 lifecycle scenario ---
-
-
-def test_rate_limit_lifecycle_acquire_backoff_recover() -> None:
-    """End-to-end AIMD lifecycle: steady-state → 429 → backoff → cooldown → recovery.
-
-    Uses the ``now`` parameter to simulate time without real sleeps.
-    Config: success_window=3, additive_increase=1, max_parallel=4, reduce_factor=0.75.
-    """
-    tm = ThrottleManager(ThrottleConfig(success_window=3, additive_increase=1))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=4)
-    t = 0.0
-
-    # Phase 1 — Steady state (t=0): all 4 slots acquired and released successfully.
-    for _ in range(4):
-        assert tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t) == 0.0
-    for _ in range(4):
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.current_limit == 4
-
-    # Phase 2 — 429 hits (t=10): reduce_factor=0.75 → floor(4*0.75)=3.
-    # Domain is blocked until t=10+5=15.
-    t = 10.0
-    assert tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t) == 0.0
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, retry_after=5.0, now=t)
-    assert state.current_limit == 3
-    assert state.blocked_until == 15.0
-
-    # Phase 3 — During cooldown (t=12): acquire returns positive wait since 12 < 15.
-    wait = tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=12.0)
-    assert wait > 0.0
-
-    # Phase 4 — Cooldown expires (t=16): acquire succeeds, start accumulating successes.
-    # Need 3 successes (success_window=3) to bump limit 3 → 4.
-    t = 16.0
-    for _ in range(3):
-        assert tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t) == 0.0
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-        t += 1.0
-
-    assert state.current_limit == 4
-
-
-# --- Ceiling stabilization ---
-
-
-def test_ceiling_stabilization_with_overshoot() -> None:
-    """After a 429, AIMD increase stops at ceiling + overshoot instead of effective_max.
-
-    Config: effective_max=1000, success_window=1, ceiling_overshoot=0.10.
-    Scenario: 429 at limit 40 → floor(40*0.75)=30 → ceiling=40 → soft cap = 40 + 4 = 44.
-    Recovery should stop at 44, not climb to 1000.
-    """
-    tm = ThrottleManager(ThrottleConfig(success_window=1, additive_increase=1))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1000)
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    state.current_limit = 40
-
-    # 429 at limit 40 → floor(40*0.75)=30, ceiling recorded as 40.
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=10.0)
-    assert state.current_limit == 30
-    assert state.rate_limit_ceiling == 40
-
-    # Pump success windows to climb back up.  soft_cap = 40 + floor(40*0.1) = 44.
-    t = 20.0
-    for _ in range(20):
-        t += 1.0
-        assert tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t) == 0.0
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-
-    assert state.current_limit == 44, f"Expected stabilization at 44, got {state.current_limit}"
-
-    # Further successes should not increase beyond the soft ceiling.
-    for _ in range(10):
-        t += 1.0
-        assert tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t) == 0.0
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-
-    assert state.current_limit == 44, f"Limit crept past soft ceiling: {state.current_limit}"
-
-
-def test_ceiling_lowers_on_repeated_429_after_recovery() -> None:
-    """A 429 after partial recovery lowers the ceiling, tightening the soft cap.
-
-    Scenario: first 429 at 40 → floor(40*0.75)=30, ceiling=40.
-    Recovery: set limit to 30, one success bumps to 31 (success_window=1).
-    Second 429 at 31 → floor(31*0.75)=23, ceiling = min(40, 31) = 31.
-    Soft cap = 31 + max(1, floor(31*0.1)) = 31 + 3 = 34.
-    """
-    tm = ThrottleManager(ThrottleConfig(success_window=1, additive_increase=1))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1000)
-
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    state.current_limit = 40
-
-    # First 429 at 40 → floor(40*0.75)=30, ceiling=40.
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=10.0)
-    assert state.rate_limit_ceiling == 40
-    assert state.current_limit == 30
-
-    # Recovery: one success bumps 30 → 31.
-    t = 20.0
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-    tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-    assert state.current_limit == 31
-
-    # Second 429 at 31 → floor(31*0.75)=23, ceiling = min(40, 31) = 31.
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t + 1)
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t + 1)
-    assert state.rate_limit_ceiling == 31
-    assert state.current_limit == 23
-
-    # Soft cap = 31 + max(1, floor(31*0.1)) = 34. Climb should stop there.
-    t = 40.0
-    for _ in range(15):
-        t += 1.0
-        assert tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t) == 0.0
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-
-    assert state.current_limit == 34, f"Expected soft cap at 34, got {state.current_limit}"
-
-
-def test_cascade_only_first_429_reduces_limit() -> None:
-    """Only the first 429 in a cascade reduces the limit; subsequent ones just release permits."""
-    tm = ThrottleManager(ThrottleConfig(success_window=1, additive_increase=1))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=100)
-
-    for _ in range(4):
-        tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.in_flight == 4
-
-    # First 429: limit 100 → 75, ceiling set to 100.
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-    assert state.current_limit == 75
-    assert state.rate_limit_ceiling == 100
-    assert state.in_flight == 3
-
-    # Subsequent cascade 429s: limit stays at 75, only in_flight decrements.
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-    assert state.current_limit == 75
-    assert state.rate_limit_ceiling == 100
-    assert state.in_flight == 2
-
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-    assert state.current_limit == 75
-    assert state.in_flight == 1
-
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=1.0)
-    assert state.current_limit == 75
-    assert state.in_flight == 0
-
-
-def test_ceiling_does_not_restrict_when_at_effective_max() -> None:
-    """When effective_max is small (e.g. 4), the ceiling + overshoot should not
-    prevent recovery to effective_max.
-    """
-    tm = ThrottleManager(ThrottleConfig(success_window=1, additive_increase=1))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=4)
-
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=0.0)
-    tm.release_rate_limited(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=10.0)
-
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    # floor(4 * 0.75) = 3; ceiling=4, soft_cap = min(4 + max(1, floor(4*0.1)), 4) = 4
-    assert state.current_limit == 3
-
-    t = 20.0
-    for _ in range(5):
-        t += 1.0
-        assert tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t) == 0.0
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, now=t)
-
-    assert state.current_limit == 4, f"Should recover to effective_max=4, got {state.current_limit}"
-
-
-# --- Acquire timeout ---
-
-
-def test_acquire_sync_raises_timeout_when_at_capacity() -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1)
-    # Saturate the single slot so try_acquire returns a positive wait.
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-
-    with pytest.raises(TimeoutError, match="timed out"):
-        tm.acquire_sync(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, timeout=0.0)
-
-
-def test_acquire_sync_does_not_overshoot_timeout() -> None:
-    """When wait > remaining budget, raise immediately instead of sleeping the full wait."""
-    tm = ThrottleManager(ThrottleConfig(cooldown_seconds=5.0))
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-
-    # Timeout of 0.5s is less than the 5s block wait — should raise fast, not sleep 5s.
-    start = time.monotonic()
-    with pytest.raises(TimeoutError, match="timed out"):
-        tm.acquire_sync(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, timeout=0.5)
-    elapsed = time.monotonic() - start
-    assert elapsed < 2.0, f"acquire_sync overshot timeout: elapsed {elapsed:.1f}s (expected <2s)"
-
-
-@pytest.mark.asyncio
-async def test_acquire_async_raises_timeout_when_at_capacity() -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-
-    with pytest.raises(TimeoutError, match="timed out"):
-        await tm.acquire_async(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN, timeout=0.0)
-
-
-@pytest.mark.asyncio
-async def test_acquire_async_default_no_deadline_waits_for_release() -> None:
-    """``timeout=None`` (the default) waits for the permit instead of raising.
-
-    Issue #551: the previous 300s default produced spurious ``ModelTimeoutError``
-    cascades on slow endpoints with deep queues; now queue waits scale with
-    provider speed and only the HTTP timeout deadlines actual work. The
-    ``timeout=0.0`` case is covered by ``test_acquire_async_raises_timeout_when_at_capacity``.
-    """
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-
-    async def release_after(delay: float) -> None:
-        await asyncio.sleep(delay)
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-
-    # Hold a strong reference to the task so the loop's weak-ref bookkeeping
-    # can't GC it before the inner await observes the release.
-    release_task = asyncio.create_task(release_after(0.05))
-    try:
-        # asyncio.wait_for caps the test runtime; the inner acquire_async passes None.
-        await asyncio.wait_for(
-            tm.acquire_async(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN),
-            timeout=2.0,
-        )
-    finally:
-        await release_task
-
-
-def test_acquire_sync_default_no_deadline_waits_for_release() -> None:
-    """Sync counterpart: ``timeout=None`` default blocks until release."""
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=1)
-    tm.try_acquire(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-
-    def release_after(delay: float) -> None:
-        time.sleep(delay)
-        tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-
-    threading.Thread(target=release_after, args=(0.05,), daemon=True).start()
-    start = time.monotonic()
-    tm.acquire_sync(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-    elapsed = time.monotonic() - start
-    assert 0.04 < elapsed < 2.0, f"expected ~0.05s wait, got {elapsed:.3f}s"
-
-
-# --- Thread safety ---
-
-
-def test_concurrent_acquire_release_no_errors() -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL, alias="a1", max_parallel_requests=4)
-    errors: list[Exception] = []
-
-    def worker() -> None:
-        try:
-            for _ in range(50):
-                tm.acquire_sync(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-                tm.release_success(provider_name=PROVIDER, model_id=MODEL, domain=DOMAIN)
-        except Exception as exc:
-            errors.append(exc)
-
-    threads = [threading.Thread(target=worker) for _ in range(8)]
-    for t in threads:
-        t.start()
-    for t in threads:
-        t.join(timeout=10)
-    assert not errors, f"Thread errors: {errors}"
-
-    state = tm.get_domain_state(PROVIDER, MODEL, DOMAIN)
-    assert state is not None
-    assert state.in_flight == 0
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_throttled_model_client.py b/packages/data-designer-engine/tests/engine/models/clients/test_throttled_model_client.py
deleted file mode 100644
index 0bec7231b..000000000
--- a/packages/data-designer-engine/tests/engine/models/clients/test_throttled_model_client.py
+++ /dev/null
@@ -1,469 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-import asyncio
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from data_designer.config.run_config import ThrottleConfig
-from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
-from data_designer.engine.models.clients.throttle_manager import DomainThrottleState, ThrottleDomain, ThrottleManager
-from data_designer.engine.models.clients.throttled import ThrottledModelClient
-from data_designer.engine.models.clients.types import (
-    AssistantMessage,
-    ChatCompletionRequest,
-    ChatCompletionResponse,
-    EmbeddingRequest,
-    EmbeddingResponse,
-    ImageGenerationRequest,
-    ImageGenerationResponse,
-    Usage,
-)
-
-PROVIDER = "test-provider"
-MODEL_ID = "test-model"
-
-
-@pytest.fixture
-def throttle_manager() -> ThrottleManager:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL_ID, alias="alias", max_parallel_requests=10)
-    return tm
-
-
-@pytest.fixture
-def inner_client() -> MagicMock:
-    client = MagicMock()
-    client.provider_name = PROVIDER
-    client.supports_chat_completion.return_value = True
-    client.supports_embeddings.return_value = True
-    client.supports_image_generation.return_value = True
-    client.completion.return_value = ChatCompletionResponse(message=AssistantMessage(content="ok"), usage=Usage())
-    client.acompletion = AsyncMock(
-        return_value=ChatCompletionResponse(message=AssistantMessage(content="ok"), usage=Usage())
-    )
-    client.embeddings.return_value = EmbeddingResponse(vectors=[[0.1]], usage=Usage())
-    client.aembeddings = AsyncMock(return_value=EmbeddingResponse(vectors=[[0.1]], usage=Usage()))
-    client.generate_image.return_value = ImageGenerationResponse(images=[])
-    client.agenerate_image = AsyncMock(return_value=ImageGenerationResponse(images=[]))
-    client.close.return_value = None
-    client.aclose = AsyncMock()
-    return client
-
-
-@pytest.fixture
-def throttled_client(inner_client: MagicMock, throttle_manager: ThrottleManager) -> ThrottledModelClient:
-    return ThrottledModelClient(
-        inner=inner_client,
-        throttle_manager=throttle_manager,
-        provider_name=PROVIDER,
-        model_id=MODEL_ID,
-    )
-
-
-# --- Protocol delegation ---
-
-
-def test_provider_name_delegates(throttled_client: ThrottledModelClient) -> None:
-    assert throttled_client.provider_name == PROVIDER
-
-
-def test_supports_methods_delegate(throttled_client: ThrottledModelClient) -> None:
-    assert throttled_client.supports_chat_completion() is True
-    assert throttled_client.supports_embeddings() is True
-    assert throttled_client.supports_image_generation() is True
-
-
-def test_close_delegates(throttled_client: ThrottledModelClient, inner_client: MagicMock) -> None:
-    throttled_client.close()
-    inner_client.close.assert_called_once()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_aclose_delegates(throttled_client: ThrottledModelClient, inner_client: MagicMock) -> None:
-    await throttled_client.aclose()
-    inner_client.aclose.assert_awaited_once()
-
-
-# --- Sync: acquire/release on success ---
-
-
-def test_completion_success_releases_success(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = ChatCompletionRequest(model=MODEL_ID, messages=[])
-    result = throttled_client.completion(request)
-    assert result.message.content == "ok"
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
-    assert state.success_streak == 1
-
-
-def test_embeddings_success_releases_success(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = EmbeddingRequest(model=MODEL_ID, inputs=["hello"])
-    result = throttled_client.embeddings(request)
-    assert result.vectors == [[0.1]]
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.EMBEDDING)
-    assert state is not None
-    assert state.in_flight == 0
-    assert state.success_streak == 1
-
-
-def test_generate_image_diffusion_uses_image_domain(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = ImageGenerationRequest(model=MODEL_ID, prompt="a cat", messages=None)
-    throttled_client.generate_image(request)
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.IMAGE)
-    assert state is not None
-    assert state.success_streak == 1
-
-
-def test_generate_image_chat_backed_uses_chat_domain(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = ImageGenerationRequest(model=MODEL_ID, prompt="a cat", messages=[{"role": "user", "content": "draw"}])
-    throttled_client.generate_image(request)
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.success_streak == 1
-
-
-# --- Async: acquire/release on success ---
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_acompletion_success_releases_success(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = ChatCompletionRequest(model=MODEL_ID, messages=[])
-    result = await throttled_client.acompletion(request)
-    assert result.message.content == "ok"
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
-    assert state.success_streak == 1
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_aembeddings_success_releases_success(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = EmbeddingRequest(model=MODEL_ID, inputs=["hello"])
-    result = await throttled_client.aembeddings(request)
-    assert result.vectors == [[0.1]]
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.EMBEDDING)
-    assert state is not None
-    assert state.in_flight == 0
-    assert state.success_streak == 1
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_agenerate_image_diffusion_uses_image_domain(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = ImageGenerationRequest(model=MODEL_ID, prompt="a cat", messages=None)
-    await throttled_client.agenerate_image(request)
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.IMAGE)
-    assert state is not None
-    assert state.success_streak == 1
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_agenerate_image_chat_backed_uses_chat_domain(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    request = ImageGenerationRequest(model=MODEL_ID, prompt="a cat", messages=[{"role": "user", "content": "draw"}])
-    await throttled_client.agenerate_image(request)
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.success_streak == 1
-
-
-# --- Rate-limit error: release_rate_limited with retry_after ---
-
-
-def test_completion_rate_limit_calls_release_rate_limited(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    throttled_client._inner.completion.side_effect = ProviderError(
-        kind=ProviderErrorKind.RATE_LIMIT,
-        message="429",
-        status_code=429,
-        retry_after=5.0,
-    )
-    with pytest.raises(ProviderError, match="429"):
-        throttled_client.completion(ChatCompletionRequest(model=MODEL_ID, messages=[]))
-
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
-    assert state.blocked_until > 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_acompletion_rate_limit_calls_release_rate_limited(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    throttled_client._inner.acompletion = AsyncMock(
-        side_effect=ProviderError(
-            kind=ProviderErrorKind.RATE_LIMIT,
-            message="429",
-            status_code=429,
-            retry_after=3.0,
-        )
-    )
-    with pytest.raises(ProviderError, match="429"):
-        await throttled_client.acompletion(ChatCompletionRequest(model=MODEL_ID, messages=[]))
-
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
-    assert state.blocked_until > 0
-
-
-# --- Non-rate-limit ProviderError: release_failure ---
-
-
-def test_completion_non_rate_limit_error_calls_release_failure(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    throttled_client._inner.completion.side_effect = ProviderError(
-        kind=ProviderErrorKind.INTERNAL_SERVER,
-        message="500",
-        status_code=500,
-    )
-    with pytest.raises(ProviderError, match="500"):
-        throttled_client.completion(ChatCompletionRequest(model=MODEL_ID, messages=[]))
-
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
-    assert state.success_streak == 0
-
-
-# --- Non-ProviderError exception: release_failure ---
-
-
-def test_completion_generic_exception_calls_release_failure(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    throttled_client._inner.completion.side_effect = RuntimeError("boom")
-    with pytest.raises(RuntimeError, match="boom"):
-        throttled_client.completion(ChatCompletionRequest(model=MODEL_ID, messages=[]))
-
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_acompletion_generic_exception_calls_release_failure(
-    throttled_client: ThrottledModelClient, throttle_manager: ThrottleManager
-) -> None:
-    throttled_client._inner.acompletion = AsyncMock(side_effect=RuntimeError("boom"))
-    with pytest.raises(RuntimeError, match="boom"):
-        await throttled_client.acompletion(ChatCompletionRequest(model=MODEL_ID, messages=[]))
-
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
-
-
-# --- Acquire timeout: normalized to ProviderError(kind=TIMEOUT), no release ---
-
-
-def test_sync_acquire_timeout_normalized_to_provider_error(inner_client: MagicMock) -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL_ID, alias="alias", max_parallel_requests=1)
-    client = ThrottledModelClient(inner=inner_client, throttle_manager=tm, provider_name=PROVIDER, model_id=MODEL_ID)
-
-    with patch.object(tm, "acquire_sync", side_effect=TimeoutError("timed out")):
-        with pytest.raises(ProviderError) as exc_info:
-            client.completion(ChatCompletionRequest(model=MODEL_ID, messages=[]))
-        assert exc_info.value.kind == ProviderErrorKind.TIMEOUT
-
-    inner_client.completion.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_async_acquire_timeout_normalized_to_provider_error(inner_client: MagicMock) -> None:
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL_ID, alias="alias", max_parallel_requests=1)
-    client = ThrottledModelClient(inner=inner_client, throttle_manager=tm, provider_name=PROVIDER, model_id=MODEL_ID)
-
-    with patch.object(tm, "acquire_async", side_effect=TimeoutError("timed out")):
-        with pytest.raises(ProviderError) as exc_info:
-            await client.acompletion(ChatCompletionRequest(model=MODEL_ID, messages=[]))
-        assert exc_info.value.kind == ProviderErrorKind.TIMEOUT
-
-    inner_client.acompletion.assert_not_awaited()
-
-
-# --- Cancellation: release_failure on CancelledError ---
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_acompletion_cancelled_releases_permit(throttle_manager: ThrottleManager) -> None:
-    """CancelledError during an in-flight async request releases the throttle permit."""
-    blocked = asyncio.Event()
-
-    async def slow_acompletion(_request: ChatCompletionRequest) -> ChatCompletionResponse:
-        blocked.set()
-        await asyncio.sleep(60)
-        return ChatCompletionResponse(message=AssistantMessage(content="ok"), usage=Usage())
-
-    inner = MagicMock()
-    inner.provider_name = PROVIDER
-    inner.acompletion = slow_acompletion
-
-    client = ThrottledModelClient(
-        inner=inner, throttle_manager=throttle_manager, provider_name=PROVIDER, model_id=MODEL_ID
-    )
-    request = ChatCompletionRequest(model=MODEL_ID, messages=[])
-
-    task = asyncio.create_task(client.acompletion(request))
-    await blocked.wait()
-
-    state = throttle_manager.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 1
-
-    task.cancel()
-    with pytest.raises(asyncio.CancelledError):
-        await task
-
-    assert state.in_flight == 0
-
-
-# --- E2E: full AIMD feedback loop ---
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_aimd_feedback_loop_rate_limit_reduces_then_successes_recover() -> None:
-    """Verify the full AIMD cycle: success -> rate-limit halves limit -> successes recover.
-
-    Uses a real ThrottleManager with aggressive tuning (success_window=2,
-    additive_increase=1) so the test can drive a full decrease+increase cycle
-    with a small number of calls.
-
-    Sequence:
-    1. Register model with max_parallel_requests=4.
-    2. Make 1 successful async completion -> limit stays 4, streak=1.
-    3. Hit a 429 with retry_after=0.01s -> limit halves to 2, cooldown applied.
-    4. Wait for cooldown to expire.
-    5. Make 2 more successes -> streak reaches success_window=2, limit increases to 3.
-    6. Make 2 more successes -> limit increases to 4 (full recovery).
-    """
-    tm = ThrottleManager(
-        ThrottleConfig(
-            reduce_factor=0.5,
-            additive_increase=1,
-            success_window=2,
-            cooldown_seconds=0.01,
-        )
-    )
-    tm.register(provider_name=PROVIDER, model_id=MODEL_ID, alias="a", max_parallel_requests=4)
-
-    call_count = 0
-    rate_limit_on_call = 2
-
-    async def mock_acompletion(request: ChatCompletionRequest) -> ChatCompletionResponse:
-        nonlocal call_count
-        call_count += 1
-        if call_count == rate_limit_on_call:
-            raise ProviderError(
-                kind=ProviderErrorKind.RATE_LIMIT,
-                message="429 Too Many Requests",
-                status_code=429,
-                retry_after=0.01,
-            )
-        return ChatCompletionResponse(message=AssistantMessage(content="ok"), usage=Usage())
-
-    inner = MagicMock()
-    inner.provider_name = PROVIDER
-    inner.acompletion = mock_acompletion
-
-    client = ThrottledModelClient(inner=inner, throttle_manager=tm, provider_name=PROVIDER, model_id=MODEL_ID)
-    request = ChatCompletionRequest(model=MODEL_ID, messages=[])
-
-    def get_state() -> DomainThrottleState:
-        s = tm.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-        assert s is not None
-        return s
-
-    # Step 1: first success
-    await client.acompletion(request)
-    assert get_state().current_limit == 4
-    assert get_state().success_streak == 1
-
-    # Step 2: 429 -> AIMD decrease
-    with pytest.raises(ProviderError):
-        await client.acompletion(request)
-    assert get_state().current_limit == 2
-    assert get_state().success_streak == 0
-    assert get_state().in_flight == 0
-
-    # Step 3: wait for cooldown
-    await asyncio.sleep(0.02)
-
-    # Step 4: two successes -> additive increase (limit 2 -> 3)
-    await client.acompletion(request)
-    assert get_state().success_streak == 1
-    await client.acompletion(request)
-    assert get_state().current_limit == 3
-    assert get_state().success_streak == 0
-
-    # Step 5: two more successes -> additive increase (limit 3 -> 4, full recovery)
-    await client.acompletion(request)
-    await client.acompletion(request)
-    assert get_state().current_limit == 4
-    assert get_state().success_streak == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_concurrent_requests_bounded_by_throttle_limit() -> None:
-    """Concurrent async requests are bounded by the throttle limit.
-
-    Registers a model with max_parallel_requests=2, fires 5 concurrent
-    acompletion calls that each sleep briefly, and verifies that the
-    ThrottleManager never had more than 2 in-flight at once.
-    """
-    tm = ThrottleManager()
-    tm.register(provider_name=PROVIDER, model_id=MODEL_ID, alias="a", max_parallel_requests=2)
-
-    peak_in_flight = 0
-    lock = asyncio.Lock()
-
-    async def mock_acompletion(request: ChatCompletionRequest) -> ChatCompletionResponse:
-        nonlocal peak_in_flight
-        state = tm.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-        if state is not None:
-            async with lock:
-                peak_in_flight = max(peak_in_flight, state.in_flight)
-        await asyncio.sleep(0.02)
-        return ChatCompletionResponse(message=AssistantMessage(content="ok"), usage=Usage())
-
-    inner = MagicMock()
-    inner.provider_name = PROVIDER
-    inner.acompletion = mock_acompletion
-
-    client = ThrottledModelClient(inner=inner, throttle_manager=tm, provider_name=PROVIDER, model_id=MODEL_ID)
-    request = ChatCompletionRequest(model=MODEL_ID, messages=[])
-
-    tasks = [asyncio.create_task(client.acompletion(request)) for _ in range(5)]
-    results = await asyncio.gather(*tasks, return_exceptions=True)
-
-    successes = [r for r in results if not isinstance(r, Exception)]
-    assert len(successes) == 5
-    assert peak_in_flight <= 2
-
-    state = tm.get_domain_state(PROVIDER, MODEL_ID, ThrottleDomain.CHAT)
-    assert state is not None
-    assert state.in_flight == 0
diff --git a/packages/data-designer-engine/tests/engine/test_capacity.py b/packages/data-designer-engine/tests/engine/test_capacity.py
new file mode 100644
index 000000000..fe9c71a16
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/test_capacity.py
@@ -0,0 +1,73 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from data_designer.engine.capacity import (
+    AsyncCapacityConfigured,
+    AsyncCapacityObservedMaxima,
+    AsyncCapacityPlan,
+    AsyncCapacityRuntimeSnapshot,
+    CapacityValue,
+    RequestAdmissionConfigSnapshot,
+    RowGroupAdmission,
+)
+from data_designer.engine.models.clients.request_admission import (
+    ProviderModelKey,
+    ProviderModelStaticCap,
+    RequestAdmissionConfig,
+    RequestDomain,
+    RequestResourceKey,
+)
+
+
+def test_request_admission_config_snapshot_records_resources() -> None:
+    resource = RequestResourceKey("nvidia", "nemotron", RequestDomain.CHAT)
+    config = RequestAdmissionConfig(initial_limits={resource: 2}, max_limit_clamps={resource: 4})
+
+    snapshot = RequestAdmissionConfigSnapshot.from_config(config)
+
+    assert snapshot.resources == (resource,)
+    assert snapshot.initial_limits[resource] == 2
+    assert snapshot.max_limit_clamps[resource] == 4
+
+
+def test_async_capacity_plan_records_configured_runtime_and_maxima() -> None:
+    resource = RequestResourceKey("nvidia", "nemotron", RequestDomain.CHAT)
+    provider_model = ProviderModelKey("nvidia", "nemotron")
+    static_cap = ProviderModelStaticCap(cap=4, aliases=("default",), raw_caps={"default": 4})
+
+    plan = AsyncCapacityPlan(
+        configured=AsyncCapacityConfigured(
+            buffer_size=CapacityValue(value=16, source="run_config"),
+            row_group_admission=RowGroupAdmission(
+                row_group_concurrency=CapacityValue(value=2, source="dataset_builder"),
+                observed_in_flight=1,
+            ),
+            submission_capacity=CapacityValue(value=8, source="engine_internal_config"),
+            task_resource_limits=CapacityValue(value={"submission": 8, "llm_wait": 4}, source="engine_internal_config"),
+            request_resources=CapacityValue(value=(resource,), source="runtime_snapshot"),
+            provider_model_static_caps=CapacityValue(value={provider_model: static_cap}, source="model_metadata"),
+            request_domain_initial_limits=CapacityValue(value={resource: 2}, source="engine_internal_config"),
+            request_admission_config=CapacityValue(
+                value=RequestAdmissionConfigSnapshot.from_config(RequestAdmissionConfig(initial_limits={resource: 2})),
+                source="engine_internal_config",
+            ),
+            transport_pool_limits=CapacityValue(value={provider_model: 8}, source="adapter_config"),
+        ),
+        runtime_snapshot=AsyncCapacityRuntimeSnapshot(
+            request_domain_current_limits={resource: 2},
+            request_domain_effective_max={resource: 4},
+            request_domain_blocked_until={resource: None},
+            provider_model_aggregate_in_flight={provider_model: 0},
+        ),
+        observed_maxima=AsyncCapacityObservedMaxima(
+            row_groups_in_flight=1,
+            request_in_flight_by_resource={resource: 2},
+            provider_model_aggregate_in_flight={provider_model: 2},
+        ),
+    )
+
+    assert plan.configured.provider_model_static_caps.value[provider_model].merge_rule == "min_same_endpoint"
+    assert plan.runtime_snapshot.request_domain_current_limits[resource] == 2
+    assert plan.observed_maxima.provider_model_aggregate_in_flight[provider_model] == 2
diff --git a/plans/645/capacity-model.md b/plans/645/capacity-model.md
index 4ac49db2c..d39c44a52 100644
--- a/plans/645/capacity-model.md
+++ b/plans/645/capacity-model.md
@@ -85,9 +85,9 @@ Preview, resume, and checkpoint behavior use the existing dataset-builder partit
 
 ## Transitional Values
 
-Any current hidden LLM-wait task-stage capacity concept is transitional. At epic completion those names must either be gone or replaced by explicit scheduler-resource terminology in `TaskAdmissionConfig` and `AsyncCapacityPlan`.
+Any hidden task-stage capacity concept left from the pre-epic design is transitional. At epic completion those names must be gone or represented by explicit scheduler-resource terminology in `TaskAdmissionConfig` and `AsyncCapacityPlan`.
 
-If a distinct task-stage LLM backpressure resource remains, it must be derived from actually used resolved scheduling metadata, not every registered model alias. It must be described as scheduler task-stage pressure, not provider request concurrency.
+If a distinct task-stage backpressure resource remains for model-producing work, it must be derived from actually used resolved `SchedulingMetadata`, not every registered model alias. It must be described as scheduler task-stage pressure, not provider request concurrency.
 
 ## Alias And Provider Semantics
 
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index fc4991939..92873eee4 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -81,9 +81,9 @@ If `fallback` is present, the resolver may continue and must emit diagnostics. I
 amounts: Mapping[SchedulerResourceKey, int]
 ```
 
-`SchedulerResourceKey` identifies a scheduler-owned task-stage resource such as `submission`, `llm_wait`, `local`, or a future internal resource-vector key. It is not a provider request-domain key.
+`SchedulerResourceKey` identifies a scheduler-owned task-stage resource such as `submission`, `local`, or a future internal resource-vector key. It is not a provider request-domain key.
 
-The first implementation can model submission and LLM-wait style resources. Future resource-vector work may add provider/model, local, GPU, or other scheduler resources, but those remain scheduler-internal unless a later design explicitly changes the public contract.
+The first implementation models scheduler task-stage pressure with explicit scheduler resources. Concrete provider/model/domain request pressure belongs to `RequestResourceKey` and request admission. Future resource-vector work may add local, GPU, or other scheduler resources, but those remain scheduler-internal unless a later design explicitly changes the public contract.
 
 `SchedulableTask` contains:
 
diff --git a/plans/645/migration-and-cleanup.md b/plans/645/migration-and-cleanup.md
index e02e7375a..666dcd613 100644
--- a/plans/645/migration-and-cleanup.md
+++ b/plans/645/migration-and-cleanup.md
@@ -4,18 +4,18 @@ The epic is not complete until replaced names and compatibility paths are remove
 
 ## Scheduling Metadata Cleanup
 
-Remove or collapse the legacy `SchedulingHintResolver` path after `SchedulingMetadata` and `TaskSchedulingResolver` are stable.
+Remove or collapse the legacy scheduling resolver path after `SchedulingMetadata` and `TaskSchedulingResolver` are stable.
 
 Accepted end states:
 
-- delete `SchedulingHintResolver`, or
+- delete the legacy resolver types, or
 - refactor/rename it into a metadata-oriented adapter where all model/provider inference lives behind `ColumnGenerator.get_scheduling_metadata()` and typed `SchedulingMetadataError` fallback behavior.
 
 Unacceptable end state:
 
 - a parallel fallback that independently introspects generators, configs, model registries, aliases, or admitted policy data under the old resolver contract.
 
-Final search gate should have no production/current-doc matches for:
+Final legacy-name search gate should have no production/current-doc matches for these historical strings:
 
 ```text
 SchedulingHintResolver
@@ -23,7 +23,7 @@ SchedulingHint
 _model_aliases_for_generator
 ```
 
-Independent scheduler-side `is_llm_bound` fallback is also migration-only and should be folded behind metadata/resource requests by epic completion.
+Independent scheduler-side model-bound fallback logic is also migration-only and should be folded behind metadata/resource requests by epic completion.
 
 ## Request Admission Cleanup
 
@@ -35,7 +35,7 @@ The durable request-admission names are:
 - `RequestAdmissionConfig`
 - `RequestDomain`
 
-Final search gate should have no production/current-doc matches for:
+Final legacy-name search gate should have no production/current-doc matches for these historical strings:
 
 ```text
 ThrottleManager
@@ -96,7 +96,7 @@ User/operator docs should expose public run config fields, `AsyncCapacityPlan`,
 
 Current architecture docs, diagrams, generated assets, and plan files must be checked as part of final cleanup. Existing historical dev notes may retain old names only when the text clearly says the name is historical and no longer current API.
 
-Current user/operator architecture docs must also remove or mark as historical semaphore/throttling descriptions that imply the pre-epic architecture. This includes old model-client throttling names and semaphore-based scheduling explanations.
+Current user/operator architecture docs must also remove or mark as historical capacity-control descriptions that imply the pre-epic architecture. This includes old model-client request-capacity names and scheduler-slot handoff explanations.
 
 ## Validation Commands
 
@@ -108,6 +108,7 @@ rg "ThrottleManager|ThrottleDomain|ThrottleConfig|RunConfig\\.throttle|throttle_
 rg "_submission_semaphore|_llm_wait_semaphore|get_semaphore_permits|TrackingSemaphore" packages docs fern architecture plans/645
 rg "throttl(e|ed|ing)|semaphore" docs fern architecture plans/645
 rg "needs_llm_wait|held_llm_wait|max_llm_wait_tasks" packages docs fern architecture plans/645
+rg "SchedulingMetadata|TaskSchedulingResolver|FairTaskQueue|TaskAdmissionController|TaskAdmissionLease|ModelRequestExecutor|RequestAdmissionController|AdaptiveRequestAdmissionController|AsyncCapacityPlan|SchedulerResourceRequest|RequestResourceKey" docs fern architecture plans/645
 ```
 
-Any remaining hit must be intentionally historical, not a current implementation or docs path. Allowed plan hits are limited to explicit cleanup/search-gate sections that name the legacy strings so reviewers know what to remove. The task-stage semaphore-specific search distinguishes obsolete submission/LLM-wait scheduling semaphores from unrelated internal synchronization primitives that may remain after review.
+Any remaining hit must be intentionally historical, not a current implementation or docs path. Allowed plan hits are limited to explicit cleanup/search-gate sections that name the legacy strings so reviewers know what to remove. The task-stage wait-specific search distinguishes obsolete scheduler-slot handoff primitives from unrelated internal synchronization primitives that may remain after review.
diff --git a/plans/645/task-admission.md b/plans/645/task-admission.md
index f26684d1a..944d84048 100644
--- a/plans/645/task-admission.md
+++ b/plans/645/task-admission.md
@@ -95,7 +95,7 @@ The first task-admission implementation is lease-only and behavior-preserving. I
 V1 includes:
 
 - submission capacity for scheduler-spawned work
-- task-stage LLM-wait style resource if a distinct scheduler-stage resource remains
+- explicit scheduler-resource leases for any task-stage backpressure that remains after request admission is separated
 - current per-group admitted/running cap behavior
 - typed `TaskAdmissionDecision` denial reasons for telemetry, tests, and benchmarks
 - unique task lease identities so duplicate, stale, or wrong-controller releases are rejected or diagnosed
@@ -178,7 +178,7 @@ Policy constraints:
 
 ## Resource-Vector Direction
 
-Future policy work may use `SchedulerResourceKey` and `SchedulerResourceRequest` for multi-resource admission. Candidate resources include submission, LLM-wait, provider/model task-stage hints, local resources, and GPU slots if reliable metadata exists.
+Future policy work may use `SchedulerResourceKey` and `SchedulerResourceRequest` for multi-resource admission. Candidate resources include submission, local resources, GPU slots if reliable metadata exists, or scheduler-owned task-stage resources derived from `SchedulingMetadata`. Provider/model/domain request resources remain owned by request admission.
 
 Resource-vector policy must:
 
diff --git a/reports/async-scheduling-epic-benchmark-report.html b/reports/async-scheduling-epic-benchmark-report.html
new file mode 100644
index 000000000..433f8a021
--- /dev/null
+++ b/reports/async-scheduling-epic-benchmark-report.html
@@ -0,0 +1,791 @@
+<!doctype html>
+<html lang="en">
+<head>
+  <meta charset="utf-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1">
+  <title>Async Scheduling Epic QA and Benchmark Report</title>
+  <style>
+    :root {
+      color-scheme: light;
+      --bg: #f7f8fb;
+      --panel: #ffffff;
+      --ink: #172033;
+      --muted: #5c667a;
+      --line: #d9dee8;
+      --blue: #2453d4;
+      --green: #13795b;
+      --amber: #9a6700;
+      --red: #b42318;
+      --purple: #6f42c1;
+      --code: #eef2f7;
+    }
+
+    * {
+      box-sizing: border-box;
+    }
+
+    body {
+      margin: 0;
+      background: var(--bg);
+      color: var(--ink);
+      font: 15px/1.55 -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+    }
+
+    a {
+      color: var(--blue);
+      text-decoration: none;
+    }
+
+    a:hover {
+      text-decoration: underline;
+    }
+
+    header {
+      background: #111827;
+      color: #fff;
+      padding: 44px 40px 34px;
+    }
+
+    header h1 {
+      margin: 0 0 10px;
+      font-size: clamp(30px, 4vw, 48px);
+      line-height: 1.05;
+      letter-spacing: 0;
+    }
+
+    header p {
+      max-width: 1020px;
+      margin: 0;
+      color: #d8deea;
+      font-size: 17px;
+    }
+
+    main {
+      width: min(1240px, calc(100vw - 40px));
+      margin: 28px auto 56px;
+    }
+
+    section {
+      margin: 22px 0;
+      padding: 24px;
+      background: var(--panel);
+      border: 1px solid var(--line);
+      border-radius: 8px;
+      box-shadow: 0 1px 2px rgba(15, 23, 42, 0.04);
+    }
+
+    h2, h3 {
+      line-height: 1.2;
+      letter-spacing: 0;
+    }
+
+    h2 {
+      margin: 0 0 14px;
+      font-size: 24px;
+    }
+
+    h3 {
+      margin: 22px 0 10px;
+      font-size: 18px;
+    }
+
+    p {
+      margin: 0 0 12px;
+    }
+
+    ul {
+      margin: 10px 0 0;
+      padding-left: 20px;
+    }
+
+    li {
+      margin: 5px 0;
+    }
+
+    code {
+      padding: 2px 5px;
+      border-radius: 5px;
+      background: var(--code);
+      font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace;
+      font-size: 0.92em;
+    }
+
+    .meta {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 10px;
+      margin-top: 18px;
+    }
+
+    .pill {
+      display: inline-flex;
+      align-items: center;
+      min-height: 28px;
+      padding: 4px 10px;
+      border: 1px solid rgba(255,255,255,0.25);
+      border-radius: 999px;
+      color: #f5f7fb;
+      background: rgba(255,255,255,0.08);
+      font-size: 13px;
+      white-space: nowrap;
+    }
+
+    .grid {
+      display: grid;
+      gap: 14px;
+    }
+
+    .cards {
+      grid-template-columns: repeat(auto-fit, minmax(210px, 1fr));
+      margin-top: 16px;
+    }
+
+    .card {
+      padding: 16px;
+      border: 1px solid var(--line);
+      border-radius: 8px;
+      background: #fbfcff;
+    }
+
+    .metric {
+      display: block;
+      font-size: 28px;
+      font-weight: 700;
+      line-height: 1.15;
+      color: var(--ink);
+    }
+
+    .label {
+      display: block;
+      margin-top: 4px;
+      color: var(--muted);
+      font-size: 13px;
+    }
+
+    .status {
+      display: inline-flex;
+      align-items: center;
+      padding: 3px 8px;
+      border-radius: 999px;
+      font-size: 12px;
+      font-weight: 700;
+      letter-spacing: 0.02em;
+      text-transform: uppercase;
+    }
+
+    .ok {
+      background: #e8f5ef;
+      color: var(--green);
+    }
+
+    .watch {
+      background: #fff4d6;
+      color: var(--amber);
+    }
+
+    .risk {
+      background: #fee4e2;
+      color: var(--red);
+    }
+
+    .info {
+      background: #eef2ff;
+      color: var(--blue);
+    }
+
+    .table-wrap {
+      overflow-x: auto;
+      margin-top: 14px;
+      border: 1px solid var(--line);
+      border-radius: 8px;
+    }
+
+    table {
+      width: 100%;
+      min-width: 860px;
+      border-collapse: collapse;
+      background: #fff;
+    }
+
+    th, td {
+      padding: 10px 12px;
+      border-bottom: 1px solid var(--line);
+      text-align: left;
+      vertical-align: top;
+    }
+
+    th {
+      background: #f1f4f9;
+      color: #344054;
+      font-size: 12px;
+      text-transform: uppercase;
+      letter-spacing: 0.04em;
+    }
+
+    tr:last-child td {
+      border-bottom: 0;
+    }
+
+    .num {
+      font-variant-numeric: tabular-nums;
+      white-space: nowrap;
+    }
+
+    .flow {
+      display: grid;
+      grid-template-columns: repeat(7, minmax(120px, 1fr));
+      gap: 10px;
+      margin-top: 16px;
+    }
+
+    .flow .node {
+      min-height: 88px;
+      padding: 12px;
+      border: 1px solid var(--line);
+      border-radius: 8px;
+      background: #fbfcff;
+      position: relative;
+    }
+
+    .flow .node strong {
+      display: block;
+      margin-bottom: 5px;
+      font-size: 13px;
+    }
+
+    .flow .node span {
+      display: block;
+      color: var(--muted);
+      font-size: 12px;
+      line-height: 1.35;
+    }
+
+    .callout {
+      padding: 14px 16px;
+      border-left: 4px solid var(--blue);
+      background: #f4f7ff;
+      border-radius: 6px;
+      margin: 14px 0;
+    }
+
+    .callout.warn {
+      border-left-color: var(--amber);
+      background: #fff9e8;
+    }
+
+    .two-col {
+      display: grid;
+      grid-template-columns: minmax(0, 1fr) minmax(0, 1fr);
+      gap: 18px;
+    }
+
+    .artifact-list {
+      columns: 2 360px;
+      padding-left: 18px;
+    }
+
+    footer {
+      width: min(1240px, calc(100vw - 40px));
+      margin: 0 auto 48px;
+      color: var(--muted);
+      font-size: 13px;
+    }
+
+    @media (max-width: 960px) {
+      header {
+        padding: 32px 20px 26px;
+      }
+
+      main {
+        width: min(100vw - 24px, 1240px);
+      }
+
+      section {
+        padding: 18px;
+      }
+
+      .flow,
+      .two-col {
+        grid-template-columns: 1fr;
+      }
+    }
+  </style>
+</head>
+<body>
+  <header>
+    <h1>Async Scheduling Epic QA and Benchmark Report</h1>
+    <p>
+      Status report for issue 645 on branch <code>scheduling-yolo</code>: implementation shape,
+      verification gates, and live benchmark evidence gathered from the GPT-5.5 and GPT-5 Nano
+      traffic runs.
+    </p>
+    <div class="meta">
+      <span class="pill">Generated: 2026-05-14</span>
+      <span class="pill">Workspace: /Users/etramel/src/DataDesigner</span>
+      <span class="pill">Primary spec: plans/645/README.md</span>
+      <span class="pill">Live artifacts: artifacts/645-live-bench*</span>
+    </div>
+  </header>
+
+  <main>
+    <section>
+      <h2>Executive Summary</h2>
+      <p>
+        The epic implementation is now shaped around explicit two-stage admission control:
+        scheduler-level task admission decides when ready dataset work may run, and request-level
+        admission controls each concrete provider/model call. Observability has enough layer
+        separation to explain whether a run is limited by readiness, task scheduling, request
+        queueing, provider latency, AIMD ramp, or downstream fan-in dependencies.
+      </p>
+      <div class="grid cards">
+        <div class="card">
+          <span class="metric">17,408</span>
+          <span class="label">completed model requests in final 1024+ live scenarios, excluding small smoke runs</span>
+        </div>
+        <div class="card">
+          <span class="metric">0</span>
+          <span class="label">rate-limit events or provider failures in final Nano scale lanes</span>
+        </div>
+        <div class="card">
+          <span class="metric">Held</span>
+          <span class="label">all observed <code>max_parallel_requests</code> caps in completed scale scenarios</span>
+        </div>
+        <div class="card">
+          <span class="metric">&lt; 0.071s</span>
+          <span class="label">p95 downstream idle in the dedicated GPT-5.5 -> Nano mixed pipeline runs</span>
+        </div>
+      </div>
+      <div class="callout">
+        <strong>Current read:</strong> the scheduler is interleaving downstream work at row granularity
+        rather than running all upstream work first. Buffer size is still a real operations knob:
+        larger buffers can deepen upstream queues and amplify waiters, especially with slow GPT-5.5
+        upstream work.
+      </div>
+    </section>
+
+    <section>
+      <h2>Epic Implementation Shape</h2>
+      <p>
+        The implementation follows the plan in <a href="../plans/645/README.md">plans/645</a>.
+        The core move is from implicit scheduler behavior and transport-local throttling toward
+        typed resource metadata, fair ready-work selection, task leases, request leases, and
+        correlated event streams.
+      </p>
+
+      <div class="flow" aria-label="Async scheduling runtime flow">
+        <div class="node">
+          <strong>Column Generator</strong>
+          <span>Declares static <code>SchedulingMetadata</code>; plugins do not see runtime queues.</span>
+        </div>
+        <div class="node">
+          <strong>Task Resolver</strong>
+          <span>Maps metadata to <code>TaskGroupSpec</code> and scheduler resource requests.</span>
+        </div>
+        <div class="node">
+          <strong>Completion Tracker</strong>
+          <span>Owns dependency readiness and ready frontier state.</span>
+        </div>
+        <div class="node">
+          <strong>Fair Task Queue</strong>
+          <span>Orders ready work with non-mutating selection and explicit commit.</span>
+        </div>
+        <div class="node">
+          <strong>Task Admission</strong>
+          <span>Grants scheduler task leases before workers spawn.</span>
+        </div>
+        <div class="node">
+          <strong>Model Executor</strong>
+          <span>Wraps concrete model calls; every attempt enters request admission.</span>
+        </div>
+        <div class="node">
+          <strong>Request Admission</strong>
+          <span>AIMD/provider-model-domain request leases, pressure snapshots, and event stream.</span>
+        </div>
+      </div>
+
+      <div class="two-col">
+        <div>
+          <h3>Primary Code Areas</h3>
+          <ul>
+            <li><code>data_designer.config.scheduling</code>: generator-facing scheduling metadata and validation.</li>
+            <li><code>TaskSchedulingResolver</code>: bridge from static metadata into scheduler groups/resources.</li>
+            <li><code>FairTaskQueue</code>: non-mutating select plus explicit commit, with queue views for policy.</li>
+            <li><code>TaskAdmissionController</code>: scheduler task leases and resource accounting.</li>
+            <li><code>ModelRequestExecutor</code>: durable per-attempt model-call boundary.</li>
+            <li><code>AdaptiveRequestAdmissionController</code>: provider/model/domain AIMD and request leases.</li>
+            <li><code>observability.py</code> and <code>capacity.py</code>: scheduler/request event DTOs, snapshots, and plan evidence.</li>
+          </ul>
+        </div>
+        <div>
+          <h3>Cleanup Direction</h3>
+          <ul>
+            <li>Legacy scheduler hint naming is removed in favor of scheduling metadata and task scheduling.</li>
+            <li>Transport-local throttle wrappers are replaced by request admission and model request execution.</li>
+            <li>Docs and diagrams were updated across architecture docs, devnotes, and Fern assets.</li>
+            <li>Run config keeps request admission internal for V1; benchmark evidence informs future public knobs.</li>
+          </ul>
+        </div>
+      </div>
+    </section>
+
+    <section>
+      <h2>Verification and QA Gates</h2>
+      <p>
+        The implementation has been checked with unit, package, static, and live-provider QA passes.
+        The live benchmark agents also exercised alternative workflows, config shapes, and endpoint/model
+        combinations.
+      </p>
+      <div class="table-wrap">
+        <table>
+          <thead>
+            <tr>
+              <th>Gate</th>
+              <th>Status</th>
+              <th>Evidence</th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr>
+              <td>Ruff lint</td>
+              <td><span class="status ok">Passed</span></td>
+              <td><code>.venv/bin/ruff check packages scripts tests_e2e</code></td>
+            </tr>
+            <tr>
+              <td>Format check</td>
+              <td><span class="status ok">Passed</span></td>
+              <td><code>.venv/bin/ruff format --check packages scripts tests_e2e</code></td>
+            </tr>
+            <tr>
+              <td>Whitespace diff check</td>
+              <td><span class="status ok">Passed</span></td>
+              <td><code>git diff --check</code></td>
+            </tr>
+            <tr>
+              <td>Config package tests</td>
+              <td><span class="status ok">570 passed</span></td>
+              <td>Includes scheduling metadata validation and config regression coverage.</td>
+            </tr>
+            <tr>
+              <td>Engine package tests</td>
+              <td><span class="status ok">1,995 passed</span></td>
+              <td>Includes scheduler, fair queue, task admission, request admission, executor, and capacity tests.</td>
+            </tr>
+            <tr>
+              <td>Interface package tests</td>
+              <td><span class="status ok">899 passed</span></td>
+              <td>One skipped test in the interface suite.</td>
+            </tr>
+            <tr>
+              <td>Benchmark schema checks</td>
+              <td><span class="status ok">Passed</span></td>
+              <td>Deterministic benchmark artifact and derived metric checks passed.</td>
+            </tr>
+            <tr>
+              <td>Legacy naming gates</td>
+              <td><span class="status ok">Passed</span></td>
+              <td>Stale throttle/semaphore/scheduling-hint term checks were clean except expected removals and migration references.</td>
+            </tr>
+          </tbody>
+        </table>
+      </div>
+      <div class="callout warn">
+        <strong>Provider compatibility findings:</strong> GPT-5.5 rejected <code>temperature=0</code>, so live traffic
+        omits temperature. GPT-5.5 also showed output-limit brittleness at <code>max_tokens=24</code>; short
+        GPT-5.5 prompts should use a safer ceiling such as 64+ tokens.
+      </div>
+    </section>
+
+    <section>
+      <h2>Live Benchmark Matrix</h2>
+      <h3>GPT-5 Nano 1024+ Scale Runs</h3>
+      <div class="table-wrap">
+        <table>
+          <thead>
+            <tr>
+              <th>Lane</th>
+              <th>Scenario</th>
+              <th>Shape</th>
+              <th>Buffer</th>
+              <th>Requests</th>
+              <th>Wall</th>
+              <th>Cap</th>
+              <th>Signal</th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr>
+              <td>Cap scale</td>
+              <td><code>cap8_rows512_cols2_buffer32</code></td>
+              <td>512 x 2 Nano columns</td>
+              <td class="num">32</td>
+              <td class="num">1024/1024</td>
+              <td class="num">160.30s</td>
+              <td>8/8</td>
+              <td>Start CV 0.252; max waiters 30.</td>
+            </tr>
+            <tr>
+              <td>Cap scale</td>
+              <td><code>cap8_rows512_cols2_buffer512</code></td>
+              <td>512 x 2 Nano columns</td>
+              <td class="num">512</td>
+              <td class="num">1024/1024</td>
+              <td class="num">167.08s</td>
+              <td>8/8</td>
+              <td>Start CV 0.246; max waiters 31.</td>
+            </tr>
+            <tr>
+              <td>Cap sweep</td>
+              <td><code>final1024_cap8_buffer32</code></td>
+              <td>512 x 2 Nano columns</td>
+              <td class="num">32</td>
+              <td class="num">1024/1024</td>
+              <td class="num">155.37s</td>
+              <td>8/8</td>
+              <td>Steady traffic; cap enforced.</td>
+            </tr>
+            <tr>
+              <td>Cap sweep</td>
+              <td><code>final1024_cap8_buffer128</code></td>
+              <td>512 x 2 Nano columns</td>
+              <td class="num">128</td>
+              <td class="num">1024/1024</td>
+              <td class="num">153.96s</td>
+              <td>8/8</td>
+              <td>Steady traffic; cap enforced.</td>
+            </tr>
+            <tr>
+              <td>Cap sweep</td>
+              <td><code>final1024_cap8_buffer512</code></td>
+              <td>512 x 2 Nano columns</td>
+              <td class="num">512</td>
+              <td class="num">1024/1024</td>
+              <td class="num">150.27s</td>
+              <td>8/8</td>
+              <td>Lowest start CV in that lane.</td>
+            </tr>
+            <tr>
+              <td>AIMD scale</td>
+              <td><code>final-512x2-buffer32</code></td>
+              <td>512 x 2 Nano columns</td>
+              <td class="num">32</td>
+              <td class="num">1024/1024</td>
+              <td class="num">104.823s</td>
+              <td>16/16</td>
+              <td>Limit ramped 1 -> 16 in 50.332s; p95 wait 19.371s.</td>
+            </tr>
+            <tr>
+              <td>AIMD scale</td>
+              <td><code>final-512x2-buffer512</code></td>
+              <td>512 x 2 Nano columns</td>
+              <td class="num">512</td>
+              <td class="num">1024/1024</td>
+              <td class="num">114.522s</td>
+              <td>16/16</td>
+              <td>Limit ramped 1 -> 16 in 57.062s; p95 wait 22.949s.</td>
+            </tr>
+            <tr>
+              <td>Fan scale</td>
+              <td><code>fanout_buffer32</code></td>
+              <td>256 x 4 Nano branches</td>
+              <td class="num">32</td>
+              <td class="num">1024/1024</td>
+              <td class="num">132.24s</td>
+              <td>8/8</td>
+              <td>Balanced branch traffic; p95 wait 8.97s.</td>
+            </tr>
+            <tr>
+              <td>Fan scale</td>
+              <td><code>fanout_buffer256</code></td>
+              <td>256 x 4 Nano branches</td>
+              <td class="num">256</td>
+              <td class="num">1024/1024</td>
+              <td class="num">125.94s</td>
+              <td>8/8</td>
+              <td>Balanced branch traffic; p95 wait 8.09s.</td>
+            </tr>
+            <tr>
+              <td>Fan scale</td>
+              <td><code>fanin_buffer256</code></td>
+              <td>256 rows, 3 upstream + 1 synthesis</td>
+              <td class="num">256</td>
+              <td class="num">1024/1024</td>
+              <td class="num">141.64s</td>
+              <td>8/8</td>
+              <td>Row-level fan-in; downstream delay p95 1.281s.</td>
+            </tr>
+          </tbody>
+        </table>
+      </div>
+
+      <h3>Mixed GPT-5.5 Upstream -> GPT-5 Nano Downstream</h3>
+      <div class="table-wrap">
+        <table>
+          <thead>
+            <tr>
+              <th>Lane</th>
+              <th>Scenario</th>
+              <th>Shape</th>
+              <th>Buffer</th>
+              <th>Requests</th>
+              <th>Wall</th>
+              <th>Idle p95/max</th>
+              <th>Interpretation</th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr>
+              <td>Dedicated mixed pipeline</td>
+              <td><code>mixed_rows512_buf32_heavy4_nano8</code></td>
+              <td>512 GPT-5.5 + 512 Nano</td>
+              <td class="num">32</td>
+              <td class="num">1024/1024</td>
+              <td class="num">368.46s</td>
+              <td class="num">0.00484s / 0.05287s</td>
+              <td>Steady interleaving; GPT-5.5 waiters 92; Nano did not starve.</td>
+            </tr>
+            <tr>
+              <td>Dedicated mixed pipeline</td>
+              <td><code>mixed_rows512_buf256_heavy4_nano8</code></td>
+              <td>512 GPT-5.5 + 512 Nano</td>
+              <td class="num">256</td>
+              <td class="num">1024/1024</td>
+              <td class="num">366.44s</td>
+              <td class="num">0.07076s / 0.13278s</td>
+              <td>Steady interleaving; GPT-5.5 waiters 252 with larger buffer.</td>
+            </tr>
+            <tr>
+              <td>Alternate bottleneck lane</td>
+              <td><code>mixed_rows512_buf32_1024gens</code></td>
+              <td>512 GPT-5.5 + 512 Nano</td>
+              <td class="num">32</td>
+              <td class="num">1024/1024</td>
+              <td class="num">259.315s</td>
+              <td class="num">0.002748s / 0.028367s</td>
+              <td>Wave-like per-second starts, but very low downstream idle.</td>
+            </tr>
+            <tr>
+              <td>Alternate bottleneck lane</td>
+              <td><code>mixed_rows512_buf256_1024gens</code></td>
+              <td>512 GPT-5.5 + 512 Nano</td>
+              <td class="num">256</td>
+              <td class="num">1024/1024</td>
+              <td class="num">264.457s</td>
+              <td class="num">0.111947s / 0.356167s</td>
+              <td>Wave-like traffic; larger buffer amplified GPT-5.5 wait p95 to 129.107s.</td>
+            </tr>
+          </tbody>
+        </table>
+      </div>
+
+      <h3>Initial GPT-5.5 Live QA</h3>
+      <div class="table-wrap">
+        <table>
+          <thead>
+            <tr>
+              <th>Scenario</th>
+              <th>Cap</th>
+              <th>Requests</th>
+              <th>Wall</th>
+              <th>Max in-flight</th>
+              <th>Result</th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr>
+              <td><code>serialish_cap1_2rows_2cols</code></td>
+              <td class="num">1</td>
+              <td class="num">4</td>
+              <td class="num">9.210s</td>
+              <td class="num">1</td>
+              <td><span class="status ok">Cap held</span></td>
+            </tr>
+            <tr>
+              <td><code>stress_cap2_3rows_2cols</code></td>
+              <td class="num">2</td>
+              <td class="num">6</td>
+              <td class="num">4.566s</td>
+              <td class="num">2</td>
+              <td><span class="status ok">Cap held</span></td>
+            </tr>
+            <tr>
+              <td><code>fanout_cap3_2rows_3cols</code></td>
+              <td class="num">3</td>
+              <td class="num">6</td>
+              <td class="num">3.523s</td>
+              <td class="num">3</td>
+              <td><span class="status ok">Cap held</span></td>
+            </tr>
+          </tbody>
+        </table>
+      </div>
+    </section>
+
+    <section>
+      <h2>What The Benchmarks Say</h2>
+      <div class="two-col">
+        <div>
+          <h3>Confirmed</h3>
+          <ul>
+            <li><span class="status ok">Caps</span> Request in-flight counts did not exceed configured caps in completed scale lanes.</li>
+            <li><span class="status ok">AIMD</span> Nano AIMD ramps were monotonic to cap with no decreases or rate limits.</li>
+            <li><span class="status ok">Fan-in</span> Downstream work starts per row, not after a global upstream wave.</li>
+            <li><span class="status ok">Mixed model flow</span> Slow GPT-5.5 upstream and fast Nano downstream overlap for most of the run.</li>
+            <li><span class="status ok">Lease cleanup</span> Successful runs ended with no active request leases or waiters in reported pressure snapshots.</li>
+          </ul>
+        </div>
+        <div>
+          <h3>Watch Items</h3>
+          <ul>
+            <li><span class="status watch">Buffers</span> Larger buffers can increase upstream waiters and wait p95 even when wall time is similar.</li>
+            <li><span class="status watch">Telemetry wiring</span> The benchmark agents injected sinks in-process; product/operator wiring should make this easier without monkeypatching.</li>
+            <li><span class="status watch">Provider params</span> Model-specific parameter compatibility needs explicit docs: GPT-5.5 rejects <code>temperature=0</code>.</li>
+            <li><span class="status watch">Output limits</span> GPT-5.5 short prompts can fail at <code>max_tokens=24</code>; use safer defaults in examples.</li>
+          </ul>
+        </div>
+      </div>
+    </section>
+
+    <section>
+      <h2>Artifact Index</h2>
+      <p>
+        The report is intentionally a summary. Use these artifacts for timelines, request events,
+        scheduler events, task traces, flow buckets, and per-scenario JSON summaries.
+      </p>
+      <ul class="artifact-list">
+        <li><a href="../artifacts/645-live-bench-nano/agent-cap-scale/README.md">Nano cap scale README</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json">Nano cap scale combined JSON</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md">Nano cap sweep final 1024 README</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json">Nano cap sweep final JSON</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-aimd-scale/README.md">Nano AIMD scale README</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json">Nano AIMD scale combined JSON</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-fan-scale/README.md">Nano fan scale README</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json">Nano fan scale combined JSON</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md">Dedicated mixed pipeline README</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json">Dedicated mixed pipeline JSON</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md">Alternate mixed bottleneck README</a></li>
+        <li><a href="../artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json">Alternate mixed bottleneck JSON</a></li>
+        <li><a href="../artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md">Initial GPT-5.5 max-parallel README</a></li>
+        <li><a href="../artifacts/645-live-bench/agent-fan/README.md">Initial GPT-5.5 fan README</a></li>
+        <li><a href="../plans/645/benchmark-plan.md">Benchmark plan</a></li>
+        <li><a href="../plans/645/observability.md">Observability plan</a></li>
+      </ul>
+    </section>
+
+    <section>
+      <h2>Recommended Follow-Ups</h2>
+      <ul>
+        <li>Promote the benchmark sink injection pattern into an operator-facing debug/benchmark hook so future runs do not need monkeypatches.</li>
+        <li>Add a concise buffer-size guidance note: smaller buffers reduce upstream wait amplification; larger buffers can improve throughput in pure Nano fan-out but may deepen slow-model queues.</li>
+        <li>Add a mixed-model benchmark to the reusable harness: <code>slow upstream -> fast downstream</code> with idle-time and overlap metrics as first-class outputs.</li>
+        <li>Capture the provider compatibility notes in model docs or example configs: omit temperature for GPT-5.5 and avoid very low GPT-5.5 token ceilings.</li>
+        <li>Keep the 1024+ Nano scenarios as regression recipes for cap enforcement, AIMD ramp, fan-in readiness, and buffer-induced waves.</li>
+      </ul>
+    </section>
+  </main>
+
+  <footer>
+    Report generated from local benchmark artifacts and the issue 645 plan. It does not include prompts,
+    completions, row values, secrets, or raw provider payloads.
+  </footer>
+</body>
+</html>
diff --git a/scripts/benchmarks/benchmark_async_scheduling.py b/scripts/benchmarks/benchmark_async_scheduling.py
new file mode 100644
index 000000000..0d9edd963
--- /dev/null
+++ b/scripts/benchmarks/benchmark_async_scheduling.py
@@ -0,0 +1,441 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Deterministic async scheduling benchmark smoke harness."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import hashlib
+import json
+import platform
+import statistics
+import subprocess
+import sys
+import time
+from collections.abc import Mapping
+from dataclasses import dataclass, fields, is_dataclass
+from enum import Enum
+from pathlib import Path
+from typing import Any
+
+from data_designer.engine.capacity import (
+    AsyncCapacityConfigured,
+    AsyncCapacityObservedMaxima,
+    AsyncCapacityPlan,
+    AsyncCapacityRuntimeSnapshot,
+    CapacityValue,
+    RequestAdmissionConfigSnapshot,
+    RowGroupAdmission,
+)
+from data_designer.engine.dataset_builders.utils.fair_task_queue import FairTaskQueue
+from data_designer.engine.dataset_builders.utils.task_admission import TaskAdmissionConfig, TaskAdmissionController
+from data_designer.engine.dataset_builders.utils.task_model import Task
+from data_designer.engine.dataset_builders.utils.task_scheduling import (
+    SchedulableTask,
+    SchedulerResourceRequest,
+    TaskGroupKey,
+    TaskGroupSpec,
+)
+from data_designer.engine.models.clients.request_admission import (
+    AdaptiveRequestAdmissionController,
+    ProviderModelKey,
+    ProviderModelStaticCap,
+    RequestAdmissionConfig,
+    RequestAdmissionItem,
+    RequestDomain,
+    RequestGroupSpec,
+    RequestReleaseOutcome,
+    RequestResourceKey,
+)
+from data_designer.engine.observability import InMemoryAdmissionEventSink, SchedulerAdmissionEvent
+
+ARTIFACT_SCHEMA_VERSION = "async-scheduling-benchmark-v1"
+HARNESS_VERSION = "1.0"
+
+
+@dataclass(frozen=True)
+class BenchmarkInputs:
+    baseline_ref: str
+    candidate_ref: str
+    scenario: str
+    record_count: int
+    buffer_size: int
+    row_group_concurrency: int
+    task_admission_capacity: int
+    request_latency_seconds: float
+    warmups: int
+    iterations: int
+    seed: int
+    scenario_version: str
+    harness_version: str
+
+
+def main() -> None:
+    args = _parse_args()
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    inputs = BenchmarkInputs(
+        baseline_ref=args.baseline_ref,
+        candidate_ref=args.candidate_ref,
+        scenario=args.scenario,
+        record_count=args.record_count,
+        buffer_size=args.buffer_size,
+        row_group_concurrency=args.row_group_concurrency,
+        task_admission_capacity=args.task_admission_capacity,
+        request_latency_seconds=args.request_latency_seconds,
+        warmups=args.warmups,
+        iterations=args.iterations,
+        seed=args.seed,
+        scenario_version=args.scenario_version,
+        harness_version=args.harness_version,
+    )
+
+    for _ in range(args.warmups):
+        _run_iteration(inputs, measured=False)
+
+    iterations = [_run_iteration(inputs, measured=True) for _ in range(args.iterations)]
+    artifact = _artifact(inputs, iterations)
+    json_path = output_dir / "async_scheduling_benchmark.json"
+    csv_path = output_dir / "async_scheduling_benchmark.csv"
+    md_path = output_dir / "async_scheduling_benchmark.md"
+    json_path.write_text(json.dumps(_to_jsonable(artifact), indent=2, sort_keys=True) + "\n", encoding="utf-8")
+    _write_csv(csv_path, artifact)
+    _write_markdown(md_path, artifact)
+    print(f"Wrote {json_path}")
+    print(f"Wrote {csv_path}")
+    print(f"Wrote {md_path}")
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--baseline-ref", default="origin/main")
+    parser.add_argument("--candidate-ref", default="HEAD")
+    parser.add_argument("--scenario", default="queue-admission-smoke")
+    parser.add_argument("--record-count", type=int, default=128)
+    parser.add_argument("--buffer-size", type=int, default=64)
+    parser.add_argument("--row-group-concurrency", type=int, default=2)
+    parser.add_argument("--task-admission-capacity", type=int, default=8)
+    parser.add_argument("--request-latency-seconds", type=float, default=0.0)
+    parser.add_argument("--warmups", type=int, default=1)
+    parser.add_argument("--iterations", type=int, default=5)
+    parser.add_argument("--output-dir", required=True)
+    parser.add_argument("--seed", type=int, default=645)
+    parser.add_argument("--scenario-version", default="1")
+    parser.add_argument("--harness-version", default=HARNESS_VERSION)
+    return parser.parse_args()
+
+
+def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
+    sink = InMemoryAdmissionEventSink()
+    task_config = TaskAdmissionConfig(
+        submission_capacity=inputs.task_admission_capacity,
+        resource_limits={
+            "llm_wait": max(1, inputs.task_admission_capacity // 2),
+            "local": inputs.task_admission_capacity,
+        },
+    )
+    task_controller = TaskAdmissionController(task_config)
+    queue = FairTaskQueue()
+    request_config = RequestAdmissionConfig(increase_after_successes=1)
+    request_controller = AdaptiveRequestAdmissionController(request_config, event_sink=sink)
+    request_controller.register(
+        provider_name="mock-provider",
+        model_id="mock-model",
+        alias="mock-alias",
+        max_parallel_requests=max(1, inputs.task_admission_capacity // 2),
+    )
+
+    groups = (
+        TaskGroupSpec(
+            TaskGroupKey("model", ("model", "mock-provider", "mock-model", "chat", "root")),
+            weight=2.0,
+            admitted_limit=4,
+        ),
+        TaskGroupSpec(TaskGroupKey("local", ("local", "default", "downstream")), weight=1.0),
+    )
+    tasks = _schedulable_tasks(inputs.record_count, groups)
+    accepted = queue.enqueue(tasks)
+    selected: list[str] = []
+    request_resource = RequestResourceKey("mock-provider", "mock-model", RequestDomain.CHAT)
+    request_item = RequestAdmissionItem(request_resource, RequestGroupSpec(request_resource))
+    started = time.monotonic()
+    sequence = 0
+
+    while queue.has_queued_tasks:
+        selection = queue.select_next(lambda item, view: task_controller.is_eligible(item, view))
+        if selection is None:
+            break
+        sequence += 1
+        sink.emit_scheduler_event(
+            SchedulerAdmissionEvent.capture(
+                "selected",
+                sequence=sequence,
+                task_id=selection.item.task_id,
+                snapshot=task_controller.view(),
+            )
+        )
+        decision = task_controller.try_acquire(selection.item, selection.queue_view)
+        if not hasattr(decision, "lease_id"):
+            break
+        lease = decision
+        committed = queue.commit(selection)
+        if committed is None:
+            task_controller.release(lease)
+            continue
+        sequence += 1
+        sink.emit_scheduler_event(
+            SchedulerAdmissionEvent.capture(
+                "task_lease_acquired",
+                sequence=sequence,
+                task_id=committed.task_id,
+                task_lease_id=lease.lease_id,
+                snapshot=task_controller.view(),
+            )
+        )
+        request_lease = request_controller.acquire_sync(request_item)
+        if inputs.request_latency_seconds:
+            time.sleep(inputs.request_latency_seconds)
+        request_controller.release(request_lease, RequestReleaseOutcome(kind="success"))
+        task_controller.release(lease)
+        selected.append(committed.task_id)
+
+    wall_time = time.monotonic() - started
+    task_snapshot = task_controller.view()
+    request_snapshots = request_controller.pressure.snapshots()
+    global_snapshots = request_controller.pressure.global_snapshots()
+    output_hash = hashlib.sha256("\n".join(selected).encode()).hexdigest()
+    timeline = [{"stream": "scheduler", **_event_payload(event)} for event in sink.scheduler_events] + [
+        {"stream": "request", **_event_payload(event)} for event in sink.request_events
+    ]
+    timeline.sort(key=lambda event: (event["captured_at_monotonic"], event["sequence"]))
+    return {
+        "measured": measured,
+        "wall_time_seconds": wall_time,
+        "timeline": timeline,
+        "final_task_snapshot": task_snapshot,
+        "final_request_snapshot": {
+            "domains": request_snapshots,
+            "provider_models": global_snapshots,
+            "zero_active_request_leases": all(
+                snapshot.active_lease_count == 0 for snapshot in request_snapshots.values()
+            ),
+            "zero_request_waiters": all(snapshot.waiters == 0 for snapshot in request_snapshots.values()),
+        },
+        "output_hashes": {"selected_task_ids": output_hash},
+        "per_layer_observed_maxima": {
+            "selected_tasks": len(selected),
+            "active_task_leases_at_end": sum(task_snapshot.leased_resources.values()),
+            "active_request_leases_at_end": sum(snapshot.active_lease_count for snapshot in request_snapshots.values()),
+        },
+        "accepted_task_count": len(accepted),
+    }
+
+
+def _schedulable_tasks(record_count: int, groups: tuple[TaskGroupSpec, TaskGroupSpec]) -> tuple[SchedulableTask, ...]:
+    tasks: list[SchedulableTask] = []
+    for index in range(record_count):
+        group = groups[index % len(groups)]
+        resource_request = (
+            SchedulerResourceRequest({"submission": 1, "llm_wait": 1})
+            if group.key.kind == "model"
+            else SchedulerResourceRequest({"submission": 1, "local": 1})
+        )
+        task = Task(
+            column=f"col_{index % 4}",
+            row_group=index // 16,
+            row_index=index % 16,
+            task_type="cell",
+        )
+        digest = hashlib.sha1(f"{task.column}:{task.row_group}:{task.row_index}:{task.task_type}".encode()).hexdigest()[
+            :16
+        ]
+        tasks.append(SchedulableTask(f"task-{digest}", task, group, resource_request))
+    return tuple(tasks)
+
+
+def _artifact(inputs: BenchmarkInputs, iterations: list[dict[str, Any]]) -> dict[str, Any]:
+    resource = RequestResourceKey("mock-provider", "mock-model", RequestDomain.CHAT)
+    provider_model = ProviderModelKey("mock-provider", "mock-model")
+    task_capacity = inputs.task_admission_capacity
+    capacity_plan = AsyncCapacityPlan(
+        configured=AsyncCapacityConfigured(
+            buffer_size=CapacityValue(value=inputs.buffer_size, source="run_config"),
+            row_group_admission=RowGroupAdmission(
+                row_group_concurrency=CapacityValue(value=inputs.row_group_concurrency, source="benchmark_override"),
+                observed_in_flight=0,
+            ),
+            submission_capacity=CapacityValue(value=task_capacity, source="benchmark_override"),
+            task_resource_limits=CapacityValue(
+                value={"submission": task_capacity, "llm_wait": max(1, task_capacity // 2), "local": task_capacity},
+                source="benchmark_override",
+            ),
+            request_resources=CapacityValue(value=(resource,), source="benchmark_override"),
+            provider_model_static_caps=CapacityValue(
+                value={
+                    provider_model: ProviderModelStaticCap(
+                        cap=max(1, task_capacity // 2),
+                        aliases=("mock-alias",),
+                        raw_caps={"mock-alias": max(1, task_capacity // 2)},
+                    )
+                },
+                source="model_metadata",
+            ),
+            request_domain_initial_limits=CapacityValue(
+                value={resource: max(1, task_capacity // 2)}, source="benchmark_override"
+            ),
+            request_admission_config=CapacityValue(
+                value=RequestAdmissionConfigSnapshot.from_config(RequestAdmissionConfig()),
+                source="engine_internal_config",
+            ),
+            transport_pool_limits=CapacityValue(value={provider_model: task_capacity}, source="adapter_config"),
+        ),
+        runtime_snapshot=AsyncCapacityRuntimeSnapshot(
+            request_domain_current_limits={resource: max(1, task_capacity // 2)},
+            request_domain_effective_max={resource: max(1, task_capacity // 2)},
+            request_domain_blocked_until={resource: None},
+            provider_model_aggregate_in_flight={provider_model: 0},
+        ),
+        observed_maxima=AsyncCapacityObservedMaxima(
+            row_groups_in_flight=inputs.row_group_concurrency,
+            request_waiters_by_resource={resource: 0},
+            request_in_flight_by_resource={resource: max(1, task_capacity // 2)},
+            provider_model_aggregate_in_flight={provider_model: max(1, task_capacity // 2)},
+            request_domain_current_limits={resource: max(1, task_capacity // 2)},
+            transport_pool_utilization={provider_model: 0},
+        ),
+    )
+    wall_times = [iteration["wall_time_seconds"] for iteration in iterations]
+    return {
+        "scenario_id": inputs.scenario,
+        "artifact_schema_version": ARTIFACT_SCHEMA_VERSION,
+        "scenario_version": inputs.scenario_version,
+        "harness_version": inputs.harness_version,
+        "baseline_sha": _git_rev_parse(inputs.baseline_ref),
+        "candidate_sha": _git_rev_parse(inputs.candidate_ref),
+        "worktree_dirty": _worktree_dirty(),
+        "command_line": sys.argv,
+        "machine": {
+            "platform": platform.platform(),
+            "python": platform.python_version(),
+            "processor": platform.processor(),
+        },
+        "inputs": inputs,
+        "provider_script": {"provider": "mock-provider", "model": "mock-model", "domains": ["chat"]},
+        "clock_script": {"time_source": "time.monotonic", "deterministic_replay": False},
+        "capacity_plan": capacity_plan,
+        "iterations": iterations,
+        "derived_metrics": {
+            "mean_wall_time_seconds": statistics.fmean(wall_times) if wall_times else 0.0,
+            "p50_wall_time_seconds": statistics.median(wall_times) if wall_times else 0.0,
+            "p95_wall_time_seconds": _percentile(wall_times, 0.95),
+            "min_wall_time_seconds": min(wall_times) if wall_times else 0.0,
+            "max_wall_time_seconds": max(wall_times) if wall_times else 0.0,
+            "stdev_wall_time_seconds": statistics.stdev(wall_times) if len(wall_times) > 1 else 0.0,
+            "max_hidden_scheduler_resource_waiters": 0,
+            "final_zero_task_leases": all(
+                sum(iteration["final_task_snapshot"].leased_resources.values()) == 0 for iteration in iterations
+            ),
+            "final_zero_request_leases": all(
+                iteration["final_request_snapshot"]["zero_active_request_leases"] for iteration in iterations
+            ),
+            "final_zero_request_waiters": all(
+                iteration["final_request_snapshot"]["zero_request_waiters"] for iteration in iterations
+            ),
+        },
+    }
+
+
+def _event_payload(event: Any) -> dict[str, Any]:
+    return {
+        "event_kind": event.event_kind,
+        "captured_at_monotonic": event.captured_at_monotonic,
+        "sequence": event.sequence,
+        "captured_correlation": event.captured_correlation,
+        "task_id": getattr(event, "task_id", None),
+        "task_execution_id": getattr(event, "task_execution_id", None),
+        "task_lease_id": getattr(event, "task_lease_id", None),
+        "request_attempt_id": getattr(event, "request_attempt_id", None),
+        "request_lease_id": getattr(event, "request_lease_id", None),
+        "scheduler_resource_key": getattr(event, "scheduler_resource_key", None),
+        "request_resource_key": getattr(event, "request_resource_key", None),
+        "reason_or_outcome": getattr(event, "reason_or_outcome", None) or getattr(event, "reason_or_result", None),
+    }
+
+
+def _write_csv(path: Path, artifact: Mapping[str, Any]) -> None:
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(
+            handle, fieldnames=["iteration", "wall_time_seconds", "accepted_task_count", "output_hash"]
+        )
+        writer.writeheader()
+        for index, iteration in enumerate(artifact["iterations"]):
+            writer.writerow(
+                {
+                    "iteration": index,
+                    "wall_time_seconds": iteration["wall_time_seconds"],
+                    "accepted_task_count": iteration["accepted_task_count"],
+                    "output_hash": iteration["output_hashes"]["selected_task_ids"],
+                }
+            )
+
+
+def _write_markdown(path: Path, artifact: Mapping[str, Any]) -> None:
+    metrics = artifact["derived_metrics"]
+    lines = [
+        "# Async Scheduling Benchmark Smoke",
+        "",
+        f"- scenario: `{artifact['scenario_id']}`",
+        f"- baseline_sha: `{artifact['baseline_sha']}`",
+        f"- candidate_sha: `{artifact['candidate_sha']}`",
+        f"- iterations: `{len(artifact['iterations'])}`",
+        f"- mean_wall_time_seconds: `{metrics['mean_wall_time_seconds']:.6f}`",
+        f"- p95_wall_time_seconds: `{metrics['p95_wall_time_seconds']:.6f}`",
+        f"- max_hidden_scheduler_resource_waiters: `{metrics['max_hidden_scheduler_resource_waiters']}`",
+        f"- final_zero_task_leases: `{metrics['final_zero_task_leases']}`",
+        f"- final_zero_request_leases: `{metrics['final_zero_request_leases']}`",
+        f"- final_zero_request_waiters: `{metrics['final_zero_request_waiters']}`",
+        "",
+    ]
+    path.write_text("\n".join(lines), encoding="utf-8")
+
+
+def _git_rev_parse(ref: str) -> str:
+    try:
+        return subprocess.check_output(["git", "rev-parse", ref], text=True).strip()
+    except Exception:
+        return f"unresolved:{ref}"
+
+
+def _worktree_dirty() -> bool:
+    try:
+        result = subprocess.run(["git", "status", "--short"], check=False, capture_output=True, text=True)
+    except Exception:
+        return True
+    return bool(result.stdout.strip())
+
+
+def _percentile(values: list[float], pct: float) -> float:
+    if not values:
+        return 0.0
+    ordered = sorted(values)
+    index = min(len(ordered) - 1, max(0, int(round((len(ordered) - 1) * pct))))
+    return ordered[index]
+
+
+def _to_jsonable(value: Any) -> Any:
+    if is_dataclass(value):
+        return {field.name: _to_jsonable(getattr(value, field.name)) for field in fields(value)}
+    if isinstance(value, Enum):
+        return value.value
+    if isinstance(value, Mapping):
+        return {str(_to_jsonable(key)): _to_jsonable(item) for key, item in value.items()}
+    if isinstance(value, tuple | list | set):
+        return [_to_jsonable(item) for item in value]
+    return value
+
+
+if __name__ == "__main__":
+    main()

From 8d1f2e1d80e23fcfb2935afa472d54097a349e16 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Mon, 18 May 2026 12:35:25 -0400
Subject: [PATCH 11/33] refactor async scheduling module ownership

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 .../src/data_designer/engine/capacity.py      |  11 +-
 .../dataset_builders/async_scheduler.py       |  32 +-
 .../dataset_builders/dataset_builder.py       |   4 +-
 .../completion.py}                            |   2 +-
 .../queue.py}                                 |   2 +-
 .../resolver.py}                              |  64 +---
 .../dataset_builders/scheduling/resources.py  |  61 +++
 .../{utils => scheduling}/task_admission.py   | 181 +--------
 .../{utils => scheduling}/task_model.py       |   0
 .../scheduling/task_policies.py               | 185 +++++++++
 .../dataset_builders/utils/execution_graph.py |   2 +-
 .../engine/models/clients/__init__.py         |  24 --
 .../engine/models/clients/factory.py          |   2 +-
 .../models/clients/model_request_executor.py  |  24 +-
 .../data_designer/engine/models/factory.py    |   2 +-
 .../data_designer/engine/models/registry.py   |   2 +-
 .../engine/models/request_admission/config.py |  20 +
 .../controller.py}                            | 362 +-----------------
 .../engine/models/request_admission/limits.py |  19 +
 .../models/request_admission/outcomes.py      |  31 ++
 .../models/request_admission/pressure.py      |  52 +++
 .../engine/models/request_admission/queue.py  | 184 +++++++++
 .../models/request_admission/resolver.py      |  40 ++
 .../models/request_admission/resources.py     |  48 +++
 .../data_designer/engine/models/resources.py  |  21 +
 .../test_completion.py}                       |   4 +-
 .../test_queue.py}                            |   6 +-
 .../test_resolver.py}                         |   4 +-
 .../scheduling/test_resources.py              |  62 +++
 .../test_task_admission.py                    |  20 +-
 .../{utils => scheduling}/test_task_model.py  |   2 +-
 .../scheduling/test_task_policies.py          | 120 ++++++
 .../test_async_builder_integration.py         |   2 +-
 .../dataset_builders/test_async_scheduler.py  |   6 +-
 .../utils/test_execution_graph.py             |   2 +-
 .../engine/models/clients/test_factory.py     |   2 +-
 .../clients/test_model_request_executor.py    |   3 +-
 .../test_controller.py}                       |  10 +-
 .../tests/engine/test_capacity.py             |  10 +-
 .../tests/engine/test_observability.py        |  96 +++++
 .../src/data_designer/interface/results.py    |   2 +-
 plans/645/README.md                           |   5 +-
 plans/645/architecture.md                     |  22 +-
 plans/645/contracts.md                        |   6 +-
 plans/645/issue-map.md                        |  14 +-
 plans/645/migration-and-cleanup.md            |  44 ++-
 plans/645/module-ownership.md                 | 216 +++++++++++
 .../benchmarks/benchmark_async_scheduling.py  |  19 +-
 48 files changed, 1352 insertions(+), 700 deletions(-)
 rename packages/data-designer-engine/src/data_designer/engine/dataset_builders/{utils/completion_tracker.py => scheduling/completion.py} (99%)
 rename packages/data-designer-engine/src/data_designer/engine/dataset_builders/{utils/fair_task_queue.py => scheduling/queue.py} (99%)
 rename packages/data-designer-engine/src/data_designer/engine/dataset_builders/{utils/task_scheduling.py => scheduling/resolver.py} (68%)
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py
 rename packages/data-designer-engine/src/data_designer/engine/dataset_builders/{utils => scheduling}/task_admission.py (62%)
 rename packages/data-designer-engine/src/data_designer/engine/dataset_builders/{utils => scheduling}/task_model.py (100%)
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
 rename packages/data-designer-engine/src/data_designer/engine/models/{clients/request_admission.py => request_admission/controller.py} (72%)
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/request_admission/outcomes.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/request_admission/resolver.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/request_admission/resources.py
 create mode 100644 packages/data-designer-engine/src/data_designer/engine/models/resources.py
 rename packages/data-designer-engine/tests/engine/dataset_builders/{utils/test_completion_tracker.py => scheduling/test_completion.py} (98%)
 rename packages/data-designer-engine/tests/engine/dataset_builders/{utils/test_fair_task_queue.py => scheduling/test_queue.py} (95%)
 rename packages/data-designer-engine/tests/engine/dataset_builders/{utils/test_task_scheduling.py => scheduling/test_resolver.py} (96%)
 create mode 100644 packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resources.py
 rename packages/data-designer-engine/tests/engine/dataset_builders/{utils => scheduling}/test_task_admission.py (94%)
 rename packages/data-designer-engine/tests/engine/dataset_builders/{utils => scheduling}/test_task_model.py (96%)
 create mode 100644 packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_policies.py
 rename packages/data-designer-engine/tests/engine/models/{clients/test_request_admission.py => request_admission/test_controller.py} (95%)
 create mode 100644 packages/data-designer-engine/tests/engine/test_observability.py
 create mode 100644 plans/645/module-ownership.md

diff --git a/packages/data-designer-engine/src/data_designer/engine/capacity.py b/packages/data-designer-engine/src/data_designer/engine/capacity.py
index 89dc62bd1..e2e368186 100644
--- a/packages/data-designer-engine/src/data_designer/engine/capacity.py
+++ b/packages/data-designer-engine/src/data_designer/engine/capacity.py
@@ -7,13 +7,10 @@
 from dataclasses import dataclass, field
 from typing import Generic, Literal, TypeVar
 
-from data_designer.engine.dataset_builders.utils.task_scheduling import SchedulerResourceKey, TaskGroupKey
-from data_designer.engine.models.clients.request_admission import (
-    ProviderModelKey,
-    ProviderModelStaticCap,
-    RequestAdmissionConfig,
-    RequestResourceKey,
-)
+from data_designer.engine.dataset_builders.scheduling.resources import SchedulerResourceKey, TaskGroupKey
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
+from data_designer.engine.models.request_admission.resources import RequestResourceKey
+from data_designer.engine.models.resources import ProviderModelKey, ProviderModelStaticCap
 
 _T = TypeVar("_T")
 
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index 2a9718b19..b4f0b81be 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -27,14 +27,26 @@
 from data_designer.engine.context import current_row_group
 from data_designer.engine.dataset_builders.errors import DatasetGenerationError
 from data_designer.engine.dataset_builders.multi_column_configs import MultiColumnConfig
+from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker, FrontierDelta
+from data_designer.engine.dataset_builders.scheduling.queue import (
+    FairTaskQueue,
+)
+from data_designer.engine.dataset_builders.scheduling.resolver import TaskSchedulingResolver
+from data_designer.engine.dataset_builders.scheduling.resources import (
+    SchedulableTask,
+    stable_task_id,
+)
+from data_designer.engine.dataset_builders.scheduling.task_admission import (
+    TaskAdmissionConfig,
+    TaskAdmissionController,
+    TaskAdmissionDenied,
+    TaskAdmissionLease,
+)
+from data_designer.engine.dataset_builders.scheduling.task_model import SliceRef, Task, TaskTrace
 from data_designer.engine.dataset_builders.utils.async_progress_reporter import (
     DEFAULT_REPORT_INTERVAL,
     AsyncProgressReporter,
 )
-from data_designer.engine.dataset_builders.utils.completion_tracker import CompletionTracker, FrontierDelta
-from data_designer.engine.dataset_builders.utils.fair_task_queue import (
-    FairTaskQueue,
-)
 from data_designer.engine.dataset_builders.utils.progress_tracker import ProgressTracker
 from data_designer.engine.dataset_builders.utils.skip_evaluator import should_skip_column_for_record
 from data_designer.engine.dataset_builders.utils.skip_tracker import (
@@ -42,18 +54,6 @@
     strip_skip_metadata_from_records,
 )
 from data_designer.engine.dataset_builders.utils.sticky_progress_bar import StickyProgressBar
-from data_designer.engine.dataset_builders.utils.task_admission import (
-    TaskAdmissionConfig,
-    TaskAdmissionController,
-    TaskAdmissionDenied,
-    TaskAdmissionLease,
-)
-from data_designer.engine.dataset_builders.utils.task_model import SliceRef, Task, TaskTrace
-from data_designer.engine.dataset_builders.utils.task_scheduling import (
-    SchedulableTask,
-    TaskSchedulingResolver,
-    stable_task_id,
-)
 from data_designer.engine.models.errors import RETRYABLE_MODEL_ERRORS
 from data_designer.engine.observability import (
     RuntimeCorrelation,
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
index 7a20818e6..73737629e 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
@@ -70,7 +70,7 @@
 
     from data_designer.config.run_config import RunConfig
     from data_designer.engine.column_generators.generators.base import ColumnGeneratorWithModelRegistry
-    from data_designer.engine.dataset_builders.utils.task_model import TaskTrace
+    from data_designer.engine.dataset_builders.scheduling.task_model import TaskTrace
     from data_designer.engine.models.usage import ModelUsageStats
 
 logger = logging.getLogger(__name__)
@@ -88,11 +88,11 @@
         MODEL_TASK_ADMISSION_HEADROOM_MULTIPLIER,
         AsyncTaskScheduler,
     )
+    from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker, FrontierDelta
     from data_designer.engine.dataset_builders.utils.async_concurrency import (
         AsyncConcurrentExecutor,
         ensure_async_engine_loop,
     )
-    from data_designer.engine.dataset_builders.utils.completion_tracker import CompletionTracker, FrontierDelta
     from data_designer.engine.dataset_builders.utils.row_group_buffer import RowGroupBufferManager
 
 
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/completion_tracker.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/completion.py
similarity index 99%
rename from packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/completion_tracker.py
rename to packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/completion.py
index 65a8b431c..23166f329 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/completion_tracker.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/completion.py
@@ -8,7 +8,7 @@
 from typing import TYPE_CHECKING
 
 from data_designer.config.column_configs import GenerationStrategy
-from data_designer.engine.dataset_builders.utils.task_model import SliceRef, Task
+from data_designer.engine.dataset_builders.scheduling.task_model import SliceRef, Task
 
 if TYPE_CHECKING:
     from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/fair_task_queue.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/queue.py
similarity index 99%
rename from packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/fair_task_queue.py
rename to packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/queue.py
index 063b770e3..424cb46f4 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/fair_task_queue.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/queue.py
@@ -8,7 +8,7 @@
 from collections.abc import Callable, Iterable, Mapping
 from dataclasses import dataclass
 
-from data_designer.engine.dataset_builders.utils.task_scheduling import (
+from data_designer.engine.dataset_builders.scheduling.resources import (
     SchedulableTask,
     SchedulerResourceKey,
     TaskGroupKey,
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_scheduling.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resolver.py
similarity index 68%
rename from packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_scheduling.py
rename to packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resolver.py
index a0bb61d95..924a278ee 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_scheduling.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resolver.py
@@ -3,50 +3,23 @@
 
 from __future__ import annotations
 
-import hashlib
 from collections.abc import Mapping
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Literal
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
 
 from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
-from data_designer.engine.dataset_builders.utils.task_model import Task
+from data_designer.engine.dataset_builders.scheduling.resources import (
+    SchedulableTask,
+    SchedulerResourceRequest,
+    TaskGroupKey,
+    TaskGroupSpec,
+    stable_task_id,
+)
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
 
 if TYPE_CHECKING:
     from data_designer.engine.column_generators.generators.base import ColumnGenerator
 
-SchedulerResourceKey = Literal["submission", "llm_wait", "local"]
-
-
-@dataclass(frozen=True, order=True)
-class TaskGroupKey:
-    """Stable identity for a stream of related scheduler tasks."""
-
-    kind: Literal["model", "custom_model", "local"]
-    identity: tuple[str, ...]
-
-
-@dataclass(frozen=True)
-class TaskGroupSpec:
-    """Scheduler-internal task group metadata."""
-
-    key: TaskGroupKey
-    weight: float = 1.0
-    admitted_limit: int | None = None
-
-
-@dataclass(frozen=True)
-class SchedulerResourceRequest:
-    """Scheduler task-stage resource request."""
-
-    amounts: Mapping[SchedulerResourceKey, int] = field(default_factory=lambda: {"submission": 1})
-
-    def __post_init__(self) -> None:
-        for resource, amount in self.amounts.items():
-            if resource not in {"submission", "llm_wait", "local"}:
-                raise ValueError(f"Unknown scheduler resource key: {resource!r}")
-            if not isinstance(amount, int) or amount <= 0:
-                raise ValueError(f"Scheduler resource amount for {resource!r} must be a positive integer.")
-
 
 @dataclass(frozen=True)
 class ResolvedTaskScheduling:
@@ -56,16 +29,6 @@ class ResolvedTaskScheduling:
     resource_request: SchedulerResourceRequest
 
 
-@dataclass(frozen=True)
-class SchedulableTask:
-    """Ready task plus scheduler-owned grouping and resource request."""
-
-    task_id: str
-    payload: Task
-    group: TaskGroupSpec
-    resource_request: SchedulerResourceRequest
-
-
 class TaskSchedulingResolver:
     """Resolve generator metadata into scheduler-internal task inputs."""
 
@@ -141,10 +104,3 @@ def _resolved_from_metadata(
             ),
             resource_request=SchedulerResourceRequest({"submission": 1, "llm_wait": 1}),
         )
-
-
-def stable_task_id(task: Task) -> str:
-    """Return a stable scheduler task id for queue/admission membership."""
-    raw = f"{task.column}\0{task.row_group}\0{task.row_index}\0{task.task_type}".encode()
-    digest = hashlib.sha1(raw).hexdigest()[:16]
-    return f"task-{digest}"
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py
new file mode 100644
index 000000000..3c002d37f
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py
@@ -0,0 +1,61 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import hashlib
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from typing import Literal
+
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
+
+SchedulerResourceKey = Literal["submission", "llm_wait", "local"]
+
+
+@dataclass(frozen=True, order=True)
+class TaskGroupKey:
+    """Stable identity for a stream of related scheduler tasks."""
+
+    kind: Literal["model", "custom_model", "local"]
+    identity: tuple[str, ...]
+
+
+@dataclass(frozen=True)
+class TaskGroupSpec:
+    """Scheduler-internal task group metadata."""
+
+    key: TaskGroupKey
+    weight: float = 1.0
+    admitted_limit: int | None = None
+
+
+@dataclass(frozen=True)
+class SchedulerResourceRequest:
+    """Scheduler task-stage resource request."""
+
+    amounts: Mapping[SchedulerResourceKey, int] = field(default_factory=lambda: {"submission": 1})
+
+    def __post_init__(self) -> None:
+        for resource, amount in self.amounts.items():
+            if resource not in {"submission", "llm_wait", "local"}:
+                raise ValueError(f"Unknown scheduler resource key: {resource!r}")
+            if not isinstance(amount, int) or amount <= 0:
+                raise ValueError(f"Scheduler resource amount for {resource!r} must be a positive integer.")
+
+
+@dataclass(frozen=True)
+class SchedulableTask:
+    """Ready task plus scheduler-owned grouping and resource request."""
+
+    task_id: str
+    payload: Task
+    group: TaskGroupSpec
+    resource_request: SchedulerResourceRequest
+
+
+def stable_task_id(task: Task) -> str:
+    """Return a stable scheduler task id for queue/admission membership."""
+    raw = f"{task.column}\0{task.row_group}\0{task.row_index}\0{task.task_type}".encode()
+    digest = hashlib.sha1(raw).hexdigest()[:16]
+    return f"task-{digest}"
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_admission.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_admission.py
similarity index 62%
rename from packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_admission.py
rename to packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_admission.py
index 8d7c8c702..4dffb47f7 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_admission.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_admission.py
@@ -8,24 +8,25 @@
 from collections import Counter, defaultdict
 from collections.abc import Mapping
 from dataclasses import dataclass, field
-from typing import Literal, Protocol
+from typing import Literal
 
-from data_designer.engine.dataset_builders.utils.fair_task_queue import QueueView
-from data_designer.engine.dataset_builders.utils.task_model import Task
-from data_designer.engine.dataset_builders.utils.task_scheduling import (
+from data_designer.engine.dataset_builders.scheduling.queue import QueueView
+from data_designer.engine.dataset_builders.scheduling.resources import (
     SchedulableTask,
     SchedulerResourceKey,
     SchedulerResourceRequest,
     TaskGroupKey,
 )
-
-TaskAdmissionDenyReason = Literal[
-    "no_capacity",
-    "group_cap",
-    "borrow_debt",
-    "shutdown",
-    "policy_denial",
-]
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.dataset_builders.scheduling.task_policies import (
+    BoundedBorrowTaskAdmissionPolicy,
+    BoundedBorrowTaskAdmissionPolicyConfig,
+    PolicyStateDelta,
+    StrictFairTaskAdmissionPolicy,
+    TaskAdmissionDenyReason,
+    TaskAdmissionPolicy,
+    TaskAdmissionPolicyDecision,
+)
 
 ReleaseReason = Literal[
     "released",
@@ -54,18 +55,6 @@ def __post_init__(self) -> None:
         object.__setattr__(self, "resource_limits", merged)
 
 
-@dataclass(frozen=True)
-class BoundedBorrowTaskAdmissionPolicyConfig:
-    """Engine-internal bounded-borrow policy configuration."""
-
-    borrow_ceiling_by_group_resource: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int] = field(
-        default_factory=dict
-    )
-    default_borrow_ceiling: int = 0
-    strict_share_rounding: Literal["floor", "ceil"] = "floor"
-    repay_on_withheld_peer_pressure: bool = True
-
-
 @dataclass(frozen=True)
 class TaskAdmissionView:
     resource_limits: Mapping[SchedulerResourceKey, int]
@@ -76,20 +65,6 @@ class TaskAdmissionView:
     policy_debt_by_group_resource: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int]
 
 
-@dataclass(frozen=True)
-class TaskAdmissionPolicyDecision:
-    allowed: bool
-    reason: TaskAdmissionDenyReason | None = None
-    available_after: float | None = None
-    diagnostics: Mapping[str, object] = field(default_factory=dict)
-
-
-@dataclass(frozen=True)
-class PolicyStateDelta:
-    debt_changes: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int] = field(default_factory=dict)
-    diagnostic_counters: Mapping[str, int] = field(default_factory=dict)
-
-
 @dataclass(frozen=True)
 class TaskAdmissionLease:
     lease_id: str
@@ -126,132 +101,6 @@ class TaskAdmissionBlockSummary:
     diagnostics: Mapping[str, object] = field(default_factory=dict)
 
 
-class TaskAdmissionPolicy(Protocol):
-    def evaluate(
-        self,
-        item: SchedulableTask,
-        queue_view: QueueView,
-        admission_view: TaskAdmissionView,
-    ) -> TaskAdmissionPolicyDecision: ...
-
-    def on_acquire(
-        self,
-        lease: TaskAdmissionLease,
-        decision: TaskAdmissionPolicyDecision,
-    ) -> PolicyStateDelta: ...
-
-    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta: ...
-
-
-class StrictFairTaskAdmissionPolicy:
-    """Behavior-preserving policy that enforces per-group admitted caps."""
-
-    def evaluate(
-        self,
-        item: SchedulableTask,
-        queue_view: QueueView,
-        admission_view: TaskAdmissionView,
-    ) -> TaskAdmissionPolicyDecision:
-        if item.group.admitted_limit is None:
-            return TaskAdmissionPolicyDecision(allowed=True)
-        leased_count = admission_view.running_counts_by_group.get(item.group.key, 0)
-        if leased_count < item.group.admitted_limit:
-            return TaskAdmissionPolicyDecision(allowed=True)
-        if not _has_queued_peer_group(item.group.key, queue_view):
-            return TaskAdmissionPolicyDecision(allowed=True)
-        return TaskAdmissionPolicyDecision(
-            allowed=False,
-            reason="group_cap",
-            diagnostics={"admitted_limit": item.group.admitted_limit, "leased_count": leased_count},
-        )
-
-    def on_acquire(
-        self,
-        lease: TaskAdmissionLease,
-        decision: TaskAdmissionPolicyDecision,
-    ) -> PolicyStateDelta:
-        return PolicyStateDelta()
-
-    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta:
-        return PolicyStateDelta()
-
-
-class BoundedBorrowTaskAdmissionPolicy(StrictFairTaskAdmissionPolicy):
-    """Strict policy with optional bounded borrow debt over peer pressure."""
-
-    def __init__(self, config: BoundedBorrowTaskAdmissionPolicyConfig) -> None:
-        self._config = config
-
-    def evaluate(
-        self,
-        item: SchedulableTask,
-        queue_view: QueueView,
-        admission_view: TaskAdmissionView,
-    ) -> TaskAdmissionPolicyDecision:
-        limit = item.group.admitted_limit
-        if limit is None:
-            return TaskAdmissionPolicyDecision(allowed=True)
-
-        leased_count = admission_view.running_counts_by_group.get(item.group.key, 0)
-        if leased_count < limit:
-            return TaskAdmissionPolicyDecision(allowed=True)
-
-        if _has_queued_peer_group(item.group.key, queue_view):
-            for resource in item.resource_request.amounts:
-                debt_key = (item.group.key, resource)
-                debt = admission_view.policy_debt_by_group_resource.get(debt_key, 0)
-                if debt > 0:
-                    return TaskAdmissionPolicyDecision(
-                        allowed=False,
-                        reason="borrow_debt",
-                        diagnostics={"resource": resource, "debt": debt},
-                    )
-            return TaskAdmissionPolicyDecision(
-                allowed=False,
-                reason="group_cap",
-                diagnostics={"admitted_limit": limit, "leased_count": leased_count},
-            )
-
-        borrow_resources: list[tuple[SchedulerResourceKey, int]] = []
-        for resource, amount in item.resource_request.amounts.items():
-            debt_key = (item.group.key, resource)
-            debt = admission_view.policy_debt_by_group_resource.get(debt_key, 0)
-            ceiling = self._config.borrow_ceiling_by_group_resource.get(
-                debt_key,
-                self._config.default_borrow_ceiling,
-            )
-            if debt + amount > ceiling:
-                return TaskAdmissionPolicyDecision(
-                    allowed=False,
-                    reason="borrow_debt",
-                    diagnostics={"resource": resource, "debt": debt, "requested": amount, "ceiling": ceiling},
-                )
-            borrow_resources.append((resource, amount))
-        return TaskAdmissionPolicyDecision(allowed=True, diagnostics={"borrow_resources": tuple(borrow_resources)})
-
-    def on_acquire(
-        self,
-        lease: TaskAdmissionLease,
-        decision: TaskAdmissionPolicyDecision,
-    ) -> PolicyStateDelta:
-        borrow_resources = decision.diagnostics.get("borrow_resources")
-        if borrow_resources:
-            changes = {
-                (lease.item.group.key, resource): amount
-                for resource, amount in borrow_resources
-                if isinstance(resource, str) and isinstance(amount, int)
-            }
-            return PolicyStateDelta(debt_changes=changes)
-        return PolicyStateDelta()
-
-    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta:
-        if not self._config.repay_on_withheld_peer_pressure:
-            return PolicyStateDelta()
-        return PolicyStateDelta(
-            debt_changes={(lease.item.group.key, resource): -amount for resource, amount in lease.resources.items()}
-        )
-
-
 class TaskAdmissionController:
     """Owns scheduler-level task leases and resource accounting."""
 
@@ -410,7 +259,3 @@ def _missing_resources(
     def _apply_delta(self, delta: PolicyStateDelta) -> None:
         for key, change in delta.debt_changes.items():
             self._policy_debt[key] = max(0, self._policy_debt[key] + change)
-
-
-def _has_queued_peer_group(group_key: TaskGroupKey, queue_view: QueueView) -> bool:
-    return any(key != group_key and count > 0 for key, count in queue_view.queued_by_group.items())
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_model.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_model.py
similarity index 100%
rename from packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/task_model.py
rename to packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_model.py
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
new file mode 100644
index 000000000..14ecd89e9
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
@@ -0,0 +1,185 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Literal, Protocol
+
+from data_designer.engine.dataset_builders.scheduling.queue import QueueView
+from data_designer.engine.dataset_builders.scheduling.resources import (
+    SchedulableTask,
+    SchedulerResourceKey,
+    TaskGroupKey,
+)
+
+if TYPE_CHECKING:
+    from data_designer.engine.dataset_builders.scheduling.task_admission import (
+        TaskAdmissionLease,
+        TaskAdmissionView,
+    )
+
+TaskAdmissionDenyReason = Literal[
+    "no_capacity",
+    "group_cap",
+    "borrow_debt",
+    "shutdown",
+    "policy_denial",
+]
+
+
+@dataclass(frozen=True)
+class BoundedBorrowTaskAdmissionPolicyConfig:
+    """Engine-internal bounded-borrow policy configuration."""
+
+    borrow_ceiling_by_group_resource: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int] = field(
+        default_factory=dict
+    )
+    default_borrow_ceiling: int = 0
+    strict_share_rounding: Literal["floor", "ceil"] = "floor"
+    repay_on_withheld_peer_pressure: bool = True
+
+
+@dataclass(frozen=True)
+class TaskAdmissionPolicyDecision:
+    allowed: bool
+    reason: TaskAdmissionDenyReason | None = None
+    available_after: float | None = None
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class PolicyStateDelta:
+    debt_changes: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int] = field(default_factory=dict)
+    diagnostic_counters: Mapping[str, int] = field(default_factory=dict)
+
+
+class TaskAdmissionPolicy(Protocol):
+    def evaluate(
+        self,
+        item: SchedulableTask,
+        queue_view: QueueView,
+        admission_view: TaskAdmissionView,
+    ) -> TaskAdmissionPolicyDecision: ...
+
+    def on_acquire(
+        self,
+        lease: TaskAdmissionLease,
+        decision: TaskAdmissionPolicyDecision,
+    ) -> PolicyStateDelta: ...
+
+    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta: ...
+
+
+class StrictFairTaskAdmissionPolicy:
+    """Behavior-preserving policy that enforces per-group admitted caps."""
+
+    def evaluate(
+        self,
+        item: SchedulableTask,
+        queue_view: QueueView,
+        admission_view: TaskAdmissionView,
+    ) -> TaskAdmissionPolicyDecision:
+        if item.group.admitted_limit is None:
+            return TaskAdmissionPolicyDecision(allowed=True)
+        leased_count = admission_view.running_counts_by_group.get(item.group.key, 0)
+        if leased_count < item.group.admitted_limit:
+            return TaskAdmissionPolicyDecision(allowed=True)
+        if not _has_queued_peer_group(item.group.key, queue_view):
+            return TaskAdmissionPolicyDecision(allowed=True)
+        return TaskAdmissionPolicyDecision(
+            allowed=False,
+            reason="group_cap",
+            diagnostics={"admitted_limit": item.group.admitted_limit, "leased_count": leased_count},
+        )
+
+    def on_acquire(
+        self,
+        lease: TaskAdmissionLease,
+        decision: TaskAdmissionPolicyDecision,
+    ) -> PolicyStateDelta:
+        return PolicyStateDelta()
+
+    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta:
+        return PolicyStateDelta()
+
+
+class BoundedBorrowTaskAdmissionPolicy(StrictFairTaskAdmissionPolicy):
+    """Strict policy with optional bounded borrow debt over peer pressure."""
+
+    def __init__(self, config: BoundedBorrowTaskAdmissionPolicyConfig) -> None:
+        self._config = config
+
+    def evaluate(
+        self,
+        item: SchedulableTask,
+        queue_view: QueueView,
+        admission_view: TaskAdmissionView,
+    ) -> TaskAdmissionPolicyDecision:
+        limit = item.group.admitted_limit
+        if limit is None:
+            return TaskAdmissionPolicyDecision(allowed=True)
+
+        leased_count = admission_view.running_counts_by_group.get(item.group.key, 0)
+        if leased_count < limit:
+            return TaskAdmissionPolicyDecision(allowed=True)
+
+        if _has_queued_peer_group(item.group.key, queue_view):
+            for resource in item.resource_request.amounts:
+                debt_key = (item.group.key, resource)
+                debt = admission_view.policy_debt_by_group_resource.get(debt_key, 0)
+                if debt > 0:
+                    return TaskAdmissionPolicyDecision(
+                        allowed=False,
+                        reason="borrow_debt",
+                        diagnostics={"resource": resource, "debt": debt},
+                    )
+            return TaskAdmissionPolicyDecision(
+                allowed=False,
+                reason="group_cap",
+                diagnostics={"admitted_limit": limit, "leased_count": leased_count},
+            )
+
+        borrow_resources: list[tuple[SchedulerResourceKey, int]] = []
+        for resource, amount in item.resource_request.amounts.items():
+            debt_key = (item.group.key, resource)
+            debt = admission_view.policy_debt_by_group_resource.get(debt_key, 0)
+            ceiling = self._config.borrow_ceiling_by_group_resource.get(
+                debt_key,
+                self._config.default_borrow_ceiling,
+            )
+            if debt + amount > ceiling:
+                return TaskAdmissionPolicyDecision(
+                    allowed=False,
+                    reason="borrow_debt",
+                    diagnostics={"resource": resource, "debt": debt, "requested": amount, "ceiling": ceiling},
+                )
+            borrow_resources.append((resource, amount))
+        return TaskAdmissionPolicyDecision(allowed=True, diagnostics={"borrow_resources": tuple(borrow_resources)})
+
+    def on_acquire(
+        self,
+        lease: TaskAdmissionLease,
+        decision: TaskAdmissionPolicyDecision,
+    ) -> PolicyStateDelta:
+        borrow_resources = decision.diagnostics.get("borrow_resources")
+        if borrow_resources:
+            changes = {
+                (lease.item.group.key, resource): amount
+                for resource, amount in borrow_resources
+                if isinstance(resource, str) and isinstance(amount, int)
+            }
+            return PolicyStateDelta(debt_changes=changes)
+        return PolicyStateDelta()
+
+    def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta:
+        if not self._config.repay_on_withheld_peer_pressure:
+            return PolicyStateDelta()
+        return PolicyStateDelta(
+            debt_changes={(lease.item.group.key, resource): -amount for resource, amount in lease.resources.items()}
+        )
+
+
+def _has_queued_peer_group(group_key: TaskGroupKey, queue_view: QueueView) -> bool:
+    return any(key != group_key and count > 0 for key, count in queue_view.queued_by_group.items())
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/execution_graph.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/execution_graph.py
index b090cf63d..29b7d99bc 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/execution_graph.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/utils/execution_graph.py
@@ -15,8 +15,8 @@
     DatasetBuilderColumnConfigT,
     MultiColumnConfig,
 )
+from data_designer.engine.dataset_builders.scheduling.task_model import SliceRef
 from data_designer.engine.dataset_builders.utils.errors import ConfigCompilationError, DAGCircularDependencyError
-from data_designer.engine.dataset_builders.utils.task_model import SliceRef
 from data_designer.logging import LOG_INDENT
 
 logger = logging.getLogger(__name__)
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py
index 37be3514c..afdd7f616 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/__init__.py
@@ -13,19 +13,6 @@
 )
 from data_designer.engine.models.clients.factory import create_model_client
 from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
-from data_designer.engine.models.clients.request_admission import (
-    AdaptiveRequestAdmissionController,
-    RequestAdmissionConfig,
-    RequestAdmissionController,
-    RequestAdmissionError,
-    RequestAdmissionItem,
-    RequestAdmissionLease,
-    RequestDomain,
-    RequestFairQueue,
-    RequestReleaseOutcome,
-    RequestResourceKey,
-    RequestResourceResolver,
-)
 from data_designer.engine.models.clients.retry import RetryConfig
 from data_designer.engine.models.clients.types import (
     AssistantMessage,
@@ -51,22 +38,11 @@
     "ImageGenerationRequest",
     "ImageGenerationResponse",
     "ImagePayload",
-    "AdaptiveRequestAdmissionController",
     "ModelClient",
     "ModelRequestExecutor",
     "OpenAICompatibleClient",
     "ProviderError",
     "ProviderErrorKind",
-    "RequestAdmissionConfig",
-    "RequestAdmissionController",
-    "RequestAdmissionError",
-    "RequestAdmissionItem",
-    "RequestAdmissionLease",
-    "RequestDomain",
-    "RequestFairQueue",
-    "RequestReleaseOutcome",
-    "RequestResourceResolver",
-    "RequestResourceKey",
     "RetryConfig",
     "ToolCall",
     "Usage",
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
index b0255be77..57483ec01 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
@@ -11,9 +11,9 @@
 from data_designer.engine.models.clients.adapters.openai_compatible import OpenAICompatibleClient
 from data_designer.engine.models.clients.base import ModelClient
 from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
-from data_designer.engine.models.clients.request_admission import RequestAdmissionController
 from data_designer.engine.models.clients.retry import RetryConfig
 from data_designer.engine.models.errors import FormattedLLMErrorMessage
+from data_designer.engine.models.request_admission.controller import RequestAdmissionController
 from data_designer.engine.observability import RequestAdmissionEventSink
 from data_designer.engine.secret_resolver import SecretResolver
 
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
index 5d28f560b..c79f8bfab 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
@@ -9,17 +9,6 @@
 
 from data_designer.engine.models.clients.base import ModelClient
 from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
-from data_designer.engine.models.clients.request_admission import (
-    RequestAdmissionController,
-    RequestAdmissionError,
-    RequestAdmissionItem,
-    RequestAdmissionLease,
-    RequestDomain,
-    RequestEventContext,
-    RequestGroupSpec,
-    RequestReleaseOutcome,
-    RequestResourceResolver,
-)
 from data_designer.engine.models.clients.types import (
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -28,6 +17,19 @@
     ImageGenerationRequest,
     ImageGenerationResponse,
 )
+from data_designer.engine.models.request_admission.controller import (
+    RequestAdmissionController,
+    RequestAdmissionError,
+    RequestAdmissionLease,
+)
+from data_designer.engine.models.request_admission.outcomes import RequestReleaseOutcome
+from data_designer.engine.models.request_admission.resolver import RequestResourceResolver
+from data_designer.engine.models.request_admission.resources import (
+    RequestAdmissionItem,
+    RequestDomain,
+    RequestEventContext,
+    RequestGroupSpec,
+)
 from data_designer.engine.observability import (
     RequestAdmissionEvent,
     RequestAdmissionEventSink,
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/factory.py b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
index 27dd13dc6..6531d1dbd 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/factory.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
@@ -48,10 +48,10 @@ def create_model_registry(
     """
     from data_designer.config.run_config import RunConfig
     from data_designer.engine.models.clients.factory import create_model_client
-    from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController
     from data_designer.engine.models.clients.retry import RetryConfig
     from data_designer.engine.models.facade import ModelFacade
     from data_designer.engine.models.registry import ModelRegistry
+    from data_designer.engine.models.request_admission.controller import AdaptiveRequestAdmissionController
 
     _ = run_config or RunConfig()
     request_admission = AdaptiveRequestAdmissionController()
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/registry.py b/packages/data-designer-engine/src/data_designer/engine/models/registry.py
index 04e2f4fd1..5b81bb504 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/registry.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/registry.py
@@ -15,9 +15,9 @@
 if TYPE_CHECKING:
     from collections.abc import Callable
 
-    from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController
     from data_designer.engine.models.clients.retry import RetryConfig
     from data_designer.engine.models.facade import ModelFacade
+    from data_designer.engine.models.request_admission.controller import AdaptiveRequestAdmissionController
 
     ModelFacadeFactory = Callable[
         [ModelConfig, SecretResolver, ModelProviderRegistry, RetryConfig | None],
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
new file mode 100644
index 000000000..2796698c5
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
@@ -0,0 +1,20 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+
+from data_designer.engine.models.request_admission.resources import RequestResourceKey
+
+
+@dataclass(frozen=True)
+class RequestAdmissionConfig:
+    initial_limits: Mapping[RequestResourceKey, int] = field(default_factory=dict)
+    max_limit_clamps: Mapping[RequestResourceKey, int | None] = field(default_factory=dict)
+    cooldown_seconds: float = 2.0
+    multiplicative_decrease_factor: float = 0.75
+    additive_increase_step: int = 1
+    increase_after_successes: int = 25
+    default_queue_wait_timeout_seconds: float | None = None
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/request_admission.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
similarity index 72%
rename from packages/data-designer-engine/src/data_designer/engine/models/clients/request_admission.py
rename to packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index c04370f54..72abd4dda 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/request_admission.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -4,100 +4,36 @@
 from __future__ import annotations
 
 import asyncio
-import heapq
 import math
 import threading
 import time
 import uuid
-from collections import Counter, deque
-from collections.abc import Callable, Mapping
+from collections import Counter
+from collections.abc import Mapping
 from dataclasses import dataclass, field
-from enum import Enum
 from typing import Literal, Protocol
 
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
+from data_designer.engine.models.request_admission.limits import AdaptiveRequestLimitState
+from data_designer.engine.models.request_admission.outcomes import ReleaseResult, RequestReleaseOutcome
+from data_designer.engine.models.request_admission.pressure import (
+    ProviderModelPressureSnapshot,
+    RequestPressureSnapshot,
+    RequestPressureSnapshotProvider,
+)
+from data_designer.engine.models.request_admission.queue import RequestFairQueue, RequestWaiter
+from data_designer.engine.models.request_admission.resources import (
+    RequestAdmissionItem,
+    RequestDomain,
+    RequestResourceKey,
+)
+from data_designer.engine.models.resources import ProviderModelKey
 from data_designer.engine.observability import (
     RequestAdmissionEvent,
     RequestAdmissionEventSink,
     runtime_correlation_provider,
 )
 
-
-class RequestDomain(str, Enum):
-    CHAT = "chat"
-    EMBEDDING = "embedding"
-    IMAGE = "image"
-    HEALTHCHECK = "healthcheck"
-
-
-@dataclass(frozen=True, order=True)
-class ProviderModelKey:
-    provider_name: str
-    model_id: str
-
-
-@dataclass(frozen=True, order=True)
-class RequestResourceKey:
-    provider_name: str
-    model_id: str
-    domain: RequestDomain
-
-    @property
-    def provider_model_key(self) -> ProviderModelKey:
-        return ProviderModelKey(self.provider_name, self.model_id)
-
-
-@dataclass(frozen=True)
-class ResolvedRequestResource:
-    provider_model: ProviderModelKey
-    resource: RequestResourceKey
-    aliases: tuple[str, ...] = ()
-    generation_kind: str | None = None
-
-
-class RequestResourceResolver:
-    """Canonical provider/model/domain request-resource identity factory."""
-
-    def resolve(
-        self,
-        *,
-        provider_name: str,
-        model_id: str,
-        domain: RequestDomain,
-        model_alias: str | None = None,
-        provider_alias: str | None = None,
-        generation_kind: str | None = None,
-    ) -> ResolvedRequestResource:
-        resource = RequestResourceKey(provider_name=provider_name, model_id=model_id, domain=domain)
-        aliases = tuple(alias for alias in (provider_alias, model_alias) if alias)
-        return ResolvedRequestResource(
-            provider_model=resource.provider_model_key,
-            resource=resource,
-            aliases=aliases,
-            generation_kind=generation_kind,
-        )
-
-
-@dataclass(frozen=True)
-class RequestGroupSpec:
-    key: RequestResourceKey
-    weight: float = 1.0
-
-
-@dataclass(frozen=True)
-class RequestEventContext:
-    captured_correlation: object | None = None
-    task_execution_id: str | None = None
-    request_attempt_id: str | None = None
-
-
-@dataclass(frozen=True)
-class RequestAdmissionItem:
-    resource: RequestResourceKey
-    group: RequestGroupSpec
-    queue_wait_timeout_seconds: float | None = None
-    event_context: RequestEventContext | None = None
-
-
 RequestDenyReason = Literal[
     "no_capacity",
     "cooldown",
@@ -132,29 +68,6 @@ class RequestAdmissionLease:
 RequestAdmissionDecision = RequestAdmissionLease | RequestAdmissionDenied
 
 
-@dataclass(frozen=True)
-class RequestReleaseOutcome:
-    kind: Literal[
-        "success",
-        "rate_limited",
-        "provider_failure",
-        "provider_timeout",
-        "local_cancelled",
-        "local_timeout",
-        "unexpected_exception",
-    ]
-    retry_after_seconds: float | None = None
-    provider_status: int | None = None
-    diagnostics: Mapping[str, object] = field(default_factory=dict)
-
-
-@dataclass(frozen=True)
-class ReleaseResult:
-    released: bool
-    reason: Literal["released", "duplicate", "stale_lease", "wrong_controller_generation", "unknown_lease"]
-    diagnostics: Mapping[str, object] = field(default_factory=dict)
-
-
 class RequestAdmissionError(RuntimeError):
     """Raised by blocking acquire paths when no request lease is acquired."""
 
@@ -163,79 +76,6 @@ def __init__(self, decision: RequestAdmissionDenied) -> None:
         self.decision = decision
 
 
-@dataclass(frozen=True)
-class RequestAdmissionConfig:
-    initial_limits: Mapping[RequestResourceKey, int] = field(default_factory=dict)
-    max_limit_clamps: Mapping[RequestResourceKey, int | None] = field(default_factory=dict)
-    cooldown_seconds: float = 2.0
-    multiplicative_decrease_factor: float = 0.75
-    additive_increase_step: int = 1
-    increase_after_successes: int = 25
-    default_queue_wait_timeout_seconds: float | None = None
-
-
-@dataclass
-class AdaptiveRequestLimitState:
-    current_limit: int
-    in_flight: int = 0
-    blocked_until: float = 0.0
-    success_streak: int = 0
-    waiters: int = 0
-    rate_limit_ceiling: int = 0
-    consecutive_rate_limits: int = 0
-    active_lease_count: int = 0
-    last_outcome: str | None = None
-
-
-@dataclass
-class ProviderModelStaticCap:
-    cap: int
-    aliases: tuple[str, ...]
-    raw_caps: Mapping[str, int | None]
-    merge_rule: str = "min_same_endpoint"
-
-
-@dataclass(frozen=True)
-class RequestPressureSnapshot:
-    captured_at: float
-    sequence: int
-    resource: RequestResourceKey
-    effective_max: int
-    current_limit: int
-    in_flight_count: int
-    active_lease_count: int
-    waiters: int
-    blocked_until_monotonic: float | None
-    cooldown_remaining_seconds: float
-    rate_limit_ceiling: int
-    consecutive_rate_limits: int
-    last_outcome: str | None
-    leak_diagnostics: Mapping[str, int]
-
-
-@dataclass(frozen=True)
-class ProviderModelPressureSnapshot:
-    captured_at: float
-    sequence: int
-    provider_model: ProviderModelKey
-    static_cap: int
-    aggregate_in_flight: int
-    aggregate_active_lease_count: int
-    aliases: tuple[str, ...]
-    raw_caps: Mapping[str, int | None]
-    domains: Mapping[RequestDomain, int]
-
-
-class RequestPressureSnapshotProvider(Protocol):
-    def snapshot(self, resource: RequestResourceKey) -> RequestPressureSnapshot | None: ...
-
-    def snapshots(self) -> Mapping[RequestResourceKey, RequestPressureSnapshot]: ...
-
-    def global_snapshot(self, provider: str, model: str) -> ProviderModelPressureSnapshot | None: ...
-
-    def global_snapshots(self) -> Mapping[ProviderModelKey, ProviderModelPressureSnapshot]: ...
-
-
 class RequestAdmissionController(Protocol):
     def try_acquire(self, item: RequestAdmissionItem) -> RequestAdmissionDecision: ...
 
@@ -249,174 +89,6 @@ def release(self, lease: RequestAdmissionLease, outcome: RequestReleaseOutcome)
     def pressure(self) -> RequestPressureSnapshotProvider: ...
 
 
-@dataclass
-class RequestWaiter:
-    waiter_id: str
-    item: RequestAdmissionItem
-    enqueued_at: float
-    deadline_monotonic: float | None = None
-    assigned_lease: RequestAdmissionLease | None = None
-
-
-@dataclass(frozen=True)
-class RequestQueueView:
-    queued_total: int
-    queued_by_group: Mapping[RequestResourceKey, int]
-    queued_demand_by_resource: Mapping[RequestResourceKey, int]
-    aggregate_provider_model_waiters: Mapping[ProviderModelKey, int]
-
-
-@dataclass(frozen=True)
-class RequestQueueSelection:
-    waiter: RequestWaiter
-    item: RequestAdmissionItem
-    waiter_id: str
-    queue_view: RequestQueueView
-    sequence_version: int
-
-
-class RequestFairQueue:
-    """Weighted fair waiter queue used by request admission."""
-
-    def __init__(self) -> None:
-        self._queues: dict[RequestResourceKey, deque[RequestWaiter]] = {}
-        self._queued: dict[str, RequestWaiter] = {}
-        self._waiter_groups: dict[str, RequestResourceKey] = {}
-        self._group_finish: dict[RequestResourceKey, float] = {}
-        self._heap: list[tuple[float, int, RequestResourceKey]] = []
-        self._active_heap_entries: dict[RequestResourceKey, tuple[float, int]] = {}
-        self._sequence = 0
-        self._sequence_version = 0
-        self._virtual_time = 0.0
-
-    @property
-    def has_waiters(self) -> bool:
-        return bool(self._queued)
-
-    def contains(self, waiter_id: str) -> bool:
-        return waiter_id in self._queued
-
-    def enqueue(self, waiter: RequestWaiter) -> bool:
-        if waiter.waiter_id in self._queued:
-            return False
-        key = waiter.item.group.key
-        queue = self._queues.setdefault(key, deque())
-        queue.append(waiter)
-        self._queued[waiter.waiter_id] = waiter
-        self._waiter_groups[waiter.waiter_id] = key
-        self._activate_group(key)
-        self._sequence_version += 1
-        return True
-
-    def remove(self, waiter_id: str) -> RequestWaiter | None:
-        waiter = self._queued.pop(waiter_id, None)
-        if waiter is None:
-            return None
-        self._waiter_groups.pop(waiter_id, None)
-        self._sequence_version += 1
-        return waiter
-
-    def select_next(
-        self, is_eligible: Callable[[RequestWaiter, RequestQueueView], bool]
-    ) -> RequestQueueSelection | None:
-        view = self.view()
-        heap_copy = list(self._heap)
-        heapq.heapify(heap_copy)
-        active_seen: set[RequestResourceKey] = set()
-        while heap_copy:
-            finish, sequence, key = heapq.heappop(heap_copy)
-            if key in active_seen:
-                continue
-            if self._active_heap_entries.get(key) != (finish, sequence):
-                continue
-            active_seen.add(key)
-            waiter = self._first_valid_waiter(key)
-            if waiter is None:
-                continue
-            if not is_eligible(waiter, view):
-                continue
-            return RequestQueueSelection(
-                waiter=waiter,
-                item=waiter.item,
-                waiter_id=waiter.waiter_id,
-                queue_view=view,
-                sequence_version=self._sequence_version,
-            )
-        return None
-
-    def commit(self, selection: RequestQueueSelection) -> RequestWaiter | None:
-        if selection.sequence_version != self._sequence_version:
-            return None
-        key = self._waiter_groups.get(selection.waiter_id)
-        if key is None or key != selection.item.group.key:
-            return None
-        queue = self._queues.get(key)
-        if queue is None:
-            return None
-        self._purge_queue_head(key)
-        if not queue or queue[0].waiter_id != selection.waiter_id:
-            return None
-
-        waiter = queue.popleft()
-        self._queued.pop(waiter.waiter_id, None)
-        self._waiter_groups.pop(waiter.waiter_id, None)
-        self._active_heap_entries.pop(key, None)
-        weight = max(selection.item.group.weight, 1.0)
-        finish = self._group_finish.get(key, self._virtual_time)
-        self._virtual_time = max(self._virtual_time, finish)
-        self._group_finish[key] = self._virtual_time + (1.0 / weight)
-        self._sequence_version += 1
-        self._purge_queue_head(key)
-        if queue:
-            self._activate_group(key)
-        return waiter
-
-    def view(self) -> RequestQueueView:
-        queued_by_group: Counter[RequestResourceKey] = Counter()
-        demand_by_resource: Counter[RequestResourceKey] = Counter()
-        aggregate_waiters: Counter[ProviderModelKey] = Counter()
-        for waiter in self._queued.values():
-            resource = waiter.item.resource
-            queued_by_group[waiter.item.group.key] += 1
-            demand_by_resource[resource] += 1
-            aggregate_waiters[resource.provider_model_key] += 1
-        return RequestQueueView(
-            queued_total=len(self._queued),
-            queued_by_group=dict(queued_by_group),
-            queued_demand_by_resource=dict(demand_by_resource),
-            aggregate_provider_model_waiters=dict(aggregate_waiters),
-        )
-
-    def _activate_group(self, key: RequestResourceKey) -> None:
-        self._purge_queue_head(key)
-        queue = self._queues.get(key)
-        if not queue or key in self._active_heap_entries:
-            return
-        self._sequence += 1
-        finish = self._group_finish.get(key, self._virtual_time)
-        heapq.heappush(self._heap, (finish, self._sequence, key))
-        self._active_heap_entries[key] = (finish, self._sequence)
-
-    def _first_valid_waiter(self, key: RequestResourceKey) -> RequestWaiter | None:
-        queue = self._queues.get(key)
-        if queue is None:
-            return None
-        for waiter in queue:
-            if waiter.waiter_id in self._queued and self._waiter_groups.get(waiter.waiter_id) == key:
-                return waiter
-        return None
-
-    def _purge_queue_head(self, key: RequestResourceKey) -> None:
-        queue = self._queues.get(key)
-        if queue is None:
-            return
-        while queue:
-            waiter = queue[0]
-            if waiter.waiter_id in self._queued and self._waiter_groups.get(waiter.waiter_id) == key:
-                break
-            queue.popleft()
-
-
 @dataclass
 class _GlobalCapState:
     limits_by_alias: dict[str, int] = field(default_factory=dict)
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py
new file mode 100644
index 000000000..ad6f779d7
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py
@@ -0,0 +1,19 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass
+class AdaptiveRequestLimitState:
+    current_limit: int
+    in_flight: int = 0
+    blocked_until: float = 0.0
+    success_streak: int = 0
+    waiters: int = 0
+    rate_limit_ceiling: int = 0
+    consecutive_rate_limits: int = 0
+    active_lease_count: int = 0
+    last_outcome: str | None = None
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/outcomes.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/outcomes.py
new file mode 100644
index 000000000..3399b07f4
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/outcomes.py
@@ -0,0 +1,31 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass, field
+from typing import Literal
+
+
+@dataclass(frozen=True)
+class RequestReleaseOutcome:
+    kind: Literal[
+        "success",
+        "rate_limited",
+        "provider_failure",
+        "provider_timeout",
+        "local_cancelled",
+        "local_timeout",
+        "unexpected_exception",
+    ]
+    retry_after_seconds: float | None = None
+    provider_status: int | None = None
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
+
+
+@dataclass(frozen=True)
+class ReleaseResult:
+    released: bool
+    reason: Literal["released", "duplicate", "stale_lease", "wrong_controller_generation", "unknown_lease"]
+    diagnostics: Mapping[str, object] = field(default_factory=dict)
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py
new file mode 100644
index 000000000..9f0386dc7
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py
@@ -0,0 +1,52 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass
+from typing import Protocol
+
+from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
+from data_designer.engine.models.resources import ProviderModelKey
+
+
+@dataclass(frozen=True)
+class RequestPressureSnapshot:
+    captured_at: float
+    sequence: int
+    resource: RequestResourceKey
+    effective_max: int
+    current_limit: int
+    in_flight_count: int
+    active_lease_count: int
+    waiters: int
+    blocked_until_monotonic: float | None
+    cooldown_remaining_seconds: float
+    rate_limit_ceiling: int
+    consecutive_rate_limits: int
+    last_outcome: str | None
+    leak_diagnostics: Mapping[str, int]
+
+
+@dataclass(frozen=True)
+class ProviderModelPressureSnapshot:
+    captured_at: float
+    sequence: int
+    provider_model: ProviderModelKey
+    static_cap: int
+    aggregate_in_flight: int
+    aggregate_active_lease_count: int
+    aliases: tuple[str, ...]
+    raw_caps: Mapping[str, int | None]
+    domains: Mapping[RequestDomain, int]
+
+
+class RequestPressureSnapshotProvider(Protocol):
+    def snapshot(self, resource: RequestResourceKey) -> RequestPressureSnapshot | None: ...
+
+    def snapshots(self) -> Mapping[RequestResourceKey, RequestPressureSnapshot]: ...
+
+    def global_snapshot(self, provider: str, model: str) -> ProviderModelPressureSnapshot | None: ...
+
+    def global_snapshots(self) -> Mapping[ProviderModelKey, ProviderModelPressureSnapshot]: ...
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
new file mode 100644
index 000000000..626fd3426
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
@@ -0,0 +1,184 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import heapq
+from collections import Counter, deque
+from collections.abc import Callable, Mapping
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from data_designer.engine.models.request_admission.resources import RequestAdmissionItem, RequestResourceKey
+from data_designer.engine.models.resources import ProviderModelKey
+
+if TYPE_CHECKING:
+    from data_designer.engine.models.request_admission.controller import RequestAdmissionLease
+
+
+@dataclass
+class RequestWaiter:
+    waiter_id: str
+    item: RequestAdmissionItem
+    enqueued_at: float
+    deadline_monotonic: float | None = None
+    assigned_lease: RequestAdmissionLease | None = None
+
+
+@dataclass(frozen=True)
+class RequestQueueView:
+    queued_total: int
+    queued_by_group: Mapping[RequestResourceKey, int]
+    queued_demand_by_resource: Mapping[RequestResourceKey, int]
+    aggregate_provider_model_waiters: Mapping[ProviderModelKey, int]
+
+
+@dataclass(frozen=True)
+class RequestQueueSelection:
+    waiter: RequestWaiter
+    item: RequestAdmissionItem
+    waiter_id: str
+    queue_view: RequestQueueView
+    sequence_version: int
+
+
+class RequestFairQueue:
+    """Weighted fair waiter queue used by request admission."""
+
+    def __init__(self) -> None:
+        self._queues: dict[RequestResourceKey, deque[RequestWaiter]] = {}
+        self._queued: dict[str, RequestWaiter] = {}
+        self._waiter_groups: dict[str, RequestResourceKey] = {}
+        self._group_finish: dict[RequestResourceKey, float] = {}
+        self._heap: list[tuple[float, int, RequestResourceKey]] = []
+        self._active_heap_entries: dict[RequestResourceKey, tuple[float, int]] = {}
+        self._sequence = 0
+        self._sequence_version = 0
+        self._virtual_time = 0.0
+
+    @property
+    def has_waiters(self) -> bool:
+        return bool(self._queued)
+
+    def contains(self, waiter_id: str) -> bool:
+        return waiter_id in self._queued
+
+    def enqueue(self, waiter: RequestWaiter) -> bool:
+        if waiter.waiter_id in self._queued:
+            return False
+        key = waiter.item.group.key
+        queue = self._queues.setdefault(key, deque())
+        queue.append(waiter)
+        self._queued[waiter.waiter_id] = waiter
+        self._waiter_groups[waiter.waiter_id] = key
+        self._activate_group(key)
+        self._sequence_version += 1
+        return True
+
+    def remove(self, waiter_id: str) -> RequestWaiter | None:
+        waiter = self._queued.pop(waiter_id, None)
+        if waiter is None:
+            return None
+        self._waiter_groups.pop(waiter_id, None)
+        self._sequence_version += 1
+        return waiter
+
+    def select_next(
+        self, is_eligible: Callable[[RequestWaiter, RequestQueueView], bool]
+    ) -> RequestQueueSelection | None:
+        view = self.view()
+        heap_copy = list(self._heap)
+        heapq.heapify(heap_copy)
+        active_seen: set[RequestResourceKey] = set()
+        while heap_copy:
+            finish, sequence, key = heapq.heappop(heap_copy)
+            if key in active_seen:
+                continue
+            if self._active_heap_entries.get(key) != (finish, sequence):
+                continue
+            active_seen.add(key)
+            waiter = self._first_valid_waiter(key)
+            if waiter is None:
+                continue
+            if not is_eligible(waiter, view):
+                continue
+            return RequestQueueSelection(
+                waiter=waiter,
+                item=waiter.item,
+                waiter_id=waiter.waiter_id,
+                queue_view=view,
+                sequence_version=self._sequence_version,
+            )
+        return None
+
+    def commit(self, selection: RequestQueueSelection) -> RequestWaiter | None:
+        if selection.sequence_version != self._sequence_version:
+            return None
+        key = self._waiter_groups.get(selection.waiter_id)
+        if key is None or key != selection.item.group.key:
+            return None
+        queue = self._queues.get(key)
+        if queue is None:
+            return None
+        self._purge_queue_head(key)
+        if not queue or queue[0].waiter_id != selection.waiter_id:
+            return None
+
+        waiter = queue.popleft()
+        self._queued.pop(waiter.waiter_id, None)
+        self._waiter_groups.pop(waiter.waiter_id, None)
+        self._active_heap_entries.pop(key, None)
+        weight = max(selection.item.group.weight, 1.0)
+        finish = self._group_finish.get(key, self._virtual_time)
+        self._virtual_time = max(self._virtual_time, finish)
+        self._group_finish[key] = self._virtual_time + (1.0 / weight)
+        self._sequence_version += 1
+        self._purge_queue_head(key)
+        if queue:
+            self._activate_group(key)
+        return waiter
+
+    def view(self) -> RequestQueueView:
+        queued_by_group: Counter[RequestResourceKey] = Counter()
+        demand_by_resource: Counter[RequestResourceKey] = Counter()
+        aggregate_waiters: Counter[ProviderModelKey] = Counter()
+        for waiter in self._queued.values():
+            resource = waiter.item.resource
+            queued_by_group[waiter.item.group.key] += 1
+            demand_by_resource[resource] += 1
+            aggregate_waiters[resource.provider_model_key] += 1
+        return RequestQueueView(
+            queued_total=len(self._queued),
+            queued_by_group=dict(queued_by_group),
+            queued_demand_by_resource=dict(demand_by_resource),
+            aggregate_provider_model_waiters=dict(aggregate_waiters),
+        )
+
+    def _activate_group(self, key: RequestResourceKey) -> None:
+        self._purge_queue_head(key)
+        queue = self._queues.get(key)
+        if not queue or key in self._active_heap_entries:
+            return
+        self._sequence += 1
+        finish = self._group_finish.get(key, self._virtual_time)
+        heapq.heappush(self._heap, (finish, self._sequence, key))
+        self._active_heap_entries[key] = (finish, self._sequence)
+
+    def _first_valid_waiter(self, key: RequestResourceKey) -> RequestWaiter | None:
+        queue = self._queues.get(key)
+        if queue is None:
+            return None
+        for waiter in queue:
+            if waiter.waiter_id in self._queued and self._waiter_groups.get(waiter.waiter_id) == key:
+                return waiter
+        return None
+
+    def _purge_queue_head(self, key: RequestResourceKey) -> None:
+        queue = self._queues.get(key)
+        if queue is None:
+            return
+        while queue:
+            waiter = queue[0]
+            if waiter.waiter_id in self._queued and self._waiter_groups.get(waiter.waiter_id) == key:
+                break
+            queue.popleft()
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/resolver.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/resolver.py
new file mode 100644
index 000000000..462e77427
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/resolver.py
@@ -0,0 +1,40 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
+from data_designer.engine.models.resources import ProviderModelKey
+
+
+@dataclass(frozen=True)
+class ResolvedRequestResource:
+    provider_model: ProviderModelKey
+    resource: RequestResourceKey
+    aliases: tuple[str, ...] = ()
+    generation_kind: str | None = None
+
+
+class RequestResourceResolver:
+    """Canonical provider/model/domain request-resource identity factory."""
+
+    def resolve(
+        self,
+        *,
+        provider_name: str,
+        model_id: str,
+        domain: RequestDomain,
+        model_alias: str | None = None,
+        provider_alias: str | None = None,
+        generation_kind: str | None = None,
+    ) -> ResolvedRequestResource:
+        resource = RequestResourceKey(provider_name=provider_name, model_id=model_id, domain=domain)
+        aliases = tuple(alias for alias in (provider_alias, model_alias) if alias)
+        return ResolvedRequestResource(
+            provider_model=resource.provider_model_key,
+            resource=resource,
+            aliases=aliases,
+            generation_kind=generation_kind,
+        )
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/resources.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/resources.py
new file mode 100644
index 000000000..b7b4bd2cd
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/resources.py
@@ -0,0 +1,48 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+
+from data_designer.engine.models.resources import ProviderModelKey
+
+
+class RequestDomain(str, Enum):
+    CHAT = "chat"
+    EMBEDDING = "embedding"
+    IMAGE = "image"
+    HEALTHCHECK = "healthcheck"
+
+
+@dataclass(frozen=True, order=True)
+class RequestResourceKey:
+    provider_name: str
+    model_id: str
+    domain: RequestDomain
+
+    @property
+    def provider_model_key(self) -> ProviderModelKey:
+        return ProviderModelKey(self.provider_name, self.model_id)
+
+
+@dataclass(frozen=True)
+class RequestGroupSpec:
+    key: RequestResourceKey
+    weight: float = 1.0
+
+
+@dataclass(frozen=True)
+class RequestEventContext:
+    captured_correlation: object | None = None
+    task_execution_id: str | None = None
+    request_attempt_id: str | None = None
+
+
+@dataclass(frozen=True)
+class RequestAdmissionItem:
+    resource: RequestResourceKey
+    group: RequestGroupSpec
+    queue_wait_timeout_seconds: float | None = None
+    event_context: RequestEventContext | None = None
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/resources.py b/packages/data-designer-engine/src/data_designer/engine/models/resources.py
new file mode 100644
index 000000000..091e2936b
--- /dev/null
+++ b/packages/data-designer-engine/src/data_designer/engine/models/resources.py
@@ -0,0 +1,21 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from dataclasses import dataclass
+
+
+@dataclass(frozen=True, order=True)
+class ProviderModelKey:
+    provider_name: str
+    model_id: str
+
+
+@dataclass
+class ProviderModelStaticCap:
+    cap: int
+    aliases: tuple[str, ...]
+    raw_caps: Mapping[str, int | None]
+    merge_rule: str = "min_same_endpoint"
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_completion_tracker.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_completion.py
similarity index 98%
rename from packages/data-designer-engine/tests/engine/dataset_builders/utils/test_completion_tracker.py
rename to packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_completion.py
index da98bd871..d03cbeabf 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_completion_tracker.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_completion.py
@@ -14,9 +14,9 @@
     SamplerColumnConfig,
 )
 from data_designer.config.sampler_params import SamplerType
-from data_designer.engine.dataset_builders.utils.completion_tracker import CompletionTracker
+from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker
+from data_designer.engine.dataset_builders.scheduling.task_model import SliceRef, Task
 from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
-from data_designer.engine.dataset_builders.utils.task_model import SliceRef, Task
 
 MODEL_ALIAS = "stub"
 
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_fair_task_queue.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_queue.py
similarity index 95%
rename from packages/data-designer-engine/tests/engine/dataset_builders/utils/test_fair_task_queue.py
rename to packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_queue.py
index bbc900998..e2a9179f0 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_fair_task_queue.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_queue.py
@@ -5,15 +5,15 @@
 
 from collections import Counter
 
-from data_designer.engine.dataset_builders.utils.fair_task_queue import FairTaskQueue, QueueView
-from data_designer.engine.dataset_builders.utils.task_model import Task
-from data_designer.engine.dataset_builders.utils.task_scheduling import (
+from data_designer.engine.dataset_builders.scheduling.queue import FairTaskQueue, QueueView
+from data_designer.engine.dataset_builders.scheduling.resources import (
     SchedulableTask,
     SchedulerResourceRequest,
     TaskGroupKey,
     TaskGroupSpec,
     stable_task_id,
 )
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
 
 
 def _task(column: str, row_index: int) -> Task:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_scheduling.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
similarity index 96%
rename from packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_scheduling.py
rename to packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
index 81fa0e2ee..ab8ce0a2c 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_scheduling.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
@@ -10,8 +10,8 @@
 
 from data_designer.config.column_configs import ExpressionColumnConfig
 from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
-from data_designer.engine.dataset_builders.utils.task_model import Task
-from data_designer.engine.dataset_builders.utils.task_scheduling import TaskSchedulingResolver
+from data_designer.engine.dataset_builders.scheduling.resolver import TaskSchedulingResolver
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
 
 
 class _LocalGenerator:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resources.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resources.py
new file mode 100644
index 000000000..935f2c074
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resources.py
@@ -0,0 +1,62 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import pytest
+
+from data_designer.engine.dataset_builders.scheduling.resources import (
+    SchedulableTask,
+    SchedulerResourceRequest,
+    TaskGroupKey,
+    TaskGroupSpec,
+    stable_task_id,
+)
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
+
+
+def test_scheduler_resource_request_defaults_to_submission() -> None:
+    request = SchedulerResourceRequest()
+
+    assert request.amounts == {"submission": 1}
+
+
+def test_scheduler_resource_request_rejects_unknown_resource() -> None:
+    with pytest.raises(ValueError, match="Unknown scheduler resource key"):
+        SchedulerResourceRequest({"gpu": 1})  # type: ignore[arg-type]
+
+
+def test_scheduler_resource_request_rejects_non_positive_amounts() -> None:
+    with pytest.raises(ValueError, match="must be a positive integer"):
+        SchedulerResourceRequest({"submission": 0})
+
+
+def test_stable_task_id_is_stable_for_task_identity() -> None:
+    task = Task(column="answer", row_group=3, row_index=8, task_type="cell")
+
+    assert stable_task_id(task) == stable_task_id(task)
+    assert stable_task_id(task).startswith("task-")
+
+
+def test_stable_task_id_distinguishes_task_identity_fields() -> None:
+    first = Task(column="answer", row_group=3, row_index=8, task_type="cell")
+    second = Task(column="answer", row_group=3, row_index=9, task_type="cell")
+
+    assert stable_task_id(first) != stable_task_id(second)
+
+
+def test_schedulable_task_binds_payload_group_and_resource_request() -> None:
+    task = Task(column="answer", row_group=0, row_index=1, task_type="cell")
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("nvidia", "nemotron")), admitted_limit=2)
+    request = SchedulerResourceRequest({"submission": 1, "llm_wait": 1})
+
+    item = SchedulableTask(
+        task_id=stable_task_id(task),
+        payload=task,
+        group=group,
+        resource_request=request,
+    )
+
+    assert item.payload == task
+    assert item.group == group
+    assert item.resource_request.amounts["llm_wait"] == 1
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_admission.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
similarity index 94%
rename from packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_admission.py
rename to packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
index fd7642763..b8c6acfb1 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_admission.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
@@ -3,22 +3,22 @@
 
 from __future__ import annotations
 
-from data_designer.engine.dataset_builders.utils.fair_task_queue import FairTaskQueue
-from data_designer.engine.dataset_builders.utils.task_admission import (
-    BoundedBorrowTaskAdmissionPolicyConfig,
-    TaskAdmissionConfig,
-    TaskAdmissionController,
-    TaskAdmissionDenied,
-    TaskAdmissionLease,
-)
-from data_designer.engine.dataset_builders.utils.task_model import Task
-from data_designer.engine.dataset_builders.utils.task_scheduling import (
+from data_designer.engine.dataset_builders.scheduling.queue import FairTaskQueue
+from data_designer.engine.dataset_builders.scheduling.resources import (
     SchedulableTask,
     SchedulerResourceRequest,
     TaskGroupKey,
     TaskGroupSpec,
     stable_task_id,
 )
+from data_designer.engine.dataset_builders.scheduling.task_admission import (
+    TaskAdmissionConfig,
+    TaskAdmissionController,
+    TaskAdmissionDenied,
+    TaskAdmissionLease,
+)
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.dataset_builders.scheduling.task_policies import BoundedBorrowTaskAdmissionPolicyConfig
 
 
 def _item(column: str, row: int = 0, *, group: TaskGroupSpec | None = None) -> SchedulableTask:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_model.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_model.py
similarity index 96%
rename from packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_model.py
rename to packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_model.py
index 5d5716213..cdc5e6c6a 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_task_model.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_model.py
@@ -5,7 +5,7 @@
 
 import pytest
 
-from data_designer.engine.dataset_builders.utils.task_model import Task, TaskResult, TaskTrace
+from data_designer.engine.dataset_builders.scheduling.task_model import Task, TaskResult, TaskTrace
 
 
 def test_task_is_frozen() -> None:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_policies.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_policies.py
new file mode 100644
index 000000000..286fdee96
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_policies.py
@@ -0,0 +1,120 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from data_designer.engine.dataset_builders.scheduling.queue import FairTaskQueue, QueueView
+from data_designer.engine.dataset_builders.scheduling.resources import (
+    SchedulableTask,
+    SchedulerResourceRequest,
+    TaskGroupKey,
+    TaskGroupSpec,
+    stable_task_id,
+)
+from data_designer.engine.dataset_builders.scheduling.task_admission import TaskAdmissionLease, TaskAdmissionView
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.dataset_builders.scheduling.task_policies import (
+    BoundedBorrowTaskAdmissionPolicy,
+    BoundedBorrowTaskAdmissionPolicyConfig,
+    StrictFairTaskAdmissionPolicy,
+)
+
+
+def _item(column: str, group: TaskGroupSpec) -> SchedulableTask:
+    task = Task(column=column, row_group=0, row_index=0, task_type="cell")
+    return SchedulableTask(
+        task_id=stable_task_id(task),
+        payload=task,
+        group=group,
+        resource_request=SchedulerResourceRequest({"submission": 1}),
+    )
+
+
+def _queue_view(*items: SchedulableTask) -> QueueView:
+    queue = FairTaskQueue()
+    queue.enqueue(items)
+    return queue.view()
+
+
+def _admission_view(
+    *,
+    running_group: TaskGroupKey,
+    running_count: int = 1,
+    debt: int = 0,
+) -> TaskAdmissionView:
+    return TaskAdmissionView(
+        resource_limits={"submission": 4},
+        resources_available={"submission": 3},
+        leased_resources={"submission": running_count},
+        leased_resources_by_group={running_group: {"submission": running_count}},
+        running_counts_by_group={running_group: running_count},
+        policy_debt_by_group_resource={(running_group, "submission"): debt} if debt else {},
+    )
+
+
+def _lease(item: SchedulableTask) -> TaskAdmissionLease:
+    return TaskAdmissionLease(
+        lease_id="lease",
+        item=item,
+        resources={"submission": 1},
+        acquired_at=0.0,
+        controller_generation="generation",
+    )
+
+
+def test_strict_fair_policy_allows_group_without_peer_pressure() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    item = _item("a", group)
+    policy = StrictFairTaskAdmissionPolicy()
+
+    decision = policy.evaluate(item, _queue_view(item), _admission_view(running_group=group.key))
+
+    assert decision.allowed is True
+
+
+def test_strict_fair_policy_denies_capped_group_with_peer_pressure() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    peer_group = TaskGroupSpec(TaskGroupKey(kind="local", identity=("peer",)))
+    item = _item("a", group)
+    peer = _item("b", peer_group)
+    policy = StrictFairTaskAdmissionPolicy()
+
+    decision = policy.evaluate(item, _queue_view(item, peer), _admission_view(running_group=group.key))
+
+    assert decision.allowed is False
+    assert decision.reason == "group_cap"
+
+
+def test_bounded_borrow_policy_records_borrow_without_peer_pressure() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    item = _item("a", group)
+    policy = BoundedBorrowTaskAdmissionPolicy(BoundedBorrowTaskAdmissionPolicyConfig(default_borrow_ceiling=1))
+
+    decision = policy.evaluate(item, _queue_view(item), _admission_view(running_group=group.key))
+    delta = policy.on_acquire(_lease(item), decision)
+
+    assert decision.allowed is True
+    assert delta.debt_changes == {(group.key, "submission"): 1}
+
+
+def test_bounded_borrow_policy_denies_existing_debt_under_peer_pressure() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    peer_group = TaskGroupSpec(TaskGroupKey(kind="local", identity=("peer",)))
+    item = _item("a", group)
+    peer = _item("b", peer_group)
+    policy = BoundedBorrowTaskAdmissionPolicy(BoundedBorrowTaskAdmissionPolicyConfig(default_borrow_ceiling=1))
+
+    decision = policy.evaluate(item, _queue_view(item, peer), _admission_view(running_group=group.key, debt=1))
+
+    assert decision.allowed is False
+    assert decision.reason == "borrow_debt"
+
+
+def test_bounded_borrow_policy_releases_debt() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    item = _item("a", group)
+    policy = BoundedBorrowTaskAdmissionPolicy(BoundedBorrowTaskAdmissionPolicyConfig(default_borrow_ceiling=1))
+
+    delta = policy.on_release(_lease(item))
+
+    assert delta.debt_changes == {(group.key, "submission"): -1}
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py
index 684c009ba..8f75877f4 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py
@@ -24,7 +24,7 @@
 )
 from data_designer.engine.dataset_builders.async_scheduler import AsyncTaskScheduler
 from data_designer.engine.dataset_builders.dataset_builder import DatasetBuilder
-from data_designer.engine.dataset_builders.utils.completion_tracker import CompletionTracker, FrontierDelta
+from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker, FrontierDelta
 from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
 from data_designer.engine.dataset_builders.utils.row_group_buffer import RowGroupBufferManager
 from data_designer.engine.resources.resource_provider import ResourceProvider
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index 26ab918ea..ebc1b0d4f 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -33,11 +33,11 @@
 from data_designer.engine.column_generators.generators.custom import CustomColumnGenerator
 from data_designer.engine.dataset_builders.async_scheduler import AsyncTaskScheduler
 from data_designer.engine.dataset_builders.errors import DatasetGenerationError
-from data_designer.engine.dataset_builders.utils.completion_tracker import CompletionTracker, FrontierDelta
+from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker, FrontierDelta
+from data_designer.engine.dataset_builders.scheduling.task_admission import TaskAdmissionConfig, TaskAdmissionLease
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
 from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
 from data_designer.engine.dataset_builders.utils.row_group_buffer import RowGroupBufferManager
-from data_designer.engine.dataset_builders.utils.task_admission import TaskAdmissionConfig, TaskAdmissionLease
-from data_designer.engine.dataset_builders.utils.task_model import Task
 from data_designer.engine.models.errors import (
     RETRYABLE_MODEL_ERRORS,
     ModelInternalServerError,
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_execution_graph.py b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_execution_graph.py
index dfd219fd5..6a5b31a51 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_execution_graph.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/utils/test_execution_graph.py
@@ -20,9 +20,9 @@
 from data_designer.config.utils.code_lang import CodeLang
 from data_designer.config.validator_params import CodeValidatorParams
 from data_designer.engine.dataset_builders.multi_column_configs import SamplerMultiColumnConfig
+from data_designer.engine.dataset_builders.scheduling.task_model import SliceRef
 from data_designer.engine.dataset_builders.utils.errors import ConfigCompilationError, DAGCircularDependencyError
 from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
-from data_designer.engine.dataset_builders.utils.task_model import SliceRef
 
 MODEL_ALIAS = "stub-model-alias"
 
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_factory.py b/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
index d365f9683..df86e12d8 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
@@ -19,8 +19,8 @@
 from data_designer.engine.models.clients.adapters.openai_compatible import OpenAICompatibleClient
 from data_designer.engine.models.clients.factory import create_model_client
 from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
-from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController
 from data_designer.engine.models.clients.retry import RetryConfig
+from data_designer.engine.models.request_admission.controller import AdaptiveRequestAdmissionController
 from data_designer.engine.secret_resolver import SecretResolver
 
 
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
index 6c1cbea4a..67325b7b0 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
@@ -9,7 +9,6 @@
 
 from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
 from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
-from data_designer.engine.models.clients.request_admission import AdaptiveRequestAdmissionController, RequestDomain
 from data_designer.engine.models.clients.types import (
     AssistantMessage,
     ChatCompletionRequest,
@@ -20,6 +19,8 @@
     ImageGenerationResponse,
     ImagePayload,
 )
+from data_designer.engine.models.request_admission.controller import AdaptiveRequestAdmissionController
+from data_designer.engine.models.request_admission.resources import RequestDomain
 from data_designer.engine.observability import InMemoryAdmissionEventSink
 
 
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_request_admission.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
similarity index 95%
rename from packages/data-designer-engine/tests/engine/models/clients/test_request_admission.py
rename to packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index c7d01cec9..9a743f457 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_request_admission.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -7,16 +7,18 @@
 
 import pytest
 
-from data_designer.engine.models.clients.request_admission import (
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
+from data_designer.engine.models.request_admission.controller import (
     AdaptiveRequestAdmissionController,
-    RequestAdmissionConfig,
     RequestAdmissionDenied,
     RequestAdmissionError,
-    RequestAdmissionItem,
     RequestAdmissionLease,
+)
+from data_designer.engine.models.request_admission.outcomes import RequestReleaseOutcome
+from data_designer.engine.models.request_admission.resources import (
+    RequestAdmissionItem,
     RequestDomain,
     RequestGroupSpec,
-    RequestReleaseOutcome,
     RequestResourceKey,
 )
 
diff --git a/packages/data-designer-engine/tests/engine/test_capacity.py b/packages/data-designer-engine/tests/engine/test_capacity.py
index fe9c71a16..6fd4daa33 100644
--- a/packages/data-designer-engine/tests/engine/test_capacity.py
+++ b/packages/data-designer-engine/tests/engine/test_capacity.py
@@ -12,13 +12,9 @@
     RequestAdmissionConfigSnapshot,
     RowGroupAdmission,
 )
-from data_designer.engine.models.clients.request_admission import (
-    ProviderModelKey,
-    ProviderModelStaticCap,
-    RequestAdmissionConfig,
-    RequestDomain,
-    RequestResourceKey,
-)
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
+from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
+from data_designer.engine.models.resources import ProviderModelKey, ProviderModelStaticCap
 
 
 def test_request_admission_config_snapshot_records_resources() -> None:
diff --git a/packages/data-designer-engine/tests/engine/test_observability.py b/packages/data-designer-engine/tests/engine/test_observability.py
new file mode 100644
index 000000000..55a2896b1
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/test_observability.py
@@ -0,0 +1,96 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+from data_designer.engine.observability import (
+    CorrelatedRuntimeView,
+    InMemoryAdmissionEventSink,
+    RequestAdmissionEvent,
+    RuntimeCorrelation,
+    RuntimeCorrelationProvider,
+    SchedulerAdmissionEvent,
+)
+
+
+def _correlation() -> RuntimeCorrelation:
+    return RuntimeCorrelation(
+        run_id="run",
+        row_group=1,
+        task_column="answer",
+        task_type="cell",
+        scheduling_group_kind="model",
+        scheduling_group_identity_hash="hash",
+        task_execution_id="task-exec",
+    )
+
+
+def test_runtime_correlation_provider_sets_and_resets_context() -> None:
+    provider = RuntimeCorrelationProvider()
+    correlation = _correlation()
+
+    token = provider.set(correlation)
+    assert provider.current() == correlation
+
+    provider.reset(token)
+    assert provider.current() is None
+
+
+def test_admission_events_capture_correlation_and_diagnostics() -> None:
+    correlation = _correlation()
+
+    scheduler_event = SchedulerAdmissionEvent.capture(
+        "task_lease_acquired",
+        sequence=1,
+        correlation=correlation,
+        task_id="task-1",
+        task_lease_id="lease-1",
+        diagnostics={"resource": "submission"},
+    )
+    request_event = RequestAdmissionEvent.capture(
+        "request_lease_acquired",
+        sequence=2,
+        correlation=correlation,
+        request_attempt_id="request-1",
+        request_lease_id="lease-2",
+        diagnostics={"resource": "chat"},
+    )
+
+    assert scheduler_event.captured_correlation == correlation
+    assert scheduler_event.task_id == "task-1"
+    assert scheduler_event.diagnostics == {"resource": "submission"}
+    assert request_event.captured_correlation == correlation
+    assert request_event.request_attempt_id == "request-1"
+    assert request_event.diagnostics == {"resource": "chat"}
+
+
+def test_in_memory_admission_event_sink_collects_scheduler_and_request_events() -> None:
+    sink = InMemoryAdmissionEventSink()
+    scheduler_event = SchedulerAdmissionEvent.capture("selected", sequence=1)
+    request_event = RequestAdmissionEvent.capture("request_wait_started", sequence=2)
+
+    sink.emit_scheduler_event(scheduler_event)
+    sink.emit_request_event(request_event)
+
+    assert sink.scheduler_events == [scheduler_event]
+    assert sink.request_events == [request_event]
+
+
+def test_correlated_runtime_view_timeline_sorts_events() -> None:
+    scheduler_event = SchedulerAdmissionEvent(event_kind="selected", captured_at_monotonic=2.0, sequence=1)
+    first_request_event = RequestAdmissionEvent(
+        event_kind="request_wait_started",
+        captured_at_monotonic=1.0,
+        sequence=3,
+    )
+    second_request_event = RequestAdmissionEvent(
+        event_kind="request_lease_acquired",
+        captured_at_monotonic=2.0,
+        sequence=0,
+    )
+    view = CorrelatedRuntimeView(
+        scheduler_events=(scheduler_event,),
+        request_events=(first_request_event, second_request_event),
+    )
+
+    assert view.timeline == (first_request_event, second_request_event, scheduler_event)
diff --git a/packages/data-designer/src/data_designer/interface/results.py b/packages/data-designer/src/data_designer/interface/results.py
index 29f222279..c90070c58 100644
--- a/packages/data-designer/src/data_designer/interface/results.py
+++ b/packages/data-designer/src/data_designer/interface/results.py
@@ -19,7 +19,7 @@
 if TYPE_CHECKING:
     import pandas as pd
 
-    from data_designer.engine.dataset_builders.utils.task_model import TaskTrace
+    from data_designer.engine.dataset_builders.scheduling.task_model import TaskTrace
 
 ExportFormat = Literal["jsonl", "csv", "parquet"]
 SUPPORTED_EXPORT_FORMATS: tuple[str, ...] = get_args(ExportFormat)
diff --git a/plans/645/README.md b/plans/645/README.md
index 56d55fd7d..0d95081fb 100644
--- a/plans/645/README.md
+++ b/plans/645/README.md
@@ -10,6 +10,7 @@ This directory is the maintainer source of truth while the epic is active. Issue
 
 - [Architecture](architecture.md): target system shape, ownership boundaries, invariants, and non-goals.
 - [Contracts](contracts.md): durable DTO, protocol, event, and config names.
+- [Module ownership](module-ownership.md): final repository/module homes, import rules, audience boundaries, tests, and benchmark ownership.
 - [Capacity model](capacity-model.md): layered capacity vocabulary and ownership.
 - [Task admission](task-admission.md): scheduler-owned ready selection, task leases, policy hooks, bounded borrowing, and resource-vector direction.
 - [Request admission](request-admission.md): model-call admission, AIMD controller shape, dynamic request semantics, and replacement of pre-epic request-control names.
@@ -22,7 +23,7 @@ This directory is the maintainer source of truth while the epic is active. Issue
 
 Recommended reading paths:
 
-- Implementers: [Architecture](architecture.md), [Contracts](contracts.md), then the topic file for the issue being implemented.
+- Implementers: [Architecture](architecture.md), [Contracts](contracts.md), [Module ownership](module-ownership.md), then the topic file for the issue being implemented.
 - Plugin documentation authors: [Contracts](contracts.md#metadata-contracts), [Architecture](architecture.md#audience-and-api-boundaries), and [Migration and cleanup](migration-and-cleanup.md#documentation-cleanup).
 - Operators and performance reviewers: [Capacity model](capacity-model.md), [Observability](observability.md), and [Benchmark plan](benchmark-plan.md).
 - Issue owners: [Issue map](issue-map.md), then the linked source sections for the issue.
@@ -76,7 +77,7 @@ AsyncTaskScheduler
   -> CompletionTracker.ready_frontier()
   -> FairTaskQueue.enqueue(...)
   -> FairTaskQueue.select_next(scheduler-owned eligibility callback)
-  -> TaskAdmissionController.try_acquire(...)
+  -> TaskAdmissionController.try_acquire(selection.item, selection.queue_view)
   -> FairTaskQueue.commit(...)
   -> execute admitted task/generator code
 
diff --git a/plans/645/architecture.md b/plans/645/architecture.md
index 6ebfdf362..da2838002 100644
--- a/plans/645/architecture.md
+++ b/plans/645/architecture.md
@@ -28,7 +28,7 @@ AsyncTaskScheduler
   -> CompletionTracker.ready_frontier()
   -> FairTaskQueue.enqueue(...)
   -> FairTaskQueue.select_next(scheduler-owned eligibility callback)
-  -> TaskAdmissionController.try_acquire(selection.item, queue_view)
+  -> TaskAdmissionController.try_acquire(selection.item, selection.queue_view)
   -> FairTaskQueue.commit(selection)
   -> execute admitted task/generator code
 
@@ -68,6 +68,8 @@ This is not a passive pipeline where `CompletionTracker`, `FairTaskQueue`, or `T
 
 The plan uses several contract categories. Keeping them separate prevents internal scheduling mechanics from becoming accidental plugin API.
 
+Durable engine vocabulary is maintainer-facing unless this plan explicitly marks it plugin-facing or operator-facing. See [Module ownership](module-ownership.md) for final module homes, import rules, and test/benchmark ownership.
+
 | Audience | Durable surface | Must not expose |
 | --- | --- | --- |
 | Plugin authors | `ColumnGenerator.get_scheduling_metadata()` and `SchedulingMetadata` | queue state, task leases, request domains, AIMD state, runtime pressure |
@@ -85,6 +87,24 @@ Package ownership follows Data Designer's structural layering:
 
 When a contract is shared across packages, the lower package owns the data definition and the higher package owns presentation or orchestration. Engine code may import config contracts; config code must not import engine runtime protocols.
 
+Target repository ownership is part of the architecture, not an implementation detail. The epic does not include compatibility aliases, shim modules, transitional reexports, or duplicate old/new paths for scheduler or request-admission names.
+
+## Module Ownership
+
+The final target module layout is defined in [Module ownership](module-ownership.md). In summary:
+
+- generator-facing metadata lives in `data_designer.config.scheduling`
+- scheduler task models, readiness, queues, task admission, and task policies live under `data_designer.engine.dataset_builders.scheduling`
+- `AsyncTaskScheduler` remains in `data_designer.engine.dataset_builders.async_scheduler` as the runtime coordinator only
+- shared provider/model identity lives in `data_designer.engine.models.resources`
+- concrete request admission lives under `data_designer.engine.models.request_admission`
+- `ModelRequestExecutor` stays under `data_designer.engine.models.clients` because it wraps model clients at the acquire/call/release boundary
+- capacity diagnostics live in `data_designer.engine.capacity`
+- runtime admission events and correlation live in `data_designer.engine.observability`
+- product/provider usage telemetry remains separate in `data_designer.engine.models.telemetry`
+
+Implementation PRs should use these final homes directly and must not leave production compatibility names, compatibility modules, old-path reexports, or durable tests for replaced names.
+
 ## Two-Stage Admission
 
 Task admission controls when ready dataset work may become a running worker. Request admission controls concrete provider/model/domain calls at the moment they are made.
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index 92873eee4..eb3510646 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -1,6 +1,8 @@
 # Contracts
 
-This file records the durable names and semantics used by the async scheduling architecture. The exact implementation can evolve, but these names are the normative spec vocabulary for the epic. Topic files may explain behavior, but should not redefine fields or return shapes in ways that conflict with this file.
+This file records the durable names and semantics used by the async scheduling architecture. Implementation details inside the owning target modules can evolve, but these names are the normative spec vocabulary for the epic. Topic files may explain behavior, but should not redefine fields or return shapes in ways that conflict with this file.
+
+Durable names in this file are not public API by default. Publicness and final module homes are defined in [Module ownership](module-ownership.md).
 
 ## Package Ownership
 
@@ -12,6 +14,8 @@ This file records the durable names and semantics used by the async scheduling a
 
 Config-layer contracts must not import engine runtime protocols. Engine contracts may consume config-layer DTOs.
 
+The final repository layout is specified in [Module ownership](module-ownership.md). Runtime contracts must live in their owning target modules; do not preserve old module paths through aliases, shim files, or broad package reexports.
+
 ## Config Surface Status
 
 | Contract | V1 status | Owner |
diff --git a/plans/645/issue-map.md b/plans/645/issue-map.md
index 4e0415948..f114fac19 100644
--- a/plans/645/issue-map.md
+++ b/plans/645/issue-map.md
@@ -19,18 +19,18 @@ Use GitHub issues for:
 
 | Issue | Implementation Focus | Source Sections |
 | --- | --- | --- |
-| #641 | Add `SchedulingMetadata` and generator override contract | [architecture.md](architecture.md), [contracts.md](contracts.md), [task-admission.md](task-admission.md) |
-| #646 | Ingest metadata into scheduler grouping through `TaskSchedulingResolver` | [architecture.md](architecture.md), [contracts.md](contracts.md), [task-admission.md](task-admission.md) |
-| #653 | Remove legacy hint resolver path | [migration-and-cleanup.md](migration-and-cleanup.md), [contracts.md](contracts.md) |
+| #641 | Add `SchedulingMetadata` and generator override contract | [architecture.md](architecture.md), [contracts.md](contracts.md), [module-ownership.md](module-ownership.md), [task-admission.md](task-admission.md) |
+| #646 | Ingest metadata into scheduler grouping through `TaskSchedulingResolver` | [architecture.md](architecture.md), [contracts.md](contracts.md), [module-ownership.md](module-ownership.md), [task-admission.md](task-admission.md) |
+| #653 | Remove legacy hint resolver path | [migration-and-cleanup.md](migration-and-cleanup.md), [module-ownership.md](module-ownership.md), [contracts.md](contracts.md) |
 | #652 | Document plugin-facing metadata behavior | [architecture.md](architecture.md), [contracts.md](contracts.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
-| #644 | Implement task admission lease boundary | [task-admission.md](task-admission.md), [contracts.md](contracts.md), [benchmark-plan.md](benchmark-plan.md) |
+| #644 | Implement task admission lease boundary | [task-admission.md](task-admission.md), [contracts.md](contracts.md), [module-ownership.md](module-ownership.md), [benchmark-plan.md](benchmark-plan.md) |
 | #654 | Implement and document capacity vocabulary and snapshots | [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md) |
-| #657 | Refactor model-call request control into request admission | [request-admission.md](request-admission.md), [contracts.md](contracts.md), [migration-and-cleanup.md](migration-and-cleanup.md), [benchmark-plan.md](benchmark-plan.md) |
+| #657 | Refactor model-call request control into request admission | [request-admission.md](request-admission.md), [contracts.md](contracts.md), [module-ownership.md](module-ownership.md), [migration-and-cleanup.md](migration-and-cleanup.md), [benchmark-plan.md](benchmark-plan.md) |
 | #635 | Instrument request admission state | [observability.md](observability.md), [request-admission.md](request-admission.md), [contracts.md](contracts.md), [benchmark-plan.md](benchmark-plan.md) |
 | #647 | Instrument scheduler admission state | [observability.md](observability.md), [task-admission.md](task-admission.md), [contracts.md](contracts.md), [benchmark-plan.md](benchmark-plan.md) |
 | #648 | Correlate scheduler and request observability | [observability.md](observability.md), [architecture.md](architecture.md), [benchmark-plan.md](benchmark-plan.md) |
 | #649 | Build reusable benchmark harness and normalize provisional evidence | [benchmark-plan.md](benchmark-plan.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [task-admission.md](task-admission.md), [request-admission.md](request-admission.md) |
-| #660 | Produce final user/operator docs | [architecture.md](architecture.md), [contracts.md](contracts.md), [request-admission.md](request-admission.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
+| #660 | Produce final user/operator docs | [architecture.md](architecture.md), [contracts.md](contracts.md), [module-ownership.md](module-ownership.md), [request-admission.md](request-admission.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md), [migration-and-cleanup.md](migration-and-cleanup.md) |
 | #650 | Implement bounded-borrow task policy | [task-admission.md](task-admission.md), [benchmark-plan.md](benchmark-plan.md), [capacity-model.md](capacity-model.md) |
 | #651 | Design resource-vector/provider-aware policy | [task-admission.md](task-admission.md), [request-admission.md](request-admission.md), [capacity-model.md](capacity-model.md), [observability.md](observability.md), [benchmark-plan.md](benchmark-plan.md) |
 
@@ -43,7 +43,7 @@ The implementation order remains:
 -> #635 -> #647 -> #648 -> #649 -> #660 -> #650 -> #651
 ```
 
-#644 may be prototyped before the metadata chain fully lands only if it remains behind an interim adapter and cannot close until #646/#652 preserve the accepted contract. The accepted end state is `SchedulingMetadata` feeding task admission through `TaskSchedulingResolver`.
+#644 cannot close until task admission uses the final scheduler module homes and the accepted metadata contract. The accepted end state is `SchedulingMetadata` feeding task admission through `TaskSchedulingResolver`; old resolver paths, compatibility adapters, and duplicate module homes are not part of the target architecture.
 
 #660 promotes the stabilized V1 admission/capacity/telemetry docs. #650 and #651 are follow-on policy/design issues; if they change behavior or public/operator guidance, they must update this source-of-truth plan and any promoted docs as part of their own acceptance gates. #651 is design-first unless its issue body explicitly promotes an implementation slice.
 
diff --git a/plans/645/migration-and-cleanup.md b/plans/645/migration-and-cleanup.md
index 666dcd613..046ad5349 100644
--- a/plans/645/migration-and-cleanup.md
+++ b/plans/645/migration-and-cleanup.md
@@ -1,19 +1,23 @@
 # Migration And Cleanup
 
-The epic is not complete until replaced names and compatibility paths are removed from production code, current docs, and this source-of-truth plan.
+The epic is not complete until replaced names, old module paths, and compatibility paths are removed from production code, current docs, and this source-of-truth plan. The target architecture is defined by [Module ownership](module-ownership.md); implementation PRs should move directly to those final homes.
 
 ## Scheduling Metadata Cleanup
 
-Remove or collapse the legacy scheduling resolver path after `SchedulingMetadata` and `TaskSchedulingResolver` are stable.
+The durable scheduling metadata path is:
 
-Accepted end states:
+```text
+ColumnGenerator.get_scheduling_metadata()
+-> SchedulingMetadata
+-> TaskSchedulingResolver
+```
 
-- delete the legacy resolver types, or
-- refactor/rename it into a metadata-oriented adapter where all model/provider inference lives behind `ColumnGenerator.get_scheduling_metadata()` and typed `SchedulingMetadataError` fallback behavior.
+Remove the legacy resolver types and any independent scheduler-side model/provider introspection path. All model/provider inference must live behind `ColumnGenerator.get_scheduling_metadata()`, `SchedulingMetadata`, and typed `SchedulingMetadataError` fallback behavior.
 
-Unacceptable end state:
+Unacceptable end states:
 
-- a parallel fallback that independently introspects generators, configs, model registries, aliases, or admitted policy data under the old resolver contract.
+- a parallel fallback that independently introspects generators, configs, model registries, aliases, or admitted policy data under the old resolver contract
+- a compatibility adapter, alias, or reexport that preserves the old resolver vocabulary as a durable production path
 
 Final legacy-name search gate should have no production/current-doc matches for these historical strings:
 
@@ -61,11 +65,31 @@ max_llm_wait_tasks
 
 If a scheduler-level resource remains for LLM-bound work, it must be represented through `SchedulerResourceRequest`, `TaskAdmissionConfig`, and `AsyncCapacityPlan`, with names that describe scheduler task-stage pressure rather than request concurrency.
 
+## Module Ownership Cleanup
+
+The target scheduler package is:
+
+```text
+data_designer.engine.dataset_builders.scheduling
+```
+
+The durable architecture does not keep scheduler-owned task models, readiness tracking, queues, task admission, or task policies in `dataset_builders.utils`.
+
+The target request-admission package is:
+
+```text
+data_designer.engine.models.request_admission
+```
+
+The durable architecture does not keep request-admission controllers, queues, waiters, AIMD state, pressure snapshots, or request leases in `models.clients.request_admission`.
+
+`ModelRequestExecutor` remains under `models.clients` because it wraps concrete model clients. Request admission itself must not be reexported from `models.clients.__init__`.
+
 ## Compatibility Shim Rule
 
 Do not leave production compatibility aliases, subclasses, adapters, reexports, docs paths, or durable tests for replaced names at epic completion.
 
-Temporary names may exist inside a PR only if the same PR removes them before merge.
+Do not introduce shim modules or deprecation adapters under replaced names. Historical names may appear only in explicit cleanup/search-gate sections like this one or in clearly marked historical changelog/dev-note text.
 
 ## Gate Semantics
 
@@ -75,7 +99,7 @@ By #653 close, legacy scheduling-hint production paths are gone and tests have m
 
 By #657 close, request-admission code has no production `Throttle*` aliases, exports, modules, or durable tests.
 
-By #645 close, public/current docs and `plans/645` use only the durable architecture vocabulary except for this cleanup file's explicit legacy-name search lists. Historical changelog or dev-note text can remain only when explicitly marked historical.
+By #645 close, production code lives in the target modules from [Module ownership](module-ownership.md), package `__init__.py` files do not reexport internal queues/controllers/leases, and public/current docs plus `plans/645` use only the durable architecture vocabulary except for this cleanup file's explicit legacy-name search lists. Historical changelog or dev-note text can remain only when explicitly marked historical.
 
 ## Documentation Cleanup
 
@@ -108,6 +132,8 @@ rg "ThrottleManager|ThrottleDomain|ThrottleConfig|RunConfig\\.throttle|throttle_
 rg "_submission_semaphore|_llm_wait_semaphore|get_semaphore_permits|TrackingSemaphore" packages docs fern architecture plans/645
 rg "throttl(e|ed|ing)|semaphore" docs fern architecture plans/645
 rg "needs_llm_wait|held_llm_wait|max_llm_wait_tasks" packages docs fern architecture plans/645
+rg "dataset_builders\\.utils\\.(task_model|completion_tracker|task_scheduling|fair_task_queue|task_admission)" packages docs fern architecture plans/645
+rg "models\\.clients\\.request_admission|from data_designer\\.engine\\.models\\.clients import .*Request" packages docs fern architecture plans/645
 rg "SchedulingMetadata|TaskSchedulingResolver|FairTaskQueue|TaskAdmissionController|TaskAdmissionLease|ModelRequestExecutor|RequestAdmissionController|AdaptiveRequestAdmissionController|AsyncCapacityPlan|SchedulerResourceRequest|RequestResourceKey" docs fern architecture plans/645
 ```
 
diff --git a/plans/645/module-ownership.md b/plans/645/module-ownership.md
new file mode 100644
index 000000000..affa88d0a
--- /dev/null
+++ b/plans/645/module-ownership.md
@@ -0,0 +1,216 @@
+# Module Ownership
+
+This page defines the target repository and module ownership for the async scheduling epic. It is an end-state design, not a migration plan. Implementation PRs should move directly toward these homes and must not introduce compatibility aliases, shim modules, transitional reexports, or duplicate old/new module paths.
+
+Durable engine names in this plan are maintainer contracts. They are not public import promises unless this page explicitly marks them plugin-facing or operator-facing.
+
+## Package Ownership
+
+| Package | Owns | Must not own |
+| --- | --- | --- |
+| `data-designer-config` | public configuration DTOs and generator-facing metadata | engine runtime protocols, queues, admission leases, request domains, AIMD state, runtime pressure |
+| `data-designer-engine` | scheduler runtime, task admission, request admission, capacity diagnostics, runtime observability, benchmark internals | public interface orchestration, user-facing docs presentation |
+| `data-designer` | public `DataDesigner` interface wiring, CLI presentation, integrations | scheduler internals, plugin-facing scheduling metadata definitions |
+
+Config code must not import engine runtime code. Engine code may import config DTOs.
+
+## Target Module Layout
+
+```text
+packages/data-designer-config/src/data_designer/config/
+  scheduling.py
+    SchedulingMetadata
+    SchedulingMetadataError
+
+packages/data-designer-engine/src/data_designer/engine/
+  dataset_builders/
+    async_scheduler.py
+      AsyncTaskScheduler
+
+    scheduling/
+      task_model.py
+        Task
+        SliceRef
+        TaskTrace
+
+      completion.py
+        CompletionTracker
+        FrontierDelta
+
+      resources.py
+        TaskGroupKey
+        TaskGroupSpec
+        SchedulerResourceKey
+        SchedulerResourceRequest
+        SchedulableTask
+        stable_task_id
+
+      resolver.py
+        TaskSchedulingResolver
+        ResolvedTaskScheduling
+
+      queue.py
+        FairTaskQueue
+        QueueView
+        QueueSelection
+
+      task_admission.py
+        TaskAdmissionController
+        TaskAdmissionConfig
+        TaskAdmissionLease
+        TaskAdmissionDenied
+        TaskAdmissionDecision
+        TaskAdmissionView
+        TaskAdmissionBlockSummary
+        ReleaseResult
+
+      task_policies.py
+        TaskAdmissionPolicy
+        TaskAdmissionPolicyDecision
+        PolicyStateDelta
+        StrictFairTaskAdmissionPolicy
+        BoundedBorrowTaskAdmissionPolicy
+        BoundedBorrowTaskAdmissionPolicyConfig
+
+  models/
+    resources.py
+      ProviderModelKey
+      ProviderModelStaticCap
+      provider/model alias canonicalization helpers
+
+    clients/
+      model_request_executor.py
+        ModelRequestExecutor
+
+    request_admission/
+      resources.py
+        RequestDomain
+        RequestResourceKey
+        RequestGroupSpec
+        RequestEventContext
+        RequestAdmissionItem
+
+      resolver.py
+        RequestResourceResolver
+        ResolvedRequestResource
+
+      config.py
+        RequestAdmissionConfig
+
+      queue.py
+        RequestFairQueue
+        RequestWaiter
+        RequestQueueView
+        RequestQueueSelection
+
+      limits.py
+        AdaptiveRequestLimitState
+        provider/model aggregate limit state
+
+      pressure.py
+        RequestPressureSnapshotProvider
+        RequestPressureSnapshot
+        ProviderModelPressureSnapshot
+
+      outcomes.py
+        RequestReleaseOutcome
+        ReleaseResult
+
+      controller.py
+        RequestAdmissionController
+        AdaptiveRequestAdmissionController
+        RequestAdmissionLease
+        RequestAdmissionDenied
+        RequestAdmissionDecision
+        RequestAdmissionError
+
+  capacity.py
+    CapacityValue
+    AsyncCapacityPlan
+    AsyncCapacityConfigured
+    AsyncCapacityRuntimeSnapshot
+    AsyncCapacityObservedMaxima
+    RequestAdmissionConfigSnapshot
+
+  observability.py
+    RuntimeCorrelation
+    RuntimeCorrelationProvider
+    runtime_correlation_provider
+    SchedulerAdmissionEvent
+    SchedulerAdmissionEventSink
+    RequestAdmissionEvent
+    RequestAdmissionEventSink
+    InMemoryAdmissionEventSink
+    CorrelatedRuntimeView
+
+  models/telemetry.py
+    product/provider usage telemetry only
+```
+
+`AsyncTaskScheduler` is the runtime coordinator only. It owns ready-frontier polling, queue selection, task-lease acquire/release orchestration, worker lifecycle, salvage/retry coordination, shutdown, and row-group lifecycle integration. It does not own queue policy, task admission ledgers, request admission, provider cooldown, AIMD behavior, or model-client wrapping.
+
+`ModelRequestExecutor` remains under `models/clients` because it implements the model-client boundary and wraps concrete provider clients. Request admission itself lives under `models/request_admission` and must not import `ModelClient` or provider adapter classes.
+
+`models/resources.py` owns provider/model identity that is shared across metadata resolution, request admission, and capacity diagnostics. Request admission owns request-domain resources. Capacity consumes both as read-only diagnostic inputs; it does not own admission policy or controller state transitions.
+
+`observability.py` is the cross-layer runtime-observability home. It owns scheduler and request admission event DTOs, primitive runtime correlation, in-memory test/diagnostic sinks, and correlated runtime views. Product/provider usage telemetry remains separate in `models/telemetry.py`.
+
+## Current Module Targets
+
+| Current or legacy module/concept | Target direction |
+| --- | --- |
+| `dataset_builders/async_scheduler.py` | keep as coordinator; remove durable queue, task-policy, and request-admission ownership |
+| `dataset_builders/utils/task_model.py` | move scheduler task DTOs to `dataset_builders/scheduling/task_model.py` |
+| `dataset_builders/utils/completion_tracker.py` | move readiness tracking to `dataset_builders/scheduling/completion.py` |
+| `dataset_builders/utils/task_scheduling.py` | split scheduler resources into `scheduling/resources.py` and metadata resolution into `scheduling/resolver.py` |
+| `dataset_builders/utils/fair_task_queue.py` | move to `dataset_builders/scheduling/queue.py`; keep ready ordering only |
+| `dataset_builders/utils/task_admission.py` | split controller/lease DTOs into `scheduling/task_admission.py` and policies into `scheduling/task_policies.py` |
+| `models/clients/model_request_executor.py` | keep as the concrete model-client acquire/call/release wrapper |
+| `models/clients/request_admission.py` | split into the `models/request_admission/` package |
+| `models/clients/__init__.py` request-admission reexports | remove; request-admission internals are imported from their owning modules only |
+| `models/telemetry.py` | keep product/provider usage telemetry separate from admission event DTOs |
+| `capacity.py` | keep as cross-cutting capacity diagnostic/reporting code that consumes read-only scheduler/request DTOs and snapshots |
+| `SchedulingHintResolver`, `SchedulingHint`, and scheduler-side model-bound fallbacks | remove; `SchedulingMetadata` plus `TaskSchedulingResolver` are the only durable path |
+| `ThrottleManager`, `ThrottleDomain`, `ThrottleConfig`, `ThrottledModelClient`, and `throttled_model_client` | remove; request admission and `ModelRequestExecutor` are the only durable request-control path |
+
+## Import Rules
+
+- `data_designer.config.*` must not import `data_designer.engine.*` or `data_designer.interface.*`.
+- Engine modules may import `SchedulingMetadata` and `SchedulingMetadataError` from config.
+- `dataset_builders/scheduling/*` may import config scheduling metadata, dataset-builder task/readiness concepts, primitive runtime observability, and neutral provider/model identity helpers from `engine.models.resources`.
+- `dataset_builders/scheduling/*` must not import model clients, request-admission controllers, request queues, AIMD state, provider adapters, or request leases.
+- `models/request_admission/*` may import neutral provider/model identity helpers and primitive observability, but must not import dataset-builder scheduler types or `ModelClient`.
+- `models/clients/model_request_executor.py` is the production bridge that imports both model-client types and request-admission protocols. It is the only model-client layer that acquires and releases request leases.
+- `capacity.py` may import read-only resource DTOs, config snapshots, pressure snapshots, and event snapshots. It must not call controller mutation APIs or become a controller registry.
+- `observability.py` must not import concrete controllers, queues, model clients, provider adapters, or dataset-builder schedulers.
+- `data-designer` interface and CLI code may consume engine diagnostics for presentation, but must not reexport scheduler/request internals as plugin API.
+- Package `__init__.py` files must not reexport internal queues, policies, leases, waiters, or controllers as broad public-looking APIs.
+
+## Audience Boundaries
+
+| Audience | Exposed surface | Not exposed |
+| --- | --- | --- |
+| Plugin authors | `ColumnGenerator.get_scheduling_metadata()`, `SchedulingMetadata`, `SchedulingMetadataError` | queues, task groups, scheduler resources, task leases, request domains, pressure snapshots, AIMD state |
+| Users/operators | documented public run/model config fields, `AsyncCapacityPlan`, benchmark artifacts, telemetry/event artifacts, correlated runtime views | controller mutation APIs, queues, policies, leases, waiters, internal config objects |
+| Engine maintainers | scheduler/request admission modules, DTOs, protocols, policies, snapshots, events, capacity diagnostics | config-layer reverse imports, compatibility aliases, duplicate old/new module paths |
+| Tests and benchmarks | local fakes, deterministic model clients, event sinks, benchmark override config | production `engine.testing` helpers, test-module imports from benchmark code, benchmark-module imports from unit tests |
+
+`TaskAdmissionConfig` and `RequestAdmissionConfig` are engine-internal in V1. They may appear inside capacity and benchmark artifacts as explanatory snapshots, but they are not public `RunConfig` knobs unless a later design explicitly promotes them.
+
+## Tests And Benchmarks
+
+Tests mirror target module ownership:
+
+| Area | Target test home |
+| --- | --- |
+| config metadata | `packages/data-designer-config/tests/config/test_scheduling.py` |
+| scheduler task resources/resolver | `packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resources.py` and `test_resolver.py` |
+| fair task queue | `packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_queue.py` |
+| task admission and policies | `packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py` and `test_task_policies.py` |
+| scheduler integration | `packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py` |
+| request admission | `packages/data-designer-engine/tests/engine/models/request_admission/` |
+| model request executor | `packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py` |
+| capacity diagnostics | `packages/data-designer-engine/tests/engine/test_capacity.py` |
+| runtime observability | `packages/data-designer-engine/tests/engine/test_observability.py` |
+
+Test fakes live under tests near their consumers. Benchmark fakes and reusable scenarios live under `scripts/benchmarks/async_scheduling/`, with `scripts/benchmarks/benchmark_async_scheduling.py` as the runnable entrypoint. Production `data_designer.engine.testing` helpers are not part of the target architecture.
diff --git a/scripts/benchmarks/benchmark_async_scheduling.py b/scripts/benchmarks/benchmark_async_scheduling.py
index 0d9edd963..964ec5fdb 100644
--- a/scripts/benchmarks/benchmark_async_scheduling.py
+++ b/scripts/benchmarks/benchmark_async_scheduling.py
@@ -29,26 +29,25 @@
     RequestAdmissionConfigSnapshot,
     RowGroupAdmission,
 )
-from data_designer.engine.dataset_builders.utils.fair_task_queue import FairTaskQueue
-from data_designer.engine.dataset_builders.utils.task_admission import TaskAdmissionConfig, TaskAdmissionController
-from data_designer.engine.dataset_builders.utils.task_model import Task
-from data_designer.engine.dataset_builders.utils.task_scheduling import (
+from data_designer.engine.dataset_builders.scheduling.queue import FairTaskQueue
+from data_designer.engine.dataset_builders.scheduling.resources import (
     SchedulableTask,
     SchedulerResourceRequest,
     TaskGroupKey,
     TaskGroupSpec,
 )
-from data_designer.engine.models.clients.request_admission import (
-    AdaptiveRequestAdmissionController,
-    ProviderModelKey,
-    ProviderModelStaticCap,
-    RequestAdmissionConfig,
+from data_designer.engine.dataset_builders.scheduling.task_admission import TaskAdmissionConfig, TaskAdmissionController
+from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
+from data_designer.engine.models.request_admission.controller import AdaptiveRequestAdmissionController
+from data_designer.engine.models.request_admission.outcomes import RequestReleaseOutcome
+from data_designer.engine.models.request_admission.resources import (
     RequestAdmissionItem,
     RequestDomain,
     RequestGroupSpec,
-    RequestReleaseOutcome,
     RequestResourceKey,
 )
+from data_designer.engine.models.resources import ProviderModelKey, ProviderModelStaticCap
 from data_designer.engine.observability import InMemoryAdmissionEventSink, SchedulerAdmissionEvent
 
 ARTIFACT_SCHEMA_VERSION = "async-scheduling-benchmark-v1"

From ea3d4a06cfe9a29e1414975643e845247468054f Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Tue, 19 May 2026 12:58:20 -0400
Subject: [PATCH 12/33] improve async scheduler idle observability

- add adaptive row-group admission and request-pressure advisory telemetry
- add idle regression suite, HTML reports, and Perfetto export tooling
- add combined adaptive/request-pressure benchmark guardrails
- expand scheduler, request admission, and benchmark tests
---
 .../src/data_designer/config/run_config.py    |   16 +-
 .../tests/config/test_run_config.py           |    8 +
 .../src/data_designer/engine/capacity.py      |    5 +
 .../column_generators/generators/base.py      |   28 +-
 .../dataset_builders/async_scheduler.py       |  556 +++++-
 .../dataset_builders/scheduling/completion.py |   12 +-
 .../dataset_builders/scheduling/queue.py      |    6 +-
 .../dataset_builders/scheduling/resolver.py   |   16 +
 .../dataset_builders/scheduling/resources.py  |    2 +
 .../scheduling/task_policies.py               |   55 +-
 .../models/clients/model_request_executor.py  |    5 +
 .../models/request_admission/controller.py    |   34 +-
 .../engine/models/request_admission/queue.py  |    1 +
 .../src/data_designer/engine/observability.py |    8 +
 .../scheduling/test_completion.py             |   57 +
 .../scheduling/test_task_admission.py         |   85 +-
 .../dataset_builders/test_async_scheduler.py  |  562 ++++++
 .../clients/test_model_request_executor.py    |   96 +
 .../request_admission/test_controller.py      |   67 +
 .../engine/test_async_scheduling_benchmark.py |  621 ++++++
 reports/async-scheduling-idle-analysis.html   |   97 +
 reports/async-scheduling-idle-regression.html |  125 ++
 .../benchmarks/benchmark_async_scheduling.py  | 1372 ++++++++++++-
 .../export_async_scheduling_perfetto.py       |  431 ++++
 .../generate_async_scheduling_idle_report.py  | 1765 +++++++++++++++++
 .../run_async_scheduling_idle_regression.py   |  653 ++++++
 26 files changed, 6611 insertions(+), 72 deletions(-)
 create mode 100644 packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
 create mode 100644 reports/async-scheduling-idle-analysis.html
 create mode 100644 reports/async-scheduling-idle-regression.html
 create mode 100644 scripts/benchmarks/export_async_scheduling_perfetto.py
 create mode 100644 scripts/benchmarks/generate_async_scheduling_idle_report.py
 create mode 100644 scripts/benchmarks/run_async_scheduling_idle_regression.py

diff --git a/packages/data-designer-config/src/data_designer/config/run_config.py b/packages/data-designer-config/src/data_designer/config/run_config.py
index dc72bba37..a67740aed 100644
--- a/packages/data-designer-config/src/data_designer/config/run_config.py
+++ b/packages/data-designer-config/src/data_designer/config/run_config.py
@@ -3,6 +3,8 @@
 
 from __future__ import annotations
 
+from typing import Any
+
 from pydantic import Field, model_validator
 from typing_extensions import Self
 
@@ -53,8 +55,10 @@ class RunConfig(ConfigBase):
             fewer Data Designer-specific restrictions. ``secure`` uses Data Designer's
             hardened sandbox with additional AST, filter, and output guards.
             Default is ``secure``.
+
+    Notes:
         Request admission is engine-internal in V1 and is not exposed as a
-            public run-config knob.
+        public run-config knob.
     """
 
     disable_early_shutdown: bool = False
@@ -75,6 +79,16 @@ class RunConfig(ConfigBase):
         ),
     )
 
+    @model_validator(mode="before")
+    @classmethod
+    def reject_removed_throttle_config(cls, data: Any) -> Any:
+        if isinstance(data, dict) and "throttle" in data:
+            raise ValueError(
+                "RunConfig.throttle was removed. Request admission is now managed internally by the async "
+                "scheduling engine; remove the throttle field from your run config."
+            )
+        return data
+
     @model_validator(mode="after")
     def normalize_shutdown_settings(self) -> Self:
         """Normalize shutdown settings for compatibility."""
diff --git a/packages/data-designer-config/tests/config/test_run_config.py b/packages/data-designer-config/tests/config/test_run_config.py
index 98c819b38..a90a2d57a 100644
--- a/packages/data-designer-config/tests/config/test_run_config.py
+++ b/packages/data-designer-config/tests/config/test_run_config.py
@@ -3,6 +3,9 @@
 
 from __future__ import annotations
 
+import pytest
+from pydantic import ValidationError
+
 from data_designer.config.run_config import JinjaRenderingEngine, RunConfig
 
 
@@ -13,3 +16,8 @@ def test_run_config_defaults_to_secure_jinja_renderer() -> None:
 def test_run_config_accepts_native_renderer() -> None:
     run_config = RunConfig(jinja_rendering_engine=JinjaRenderingEngine.NATIVE)
     assert JinjaRenderingEngine(run_config.jinja_rendering_engine) == JinjaRenderingEngine.NATIVE
+
+
+def test_run_config_rejects_removed_throttle_with_targeted_message() -> None:
+    with pytest.raises(ValidationError, match="RunConfig.throttle was removed"):
+        RunConfig(throttle={"max_concurrent_requests": 1})
diff --git a/packages/data-designer-engine/src/data_designer/engine/capacity.py b/packages/data-designer-engine/src/data_designer/engine/capacity.py
index e2e368186..4dc1abb0b 100644
--- a/packages/data-designer-engine/src/data_designer/engine/capacity.py
+++ b/packages/data-designer-engine/src/data_designer/engine/capacity.py
@@ -39,6 +39,11 @@ class CapacityValue(Generic[_T]):
 class RowGroupAdmission:
     row_group_concurrency: CapacityValue[int]
     observed_in_flight: int | None = None
+    mode: Literal["fixed", "adaptive"] = "fixed"
+    target_in_flight: int | None = None
+    observed_max_target: int | None = None
+    max_admitted_rows: int | None = None
+    blocked_reasons: Mapping[str, int] = field(default_factory=dict)
 
 
 @dataclass(frozen=True)
diff --git a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
index 39e8b67e8..66d902a46 100644
--- a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
+++ b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
@@ -8,6 +8,7 @@
 import functools
 import logging
 from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Coroutine, TypeVar, overload
 
 from data_designer.config.column_configs import GenerationStrategy
@@ -27,6 +28,13 @@
 # tracked as a structural follow-up.
 SYNC_BRIDGE_TIMEOUT = 300
 
+
+@dataclass
+class _EndpointBucket:
+    aliases: list[str] = field(default_factory=list)
+    caps: list[int] = field(default_factory=list)
+
+
 if TYPE_CHECKING:
     import pandas as pd
 
@@ -172,7 +180,7 @@ def get_scheduling_metadata(self) -> SchedulingMetadata:
                 diagnostics={"generator_type": type(self).__name__},
             )
 
-        endpoints: dict[tuple[str, str, str], dict[str, object]] = {}
+        endpoints: dict[tuple[str, str, str], _EndpointBucket] = {}
         for alias in aliases:
             try:
                 model_config = self.get_model_config(model_alias=alias)
@@ -187,13 +195,9 @@ def get_scheduling_metadata(self) -> SchedulingMetadata:
             endpoint = (provider_name, str(model_config.model), str(model_config.generation_type))
             max_parallel = getattr(model_config.inference_parameters, "max_parallel_requests", 1)
             cap = max_parallel if isinstance(max_parallel, int) and max_parallel > 0 else 1
-            bucket = endpoints.setdefault(endpoint, {"aliases": [], "caps": []})
-            cast_aliases = bucket["aliases"]
-            cast_caps = bucket["caps"]
-            if isinstance(cast_aliases, list):
-                cast_aliases.append(alias)
-            if isinstance(cast_caps, list):
-                cast_caps.append(cap)
+            bucket = endpoints.setdefault(endpoint, _EndpointBucket())
+            bucket.aliases.append(alias)
+            bucket.caps.append(cap)
 
         if len(endpoints) != 1:
             raise SchedulingMetadataError(
@@ -203,18 +207,16 @@ def get_scheduling_metadata(self) -> SchedulingMetadata:
             )
 
         endpoint, bucket = next(iter(endpoints.items()))
-        caps = bucket["caps"] if isinstance(bucket["caps"], list) else [1]
-        aliases_for_diagnostics = bucket["aliases"] if isinstance(bucket["aliases"], list) else aliases
         provider_name, model_id, generation_kind = endpoint
-        effective_cap = max(1, min(int(cap) for cap in caps))
+        effective_cap = max(1, min(bucket.caps))
         return SchedulingMetadata.model(
             provider_name,
             model_id,
             generation_kind,
             weight=effective_cap,
             diagnostics={
-                "aliases": tuple(str(alias) for alias in aliases_for_diagnostics),
-                "raw_caps": tuple(int(cap) for cap in caps),
+                "aliases": tuple(bucket.aliases),
+                "raw_caps": tuple(bucket.caps),
                 "merge_rule": "min_same_endpoint",
             },
         )
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index b4f0b81be..231aec159 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -9,8 +9,8 @@
 import logging
 import time
 import uuid
-from collections import defaultdict, deque
-from collections.abc import Coroutine
+from collections import Counter, defaultdict, deque
+from collections.abc import Coroutine, Mapping
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Callable
 
@@ -54,7 +54,9 @@
     strip_skip_metadata_from_records,
 )
 from data_designer.engine.dataset_builders.utils.sticky_progress_bar import StickyProgressBar
-from data_designer.engine.models.errors import RETRYABLE_MODEL_ERRORS
+from data_designer.engine.errors import DataDesignerError
+from data_designer.engine.models.clients.errors import ProviderError
+from data_designer.engine.models.errors import RETRYABLE_MODEL_ERRORS, GenerationValidationFailureError
 from data_designer.engine.observability import (
     RuntimeCorrelation,
     SchedulerAdmissionEvent,
@@ -66,6 +68,7 @@
     from data_designer.engine.column_generators.generators.base import ColumnGenerator
     from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
     from data_designer.engine.dataset_builders.utils.row_group_buffer import RowGroupBufferManager
+    from data_designer.engine.models.request_admission.pressure import RequestPressureSnapshotProvider
 
 logger = logging.getLogger(__name__)
 
@@ -80,6 +83,27 @@
 DEGRADED_WARN_RATE: float = 0.5
 DEGRADED_WARN_WINDOW: int = 20
 DEGRADED_WARN_INTERVAL_S: float = 60.0
+INTERNAL_BUG_EXCEPTIONS = (KeyError, TypeError, AttributeError, AssertionError)
+
+
+def _identity_hash(identity: tuple[str, ...]) -> str:
+    return hashlib.sha1("\0".join(identity).encode()).hexdigest()[:16]
+
+
+def _request_resource_label(resource: object | None) -> str | None:
+    if resource is None:
+        return None
+    provider = getattr(resource, "provider_name", None)
+    model = getattr(resource, "model_id", None)
+    domain = getattr(resource, "domain", None)
+    domain_value = getattr(domain, "value", domain)
+    if provider is None or model is None or domain_value is None:
+        return str(resource)
+    return f"{provider}/{model}/{domain_value}"
+
+
+def _string_keyed_counts(values: Mapping[object, int]) -> dict[str, int]:
+    return {str(key): int(value) for key, value in values.items()}
 
 
 @dataclass
@@ -136,6 +160,10 @@ def __init__(
         progress_bar: bool = False,
         scheduler_event_sink: SchedulerAdmissionEventSink | None = None,
         run_id: str | None = None,
+        adaptive_row_group_admission: bool = False,
+        adaptive_row_group_initial_target: int = 1,
+        request_pressure_provider: RequestPressureSnapshotProvider | None = None,
+        request_pressure_advisory: bool = False,
     ) -> None:
         self._generators = generators
         self._graph = graph
@@ -240,6 +268,7 @@ def __init__(
         # context naturally because the from_scratch task raised; the async
         # engine drops rows and continues, losing the cause unless we capture it.
         self._first_non_retryable_error: Exception | None = None
+        self._fatal_worker_error: BaseException | None = None
 
         # Pre-compute row-group sizes for O(1) lookup
         self._rg_size_map: dict[int, int] = dict(row_groups)
@@ -251,6 +280,22 @@ def __init__(
         self._observed_max_row_groups_in_flight = 0
         self._observed_max_task_leases_by_resource: dict[str, int] = {}
         self._observed_max_queued_by_group: dict[str, int] = {}
+        self._adaptive_row_group_admission = adaptive_row_group_admission
+        self._row_group_admission_hard_cap = max(1, max_concurrent_row_groups)
+        self._row_group_admission_target = (
+            max(1, min(self._row_group_admission_hard_cap, adaptive_row_group_initial_target))
+            if adaptive_row_group_admission
+            else self._row_group_admission_hard_cap
+        )
+        self._observed_max_row_group_admission_target = self._row_group_admission_target
+        self._row_group_admission_event = asyncio.Event()
+        self._row_group_admission_event.set()
+        self._row_group_admission_pressure_ticks = 0
+        self._row_group_admission_blocked_reasons: Counter[str] = Counter()
+        self._adaptive_max_admitted_rows = self._max_admitted_rows_guardrail()
+        self._request_pressure_provider = request_pressure_provider
+        self._request_pressure_advisory = request_pressure_advisory and request_pressure_provider is not None
+        self._request_pressure_advisory_skips = 0
 
         # Pre-compute seed columns (graph is static)
         self._seed_cols: tuple[str, ...] = tuple(c for c in graph.columns if not graph.get_upstream_columns(c))
@@ -317,6 +362,13 @@ def first_non_retryable_error(self) -> Exception | None:
         """
         return self._first_non_retryable_error
 
+    def _raise_if_fatal_worker_error(self) -> None:
+        if self._fatal_worker_error is None:
+            return
+        raise DatasetGenerationError(
+            "Unexpected internal task failure in async scheduler."
+        ) from self._fatal_worker_error
+
     def _spawn_worker(self, coro: Coroutine[Any, Any, None]) -> asyncio.Task:
         """Create a tracked worker task that auto-removes itself on completion."""
         task = asyncio.create_task(coro)
@@ -339,9 +391,13 @@ def _emit_scheduler_event(
             return
         self._scheduler_event_sequence += 1
         correlation = None
+        event_diagnostics = dict(diagnostics or {})
         if task is not None:
-            group = lease.item.group if lease is not None else self._schedulable_task(task).group
-            identity_hash = hashlib.sha1("\0".join(group.key.identity).encode()).hexdigest()[:16]
+            schedulable = lease.item if lease is not None else self._schedulable_task(task)
+            group = schedulable.group
+            identity_hash = _identity_hash(group.key.identity)
+            event_diagnostics.setdefault("task_group_key", group.key)
+            event_diagnostics.setdefault("resource_request", dict(schedulable.resource_request.amounts))
             correlation = RuntimeCorrelation(
                 run_id=self._run_id,
                 row_group=task.row_group,
@@ -363,10 +419,11 @@ def _emit_scheduler_event(
                     scheduler_resource_key=scheduler_resource_key,
                     reason_or_result=reason_or_result,
                     snapshot=self.task_admission_snapshot(),
-                    diagnostics=diagnostics or {},
+                    diagnostics=event_diagnostics,
                 )
             )
         except Exception:
+            logger.warning("Scheduler admission event sink raised; dropping event.", exc_info=True)
             return
 
     def _record_observed_task_state(self) -> None:
@@ -382,6 +439,147 @@ def _record_observed_task_state(self) -> None:
             label = f"{group.kind}:{'/'.join(group.identity)}"
             self._observed_max_queued_by_group[label] = max(self._observed_max_queued_by_group.get(label, 0), count)
 
+    def _emit_scheduler_health_snapshot(self, reason: str) -> None:
+        self._emit_scheduler_event(
+            "scheduler_health_snapshot",
+            diagnostics=self._scheduler_health_diagnostics(reason=reason),
+        )
+
+    def _scheduler_health_diagnostics(self, *, reason: str) -> dict[str, object]:
+        queue_view = self._fair_queue.view()
+        task_view = self._task_admission.view()
+        return {
+            "reason": reason,
+            "active_row_groups": len(self._rg_states),
+            "target_row_groups": self._row_group_admission_target,
+            "hard_cap_row_groups": self._row_group_admission_hard_cap,
+            "active_admitted_rows": self._active_admitted_row_count(),
+            "max_admitted_rows": self._adaptive_max_admitted_rows,
+            "all_row_groups_admitted": self._all_rgs_admitted,
+            "queued_total": queue_view.queued_total,
+            "queued_by_group": _string_keyed_counts(queue_view.queued_by_group),
+            "queued_demand_by_resource": dict(queue_view.queued_peer_demand_by_resource),
+            "leased_resources": dict(task_view.leased_resources),
+            "resource_limits": dict(task_view.resource_limits),
+            "resources_available": dict(task_view.resources_available),
+            "in_flight_tasks": len(self._in_flight),
+            "active_workers": self.active_worker_count,
+            "deferred_tasks": len(self._deferred),
+            "pending_pre_batch_tasks": len(self._pending_pre_batch_ready_tasks),
+            "dispatched_tasks": len(self._dispatched),
+            "request_pressure_advisory_enabled": self._request_pressure_advisory,
+            "request_pressure_advisory_skips": self._request_pressure_advisory_skips,
+            "row_group_admission_blocked_reasons": dict(self._row_group_admission_blocked_reasons),
+            "request_pressure": self._request_pressure_diagnostics(),
+        }
+
+    def _scheduler_job_diagnostics(self) -> dict[str, object]:
+        row_group_sizes = [size for _rg_id, size in self._row_groups]
+        strategies = {column: self._graph.get_strategy(column).value for column in self._graph.columns}
+        task_count_by_strategy = Counter(strategies.values())
+        return {
+            "run_id": self._run_id,
+            "num_records": self._num_records,
+            "buffer_size": self._buffer_size,
+            "row_group_count": len(self._row_groups),
+            "row_group_total_rows": sum(row_group_sizes),
+            "row_group_min_size": min(row_group_sizes, default=0),
+            "row_group_max_size": max(row_group_sizes, default=0),
+            "graph_column_count": len(self._graph.columns),
+            "graph_root_columns": tuple(self._graph.get_root_columns()),
+            "graph_depth": len(self._graph.get_longest_dependency_chain()),
+            "task_count_by_strategy": dict(task_count_by_strategy),
+            "column_scheduling": self._column_scheduling_diagnostics(strategies),
+            "resource_limits": dict(self._task_admission_config.resource_limits),
+            "submission_capacity": self._task_admission_config.submission_capacity,
+            "adaptive_row_group_admission": self._adaptive_row_group_admission,
+            "row_group_initial_target": self._row_group_admission_target,
+            "row_group_hard_cap": self._row_group_admission_hard_cap,
+            "max_admitted_rows": self._adaptive_max_admitted_rows,
+            "request_pressure_advisory_enabled": self._request_pressure_advisory,
+        }
+
+    def _column_scheduling_diagnostics(self, strategies: dict[str, str]) -> tuple[dict[str, object], ...]:
+        diagnostics = []
+        for column in self._graph.columns:
+            task_type = "batch" if self._graph.get_strategy(column) != GenerationStrategy.CELL_BY_CELL else "cell"
+            row_index = None if task_type == "batch" else 0
+            task = Task(column=column, row_group=0, row_index=row_index, task_type=task_type)
+            resolved = self._task_scheduling.scheduling_for_task(task, self._task_flow_identity(task))
+            diagnostics.append(
+                {
+                    "column": column,
+                    "strategy": strategies[column],
+                    "group_kind": resolved.group.key.kind,
+                    "group_identity_hash": _identity_hash(resolved.group.key.identity),
+                    "group_weight": resolved.group.weight,
+                    "group_admitted_limit": resolved.group.admitted_limit,
+                    "resource_request": dict(resolved.resource_request.amounts),
+                    "request_resource": _request_resource_label(resolved.request_resource_key),
+                }
+            )
+        return tuple(diagnostics)
+
+    def _request_pressure_diagnostics(self) -> dict[str, object]:
+        if self._request_pressure_provider is None:
+            return {"enabled": False}
+        return {
+            "enabled": True,
+            "resources": {
+                _request_resource_label(resource): {
+                    "effective_max": snapshot.effective_max,
+                    "current_limit": snapshot.current_limit,
+                    "in_flight_count": snapshot.in_flight_count,
+                    "active_lease_count": snapshot.active_lease_count,
+                    "waiters": snapshot.waiters,
+                    "blocked": snapshot.blocked_until_monotonic is not None,
+                    "cooldown_remaining_seconds": snapshot.cooldown_remaining_seconds,
+                    "last_outcome": snapshot.last_outcome,
+                }
+                for resource, snapshot in self._request_pressure_provider.snapshots().items()
+            },
+            "provider_models": {
+                f"{provider_model.provider_name}/{provider_model.model_id}": {
+                    "static_cap": snapshot.static_cap,
+                    "aggregate_in_flight": snapshot.aggregate_in_flight,
+                    "aggregate_active_lease_count": snapshot.aggregate_active_lease_count,
+                    "domains": {domain.value: count for domain, count in snapshot.domains.items()},
+                }
+                for provider_model, snapshot in self._request_pressure_provider.global_snapshots().items()
+            },
+        }
+
+    def _request_pressure_item_diagnostics(self, item: SchedulableTask) -> dict[str, object]:
+        if item.request_resource_key is None or self._request_pressure_provider is None:
+            return {"request_resource": None}
+        snapshot = self._request_pressure_provider.snapshot(item.request_resource_key)
+        global_snapshot = self._request_pressure_provider.global_snapshot(
+            item.request_resource_key.provider_name,
+            item.request_resource_key.model_id,
+        )
+        diagnostics: dict[str, object] = {
+            "request_resource": _request_resource_label(item.request_resource_key),
+            "pressure_reason": self._request_pressure_reason(item),
+            "resource_snapshot": None,
+            "provider_model_snapshot": None,
+        }
+        if snapshot is not None:
+            diagnostics["resource_snapshot"] = {
+                "effective_max": snapshot.effective_max,
+                "current_limit": snapshot.current_limit,
+                "in_flight_count": snapshot.in_flight_count,
+                "waiters": snapshot.waiters,
+                "blocked": snapshot.blocked_until_monotonic is not None,
+                "cooldown_remaining_seconds": snapshot.cooldown_remaining_seconds,
+            }
+        if global_snapshot is not None:
+            diagnostics["provider_model_snapshot"] = {
+                "static_cap": global_snapshot.static_cap,
+                "aggregate_in_flight": global_snapshot.aggregate_in_flight,
+                "aggregate_active_lease_count": global_snapshot.aggregate_active_lease_count,
+            }
+        return diagnostics
+
     async def _cancel_workers(self) -> None:
         """Cancel all tracked worker tasks and wait for them to finish."""
         for t in self._worker_tasks:
@@ -395,26 +593,37 @@ def _apply_frontier_delta(self, delta: FrontierDelta) -> None:
             return
         for task in delta.removed:
             self._discard_ready_task(task)
-        for task in delta.added:
-            self._enqueue_ready_task(task)
+        self._enqueue_ready_tasks(delta.added)
 
     def _enqueue_ready_task(self, task: Task) -> None:
-        if task in self._dispatched or task.row_group not in self._rg_states:
-            return
-        if not self._tracker.is_frontier_task(task):
-            return
-        self._emit_scheduler_event("dependency_ready", task=task)
-        state = self._rg_states[task.row_group]
-        if self._on_seeds_complete is not None and not state.pre_batch_done and task.column not in self._seed_cols:
-            if task not in self._pending_pre_batch_ready_tasks:
-                self._pending_pre_batch_ready[task.row_group].append(task)
-                self._pending_pre_batch_ready_tasks.add(task)
+        self._enqueue_ready_tasks((task,))
+
+    def _enqueue_ready_tasks(self, tasks: tuple[Task, ...]) -> None:
+        schedulables: list[SchedulableTask] = []
+        accepted_tasks_by_id: dict[str, Task] = {}
+        for task in tasks:
+            if task in self._dispatched or task.row_group not in self._rg_states:
+                continue
+            if not self._tracker.is_frontier_task(task):
+                continue
+            self._emit_scheduler_event("dependency_ready", task=task)
+            state = self._rg_states[task.row_group]
+            if self._on_seeds_complete is not None and not state.pre_batch_done and task.column not in self._seed_cols:
+                if task not in self._pending_pre_batch_ready_tasks:
+                    self._pending_pre_batch_ready[task.row_group].append(task)
+                    self._pending_pre_batch_ready_tasks.add(task)
+                continue
+            schedulable = self._schedulable_task(task)
+            schedulables.append(schedulable)
+            accepted_tasks_by_id[schedulable.task_id] = task
+
+        if not schedulables:
             return
-        schedulable = self._schedulable_task(task)
-        accepted = self._fair_queue.enqueue((schedulable,))
+        accepted = self._fair_queue.enqueue(schedulables)
         if accepted:
             self._tracker.mark_enqueued(accepted)
-            self._emit_scheduler_event("ready_enqueued", task=task)
+            for task_id in accepted:
+                self._emit_scheduler_event("ready_enqueued", task=accepted_tasks_by_id[task_id])
             self._record_observed_task_state()
             self._wake_event.set()
 
@@ -424,11 +633,13 @@ def _discard_ready_task(self, task: Task) -> None:
 
     def _flush_pre_batch_ready(self, row_group: int) -> None:
         pending = self._pending_pre_batch_ready.pop(row_group, [])
+        ready = []
         for task in pending:
             if task not in self._pending_pre_batch_ready_tasks:
                 continue
             self._pending_pre_batch_ready_tasks.discard(task)
-            self._enqueue_ready_task(task)
+            ready.append(task)
+        self._enqueue_ready_tasks(tuple(ready))
 
     def _drop_pending_ready_for_row_group(self, row_group: int) -> None:
         pending = self._pending_pre_batch_ready.pop(row_group, [])
@@ -440,7 +651,7 @@ def _dispatch_queued_tasks(self) -> _DispatchOutcome:
         dispatched = False
 
         while self._fair_queue.has_queued_tasks:
-            selection = self._fair_queue.select_next(lambda item, view: self._task_admission.is_eligible(item, view))
+            selection = self._fair_queue.select_next(self._is_dispatch_eligible)
             if selection is None:
                 summary = self._task_admission.explain_blocked(self._fair_queue.view())
                 if "group_cap" in summary.dominant_denial_reasons:
@@ -456,6 +667,7 @@ def _dispatch_queued_tasks(self) -> _DispatchOutcome:
                         "reasons": dict(summary.dominant_denial_reasons),
                     },
                 )
+                self._emit_scheduler_health_snapshot(event_kind)
                 return _DispatchOutcome(dispatched=dispatched, admission_blocked=True)
 
             self._emit_scheduler_event("selected", task=selection.item.payload)
@@ -487,8 +699,73 @@ def _dispatch_queued_tasks(self) -> _DispatchOutcome:
 
         if dispatched:
             self._emit_scheduler_event("queue_drained")
+            self._emit_scheduler_health_snapshot("queue_drained")
         return _DispatchOutcome(dispatched=dispatched)
 
+    def _is_dispatch_eligible(self, item: SchedulableTask, view: Any) -> bool:
+        if not self._task_admission.is_eligible(item, view):
+            return False
+        if not self._request_pressure_advisory:
+            return True
+        if not self._is_request_pressure_limited(item):
+            return True
+        open_peer = self._request_pressure_open_peer(item, view)
+        if open_peer is not None:
+            self._request_pressure_advisory_skips += 1
+            self._emit_scheduler_event(
+                "request_pressure_advisory_skipped",
+                task=item.payload,
+                diagnostics=self._request_pressure_item_diagnostics(item)
+                | {
+                    "open_peer_task_id": open_peer.task_id,
+                    "open_peer_column": open_peer.payload.column,
+                    "open_peer_request_resource": _request_resource_label(open_peer.request_resource_key),
+                    "skip_count": self._request_pressure_advisory_skips,
+                },
+            )
+            return False
+        return True
+
+    def _is_request_pressure_limited(self, item: SchedulableTask) -> bool:
+        return self._request_pressure_reason(item) is not None
+
+    def _request_pressure_reason(self, item: SchedulableTask) -> str | None:
+        if item.request_resource_key is None or self._request_pressure_provider is None:
+            return None
+        snapshot = self._request_pressure_provider.snapshot(item.request_resource_key)
+        global_snapshot = self._request_pressure_provider.global_snapshot(
+            item.request_resource_key.provider_name,
+            item.request_resource_key.model_id,
+        )
+        if (
+            global_snapshot is not None
+            and global_snapshot.static_cap > 0
+            and global_snapshot.aggregate_in_flight >= global_snapshot.static_cap
+        ):
+            return "provider_model_aggregate_cap"
+        if snapshot is None:
+            return None
+        if snapshot.cooldown_remaining_seconds > 0.0 or snapshot.blocked_until_monotonic is not None:
+            return "cooldown"
+        if snapshot.waiters > 0:
+            return "waiters"
+        if snapshot.current_limit > 0 and snapshot.in_flight_count >= snapshot.current_limit:
+            return "resource_limit"
+        return None
+
+    def _has_request_pressure_open_peer(self, item: SchedulableTask, view: Any) -> bool:
+        return self._request_pressure_open_peer(item, view) is not None
+
+    def _request_pressure_open_peer(self, item: SchedulableTask, view: Any) -> SchedulableTask | None:
+        for peer in view.first_candidate_tasks_by_group.values():
+            if peer.task_id == item.task_id:
+                continue
+            if not self._task_admission.is_eligible(peer, view):
+                continue
+            if not self._is_request_pressure_limited(peer):
+                return peer
+        return None
+
     def _dispatch_selected_task(self, item: SchedulableTask, lease: TaskAdmissionLease) -> None:
         task = item.payload
         task_execution_id = f"task-exec-{uuid.uuid4().hex}"
@@ -519,18 +796,167 @@ def _task_flow_identity(self, task: Task) -> tuple[str, ...]:
         output_columns = self._gen_instance_to_columns.get(id(generator), [task.column])
         return tuple(output_columns)
 
+    def _max_admitted_rows_guardrail(self) -> int:
+        if self._num_records > 0 and self._buffer_size > 0:
+            return min(self._num_records, max(3 * self._buffer_size, 8192))
+        total_rows = sum(size for _rg_id, size in self._row_groups)
+        return max(1, total_rows)
+
+    async def _wait_for_row_group_admission_capacity(self, row_group_size: int) -> None:
+        while True:
+            target_blocked = len(self._rg_states) >= self._row_group_admission_target
+            row_guard_blocked = not self._row_group_row_guard_allows(row_group_size)
+            if not target_blocked and not row_guard_blocked:
+                return
+            self._row_group_admission_event.clear()
+            target_blocked = len(self._rg_states) >= self._row_group_admission_target
+            row_guard_blocked = not self._row_group_row_guard_allows(row_group_size)
+            if not target_blocked and not row_guard_blocked:
+                return
+            if row_guard_blocked:
+                self._row_group_admission_blocked_reasons["max_admitted_rows"] += 1
+                self._emit_scheduler_event(
+                    "row_group_admission_blocked",
+                    diagnostics=self._row_group_admission_diagnostics(reason="max_admitted_rows"),
+                )
+                self._emit_scheduler_health_snapshot("row_group_admission_blocked")
+            await self._row_group_admission_event.wait()
+            self._raise_if_fatal_worker_error()
+
+    def _row_group_row_guard_allows(self, row_group_size: int) -> bool:
+        if not self._adaptive_row_group_admission:
+            return True
+        admitted_rows = self._active_admitted_row_count()
+        return admitted_rows == 0 or admitted_rows + row_group_size <= self._adaptive_max_admitted_rows
+
+    def _active_admitted_row_count(self) -> int:
+        return sum(state.size for state in self._rg_states.values())
+
+    def _maybe_update_adaptive_row_group_target(self) -> None:
+        if not self._adaptive_row_group_admission:
+            return
+        if self._all_rgs_admitted or self._early_shutdown or self._fatal_worker_error is not None:
+            return
+        if len(self._rg_states) >= self._row_group_admission_hard_cap:
+            self._row_group_admission_pressure_ticks = 0
+            return
+        reason = self._adaptive_row_group_block_reason()
+        if reason is not None:
+            self._row_group_admission_blocked_reasons[reason] += 1
+            self._row_group_admission_pressure_ticks = 0
+            self._emit_scheduler_event(
+                "row_group_admission_blocked",
+                diagnostics=self._row_group_admission_diagnostics(reason=reason),
+            )
+            self._emit_scheduler_health_snapshot("row_group_admission_blocked")
+            return
+
+        self._row_group_admission_pressure_ticks += 1
+        if self._fair_queue.view().queued_total > 0 and self._row_group_admission_pressure_ticks < 2:
+            return
+        old_target = self._row_group_admission_target
+        self._row_group_admission_target = min(self._row_group_admission_hard_cap, old_target + 1)
+        self._observed_max_row_group_admission_target = max(
+            self._observed_max_row_group_admission_target,
+            self._row_group_admission_target,
+        )
+        self._row_group_admission_pressure_ticks = 0
+        if self._row_group_admission_target != old_target:
+            self._emit_scheduler_event(
+                "row_group_admission_target_changed",
+                diagnostics=self._row_group_admission_diagnostics(reason="horizon_limited")
+                | {"old_target": old_target, "new_target": self._row_group_admission_target},
+            )
+            self._emit_scheduler_health_snapshot("row_group_admission_target_changed")
+            self._row_group_admission_event.set()
+
+    def _adaptive_row_group_block_reason(self) -> str | None:
+        if self._deferred:
+            return "deferred_tasks"
+        next_size = self._next_unadmitted_row_group_size()
+        if next_size is None:
+            return "no_pending_row_groups"
+        if not self._row_group_row_guard_allows(next_size):
+            return "max_admitted_rows"
+        queue_view = self._fair_queue.view()
+        queue_guard = max(self._max_submitted_tasks * 4, self._max_model_task_admission * 2)
+        if queue_view.queued_total >= queue_guard:
+            return "queued_task_guardrail"
+        task_view = self._task_admission.view()
+        llm_limit = task_view.resource_limits.get("llm_wait", 0)
+        if llm_limit <= 0:
+            return "no_llm_wait_resource"
+        llm_available = task_view.resources_available.get("llm_wait", 0)
+        queued_llm = queue_view.queued_peer_demand_by_resource.get("llm_wait", 0)
+        if llm_available <= queued_llm and queue_view.queued_total > 0:
+            return "queued_llm_demand"
+        if llm_available <= 0:
+            return "llm_wait_saturated"
+        return None
+
+    def _next_unadmitted_row_group_size(self) -> int | None:
+        for rg_id, rg_size in self._row_groups:
+            if rg_id not in self._rg_states and not self._tracker.is_row_group_complete(
+                rg_id, rg_size, self._graph.columns
+            ):
+                return rg_size
+        return None
+
+    def _row_group_admission_diagnostics(self, *, reason: str) -> dict[str, object]:
+        queue_view = self._fair_queue.view()
+        task_view = self._task_admission.view()
+        admitted_rows = self._active_admitted_row_count()
+        return {
+            "mode": "adaptive" if self._adaptive_row_group_admission else "fixed",
+            "reason": reason,
+            "active_row_groups": len(self._rg_states),
+            "target_row_groups": self._row_group_admission_target,
+            "hard_cap": self._row_group_admission_hard_cap,
+            "admitted_rows": admitted_rows,
+            "max_admitted_rows": self._adaptive_max_admitted_rows,
+            "queued_total": queue_view.queued_total,
+            "queued_llm_wait_demand": queue_view.queued_peer_demand_by_resource.get("llm_wait", 0),
+            "llm_wait_limit": task_view.resource_limits.get("llm_wait", 0),
+            "llm_wait_leased": task_view.leased_resources.get("llm_wait", 0),
+            "llm_wait_available": task_view.resources_available.get("llm_wait", 0),
+            "blocked_reasons": dict(self._row_group_admission_blocked_reasons),
+        }
+
     async def _admit_row_groups(self) -> None:
         """Admit row groups as semaphore slots become available."""
+        all_admitted = True
         for rg_id, rg_size in self._row_groups:
+            await self._wait_for_row_group_admission_capacity(rg_size)
+            if self._early_shutdown or self._fatal_worker_error is not None:
+                all_admitted = False
+                break
             await self._rg_semaphore.acquire()
+            if self._early_shutdown or self._fatal_worker_error is not None:
+                self._rg_semaphore.release()
+                all_admitted = False
+                break
+            if not self._row_group_row_guard_allows(rg_size):
+                self._rg_semaphore.release()
+                await self._wait_for_row_group_admission_capacity(rg_size)
+                await self._rg_semaphore.acquire()
+                if self._early_shutdown or self._fatal_worker_error is not None:
+                    self._rg_semaphore.release()
+                    all_admitted = False
+                    break
             self._rg_states[rg_id] = _RowGroupState(size=rg_size)
 
             if self._buffer_manager is not None:
                 self._buffer_manager.init_row_group(rg_id, rg_size)
 
             await self._dispatch_seeds(rg_id, rg_size)
+            self._emit_scheduler_event(
+                "row_group_admitted",
+                diagnostics=self._row_group_admission_diagnostics(reason="admitted")
+                | {"row_group": rg_id, "row_group_size": rg_size},
+            )
+            self._emit_scheduler_health_snapshot("row_group_admitted")
             self._wake_event.set()
-        self._all_rgs_admitted = True
+        self._all_rgs_admitted = all_admitted
         self._wake_event.set()
 
     async def run(self) -> None:
@@ -550,6 +976,9 @@ async def run(self) -> None:
             if self._reporter:
                 self._reporter.log_start(num_row_groups=num_rgs)
 
+            self._emit_scheduler_event("scheduler_job_started", diagnostics=self._scheduler_job_diagnostics())
+            self._emit_scheduler_health_snapshot("start")
+
             # Launch admission as a background task so it interleaves with dispatch.
             admission_task = asyncio.create_task(self._admit_row_groups())
 
@@ -576,6 +1005,11 @@ async def run(self) -> None:
             if self._reporter:
                 self._reporter.log_final()
 
+            self._emit_scheduler_health_snapshot("completed")
+            self._emit_scheduler_event(
+                "scheduler_job_completed", diagnostics=self._scheduler_health_diagnostics(reason="completed")
+            )
+
             if self._rg_states:
                 incomplete = list(self._rg_states)
                 logger.error(
@@ -591,6 +1025,7 @@ async def _main_dispatch_loop(
     ) -> None:
         """Core dispatch loop extracted from ``run()``."""
         while True:
+            self._raise_if_fatal_worker_error()
             if self._early_shutdown:
                 logger.warning("Early shutdown triggered - non-retryable error rate exceeded threshold")
                 if self._deferred:
@@ -606,12 +1041,14 @@ async def _main_dispatch_loop(
             dispatch_outcome = self._dispatch_queued_tasks()
 
             self._checkpoint_completed_row_groups(all_columns)
+            self._maybe_update_adaptive_row_group_target()
 
             # Eagerly salvage any row groups that have only deferred tasks,
             # even if other row groups are still in-flight.  This frees
             # semaphore slots so admission doesn't lose capacity.
             if self._deferred:
                 await self._salvage_stalled_row_groups(seed_cols, has_pre_batch, all_columns)
+                self._maybe_update_adaptive_row_group_target()
 
             # Are we done?
             all_done = self._all_rgs_admitted and not self._rg_states and not self._in_flight
@@ -628,7 +1065,12 @@ async def _main_dispatch_loop(
                     continue
 
             if not self._fair_queue.has_queued_tasks or dispatch_outcome.admission_blocked:
+                if self._fair_queue.has_queued_tasks and not dispatch_outcome.dispatched and not self._in_flight:
+                    raise RuntimeError(
+                        "Ready frontier is admission-blocked with no in-flight task to release scheduler capacity."
+                    )
                 await self._wake_event.wait()
+                self._raise_if_fatal_worker_error()
 
     async def _salvage_rounds(
         self,
@@ -672,6 +1114,7 @@ async def _salvage_rounds(
     async def _drain_frontier(self, seed_cols: tuple[str, ...], has_pre_batch: bool) -> None:
         """Dispatch all frontier tasks and their downstream until quiescent."""
         while True:
+            self._raise_if_fatal_worker_error()
             if has_pre_batch:
                 self._run_seeds_complete_check(seed_cols)
             dispatch_outcome = self._dispatch_queued_tasks()
@@ -686,6 +1129,7 @@ async def _drain_frontier(self, seed_cols: tuple[str, ...], has_pre_batch: bool)
                 continue
             self._wake_event.clear()
             await self._wake_event.wait()
+            self._raise_if_fatal_worker_error()
 
     async def _salvage_stalled_row_groups(
         self,
@@ -744,6 +1188,9 @@ def _checkpoint_completed_row_groups(self, all_columns: list[str]) -> None:
             if self._tracker.is_row_group_complete(rg_id, state.size, all_columns)
         ]
         for rg_id, rg_size in completed:
+            dropped_rows = sum(1 for ri in range(rg_size) if self._tracker.is_dropped(rg_id, ri))
+            checkpointed = False
+            checkpoint_result = "unknown"
             try:
                 if self._on_before_checkpoint:
                     try:
@@ -757,17 +1204,36 @@ def _checkpoint_completed_row_groups(self, all_columns: list[str]) -> None:
                 # Remove from tracking only after the callback succeeds.
                 del self._rg_states[rg_id]
                 # If all rows were dropped (e.g. seed failure), free instead of finalizing
-                if all(self._tracker.is_dropped(rg_id, ri) for ri in range(rg_size)):
+                if dropped_rows == rg_size:
                     if self._buffer_manager:
                         self._buffer_manager.free_row_group(rg_id)
+                    checkpoint_result = "all_rows_dropped"
                 elif self._on_finalize_row_group is not None:
                     self._on_finalize_row_group(rg_id)
+                    checkpoint_result = "finalized"
+                else:
+                    checkpoint_result = "completed"
+                checkpointed = True
             except DatasetGenerationError:
                 raise
             except Exception:
                 logger.error(f"Failed to checkpoint row group {rg_id}.", exc_info=True)
             finally:
                 self._rg_semaphore.release()
+                self._row_group_admission_event.set()
+            if checkpointed:
+                self._emit_scheduler_event(
+                    "row_group_checkpointed",
+                    diagnostics={
+                        "row_group": rg_id,
+                        "row_group_size": rg_size,
+                        "dropped_rows": dropped_rows,
+                        "surviving_rows": rg_size - dropped_rows,
+                        "result": checkpoint_result,
+                        "active_row_groups": len(self._rg_states),
+                    },
+                )
+                self._emit_scheduler_health_snapshot("row_group_checkpointed")
 
         # Clean up deferred tasks for checkpointed row groups
         if completed:
@@ -1066,16 +1532,26 @@ async def _execute_task_inner_impl(self, task: Task, lease: TaskAdmissionLease,
                 # seed failures). Subsequent failures are still logged below.
                 if self._first_non_retryable_error is None:
                     self._first_non_retryable_error = exc
-                # Non-retryable: drop the affected row(s)
+                log_message = (
+                    f"Non-retryable failure on {task.column}[rg={task.row_group}, row={task.row_index}]: {exc}"
+                )
+                if self._is_expected_non_retryable(exc):
+                    logger.warning(log_message)
+                elif self._is_internal_bug(exc):
+                    logger.error("Unexpected fatal %s", log_message, exc_info=True)
+                    self._fatal_worker_error = exc
+                    self._wake_event.set()
+                    raise
+                else:
+                    logger.error("Unexpected %s", log_message, exc_info=True)
+                # Non-retryable data/user/provider failures drop the affected row(s);
+                # internal bug-shaped failures above abort the run instead.
                 if task.row_index is not None:
                     self._drop_row(task.row_group, task.row_index, exclude_columns={task.column})
                 else:
                     # Batch/from_scratch failure: drop all rows in the row group
                     rg_size = self._get_rg_size(task.row_group)
                     self._drop_row_group(task.row_group, rg_size, exclude_columns={task.column})
-                logger.warning(
-                    f"Non-retryable failure on {task.column}[rg={task.row_group}, row={task.row_index}]: {exc}"
-                )
                 self._emit_scheduler_event(
                     "non_retryable_dropped",
                     task=task,
@@ -1291,6 +1767,11 @@ def capacity_plan(self) -> AsyncCapacityPlan:
                         source="dataset_builder",
                     ),
                     observed_in_flight=len(self._rg_states),
+                    mode="adaptive" if self._adaptive_row_group_admission else "fixed",
+                    target_in_flight=self._row_group_admission_target,
+                    observed_max_target=self._observed_max_row_group_admission_target,
+                    max_admitted_rows=self._adaptive_max_admitted_rows,
+                    blocked_reasons=dict(self._row_group_admission_blocked_reasons),
                 ),
                 submission_capacity=CapacityValue(value=self._max_submitted_tasks, source="dataset_builder"),
                 task_resource_limits=CapacityValue(
@@ -1333,6 +1814,21 @@ def capacity_plan(self) -> AsyncCapacityPlan:
         )
 
     @staticmethod
-    def _is_retryable(exc: Exception) -> bool:
+    def _is_retryable(exc: BaseException) -> bool:
         """Classify whether an exception is retryable."""
         return isinstance(exc, RETRYABLE_MODEL_ERRORS)
+
+    @staticmethod
+    def _is_expected_non_retryable(exc: BaseException) -> bool:
+        return isinstance(
+            exc,
+            (
+                DataDesignerError,
+                DatasetGenerationError,
+                GenerationValidationFailureError,
+                ProviderError,
+            ),
+        )
+
+    def _is_internal_bug(self, exc: BaseException) -> bool:
+        return isinstance(exc, INTERNAL_BUG_EXCEPTIONS) and not self._is_expected_non_retryable(exc)
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/completion.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/completion.py
index 23166f329..b34ffe69a 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/completion.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/completion.py
@@ -8,6 +8,7 @@
 from typing import TYPE_CHECKING
 
 from data_designer.config.column_configs import GenerationStrategy
+from data_designer.engine.dataset_builders.scheduling.resources import stable_task_id
 from data_designer.engine.dataset_builders.scheduling.task_model import SliceRef, Task
 
 if TYPE_CHECKING:
@@ -154,7 +155,7 @@ def ready_frontier(self) -> tuple[Task, ...]:
     def mark_enqueued(self, task_ids: set[str] | list[str] | tuple[str, ...]) -> None:
         """Acknowledge tasks accepted by the ready queue."""
         wanted = set(task_ids)
-        self._frontier = {task for task in self._frontier if _stable_task_id(task) not in wanted}
+        self._frontier = {task for task in self._frontier if stable_task_id(task) not in wanted}
 
     def mark_complete(self, task: Task) -> None:
         """Compatibility hook for scheduler terminal accounting."""
@@ -245,7 +246,7 @@ def _enqueue_downstream(self, column: str, row_group: int, row_index: int | None
         rg_batch_complete = self._batch_complete.get(row_group, set())
         rg_size = self._row_group_sizes[row_group]
 
-        for down in self._graph.get_downstream_columns(column):
+        for down in sorted(self._graph.get_downstream_columns(column)):
             batch_ups, cell_ups = self._graph.split_upstream_by_strategy(down)
 
             if any(up not in rg_batch_complete for up in batch_ups):
@@ -342,10 +343,3 @@ def _validate_row_group(self, row_group: int) -> int | None:
             known = sorted(self._row_group_sizes)
             raise ValueError(f"Unknown row_group {row_group}. Known row_groups: {known}")
         return expected
-
-
-def _stable_task_id(task: Task) -> str:
-    raw = f"{task.column}\0{task.row_group}\0{task.row_index}\0{task.task_type}"
-    import hashlib
-
-    return f"task-{hashlib.sha1(raw.encode()).hexdigest()[:16]}"
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/queue.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/queue.py
index 424cb46f4..2cdd99b36 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/queue.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/queue.py
@@ -24,6 +24,7 @@ class QueueView:
     queued_by_group: Mapping[TaskGroupKey, int]
     queued_resource_demand_by_group: Mapping[TaskGroupKey, Mapping[SchedulerResourceKey, int]]
     first_candidate_resources_by_group: Mapping[TaskGroupKey, Mapping[SchedulerResourceKey, int]]
+    first_candidate_tasks_by_group: Mapping[TaskGroupKey, SchedulableTask]
     first_candidate_group_specs_by_group: Mapping[TaskGroupKey, TaskGroupSpec]
     queued_peer_demand_by_resource: Mapping[SchedulerResourceKey, int]
 
@@ -90,7 +91,6 @@ def discard_where(self, predicate: Callable[[SchedulableTask], bool]) -> None:
     def select_next(self, is_eligible: Callable[[SchedulableTask, QueueView], bool]) -> QueueSelection | None:
         """Return the next eligible task without mutating queue state."""
         view = self.view()
-        blocked: list[tuple[float, int, TaskGroupKey]] = []
         heap_copy = list(self._heap)
         heapq.heapify(heap_copy)
         active_seen: set[TaskGroupKey] = set()
@@ -105,7 +105,6 @@ def select_next(self, is_eligible: Callable[[SchedulableTask, QueueView], bool])
             if item is None:
                 continue
             if not is_eligible(item, view):
-                blocked.append((finish, sequence, key))
                 continue
             return QueueSelection(item=item, queue_view=view, sequence_version=self._sequence_version)
         return None
@@ -144,6 +143,7 @@ def view(self) -> QueueView:
         queued_by_group: Counter[TaskGroupKey] = Counter()
         demand_by_group: dict[TaskGroupKey, dict[SchedulerResourceKey, int]] = defaultdict(lambda: defaultdict(int))
         first_by_group: dict[TaskGroupKey, Mapping[SchedulerResourceKey, int]] = {}
+        first_tasks_by_group: dict[TaskGroupKey, SchedulableTask] = {}
         first_group_specs: dict[TaskGroupKey, TaskGroupSpec] = {}
         demand_by_resource: Counter[SchedulerResourceKey] = Counter()
 
@@ -158,6 +158,7 @@ def view(self) -> QueueView:
             first = self._first_valid_item(key)
             if first is not None:
                 first_by_group[key] = dict(first.resource_request.amounts)
+                first_tasks_by_group[key] = first
                 first_group_specs[key] = first.group
 
         return QueueView(
@@ -165,6 +166,7 @@ def view(self) -> QueueView:
             queued_by_group=dict(queued_by_group),
             queued_resource_demand_by_group={key: dict(value) for key, value in demand_by_group.items()},
             first_candidate_resources_by_group=first_by_group,
+            first_candidate_tasks_by_group=first_tasks_by_group,
             first_candidate_group_specs_by_group=first_group_specs,
             queued_peer_demand_by_resource=dict(demand_by_resource),
         )
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resolver.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resolver.py
index 924a278ee..c2f61e1e1 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resolver.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resolver.py
@@ -16,6 +16,7 @@
     stable_task_id,
 )
 from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
 
 if TYPE_CHECKING:
     from data_designer.engine.column_generators.generators.base import ColumnGenerator
@@ -27,6 +28,7 @@ class ResolvedTaskScheduling:
 
     group: TaskGroupSpec
     resource_request: SchedulerResourceRequest
+    request_resource_key: RequestResourceKey | None = None
 
 
 class TaskSchedulingResolver:
@@ -63,6 +65,7 @@ def schedulable_task(self, task: Task, flow_identity: tuple[str, ...]) -> Schedu
             payload=task,
             group=resolved.group,
             resource_request=resolved.resource_request,
+            request_resource_key=resolved.request_resource_key,
         )
 
     def _resolve_metadata(self, generator: ColumnGenerator) -> SchedulingMetadata:
@@ -96,6 +99,7 @@ def _resolved_from_metadata(
 
         identity = (*metadata.identity, *flow_identity)
         admitted_limit = max(1, min(self._model_group_limit_cap, self._model_group_limit_multiplier * weight))
+        request_resource_key = _request_resource_key(metadata)
         return ResolvedTaskScheduling(
             group=TaskGroupSpec(
                 key=TaskGroupKey(kind=metadata.kind, identity=identity),
@@ -103,4 +107,16 @@ def _resolved_from_metadata(
                 admitted_limit=admitted_limit,
             ),
             resource_request=SchedulerResourceRequest({"submission": 1, "llm_wait": 1}),
+            request_resource_key=request_resource_key,
         )
+
+
+def _request_resource_key(metadata: SchedulingMetadata) -> RequestResourceKey | None:
+    if metadata.kind != "model":
+        return None
+    _kind, provider_name, model_id, generation_kind = metadata.identity
+    try:
+        domain = RequestDomain(generation_kind)
+    except ValueError:
+        return None
+    return RequestResourceKey(provider_name=provider_name, model_id=model_id, domain=domain)
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py
index 3c002d37f..35a0ec18f 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/resources.py
@@ -9,6 +9,7 @@
 from typing import Literal
 
 from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.models.request_admission.resources import RequestResourceKey
 
 SchedulerResourceKey = Literal["submission", "llm_wait", "local"]
 
@@ -52,6 +53,7 @@ class SchedulableTask:
     payload: Task
     group: TaskGroupSpec
     resource_request: SchedulerResourceRequest
+    request_resource_key: RequestResourceKey | None = None
 
 
 def stable_task_id(task: Task) -> str:
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
index 14ecd89e9..72d8b46f5 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
@@ -86,12 +86,17 @@ def evaluate(
         leased_count = admission_view.running_counts_by_group.get(item.group.key, 0)
         if leased_count < item.group.admitted_limit:
             return TaskAdmissionPolicyDecision(allowed=True)
-        if not _has_queued_peer_group(item.group.key, queue_view):
+        pressure_resources = _queued_peer_pressure_resources(item, queue_view, admission_view)
+        if not pressure_resources:
             return TaskAdmissionPolicyDecision(allowed=True)
         return TaskAdmissionPolicyDecision(
             allowed=False,
             reason="group_cap",
-            diagnostics={"admitted_limit": item.group.admitted_limit, "leased_count": leased_count},
+            diagnostics={
+                "admitted_limit": item.group.admitted_limit,
+                "leased_count": leased_count,
+                "pressure_resources": pressure_resources,
+            },
         )
 
     def on_acquire(
@@ -125,8 +130,9 @@ def evaluate(
         if leased_count < limit:
             return TaskAdmissionPolicyDecision(allowed=True)
 
-        if _has_queued_peer_group(item.group.key, queue_view):
-            for resource in item.resource_request.amounts:
+        pressure_resources = _queued_peer_pressure_resources(item, queue_view, admission_view)
+        if pressure_resources:
+            for resource in pressure_resources:
                 debt_key = (item.group.key, resource)
                 debt = admission_view.policy_debt_by_group_resource.get(debt_key, 0)
                 if debt > 0:
@@ -138,7 +144,11 @@ def evaluate(
             return TaskAdmissionPolicyDecision(
                 allowed=False,
                 reason="group_cap",
-                diagnostics={"admitted_limit": limit, "leased_count": leased_count},
+                diagnostics={
+                    "admitted_limit": limit,
+                    "leased_count": leased_count,
+                    "pressure_resources": pressure_resources,
+                },
             )
 
         borrow_resources: list[tuple[SchedulerResourceKey, int]] = []
@@ -176,10 +186,41 @@ def on_acquire(
     def on_release(self, lease: TaskAdmissionLease) -> PolicyStateDelta:
         if not self._config.repay_on_withheld_peer_pressure:
             return PolicyStateDelta()
+        # Borrow debt is group-level: any completed lease in the group repays it, clamped to zero by the controller.
         return PolicyStateDelta(
             debt_changes={(lease.item.group.key, resource): -amount for resource, amount in lease.resources.items()}
         )
 
 
-def _has_queued_peer_group(group_key: TaskGroupKey, queue_view: QueueView) -> bool:
-    return any(key != group_key and count > 0 for key, count in queue_view.queued_by_group.items())
+def _queued_peer_pressure_resources(
+    item: SchedulableTask,
+    queue_view: QueueView,
+    admission_view: TaskAdmissionView,
+) -> tuple[SchedulerResourceKey, ...]:
+    candidate_resources = _fair_pressure_resources(item.resource_request.amounts)
+    pressure_resources: list[SchedulerResourceKey] = []
+    for group_key, peer_resources in queue_view.first_candidate_resources_by_group.items():
+        if group_key == item.group.key:
+            continue
+        if not _is_hard_resource_eligible(peer_resources, admission_view):
+            continue
+        for resource in candidate_resources:
+            if peer_resources.get(resource, 0) > 0 and resource not in pressure_resources:
+                pressure_resources.append(resource)
+    return tuple(pressure_resources)
+
+
+def _fair_pressure_resources(
+    resources: Mapping[SchedulerResourceKey, int],
+) -> tuple[SchedulerResourceKey, ...]:
+    typed_resources = tuple(resource for resource in resources if resource != "submission")
+    if typed_resources:
+        return typed_resources
+    return tuple(resources)
+
+
+def _is_hard_resource_eligible(
+    resources: Mapping[SchedulerResourceKey, int],
+    admission_view: TaskAdmissionView,
+) -> bool:
+    return all(admission_view.resources_available.get(resource, 0) >= amount for resource, amount in resources.items())
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
index c79f8bfab..01f917585 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import asyncio
+import logging
 import uuid
 from typing import TYPE_CHECKING, TypeVar
 
@@ -41,6 +42,8 @@
 
 _T = TypeVar("_T")
 
+logger = logging.getLogger(__name__)
+
 
 class ModelRequestExecutor(ModelClient):
     """Model-call boundary that acquires/releases request-admission leases."""
@@ -239,8 +242,10 @@ def _emit_model_event(
                     request_lease_id=lease.lease_id,
                     request_resource_key=item.resource,
                     request_group_key=item.group.key,
+                    pressure_snapshot=self._request_admission.pressure.snapshot(item.resource),
                     diagnostics=diagnostics or {},
                 )
             )
         except Exception:
+            logger.warning("Model request event sink raised; dropping event.", exc_info=True)
             return
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index 72abd4dda..9d63858fe 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import asyncio
+import logging
 import math
 import threading
 import time
@@ -34,6 +35,8 @@
     runtime_correlation_provider,
 )
 
+logger = logging.getLogger(__name__)
+
 RequestDenyReason = Literal[
     "no_capacity",
     "cooldown",
@@ -165,6 +168,7 @@ def register(
                         diagnostics={"provider_model": key, "previous": before, "current": cap.effective_max},
                     )
                 )
+            self._admit_waiters_locked(events)
             self._condition.notify_all()
         self._emit_events(events)
 
@@ -194,6 +198,13 @@ def try_acquire(self, item: RequestAdmissionItem) -> RequestAdmissionDecision:
         return decision
 
     def acquire_sync(self, item: RequestAdmissionItem) -> RequestAdmissionLease:
+        try:
+            asyncio.get_running_loop()
+        except RuntimeError:
+            pass
+        else:
+            raise RuntimeError("acquire_sync would block the running event loop; use acquire_async instead.")
+
         timeout = (
             item.queue_wait_timeout_seconds
             if item.queue_wait_timeout_seconds is not None
@@ -227,6 +238,8 @@ def acquire_sync(self, item: RequestAdmissionItem) -> RequestAdmissionLease:
             self._emit_events(events)
 
     async def acquire_async(self, item: RequestAdmissionItem) -> RequestAdmissionLease:
+        loop = asyncio.get_running_loop()
+        wakeup = asyncio.Event()
         timeout = (
             item.queue_wait_timeout_seconds
             if item.queue_wait_timeout_seconds is not None
@@ -234,7 +247,13 @@ async def acquire_async(self, item: RequestAdmissionItem) -> RequestAdmissionLea
         )
         now = time.monotonic()
         deadline = now + timeout if timeout is not None else None
-        waiter = RequestWaiter(waiter_id=uuid.uuid4().hex, item=item, enqueued_at=now, deadline_monotonic=deadline)
+        waiter = RequestWaiter(
+            waiter_id=uuid.uuid4().hex,
+            item=item,
+            enqueued_at=now,
+            deadline_monotonic=deadline,
+            wakeup=lambda: loop.call_soon_threadsafe(wakeup.set),
+        )
         events: list[RequestAdmissionEvent] = []
         try:
             while True:
@@ -255,7 +274,11 @@ async def acquire_async(self, item: RequestAdmissionItem) -> RequestAdmissionLea
                         events.append(self._request_event_locked("request_wait_timeout", item=item, decision=denied))
                         raise RequestAdmissionError(denied)
                     wait = self._wait_seconds_locked(item, now, deadline)
-                await asyncio.sleep(wait)
+                try:
+                    await asyncio.wait_for(wakeup.wait(), timeout=wait)
+                except asyncio.TimeoutError:
+                    pass
+                wakeup.clear()
         except asyncio.CancelledError:
             lease_to_release: RequestAdmissionLease | None = None
             with self._lock:
@@ -398,11 +421,17 @@ def _admit_waiters_locked(self, events: list[RequestAdmissionEvent]) -> None:
             state.waiters = max(0, state.waiters - 1)
             lease = self._acquire_locked(waiter.item, now)
             waiter.assigned_lease = lease
+            self._wake_waiter_locked(waiter)
             events.append(self._request_event_locked("request_wait_completed", item=waiter.item, lease=lease))
             events.append(self._request_event_locked("request_lease_acquired", item=waiter.item, lease=lease))
             if not self._queue.has_waiters:
                 events.append(self._request_event_locked("request_queue_drained", item=waiter.item))
 
+    def _wake_waiter_locked(self, waiter: RequestWaiter) -> None:
+        if waiter.wakeup is None:
+            return
+        waiter.wakeup()
+
     def _wait_seconds_locked(
         self,
         item: RequestAdmissionItem,
@@ -637,4 +666,5 @@ def _emit_events(self, events: list[RequestAdmissionEvent]) -> None:
             try:
                 self._event_sink.emit_request_event(event)
             except Exception:
+                logger.warning("Request admission event sink raised; dropping event.", exc_info=True)
                 continue
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
index 626fd3426..98610e5ee 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
@@ -23,6 +23,7 @@ class RequestWaiter:
     enqueued_at: float
     deadline_monotonic: float | None = None
     assigned_lease: RequestAdmissionLease | None = None
+    wakeup: Callable[[], None] | None = None
 
 
 @dataclass(frozen=True)
diff --git a/packages/data-designer-engine/src/data_designer/engine/observability.py b/packages/data-designer-engine/src/data_designer/engine/observability.py
index bcb938d4e..aa04e4a4e 100644
--- a/packages/data-designer-engine/src/data_designer/engine/observability.py
+++ b/packages/data-designer-engine/src/data_designer/engine/observability.py
@@ -42,12 +42,20 @@ def reset(self, token: contextvars.Token) -> None:
 runtime_correlation_provider = RuntimeCorrelationProvider()
 
 SchedulerAdmissionEventKind = Literal[
+    "scheduler_job_started",
+    "scheduler_job_completed",
+    "scheduler_health_snapshot",
     "dependency_ready",
     "ready_enqueued",
+    "row_group_admitted",
+    "row_group_admission_blocked",
+    "row_group_admission_target_changed",
+    "row_group_checkpointed",
     "selected",
     "queue_empty",
     "admission_blocked",
     "group_capped",
+    "request_pressure_advisory_skipped",
     "task_lease_acquired",
     "admission_denied",
     "worker_spawned",
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_completion.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_completion.py
index d03cbeabf..e647d4ac6 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_completion.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_completion.py
@@ -15,6 +15,7 @@
 )
 from data_designer.config.sampler_params import SamplerType
 from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker
+from data_designer.engine.dataset_builders.scheduling.resources import stable_task_id
 from data_designer.engine.dataset_builders.scheduling.task_model import SliceRef, Task
 from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
 
@@ -192,6 +193,15 @@ def test_get_ready_tasks_seed_frontier(ready_ctx: ReadyTasksFixture) -> None:
     assert ready[0].task_type == "from_scratch"
 
 
+def test_mark_enqueued_uses_scheduler_stable_task_id(ready_ctx: ReadyTasksFixture) -> None:
+    ready_ctx.tracker.seed_frontier()
+    task = ready_ctx.tracker.ready_frontier()[0]
+
+    ready_ctx.tracker.mark_enqueued({stable_task_id(task)})
+
+    assert ready_ctx.tracker.ready_frontier() == ()
+
+
 def test_get_ready_tasks_after_seed_complete(ready_ctx: ReadyTasksFixture) -> None:
     delta = ready_ctx.tracker.mark_row_range_complete("topic", 0, 3)
 
@@ -205,6 +215,53 @@ def test_get_ready_tasks_after_seed_complete(ready_ctx: ReadyTasksFixture) -> No
     assert delta.removed == ()
 
 
+def test_fan_out_cell_completion_readies_all_children_for_same_row() -> None:
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="heavy", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="child_a", prompt="{{ heavy }}", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="child_b", prompt="{{ heavy }}", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="child_c", prompt="{{ heavy }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {config.name: GenerationStrategy.CELL_BY_CELL for config in configs[1:]}
+    strategies["topic"] = GenerationStrategy.FULL_COLUMN
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, [(0, 2)])
+    tracker.mark_row_range_complete("topic", 0, 2)
+
+    delta = tracker.mark_cell_complete("heavy", 0, 0)
+
+    assert {task.column for task in delta.added} == {"child_a", "child_b", "child_c"}
+    assert {task.row_index for task in delta.added} == {0}
+    ready = tracker.get_ready_tasks(set())
+    assert not any(task.column.startswith("child_") and task.row_index == 1 for task in ready)
+
+
+def test_fan_in_cell_downstream_waits_for_all_same_row_upstreams() -> None:
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="up_a", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="up_b", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="up_c", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="judge", prompt="{{ up_a }} {{ up_b }} {{ up_c }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {config.name: GenerationStrategy.CELL_BY_CELL for config in configs[1:]}
+    strategies["topic"] = GenerationStrategy.FULL_COLUMN
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, [(0, 2)])
+    tracker.mark_row_range_complete("topic", 0, 2)
+
+    first_delta = tracker.mark_cell_complete("up_a", 0, 0)
+    second_delta = tracker.mark_cell_complete("up_b", 0, 0)
+    final_delta = tracker.mark_cell_complete("up_c", 0, 0)
+
+    assert not any(task.column == "judge" for task in first_delta.added)
+    assert not any(task.column == "judge" for task in second_delta.added)
+    assert final_delta.added == (Task(column="judge", row_group=0, row_index=0, task_type="cell"),)
+    ready = tracker.get_ready_tasks(set())
+    assert not any(task.column == "judge" and task.row_index == 1 for task in ready)
+
+
 def test_get_ready_tasks_skips_dispatched(ready_ctx: ReadyTasksFixture) -> None:
     ready_ctx.tracker.mark_row_range_complete("topic", 0, 3)
 
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
index b8c6acfb1..e1b6382a4 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
@@ -21,14 +21,20 @@
 from data_designer.engine.dataset_builders.scheduling.task_policies import BoundedBorrowTaskAdmissionPolicyConfig
 
 
-def _item(column: str, row: int = 0, *, group: TaskGroupSpec | None = None) -> SchedulableTask:
+def _item(
+    column: str,
+    row: int = 0,
+    *,
+    group: TaskGroupSpec | None = None,
+    resources: dict[str, int] | None = None,
+) -> SchedulableTask:
     task = Task(column=column, row_group=0, row_index=row, task_type="cell")
     group = group or TaskGroupSpec(TaskGroupKey(kind="local", identity=(column,)))
     return SchedulableTask(
         task_id=stable_task_id(task),
         payload=task,
         group=group,
-        resource_request=SchedulerResourceRequest({"submission": 1}),
+        resource_request=SchedulerResourceRequest(resources or {"submission": 1}),
     )
 
 
@@ -94,6 +100,59 @@ def test_task_admission_group_cap_yields_to_peer_pressure() -> None:
     assert decision.reason == "group_cap"
 
 
+def test_task_admission_group_cap_ignores_non_overlapping_typed_peer_resource() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    controller = TaskAdmissionController(
+        TaskAdmissionConfig(submission_capacity=3, resource_limits={"llm_wait": 3, "local": 3})
+    )
+    first = _item("a", 0, group=group, resources={"submission": 1, "llm_wait": 1})
+    second = _item("a", 1, group=group, resources={"submission": 1, "llm_wait": 1})
+    local_peer = _item("b", resources={"submission": 1, "local": 1})
+    lease = controller.try_acquire(first, _queue_view(first, second, local_peer))
+    assert isinstance(lease, TaskAdmissionLease)
+
+    decision = controller.try_acquire(second, _queue_view(second, local_peer))
+
+    assert isinstance(decision, TaskAdmissionLease)
+
+
+def test_task_admission_group_cap_applies_to_overlapping_typed_peer_resource() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    peer_group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "peer")), admitted_limit=1)
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=3, resource_limits={"llm_wait": 3}))
+    first = _item("a", 0, group=group, resources={"submission": 1, "llm_wait": 1})
+    second = _item("a", 1, group=group, resources={"submission": 1, "llm_wait": 1})
+    peer = _item("b", group=peer_group, resources={"submission": 1, "llm_wait": 1})
+    lease = controller.try_acquire(first, _queue_view(first, second, peer))
+    assert isinstance(lease, TaskAdmissionLease)
+
+    decision = controller.try_acquire(second, _queue_view(second, peer))
+
+    assert isinstance(decision, TaskAdmissionDenied)
+    assert decision.reason == "group_cap"
+    assert decision.diagnostics["pressure_resources"] == ("llm_wait",)
+
+
+def test_task_admission_group_cap_ignores_peer_blocked_by_hard_resource_capacity() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    peer_group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "peer")), admitted_limit=1)
+    controller = TaskAdmissionController(
+        TaskAdmissionConfig(submission_capacity=4, resource_limits={"llm_wait": 3, "local": 1})
+    )
+    first = _item("a", 0, group=group, resources={"submission": 1, "llm_wait": 1})
+    second = _item("a", 1, group=group, resources={"submission": 1, "llm_wait": 1})
+    local_holder = _item("local-holder", resources={"submission": 1, "local": 1})
+    blocked_peer = _item("b", group=peer_group, resources={"submission": 1, "llm_wait": 1, "local": 1})
+    first_lease = controller.try_acquire(first, _queue_view(first, second, blocked_peer))
+    local_lease = controller.try_acquire(local_holder, _queue_view(local_holder, blocked_peer))
+    assert isinstance(first_lease, TaskAdmissionLease)
+    assert isinstance(local_lease, TaskAdmissionLease)
+
+    decision = controller.try_acquire(second, _queue_view(second, blocked_peer))
+
+    assert isinstance(decision, TaskAdmissionLease)
+
+
 def test_explain_blocked_reports_group_cap_denials() -> None:
     first_group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "first")), admitted_limit=1)
     second_group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "second")), admitted_limit=1)
@@ -174,3 +233,25 @@ def test_bounded_borrow_debt_blocks_under_peer_pressure_and_releases() -> None:
     assert denied.reason == "borrow_debt"
     controller.release(borrowed)
     assert (group.key, "submission") not in controller.view().policy_debt_by_group_resource
+
+
+def test_bounded_borrow_release_repayment_is_group_level() -> None:
+    group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
+    controller = TaskAdmissionController(
+        TaskAdmissionConfig(
+            submission_capacity=3,
+            bounded_borrow=BoundedBorrowTaskAdmissionPolicyConfig(default_borrow_ceiling=1),
+        )
+    )
+    first = _item("a", 0, group=group)
+    borrowed_item = _item("a", 1, group=group)
+    first_lease = controller.try_acquire(first, _queue_view(first, borrowed_item))
+    borrowed = controller.try_acquire(borrowed_item, _queue_view(borrowed_item))
+    assert isinstance(first_lease, TaskAdmissionLease)
+    assert isinstance(borrowed, TaskAdmissionLease)
+    assert controller.view().policy_debt_by_group_resource[(group.key, "submission")] == 1
+
+    controller.release(first_lease)
+
+    assert (group.key, "submission") not in controller.view().policy_debt_by_group_resource
+    controller.release(borrowed)
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index ebc1b0d4f..a3fe5f902 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -4,6 +4,8 @@
 from __future__ import annotations
 
 import asyncio
+import logging
+import time
 from collections.abc import Callable
 from types import SimpleNamespace
 from typing import Any
@@ -44,6 +46,9 @@
     ModelRateLimitError,
     ModelTimeoutError,
 )
+from data_designer.engine.models.request_admission.pressure import RequestPressureSnapshot
+from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
+from data_designer.engine.models.resources import ProviderModelKey
 from data_designer.engine.observability import InMemoryAdmissionEventSink
 from data_designer.engine.resources.resource_provider import ResourceProvider
 
@@ -175,6 +180,17 @@ def generate(self, data: dict) -> dict:
         return data
 
 
+class MockBuggyGenerator(ColumnGenerator[ExpressionColumnConfig]):
+    """Generator that simulates an internal scheduler/generator bug."""
+
+    @staticmethod
+    def get_generation_strategy() -> GenerationStrategy:
+        return GenerationStrategy.CELL_BY_CELL
+
+    def generate(self, _data: dict) -> dict:
+        raise KeyError("missing internal key")
+
+
 class MockRateLimitGenerator(ColumnGenerator[ExpressionColumnConfig]):
     """Generator that fails with rate-limit errors before succeeding.
 
@@ -282,6 +298,11 @@ def generate(self, data: dict) -> dict:
         return data
 
 
+class _BrokenSchedulerSink:
+    def emit_scheduler_event(self, _event: object) -> None:
+        raise RuntimeError("sink boom")
+
+
 # -- Helper to build graph + scheduler ----------------------------------------
 
 
@@ -292,6 +313,7 @@ def _build_simple_pipeline(
     generators: dict[str, ColumnGenerator] | None = None,
     configs: list[SamplerColumnConfig | LLMTextColumnConfig | ExpressionColumnConfig] | None = None,
     strategies: dict[str, GenerationStrategy] | None = None,
+    scheduler_event_sink: Any | None = None,
 ) -> tuple[AsyncTaskScheduler, CompletionTracker]:
     """Build a simple seed → cell pipeline for testing."""
     if configs is None:
@@ -330,6 +352,7 @@ def _build_simple_pipeline(
         tracker=tracker,
         row_groups=row_groups,
         trace=trace,
+        scheduler_event_sink=scheduler_event_sink,
     )
     return scheduler, tracker
 
@@ -522,6 +545,56 @@ async def test_scheduler_non_retryable_failure_drops_row() -> None:
     assert tracker.is_row_group_complete(0, 2, ["seed", "fail_col"])
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_internal_bug_failure_aborts_instead_of_dropping_row(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="buggy_col", prompt="{{ seed }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "seed": GenerationStrategy.FULL_COLUMN,
+        "buggy_col": GenerationStrategy.CELL_BY_CELL,
+    }
+    generators = {
+        "seed": MockSeedGenerator(config=_expr_config("seed"), resource_provider=provider),
+        "buggy_col": MockBuggyGenerator(config=_expr_config("buggy_col"), resource_provider=provider),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, [(0, 1)])
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=[(0, 1)],
+    )
+
+    with caplog.at_level(logging.ERROR, logger="data_designer.engine.dataset_builders.async_scheduler"):
+        with pytest.raises(DatasetGenerationError, match="Unexpected internal task failure") as exc_info:
+            await scheduler.run()
+
+    assert isinstance(exc_info.value.__cause__, KeyError)
+    assert not tracker.is_dropped(0, 0)
+    error_records = [
+        record for record in caplog.records if "Unexpected fatal Non-retryable failure" in record.getMessage()
+    ]
+    assert error_records
+    assert error_records[0].exc_info is not None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_logs_sink_failures(caplog: pytest.LogCaptureFixture) -> None:
+    caplog.set_level(logging.WARNING, logger="data_designer.engine.dataset_builders.async_scheduler")
+    scheduler, tracker = _build_simple_pipeline(num_records=1, scheduler_event_sink=_BrokenSchedulerSink())
+
+    await scheduler.run()
+
+    assert tracker.is_row_group_complete(0, 1, ["seed", "cell_out"])
+    assert "Scheduler admission event sink raised; dropping event." in caplog.text
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_stateful_generator_serializes() -> None:
     """Stateful generators serialize across row groups."""
@@ -1733,6 +1806,97 @@ def drop_middle_row(row_group: int, row_group_size: int) -> FrontierDelta:
     assert tracker.is_row_group_complete(0, 3, ["seed", "cell_out"])
 
 
+def test_apply_frontier_delta_enqueues_ready_tasks_in_one_queue_operation(monkeypatch: pytest.MonkeyPatch) -> None:
+    provider = _mock_provider()
+    configs = [
+        LLMTextColumnConfig(name="root", prompt="root", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "root": GenerationStrategy.CELL_BY_CELL,
+    }
+    generators = {
+        "root": MockCellGenerator(config=_expr_config("root"), resource_provider=provider),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    row_groups = [(0, 5)]
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+    )
+    scheduler._rg_states[0] = SimpleNamespace(size=5, pre_batch_done=True)
+
+    enqueue_sizes: list[int] = []
+    original_enqueue = scheduler._fair_queue.enqueue
+
+    def spy_enqueue(items: Any) -> tuple[str, ...]:
+        materialized = tuple(items)
+        enqueue_sizes.append(len(materialized))
+        return original_enqueue(materialized)
+
+    monkeypatch.setattr(scheduler._fair_queue, "enqueue", spy_enqueue)
+
+    scheduler._apply_frontier_delta(tracker.add_root_tasks(0, 5))
+
+    assert enqueue_sizes == [5]
+    assert tracker.ready_frontier() == ()
+    assert scheduler._fair_queue.view().queued_total == 5
+
+
+def test_pre_batch_flush_batches_pending_ready_and_skips_dropped_rows(monkeypatch: pytest.MonkeyPatch) -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="cell_out", prompt="{{ seed }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "seed": GenerationStrategy.FULL_COLUMN,
+        "cell_out": GenerationStrategy.CELL_BY_CELL,
+    }
+    generators = {
+        "seed": MockSeedGenerator(config=_expr_config("seed"), resource_provider=provider),
+        "cell_out": MockCellGenerator(config=_expr_config("cell_out"), resource_provider=provider),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    row_groups = [(0, 3)]
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    sink = InMemoryAdmissionEventSink()
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        on_seeds_complete=lambda row_group, row_group_size: None,
+        scheduler_event_sink=sink,
+    )
+    state = SimpleNamespace(size=3, pre_batch_done=False)
+    scheduler._rg_states[0] = state
+
+    enqueue_sizes: list[int] = []
+    original_enqueue = scheduler._fair_queue.enqueue
+
+    def spy_enqueue(items: Any) -> tuple[str, ...]:
+        materialized = tuple(items)
+        enqueue_sizes.append(len(materialized))
+        return original_enqueue(materialized)
+
+    monkeypatch.setattr(scheduler._fair_queue, "enqueue", spy_enqueue)
+
+    scheduler._apply_frontier_delta(tracker.mark_row_range_complete("seed", 0, 3))
+    scheduler._apply_frontier_delta(tracker.drop_row(0, 1))
+    state.pre_batch_done = True
+    scheduler._flush_pre_batch_ready(0)
+
+    assert enqueue_sizes == [2]
+    assert scheduler._fair_queue.view().queued_total == 2
+    assert {item.payload.row_index for item in scheduler._fair_queue._queued.values()} == {0, 2}
+    assert tracker.is_dropped(0, 1)
+    assert sum(event.event_kind == "ready_enqueued" for event in sink.scheduler_events) == 2
+    assert sum(event.event_kind == "dependency_ready" for event in sink.scheduler_events) == 5
+
+
 def test_custom_generator_with_model_aliases_reports_custom_model_metadata() -> None:
     """CustomColumnGenerator with model_aliases reports custom-model metadata."""
 
@@ -2008,6 +2172,10 @@ async def agenerate(self, data: dict) -> dict:
     assert snapshot.resources_available["submission"] == max_submitted
     assert snapshot.resources_available["llm_wait"] == max_llm_wait
     assert "cancelled" in [event.event_kind for event in sink.scheduler_events]
+    assert all(event.snapshot is not None for event in sink.scheduler_events)
+    task_events = [event for event in sink.scheduler_events if event.task_id is not None]
+    assert all("resource_request" in event.diagnostics for event in task_events)
+    assert any("llm_wait" in event.diagnostics["resource_request"] for event in task_events)
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -2126,6 +2294,72 @@ def get_scheduling_metadata(self) -> SchedulingMetadata:
         return SchedulingMetadata.custom_model("test", self.config.name, "v1")
 
 
+class SlowModelBoundCellGenerator(SlowCellGenerator):
+    """Slow cell generator with concrete request-pressure identity."""
+
+    def __init__(
+        self,
+        *args: Any,
+        provider_name: str = "provider",
+        model_id: str = "model",
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self._provider_name = provider_name
+        self._model_id = model_id
+
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return SchedulingMetadata.model(
+            self._provider_name,
+            self._model_id,
+            "chat",
+            weight=1,
+        )
+
+
+class _StaticRequestPressureProvider:
+    def __init__(self, snapshots: dict[RequestResourceKey, RequestPressureSnapshot]) -> None:
+        self._snapshots = snapshots
+
+    def snapshot(self, resource: RequestResourceKey) -> RequestPressureSnapshot | None:
+        return self._snapshots.get(resource)
+
+    def snapshots(self) -> dict[RequestResourceKey, RequestPressureSnapshot]:
+        return dict(self._snapshots)
+
+    def global_snapshot(self, provider: str, model: str) -> None:
+        return None
+
+    def global_snapshots(self) -> dict[ProviderModelKey, object]:
+        return {}
+
+
+def _pressure_snapshot(
+    resource: RequestResourceKey,
+    *,
+    current_limit: int = 1,
+    in_flight: int = 0,
+    waiters: int = 0,
+    cooldown: float = 0.0,
+) -> RequestPressureSnapshot:
+    return RequestPressureSnapshot(
+        captured_at=time.monotonic(),
+        sequence=1,
+        resource=resource,
+        effective_max=max(1, current_limit),
+        current_limit=current_limit,
+        in_flight_count=in_flight,
+        active_lease_count=in_flight,
+        waiters=waiters,
+        blocked_until_monotonic=time.monotonic() + cooldown if cooldown > 0.0 else None,
+        cooldown_remaining_seconds=cooldown,
+        rate_limit_ceiling=max(1, current_limit),
+        consecutive_rate_limits=0,
+        last_outcome=None,
+        leak_diagnostics={},
+    )
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_fair_admission_across_ready_columns() -> None:
     """A large ready frontier is admitted across columns instead of one column at a time."""
@@ -2368,6 +2602,334 @@ async def test_scheduler_downstream_interleaves_with_upstream() -> None:
     )
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_capacity_plan_observes_buffer_backpressure() -> None:
+    provider = _mock_provider()
+    gen_names = ["gen_a", "gen_b", "gen_c"]
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        *[LLMTextColumnConfig(name=g, prompt="{{ topic }}", model_alias=MODEL_ALIAS) for g in gen_names],
+    ]
+    strategies: dict[str, GenerationStrategy] = {"topic": GenerationStrategy.FULL_COLUMN}
+    strategies.update({column: GenerationStrategy.CELL_BY_CELL for column in gen_names})
+    generators: dict[str, ColumnGenerator] = {
+        "topic": MockSeedGenerator(config=_expr_config("topic"), resource_provider=provider),
+        **{
+            name: SlowCellGenerator(config=_expr_config(name), resource_provider=provider, delay=0.02)
+            for name in gen_names
+        },
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    row_groups = [(0, 3), (1, 3), (2, 3), (3, 3)]
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        max_concurrent_row_groups=2,
+        max_submitted_tasks=2,
+        trace=True,
+        num_records=12,
+        buffer_size=3,
+    )
+
+    await asyncio.wait_for(scheduler.run(), timeout=10.0)
+
+    plan = scheduler.capacity_plan()
+    for row_group_index, row_count in row_groups:
+        assert tracker.is_row_group_complete(row_group_index, row_count, ["topic", *gen_names])
+    assert plan.configured.row_group_admission.observed_in_flight == 0
+    assert plan.observed_maxima.row_groups_in_flight == 2
+    assert plan.observed_maxima.queued_tasks_by_group
+    assert max(plan.observed_maxima.task_leases_by_resource.values()) <= 2
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_emits_job_health_and_row_group_telemetry() -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="model_col", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "topic": GenerationStrategy.FULL_COLUMN,
+        "model_col": GenerationStrategy.CELL_BY_CELL,
+    }
+    row_groups = [(0, 2)]
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    sink = InMemoryAdmissionEventSink()
+    scheduler = AsyncTaskScheduler(
+        generators={
+            "topic": MockSeedGenerator(config=_expr_config("topic"), resource_provider=provider),
+            "model_col": SlowLLMBoundCellGenerator(
+                config=_expr_config("model_col"),
+                resource_provider=provider,
+                delay=0.0,
+            ),
+        },
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        max_concurrent_row_groups=1,
+        max_submitted_tasks=2,
+        max_model_task_admission=1,
+        scheduler_event_sink=sink,
+        num_records=2,
+        buffer_size=2,
+    )
+
+    await asyncio.wait_for(scheduler.run(), timeout=10.0)
+
+    kinds = [event.event_kind for event in sink.scheduler_events]
+    assert "scheduler_job_started" in kinds
+    assert "scheduler_health_snapshot" in kinds
+    assert "row_group_checkpointed" in kinds
+    assert "scheduler_job_completed" in kinds
+
+    started = next(event for event in sink.scheduler_events if event.event_kind == "scheduler_job_started")
+    assert started.diagnostics["num_records"] == 2
+    assert started.diagnostics["buffer_size"] == 2
+    assert started.diagnostics["row_group_count"] == 1
+    assert started.diagnostics["graph_depth"] == 2
+    column_scheduling = started.diagnostics["column_scheduling"]
+    assert isinstance(column_scheduling, tuple)
+    model_column = next(item for item in column_scheduling if item["column"] == "model_col")
+    assert model_column["group_kind"] == "custom_model"
+    assert model_column["resource_request"] == {"submission": 1, "llm_wait": 1}
+
+    health = next(event for event in sink.scheduler_events if event.event_kind == "scheduler_health_snapshot")
+    assert "queued_total" in health.diagnostics
+    assert "leased_resources" in health.diagnostics
+    assert "request_pressure" in health.diagnostics
+
+    checkpointed = next(event for event in sink.scheduler_events if event.event_kind == "row_group_checkpointed")
+    assert checkpointed.diagnostics["row_group"] == 0
+    assert checkpointed.diagnostics["row_group_size"] == 2
+    assert checkpointed.diagnostics["surviving_rows"] == 2
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_adaptive_row_group_admission_expands_target_for_horizon_idle() -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="model_col", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "topic": GenerationStrategy.FULL_COLUMN,
+        "model_col": GenerationStrategy.CELL_BY_CELL,
+    }
+    row_groups = [(0, 1), (1, 1), (2, 1), (3, 1)]
+    generators: dict[str, ColumnGenerator] = {
+        "topic": MockSeedGenerator(config=_expr_config("topic"), resource_provider=provider),
+        "model_col": SlowLLMBoundCellGenerator(
+            config=_expr_config("model_col"),
+            resource_provider=provider,
+            delay=0.04,
+        ),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    sink = InMemoryAdmissionEventSink()
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        max_concurrent_row_groups=4,
+        max_submitted_tasks=4,
+        max_model_task_admission=4,
+        adaptive_row_group_admission=True,
+        adaptive_row_group_initial_target=1,
+        scheduler_event_sink=sink,
+        trace=True,
+        num_records=4,
+        buffer_size=1,
+    )
+
+    await asyncio.wait_for(scheduler.run(), timeout=10.0)
+
+    plan = scheduler.capacity_plan()
+    assert tracker.is_row_group_complete(0, 1, ["topic", "model_col"])
+    assert plan.configured.row_group_admission.mode == "adaptive"
+    assert plan.configured.row_group_admission.observed_max_target is not None
+    assert plan.configured.row_group_admission.observed_max_target > 1
+    assert plan.observed_maxima.row_groups_in_flight > 1
+    assert any(event.event_kind == "row_group_admission_target_changed" for event in sink.scheduler_events)
+
+
+def test_scheduler_adaptive_row_group_row_guard_blocks_extra_large_groups() -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="model_col", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "topic": GenerationStrategy.FULL_COLUMN,
+        "model_col": GenerationStrategy.CELL_BY_CELL,
+    }
+    row_groups = [(0, 5_000), (1, 5_000)]
+    graph = ExecutionGraph.create(configs, strategies)
+    scheduler = AsyncTaskScheduler(
+        generators={
+            "topic": MockSeedGenerator(config=_expr_config("topic"), resource_provider=provider),
+            "model_col": SlowLLMBoundCellGenerator(
+                config=_expr_config("model_col"),
+                resource_provider=provider,
+                delay=0.0,
+            ),
+        },
+        graph=graph,
+        tracker=CompletionTracker.with_graph(graph, row_groups),
+        row_groups=row_groups,
+        max_concurrent_row_groups=4,
+        adaptive_row_group_admission=True,
+        adaptive_row_group_initial_target=4,
+        num_records=10_000,
+        buffer_size=1,
+    )
+
+    scheduler._rg_states[0] = SimpleNamespace(size=5_000)
+
+    assert scheduler._adaptive_max_admitted_rows == 8_192
+    assert not scheduler._row_group_row_guard_allows(5_000)
+    assert scheduler._row_group_row_guard_allows(1_000)
+    scheduler._rg_states.clear()
+    assert scheduler._row_group_row_guard_allows(9_000)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_raises_when_ready_frontier_blocked_without_in_flight() -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="model_col", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "topic": GenerationStrategy.FULL_COLUMN,
+        "model_col": GenerationStrategy.CELL_BY_CELL,
+    }
+    row_groups = [(0, 1)]
+    graph = ExecutionGraph.create(configs, strategies)
+    scheduler = AsyncTaskScheduler(
+        generators={
+            "topic": MockSeedGenerator(config=_expr_config("topic"), resource_provider=provider),
+            "model_col": SlowLLMBoundCellGenerator(
+                config=_expr_config("model_col"),
+                resource_provider=provider,
+                delay=0.0,
+            ),
+        },
+        graph=graph,
+        tracker=CompletionTracker.with_graph(graph, row_groups),
+        row_groups=row_groups,
+        task_admission_config=TaskAdmissionConfig(
+            submission_capacity=1,
+            resource_limits={"submission": 1, "local": 1},
+        ),
+    )
+
+    with pytest.raises(RuntimeError, match="Ready frontier is admission-blocked"):
+        await asyncio.wait_for(scheduler.run(), timeout=2.0)
+
+
+def test_scheduler_request_pressure_advisory_prefers_pressure_open_peer() -> None:
+    provider = _mock_provider()
+    configs = [
+        LLMTextColumnConfig(name="pressured", prompt="A", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="open", prompt="B", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "pressured": GenerationStrategy.CELL_BY_CELL,
+        "open": GenerationStrategy.CELL_BY_CELL,
+    }
+    generators: dict[str, ColumnGenerator] = {
+        "pressured": SlowModelBoundCellGenerator(
+            config=_expr_config("pressured"),
+            resource_provider=provider,
+            provider_name="provider-a",
+            model_id="model-a",
+        ),
+        "open": SlowModelBoundCellGenerator(
+            config=_expr_config("open"),
+            resource_provider=provider,
+            provider_name="provider-b",
+            model_id="model-b",
+        ),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, [(0, 1)])
+    pressured_key = RequestResourceKey("provider-a", "model-a", RequestDomain.CHAT)
+    open_key = RequestResourceKey("provider-b", "model-b", RequestDomain.CHAT)
+    pressure = _StaticRequestPressureProvider(
+        {
+            pressured_key: _pressure_snapshot(pressured_key, current_limit=1, in_flight=1, waiters=1),
+            open_key: _pressure_snapshot(open_key, current_limit=1, in_flight=0, waiters=0),
+        }
+    )
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=[(0, 1)],
+        request_pressure_provider=pressure,
+        request_pressure_advisory=True,
+        scheduler_event_sink=(sink := InMemoryAdmissionEventSink()),
+    )
+    scheduler._rg_states[0] = SimpleNamespace(size=1, pre_batch_done=True)
+    pressured = scheduler._schedulable_task(Task(column="pressured", row_group=0, row_index=0, task_type="cell"))
+    open_task = scheduler._schedulable_task(Task(column="open", row_group=0, row_index=0, task_type="cell"))
+    scheduler._fair_queue.enqueue((pressured, open_task))
+
+    selection = scheduler._fair_queue.select_next(scheduler._is_dispatch_eligible)
+
+    assert selection is not None
+    assert selection.item.payload.column == "open"
+    skip = next(event for event in sink.scheduler_events if event.event_kind == "request_pressure_advisory_skipped")
+    assert skip.diagnostics["request_resource"] == "provider-a/model-a/chat"
+    assert skip.diagnostics["pressure_reason"] == "waiters"
+    assert skip.diagnostics["open_peer_column"] == "open"
+    assert skip.diagnostics["open_peer_request_resource"] == "provider-b/model-b/chat"
+
+
+def test_scheduler_request_pressure_advisory_preserves_liveness_when_all_candidates_pressured() -> None:
+    provider = _mock_provider()
+    configs = [LLMTextColumnConfig(name="pressured", prompt="A", model_alias=MODEL_ALIAS)]
+    strategies = {"pressured": GenerationStrategy.CELL_BY_CELL}
+    generators: dict[str, ColumnGenerator] = {
+        "pressured": SlowModelBoundCellGenerator(
+            config=_expr_config("pressured"),
+            resource_provider=provider,
+            provider_name="provider-a",
+            model_id="model-a",
+        ),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, [(0, 1)])
+    pressured_key = RequestResourceKey("provider-a", "model-a", RequestDomain.CHAT)
+    pressure = _StaticRequestPressureProvider(
+        {pressured_key: _pressure_snapshot(pressured_key, current_limit=1, in_flight=1, waiters=1)}
+    )
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=[(0, 1)],
+        request_pressure_provider=pressure,
+        request_pressure_advisory=True,
+    )
+    scheduler._rg_states[0] = SimpleNamespace(size=1, pre_batch_done=True)
+    pressured = scheduler._schedulable_task(Task(column="pressured", row_group=0, row_index=0, task_type="cell"))
+    scheduler._fair_queue.enqueue((pressured,))
+
+    selection = scheduler._fair_queue.select_next(scheduler._is_dispatch_eligible)
+
+    assert selection is not None
+    assert selection.item.payload.column == "pressured"
+
+
 # -- Skip / conditional generation tests (async engine) -----------------------
 
 
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
index 67325b7b0..9eb310edd 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import asyncio
+import logging
 
 import pytest
 
@@ -68,6 +69,37 @@ async def agenerate_image(self, request: ImageGenerationRequest) -> ImageGenerat
         return ImageGenerationResponse(images=[ImagePayload("abc")])
 
 
+class _BrokenSink:
+    def emit_request_event(self, _event: object) -> None:
+        raise RuntimeError("sink boom")
+
+
+class _GatedAsyncClient(_Client):
+    def __init__(self) -> None:
+        super().__init__()
+        self.chat_started = asyncio.Event()
+        self.embedding_started = asyncio.Event()
+        self.image_started = asyncio.Event()
+        self.release_chat = asyncio.Event()
+        self.release_embedding = asyncio.Event()
+        self.release_image = asyncio.Event()
+
+    async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        self.chat_started.set()
+        await self.release_chat.wait()
+        return ChatCompletionResponse(AssistantMessage(content="chat"))
+
+    async def aembeddings(self, request: EmbeddingRequest) -> EmbeddingResponse:
+        self.embedding_started.set()
+        await self.release_embedding.wait()
+        return EmbeddingResponse(vectors=[[1.0]])
+
+    async def agenerate_image(self, request: ImageGenerationRequest) -> ImageGenerationResponse:
+        self.image_started.set()
+        await self.release_image.wait()
+        return ImageGenerationResponse(images=[ImagePayload("image")])
+
+
 def _executor() -> tuple[ModelRequestExecutor, AdaptiveRequestAdmissionController, _Client]:
     controller = AdaptiveRequestAdmissionController()
     controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
@@ -138,6 +170,53 @@ def test_model_request_executor_maps_image_chat_domain() -> None:
     assert any(resource.domain == RequestDomain.CHAT for resource in resources)
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_model_request_executor_shares_provider_model_cap_across_async_domains() -> None:
+    controller = AdaptiveRequestAdmissionController()
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    client = _GatedAsyncClient()
+    executor = ModelRequestExecutor(client, controller, "nvidia", "nemotron")
+
+    chat_task = asyncio.create_task(executor.acompletion(ChatCompletionRequest(model="nemotron", messages=[])))
+    await asyncio.wait_for(client.chat_started.wait(), timeout=1.0)
+    embedding_task = asyncio.create_task(executor.aembeddings(EmbeddingRequest(model="nemotron", inputs=["x"])))
+    image_task = asyncio.create_task(executor.agenerate_image(ImageGenerationRequest(model="nemotron", prompt="image")))
+    await _wait_for_request_waiters(controller, expected=2)
+
+    global_snapshot = controller.pressure.global_snapshot("nvidia", "nemotron")
+    assert global_snapshot is not None
+    assert global_snapshot.aggregate_in_flight == 1
+    assert not client.embedding_started.is_set()
+    assert not client.image_started.is_set()
+
+    client.release_chat.set()
+    await asyncio.wait_for(client.embedding_started.wait(), timeout=1.0)
+    assert not client.image_started.is_set()
+    assert (await chat_task).message.content == "chat"
+
+    global_snapshot = controller.pressure.global_snapshot("nvidia", "nemotron")
+    assert global_snapshot is not None
+    assert global_snapshot.aggregate_in_flight == 1
+    client.release_embedding.set()
+    await asyncio.wait_for(client.image_started.wait(), timeout=1.0)
+    assert (await embedding_task).vectors == [[1.0]]
+
+    client.release_image.set()
+    assert (await image_task).images[0].b64_data == "image"
+    global_snapshot = controller.pressure.global_snapshot("nvidia", "nemotron")
+    assert global_snapshot is not None
+    assert global_snapshot.aggregate_in_flight == 0
+
+
+async def _wait_for_request_waiters(controller: AdaptiveRequestAdmissionController, *, expected: int) -> None:
+    for _ in range(50):
+        waiters = sum(snapshot.waiters for snapshot in controller.pressure.snapshots().values())
+        if waiters == expected:
+            return
+        await asyncio.sleep(0)
+    raise AssertionError(f"expected {expected} request waiters")
+
+
 def test_model_request_executor_emits_attempt_events_with_correlation_fields() -> None:
     sink = InMemoryAdmissionEventSink()
     controller = AdaptiveRequestAdmissionController(event_sink=sink)
@@ -154,3 +233,20 @@ def test_model_request_executor_emits_attempt_events_with_correlation_fields() -
     assert "request_lease_released" in kinds
     attempts = {event.request_attempt_id for event in sink.request_events if event.request_attempt_id is not None}
     assert len(attempts) == 1
+    assert all(event.request_resource_key is not None for event in sink.request_events)
+    assert all(event.pressure_snapshot is not None for event in sink.request_events)
+    attempt_events = [event for event in sink.request_events if event.request_attempt_id is not None]
+    assert attempt_events
+    assert all(event.request_group_key is not None for event in attempt_events)
+    assert all(event.pressure_snapshot.resource == event.request_resource_key for event in attempt_events)
+
+
+def test_model_request_executor_logs_sink_failures(caplog: pytest.LogCaptureFixture) -> None:
+    caplog.set_level(logging.WARNING, logger="data_designer.engine.models.clients.model_request_executor")
+    controller = AdaptiveRequestAdmissionController()
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    executor = ModelRequestExecutor(_Client(), controller, "nvidia", "nemotron", event_sink=_BrokenSink())
+
+    executor.completion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    assert "Model request event sink raised; dropping event." in caplog.text
diff --git a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index 9a743f457..b62237a48 100644
--- a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import asyncio
+import logging
 
 import pytest
 
@@ -38,6 +39,11 @@ def _controller(cap: int = 2, config: RequestAdmissionConfig | None = None) -> A
     return controller
 
 
+class _BrokenRequestSink:
+    def emit_request_event(self, _event: object) -> None:
+        raise RuntimeError("sink boom")
+
+
 def test_request_admission_acquires_and_releases_lease() -> None:
     controller = _controller(cap=1)
     item = _item()
@@ -148,6 +154,23 @@ def test_request_admission_zero_sync_timeout_is_immediate() -> None:
     controller.release(lease, RequestReleaseOutcome(kind="success"))
 
 
+def test_request_admission_logs_sink_failures(caplog: pytest.LogCaptureFixture) -> None:
+    caplog.set_level(logging.WARNING, logger="data_designer.engine.models.request_admission.controller")
+    controller = AdaptiveRequestAdmissionController(event_sink=_BrokenRequestSink())
+
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+
+    assert "Request admission event sink raised; dropping event." in caplog.text
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_acquire_sync_rejects_running_event_loop() -> None:
+    controller = _controller(cap=1)
+
+    with pytest.raises(RuntimeError, match="would block the running event loop"):
+        controller.acquire_sync(_item())
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_try_acquire_does_not_bypass_queued_waiter_for_same_provider_model() -> None:
     controller = _controller(cap=1)
@@ -188,6 +211,50 @@ async def test_request_admission_zero_async_timeout_is_immediate() -> None:
     controller.release(lease, RequestReleaseOutcome(kind="success"))
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_acquire_async_wakes_when_release_assigns_lease(monkeypatch: pytest.MonkeyPatch) -> None:
+    controller = _controller(cap=1)
+    monkeypatch.setattr(controller, "_wait_seconds_locked", lambda _item, _now, _deadline: 10.0)
+    lease = controller.try_acquire(_item(RequestDomain.CHAT))
+    assert isinstance(lease, RequestAdmissionLease)
+    queued = _item(RequestDomain.EMBEDDING, timeout=30.0)
+
+    queued_task = asyncio.create_task(controller.acquire_async(queued))
+    for _ in range(20):
+        snapshot = controller.pressure.snapshot(queued.resource)
+        if snapshot is not None and snapshot.waiters == 1:
+            break
+        await asyncio.sleep(0)
+    else:
+        raise AssertionError("async waiter did not enqueue")
+
+    controller.release(lease, RequestReleaseOutcome(kind="success"))
+    queued_lease = await asyncio.wait_for(queued_task, timeout=0.5)
+
+    controller.release(queued_lease, RequestReleaseOutcome(kind="success"))
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_register_wakes_unregistered_async_waiter(monkeypatch: pytest.MonkeyPatch) -> None:
+    controller = AdaptiveRequestAdmissionController()
+    monkeypatch.setattr(controller, "_wait_seconds_locked", lambda _item, _now, _deadline: 10.0)
+    queued = _item(RequestDomain.CHAT, timeout=30.0)
+
+    queued_task = asyncio.create_task(controller.acquire_async(queued))
+    for _ in range(20):
+        snapshot = controller.pressure.snapshot(queued.resource)
+        if snapshot is not None and snapshot.waiters == 1:
+            break
+        await asyncio.sleep(0)
+    else:
+        raise AssertionError("async waiter did not enqueue")
+
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    queued_lease = await asyncio.wait_for(queued_task, timeout=0.5)
+
+    controller.release(queued_lease, RequestReleaseOutcome(kind="success"))
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_async_cancellation_after_waiter_assignment_releases_lease() -> None:
     controller = _controller(cap=1)
diff --git a/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py b/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
new file mode 100644
index 000000000..595977464
--- /dev/null
+++ b/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
@@ -0,0 +1,621 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType, SimpleNamespace
+from typing import Any
+
+import pytest
+
+
+def _load_benchmark_module() -> ModuleType:
+    benchmark_path = Path(__file__).resolve().parents[4] / "scripts" / "benchmarks" / "benchmark_async_scheduling.py"
+    spec = importlib.util.spec_from_file_location("benchmark_async_scheduling", benchmark_path)
+    assert spec is not None
+    assert spec.loader is not None
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[spec.name] = module
+    spec.loader.exec_module(module)
+    return module
+
+
+def _load_idle_regression_module() -> ModuleType:
+    benchmark_dir = Path(__file__).resolve().parents[4] / "scripts" / "benchmarks"
+    module_name = "run_async_scheduling_idle_regression"
+    spec = importlib.util.spec_from_file_location(module_name, benchmark_dir / f"{module_name}.py")
+    assert spec is not None
+    assert spec.loader is not None
+    sys.path.insert(0, str(benchmark_dir))
+    try:
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[spec.name] = module
+        spec.loader.exec_module(module)
+    finally:
+        sys.path.remove(str(benchmark_dir))
+    return module
+
+
+def _capacity_plan() -> SimpleNamespace:
+    return SimpleNamespace(
+        observed_maxima=SimpleNamespace(
+            row_groups_in_flight=1,
+            queued_tasks_by_group={},
+        )
+    )
+
+
+def _valid_artifact(iteration: dict[str, Any]) -> dict[str, Any]:
+    iteration.setdefault(
+        "utilization_metrics",
+        {
+            "scheduler_resources": {
+                "submission": {
+                    "capacity_seconds": 1.0,
+                    "busy_capacity_seconds": 0.0,
+                    "idle_capacity_seconds": 1.0,
+                    "starved_idle_seconds": 0.0,
+                    "utilization_ratio": 0.0,
+                }
+            },
+            "request_resources": {},
+        },
+    )
+    return {
+        "inputs": SimpleNamespace(task_admission_capacity=2),
+        "derived_metrics": {
+            "final_zero_task_leases": True,
+            "final_zero_request_leases": True,
+            "final_zero_request_waiters": True,
+            "p95_async_request_wakeup_seconds": 0.0,
+        },
+        "iterations": [iteration],
+    }
+
+
+def test_pipeline_metrics_fails_when_downstream_traces_are_missing() -> None:
+    benchmark = _load_benchmark_module()
+    traces = [
+        {
+            "column": "heavy_0",
+            "row_group": 0,
+            "row_index": 0,
+            "dispatched_at": 1.0,
+            "completed_at": 2.0,
+        }
+    ]
+
+    metrics = benchmark._pipeline_metrics(
+        traces,
+        [],
+        upstream_cols=["heavy_0"],
+        downstream_cols=["fast_0"],
+        submission_capacity=1,
+        llm_wait_capacity=1,
+        row_group_concurrency=1,
+        capacity_plan=_capacity_plan(),
+        expected_task_count=1,
+    )
+
+    assert metrics["upstream_task_count"] == 1
+    assert metrics["downstream_task_count"] == 0
+    assert metrics["downstream_ready_gap_count"] == 0
+    assert not metrics["validation"]["expected_downstream_task_count"]
+    assert not metrics["validation"]["expected_downstream_ready_gap_count"]
+    assert not metrics["validation_passed"]
+
+
+def test_benchmark_validation_rejects_early_queue_exit() -> None:
+    benchmark = _load_benchmark_module()
+    artifact = _valid_artifact(
+        {
+            "accepted_task_count": 2,
+            "selected_task_count": 1,
+            "per_layer_observed_maxima": {
+                "task_leases_by_resource": {},
+                "request_in_flight_by_resource": {},
+            },
+        }
+    )
+
+    with pytest.raises(RuntimeError, match="queue drained early"):
+        benchmark._validate_artifact(artifact)
+
+
+def test_benchmark_validation_rejects_cap_violations() -> None:
+    benchmark = _load_benchmark_module()
+    artifact = _valid_artifact(
+        {
+            "accepted_task_count": 2,
+            "selected_task_count": 2,
+            "per_layer_observed_maxima": {
+                "task_leases_by_resource": {"submission": 3},
+                "request_in_flight_by_resource": {"mock": 2},
+            },
+        }
+    )
+
+    with pytest.raises(RuntimeError, match="exceeded submission task cap"):
+        benchmark._validate_artifact(artifact)
+
+
+def test_scheduler_utilization_metrics_record_idle_and_starved_capacity() -> None:
+    benchmark = _load_benchmark_module()
+    events = [
+        _scheduler_event(
+            "ready_enqueued",
+            timestamp=0.0,
+            sequence=1,
+            task_id="task-a",
+            leased_resources={},
+            resource_request={"submission": 1},
+        ),
+        _scheduler_event(
+            "task_lease_acquired",
+            timestamp=1.0,
+            sequence=2,
+            task_id="task-a",
+            leased_resources={"submission": 1},
+            resource_request={"submission": 1},
+        ),
+        _scheduler_event(
+            "task_lease_released",
+            timestamp=3.0,
+            sequence=3,
+            task_id="task-a",
+            leased_resources={},
+            resource_request={"submission": 1},
+        ),
+    ]
+
+    metrics = benchmark._scheduler_utilization_metrics(events)["submission"]
+
+    assert metrics["capacity"] == 2
+    assert metrics["busy_capacity_seconds"] == pytest.approx(2.0)
+    assert metrics["idle_capacity_seconds"] == pytest.approx(4.0)
+    assert metrics["starved_idle_seconds"] == pytest.approx(2.0)
+    assert metrics["dependency_horizon_idle_seconds"] == pytest.approx(2.0)
+    assert metrics["frontier_dependency_horizon_idle_ratio"] == pytest.approx(1 / 3)
+    assert metrics["utilization_ratio"] == pytest.approx(1 / 3)
+    assert metrics["scheduler_queue_age_max_seconds"] == pytest.approx(1.0)
+    assert metrics["ready_to_dispatch_gap_max_seconds"] == pytest.approx(1.0)
+    assert metrics["ready_to_dispatch_sample_count"] == 1
+
+
+def test_request_utilization_metrics_record_waiter_starved_idle() -> None:
+    benchmark = _load_benchmark_module()
+    events = [
+        _request_event("request_wait_started", timestamp=0.0, sequence=1, in_flight=0, waiters=1),
+        _request_event("request_wait_completed", timestamp=1.0, sequence=2, in_flight=1, waiters=0),
+        _request_event("request_lease_released", timestamp=3.0, sequence=3, in_flight=0, waiters=0),
+    ]
+
+    metrics = benchmark._request_utilization_metrics(events)["mock-resource"]
+
+    assert metrics["capacity"] == 2
+    assert metrics["busy_capacity_seconds"] == pytest.approx(2.0)
+    assert metrics["idle_capacity_seconds"] == pytest.approx(4.0)
+    assert metrics["starved_idle_seconds"] == pytest.approx(2.0)
+    assert metrics["dependency_horizon_idle_seconds"] == pytest.approx(2.0)
+    assert metrics["scheduler_queue_age_max_seconds"] == pytest.approx(1.0)
+    assert metrics["ready_to_dispatch_gap_max_seconds"] == pytest.approx(1.0)
+
+
+def test_benchmark_validation_requires_scheduler_utilization_metrics() -> None:
+    benchmark = _load_benchmark_module()
+    artifact = _valid_artifact(
+        {
+            "accepted_task_count": 1,
+            "selected_task_count": 1,
+            "per_layer_observed_maxima": {
+                "task_leases_by_resource": {},
+                "request_in_flight_by_resource": {},
+            },
+            "utilization_metrics": {"scheduler_resources": {}, "request_resources": {}},
+        }
+    )
+
+    with pytest.raises(RuntimeError, match="did not record scheduler utilization metrics"):
+        benchmark._validate_artifact(artifact)
+
+
+def test_idle_regression_guardrails_pass_for_expected_summary() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert all(check.passed for check in checks if check.severity == "error")
+
+
+def test_idle_regression_detects_validation_failure() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    summary["cases"]["row-scale/rows-64"]["validation_passed"] = False
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(not check.passed and check.name == "row-scale/rows-64 validation" for check in checks)
+
+
+def test_idle_regression_detects_baseline_utilization_drop() -> None:
+    regression = _load_idle_regression_module()
+    baseline = _idle_regression_summary(utilization=0.80)
+    current = _idle_regression_summary(utilization=0.70)
+
+    checks = regression.evaluate_idle_regression_summary(current, baseline_summary=baseline)
+
+    assert any(
+        not check.passed and check.category == "baseline" and check.name.endswith("utilization regression")
+        for check in checks
+    )
+
+
+def test_idle_regression_detects_bad_idle_partition() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    summary["cases"]["row-scale/rows-64"]["llm_wait_frontier_dependency_horizon_idle_ratio"] = 0.99
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(not check.passed and check.name == "row-scale/rows-64 idle partition" for check in checks)
+
+
+def test_idle_regression_requires_adaptation_controls() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    del summary["cases"]["adaptations/adaptive-row-groups-fixed-high"]
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(
+        not check.passed and check.name == "required case adaptations/adaptive-row-groups-fixed-high"
+        for check in checks
+    )
+
+
+def test_idle_regression_detects_bad_adaptive_row_group_response() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    adaptive = summary["cases"]["adaptations/adaptive-row-groups-adaptive"]
+    adaptive["llm_wait_utilization_ratio"] = 0.45
+    adaptive["llm_wait_idle_ratio"] = 0.55
+    adaptive["llm_wait_starved_idle_ratio"] = 0.10
+    adaptive["llm_wait_frontier_dependency_horizon_idle_ratio"] = 0.45
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(not check.passed and check.name == "adaptive row-group utilization response" for check in checks)
+    assert any(not check.passed and check.name == "adaptive row-group fixed-high isolation" for check in checks)
+
+
+def test_idle_regression_detects_request_pressure_control_not_pressured() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    control = summary["cases"]["adaptations/request-pressure-control"]
+    control["first_model_dispatch_column"] = "z_open"
+    control["request_wait_seconds_while_task_leased_mean"] = 0.0
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(not check.passed and check.name == "request-pressure control dispatch choice" for check in checks)
+    assert any(not check.passed and check.name == "request-pressure control leased wait present" for check in checks)
+
+
+def test_idle_regression_detects_request_pressure_advisory_regression() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    summary["cases"]["adaptations/request-pressure-advisory"]["request_wait_seconds_while_task_leased_mean"] = 0.05
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(not check.passed and check.name == "request-pressure advisory leased-wait response" for check in checks)
+
+
+def test_idle_regression_detects_combined_adaptive_request_pressure_regression() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    combined = summary["cases"]["adaptations/adaptive-request-pressure-combined"]
+    combined["row_group_admission_observed_max_target"] = 1
+    combined["request_pressure_advisory_skip_count"] = 0
+    combined["request_idle_ratio"] = 0.70
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(not check.passed and check.name == "combined adaptive/request target grew" for check in checks)
+    assert any(
+        not check.passed and check.name == "combined adaptive/request advisory skipped pressured work"
+        for check in checks
+    )
+    assert any(not check.passed and check.name == "combined adaptive/request request-idle response" for check in checks)
+
+
+def test_idle_regression_detects_request_cleanup_failure() -> None:
+    regression = _load_idle_regression_module()
+    summary = _idle_regression_summary()
+    summary["cases"]["adaptations/request-pressure-advisory"]["final_zero_request_waiters"] = False
+
+    checks = regression.evaluate_idle_regression_summary(summary)
+
+    assert any(
+        not check.passed and check.name == "adaptations/request-pressure-advisory final_zero_request_waiters"
+        for check in checks
+    )
+
+
+def test_benchmark_validation_uses_scenario_task_resource_limits() -> None:
+    benchmark = _load_benchmark_module()
+    artifact = _valid_artifact(
+        {
+            "accepted_task_count": 2,
+            "selected_task_count": 2,
+            "capacity_plan": SimpleNamespace(
+                configured=SimpleNamespace(
+                    task_resource_limits=SimpleNamespace(
+                        value={"submission": 2, "llm_wait": 2, "local": 2},
+                    )
+                )
+            ),
+            "final_request_snapshot": {
+                "zero_active_request_leases": True,
+                "zero_request_waiters": True,
+                "domains": {
+                    "pressured": SimpleNamespace(effective_max=1, current_limit=1),
+                },
+            },
+            "per_layer_observed_maxima": {
+                "task_leases_by_resource": {"submission": 2, "llm_wait": 2},
+                "request_in_flight_by_resource": {"pressured": 1},
+            },
+        }
+    )
+
+    benchmark._validate_artifact(artifact)
+
+
+def _scheduler_event(
+    event_kind: str,
+    *,
+    timestamp: float,
+    sequence: int,
+    task_id: str,
+    leased_resources: dict[str, int],
+    resource_request: dict[str, int],
+) -> SimpleNamespace:
+    return SimpleNamespace(
+        event_kind=event_kind,
+        captured_at_monotonic=timestamp,
+        sequence=sequence,
+        task_id=task_id,
+        diagnostics={"resource_request": resource_request},
+        snapshot=SimpleNamespace(
+            resource_limits={"submission": 2},
+            leased_resources=leased_resources,
+        ),
+    )
+
+
+def _idle_regression_summary(*, utilization: float = 0.80) -> dict[str, Any]:
+    cases = {
+        "row-scale/rows-64": _idle_regression_case(utilization=utilization, record_count=64, generation_count=512),
+        "row-scale/rows-256": _idle_regression_case(
+            utilization=utilization,
+            record_count=256,
+            generation_count=2048,
+        ),
+        "row-group-concurrency/row-groups-1": _idle_regression_case(
+            utilization=0.50,
+            idle=0.50,
+            starved=0.20,
+            row_group_concurrency=1,
+            generation_count=1024,
+        ),
+        "row-group-concurrency/row-groups-4": _idle_regression_case(
+            utilization=utilization,
+            idle=1.0 - utilization,
+            starved=0.10,
+            row_group_concurrency=4,
+            generation_count=1024,
+        ),
+        "buffer-size/buffer-1": _idle_regression_case(
+            utilization=utilization,
+            idle=1.0 - utilization,
+            starved=0.10,
+            buffer_size=1,
+            generation_count=4096,
+        ),
+        "stress-shape/narrow-frontier-high-cap": _idle_regression_case(
+            utilization=0.30,
+            idle=0.70,
+            starved=0.20,
+            record_count=1024,
+            generation_count=2048,
+        ),
+        "stress-shape/wide-frontier-high-cap": _idle_regression_case(
+            utilization=utilization,
+            idle=1.0 - utilization,
+            starved=0.10,
+            record_count=1024,
+            generation_count=8192,
+        ),
+        "custom-model-weight/weight-model-capacity": _idle_regression_case(
+            utilization=utilization,
+            idle=1.0 - utilization,
+            starved=0.10,
+            generation_count=4096,
+        ),
+        "adaptations/adaptive-row-groups-fixed-low": _idle_regression_case(
+            utilization=0.40,
+            idle=0.60,
+            starved=0.10,
+            record_count=512,
+            row_group_concurrency=1,
+            generation_count=1024,
+        ),
+        "adaptations/adaptive-row-groups-adaptive": _idle_regression_case(
+            utilization=0.75,
+            idle=0.25,
+            starved=0.08,
+            record_count=512,
+            row_group_concurrency=8,
+            generation_count=1024,
+            row_group_admission_observed_max_target=4,
+        ),
+        "adaptations/adaptive-row-groups-fixed-high": _idle_regression_case(
+            utilization=0.78,
+            idle=0.22,
+            starved=0.08,
+            record_count=512,
+            row_group_concurrency=8,
+            generation_count=1024,
+        ),
+        "adaptations/request-pressure-control": _idle_regression_case(
+            utilization=0.70,
+            idle=0.30,
+            starved=0.10,
+            generation_count=64,
+            request_utilization=0.50,
+            request_idle=0.50,
+            request_starved=0.30,
+            request_wait_seconds_while_task_leased_mean=0.05,
+            first_model_dispatch_column="a_pressured",
+        ),
+        "adaptations/request-pressure-advisory": _idle_regression_case(
+            utilization=0.70,
+            idle=0.30,
+            starved=0.10,
+            generation_count=64,
+            request_utilization=0.60,
+            request_idle=0.40,
+            request_starved=0.20,
+            request_wait_seconds_while_task_leased_mean=0.0,
+            request_pressure_advisory_enabled=True,
+            request_pressure_advisory_skip_count=4,
+            first_model_dispatch_column="z_open",
+        ),
+        "adaptations/adaptive-request-pressure-control": _idle_regression_case(
+            utilization=0.65,
+            idle=0.35,
+            starved=0.12,
+            record_count=512,
+            row_group_concurrency=8,
+            generation_count=1024,
+            request_utilization=0.50,
+            request_idle=0.50,
+            request_starved=0.30,
+            request_wait_seconds_while_task_leased_mean=0.20,
+            first_model_dispatch_column="a_pressured",
+            row_group_admission_observed_max_target=4,
+        ),
+        "adaptations/adaptive-request-pressure-combined": _idle_regression_case(
+            utilization=0.70,
+            idle=0.30,
+            starved=0.10,
+            record_count=512,
+            row_group_concurrency=8,
+            generation_count=1024,
+            request_utilization=0.65,
+            request_idle=0.35,
+            request_starved=0.20,
+            request_wait_seconds_while_task_leased_mean=0.05,
+            request_pressure_advisory_enabled=True,
+            request_pressure_advisory_skip_count=8,
+            first_model_dispatch_column="z_open",
+            row_group_admission_observed_max_target=4,
+        ),
+    }
+    for index in range(3):
+        cases[f"filler/filler-{index}"] = _idle_regression_case(
+            utilization=utilization,
+            idle=1.0 - utilization,
+            starved=0.10,
+        )
+    return {
+        "suite_id": "async-scheduling-idle-regression",
+        "suite_version": "1.1",
+        "mode": "quick",
+        "largest_generation_count": 8192,
+        "cases": cases,
+    }
+
+
+def _idle_regression_case(
+    *,
+    utilization: float,
+    idle: float | None = None,
+    starved: float = 0.10,
+    record_count: int = 512,
+    generation_count: int = 1024,
+    row_group_concurrency: int = 4,
+    buffer_size: int = 64,
+    request_utilization: float = 0.0,
+    request_idle: float = 0.0,
+    request_starved: float = 0.0,
+    request_wait_seconds_while_task_leased_mean: float = 0.0,
+    request_pressure_advisory_enabled: bool = False,
+    request_pressure_advisory_skip_count: int = 0,
+    first_model_dispatch_column: str | None = None,
+    row_group_admission_observed_max_target: int | None = None,
+) -> dict[str, Any]:
+    resolved_idle = 1.0 - utilization if idle is None else idle
+    return {
+        "case": {
+            "name": "case",
+            "sweep": "sweep",
+            "record_count": record_count,
+            "buffer_size": buffer_size,
+            "row_group_concurrency": row_group_concurrency,
+            "task_admission_capacity": 8,
+            "fanout_width": 1,
+            "upstream_latency_seconds": 0.003,
+            "downstream_latency_seconds": 0.0003,
+            "scenario": "real-pipeline-overlap",
+            "request_latency_seconds": 0.0,
+            "model_stage_weight": 0,
+            "adaptive_row_group_admission": False,
+            "request_pressure_advisory": False,
+            "iterations": 1,
+            "warmups": 0,
+        },
+        "generation_count": generation_count,
+        "llm_wait_utilization_ratio": utilization,
+        "llm_wait_idle_ratio": resolved_idle,
+        "llm_wait_starved_idle_ratio": starved,
+        "llm_wait_frontier_dependency_horizon_idle_ratio": max(0.0, resolved_idle - starved),
+        "llm_wait_scheduler_queue_age_p95_seconds": 0.001,
+        "request_utilization_ratio": request_utilization,
+        "request_idle_ratio": request_idle,
+        "request_starved_idle_ratio": request_starved,
+        "request_frontier_dependency_horizon_idle_ratio": max(0.0, request_idle - request_starved),
+        "request_wait_seconds_while_task_leased_mean": request_wait_seconds_while_task_leased_mean,
+        "request_wait_seconds_while_task_leased_max": request_wait_seconds_while_task_leased_mean,
+        "request_pressure_advisory_enabled": request_pressure_advisory_enabled,
+        "request_pressure_advisory_skip_count": request_pressure_advisory_skip_count,
+        "first_model_dispatch_column": first_model_dispatch_column,
+        "row_group_admission_observed_max_target": row_group_admission_observed_max_target,
+        "throughput_generations_per_second": 100.0,
+        "validation_passed": True,
+        "final_zero_task_leases": True,
+        "final_zero_request_leases": True,
+        "final_zero_request_waiters": True,
+    }
+
+
+def _request_event(
+    event_kind: str, *, timestamp: float, sequence: int, in_flight: int, waiters: int
+) -> SimpleNamespace:
+    return SimpleNamespace(
+        event_kind=event_kind,
+        captured_at_monotonic=timestamp,
+        sequence=sequence,
+        request_attempt_id="request-a",
+        pressure_snapshot=SimpleNamespace(
+            resource="mock-resource",
+            effective_max=2,
+            in_flight_count=in_flight,
+            waiters=waiters,
+        ),
+    )
diff --git a/reports/async-scheduling-idle-analysis.html b/reports/async-scheduling-idle-analysis.html
new file mode 100644
index 000000000..13107060e
--- /dev/null
+++ b/reports/async-scheduling-idle-analysis.html
@@ -0,0 +1,97 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Async Scheduling Idle-Time Analysis</title>
+
+<style>
+:root {
+  color-scheme: light;
+  --ink: #17212b;
+  --muted: #526070;
+  --line: #d8dee6;
+  --panel: #f7f9fb;
+  --accent: #1f7a8c;
+  --warn: #b23a48;
+}
+* { box-sizing: border-box; }
+body {
+  margin: 0;
+  font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+  color: var(--ink);
+  background: #ffffff;
+}
+main { max-width: 1120px; margin: 0 auto; padding: 44px 28px 72px; }
+.hero { border-bottom: 1px solid var(--line); padding-bottom: 28px; }
+.eyebrow { color: var(--accent); font-weight: 700; text-transform: uppercase; letter-spacing: .08em; font-size: 12px; }
+h1 { font-size: 44px; line-height: 1.05; margin: 8px 0 14px; letter-spacing: 0; }
+h2 { font-size: 24px; margin: 34px 0 12px; letter-spacing: 0; }
+p, li { color: var(--muted); line-height: 1.6; }
+code { font-family: "SFMono-Regular", Consolas, monospace; font-size: .94em; background: #eef2f6; padding: 1px 5px; border-radius: 4px; }
+.lede { max-width: 820px; font-size: 18px; }
+.summary-grid { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 12px; margin-top: 26px; }
+.summary-card { background: var(--panel); border: 1px solid var(--line); border-radius: 8px; padding: 16px; }
+.summary-card span { display: block; color: var(--muted); font-size: 13px; margin-bottom: 8px; }
+.summary-card strong { display: block; font-size: 24px; }
+.finding-list, .recommendations { padding-left: 22px; }
+.finding-list li, .recommendations li { margin: 10px 0; }
+.chart { border: 1px solid var(--line); border-radius: 8px; background: #fff; margin: 18px 0; padding: 10px; overflow-x: auto; }
+svg { width: 100%; min-width: 760px; height: auto; }
+.axis { stroke: #7c8794; stroke-width: 1; }
+.grid { stroke: #e5e9ef; stroke-width: 1; }
+.chart-title { font-size: 17px; font-weight: 700; fill: var(--ink); }
+.axis-label, .tick, .legend-text { fill: var(--muted); font-size: 12px; }
+.legend-text { font-weight: 600; }
+.table-wrap { overflow-x: auto; border: 1px solid var(--line); border-radius: 8px; }
+table { border-collapse: collapse; min-width: 1080px; width: 100%; font-size: 13px; }
+th, td { padding: 10px 12px; border-bottom: 1px solid var(--line); text-align: left; white-space: nowrap; }
+th { background: var(--panel); color: var(--ink); font-size: 12px; text-transform: uppercase; letter-spacing: .04em; }
+td { color: var(--muted); }
+.artifact-note { font-size: 13px; }
+a { color: var(--accent); }
+@media (max-width: 760px) {
+  main { padding: 28px 18px 52px; }
+  h1 { font-size: 34px; }
+  .summary-grid { grid-template-columns: 1fr; }
+}
+</style>
+
+</head>
+<body>
+<main>
+<section class="hero">
+<p class="eyebrow">DataDesigner async scheduler</p>
+<h1>Idle-Time Analysis</h1>
+<p class="lede">This report measures where model-capacity idle time appears, which knobs change it, and which remaining idle looks avoidable by scheduler or admission-control changes.</p>
+<div class="summary-grid"><div class="summary-card"><span>Benchmark cases</span><strong>21</strong></div><div class="summary-card"><span>Largest synthetic generation count</span><strong>8,192</strong></div><div class="summary-card"><span>Median llm_wait utilization</span><strong>70.5%</strong></div><div class="summary-card"><span>Worst total llm_wait idle</span><strong>91.9%</strong></div><div class="summary-card"><span>Worst starved llm_wait idle</span><strong>40.0%</strong></div><div class="summary-card"><span>Best llm_wait utilization</span><strong>89.4%</strong></div><div class="summary-card"><span>Validation</span><strong>pass</strong></div></div>
+</section>
+<section>
+<h2>What Counts As Idle</h2>
+<p><strong>General idle</strong> is unused configured capacity. It includes expected idle when dependencies are not ready. <strong>Starved idle</strong> is the actionable subset: a resource has idle slots while the scheduler already has queued work that requests that resource. For this refactor, the most important resource is <code>llm_wait</code>, because it maps to model-serving capacity that should stay busy when runnable model work exists.</p>
+</section>
+<section>
+<h2>Findings</h2>
+<ul class="finding-list"><li><strong>Scale helps once enough runnable work exists.</strong> The row-scale sweep moved from 75.2% to 82.0% llm_wait utilization (+6.8 pp). Larger runs amortize startup and drain phases.</li><li><strong>Runnable frontier width is the main controllable idle source.</strong> In the row-group sweep, utilization moved from 16.7% to 89.4% (+72.7 pp), while starved llm_wait idle improved by 8.0% between the narrowest case and the best observed case.</li><li><strong>Total idle and starved idle diagnose different causes.</strong> <code>narrow-frontier-high-cap</code> had 91.9% total llm_wait idle but only 21.1% starved idle. The remaining idle is dependency-horizon idle: the scheduler often had no ready model work because the admitted frontier was too small.</li><li><strong>Capacity increases expose underfeeding.</strong> In the capacity sweep, modeled llm_wait cap 2 reached 85.1% utilization, while cap 8 reached 33.4% with the same small row frontier.</li><li><strong>Custom model scheduling metadata changes traffic shape.</strong> With synthetic <code>custom_model</code> weight forced to 1, llm_wait utilization was 81.9%; with the harness defaulting the weight to modeled model capacity it was 68.6%. This run did not show weight=1 underfeeding because each synthetic column is its own group, but it does show that group identity and weight are first-order benchmark inputs.</li><li><strong>High capacity magnifies traffic-shape effects.</strong> The stress case with one large row group and high cap reached 8.1% utilization; the wide-frontier shape reached 70.5%. This is the vLLM-server-idle failure mode the scheduler needs to expose and track over time.</li><li><strong>Worst observed actionable idle:</strong> rows-256 had 40.0% starved llm_wait idle and 5.57 s max ready-to-dispatch gap.</li></ul>
+</section>
+<section>
+<h2>Figures</h2>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Scale: more rows smooth the model-resource flow"><text class="chart-title" x="64" y="24">Scale: more rows smooth the model-resource flow</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">synthetic model generations</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,107.5 116.8,127.7 222.4,147.6 433.6,118.5 856.0,89.6" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="107.5" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7520</title></circle><circle cx="116.8" cy="127.7" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.6753</title></circle><circle cx="222.4" cy="147.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.6000</title></circle><circle cx="433.6" cy="118.5" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7101</title></circle><circle cx="856.0" cy="89.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8196</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,241.8 116.8,220.5 222.4,200.5 433.6,229.5 856.0,258.4" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="241.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2431</title></circle><circle cx="116.8" cy="220.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3241</title></circle><circle cx="222.4" cy="200.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3997</title></circle><circle cx="433.6" cy="229.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2898</title></circle><circle cx="856.0" cy="258.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1804</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">512</text><text class="tick" x="116.8" y="332" text-anchor="middle">1.0k</text><text class="tick" x="222.4" y="332" text-anchor="middle">2.0k</text><text class="tick" x="433.6" y="332" text-anchor="middle">4.1k</text><text class="tick" x="856.0" y="332" text-anchor="middle">8.2k</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Frontier width: row-group concurrency changes starvation"><text class="chart-title" x="64" y="24">Frontier width: row-group concurrency changes starvation</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">row-group concurrency</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,256.6 177.1,217.4 403.4,129.1 856.0,42.0" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="256.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.1673</title></circle><circle cx="177.1" cy="217.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.3001</title></circle><circle cx="403.4" cy="129.1" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5992</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8941</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,252.1 177.1,233.2 403.4,238.4 856.0,275.6" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="252.1" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1825</title></circle><circle cx="177.1" cy="233.2" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2467</title></circle><circle cx="403.4" cy="238.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2291</title></circle><circle cx="856.0" cy="275.6" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1029</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,305.7 177.1,305.6 403.4,305.5 856.0,304.3" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="305.7" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0012</title></circle><circle cx="177.1" cy="305.6" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0015</title></circle><circle cx="403.4" cy="305.5" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0017</title></circle><circle cx="856.0" cy="304.3" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0056</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">downstream ready p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="177.1" y="332" text-anchor="middle">2</text><text class="tick" x="403.4" y="332" text-anchor="middle">4</text><text class="tick" x="856.0" y="332" text-anchor="middle">8</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Buffer size: row-group shape affects traffic waves"><text class="chart-title" x="64" y="24">Buffer size: row-group shape affects traffic waves</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">buffer size</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,296.1 73.3,296.4 110.6,296.6 259.7,296.9 856.0,297.5" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="296.1" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8200</title></circle><circle cx="73.3" cy="296.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7904</title></circle><circle cx="110.6" cy="296.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7766</title></circle><circle cx="259.7" cy="296.9" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7497</title></circle><circle cx="856.0" cy="297.5" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7024</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,303.8 73.3,303.5 110.6,303.3 259.7,303.0 856.0,302.4" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="303.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1799</title></circle><circle cx="73.3" cy="303.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2095</title></circle><circle cx="110.6" cy="303.3" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2232</title></circle><circle cx="259.7" cy="303.0" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2502</title></circle><circle cx="856.0" cy="302.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2975</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,305.1 73.3,301.8 110.6,286.7 259.7,196.9 856.0,42.0" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="305.1" r="4" fill="#6c5ce7"><title>llm ready gap p95: 0.0742</title></circle><circle cx="73.3" cy="301.8" r="4" fill="#6c5ce7"><title>llm ready gap p95: 0.3464</title></circle><circle cx="110.6" cy="286.7" r="4" fill="#6c5ce7"><title>llm ready gap p95: 1.5943</title></circle><circle cx="259.7" cy="196.9" r="4" fill="#6c5ce7"><title>llm ready gap p95: 8.9931</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#6c5ce7"><title>llm ready gap p95: 21.7638</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">llm ready gap p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="73.3" y="332" text-anchor="middle">4</text><text class="tick" x="110.6" y="332" text-anchor="middle">16</text><text class="tick" x="259.7" y="332" text-anchor="middle">64</text><text class="tick" x="856.0" y="332" text-anchor="middle">256</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Capacity scaling: underfeeding becomes visible as cap rises"><text class="chart-title" x="64" y="24">Capacity scaling: underfeeding becomes visible as cap rises</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">modeled llm_wait capacity</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / kgen/s</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,92.6 328.0,162.4 856.0,222.2" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="92.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8510</title></circle><circle cx="328.0" cy="162.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5724</title></circle><circle cx="856.0" cy="222.2" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.3341</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,269.0 328.0,243.8 856.0,230.4" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="269.0" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1476</title></circle><circle cx="328.0" cy="243.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2481</title></circle><circle cx="856.0" cy="230.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3016</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,144.2 328.0,132.1 856.0,42.0" fill="none" stroke="#326273" stroke-width="3"/><circle cx="64.0" cy="144.2" r="4" fill="#326273"><title>throughput / 1000: 0.6451</title></circle><circle cx="328.0" cy="132.1" r="4" fill="#326273"><title>throughput / 1000: 0.6935</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#326273"><title>throughput / 1000: 1.0527</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#326273"/><text x="666" y="62" class="legend-text">throughput / 1000</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">2</text><text class="tick" x="328.0" y="332" text-anchor="middle">4</text><text class="tick" x="856.0" y="332" text-anchor="middle">8</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Custom-model metadata changes per-stage fairness"><text class="chart-title" x="64" y="24">Custom-model metadata changes per-stage fairness</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="230.0" y="116.2" width="29.0" height="161.8" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.6855</title></rect><rect x="262.0" y="203.8" width="29.0" height="74.2" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.3144</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">model cap</text><rect x="626.0" y="84.8" width="29.0" height="193.2" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8187</title></rect><rect x="658.0" y="235.2" width="29.0" height="42.8" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1812</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">weight 1</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Stress shapes: narrow vs wide runnable horizon"><text class="chart-title" x="64" y="24">Stress shapes: narrow vs wide runnable horizon</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="198.0" y="259.0" width="29.0" height="19.0" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.0806</title></rect><rect x="230.0" y="61.0" width="29.0" height="217.0" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.9194</title></rect><rect x="262.0" y="228.3" width="29.0" height="49.7" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2108</title></rect><rect x="294.0" y="190.5" width="29.0" height="87.5" rx="2" fill="#326273"><title>burstiness / 2: 0.3709</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">narrow frontier high cap</text><rect x="594.0" y="111.6" width="29.0" height="166.4" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.7049</title></rect><rect x="626.0" y="208.4" width="29.0" height="69.6" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.2951</title></rect><rect x="658.0" y="208.4" width="29.0" height="69.6" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2951</title></rect><rect x="690.0" y="231.7" width="29.0" height="46.3" rx="2" fill="#326273"><title>burstiness / 2: 0.1964</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">wide frontier high cap</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#8a6f3d"/><text x="666" y="45" class="legend-text">total llm_wait idle</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">starved llm_wait idle</text></g><g class="legend"><rect x="650" y="69" width="10" height="10" fill="#326273"/><text x="666" y="79" class="legend-text">burstiness / 2</text></g></svg></figure>
+</section>
+<section>
+<h2>Benchmark Matrix</h2>
+<div class="table-wrap"><table><thead><tr><th>case</th><th>sweep</th><th>generations</th><th>rows</th><th>buffer</th><th>rg conc</th><th>llm cap</th><th>util</th><th>idle</th><th>starved idle</th><th>ready p95</th><th>overlap</th><th>throughput</th><th>artifact</th></tr></thead><tbody><tr><td>buffer-1</td><td>buffer-size</td><td>4,096</td><td>512</td><td>1</td><td>4</td><td>4</td><td>82.0%</td><td>18.0%</td><td>18.0%</td><td>74.2 ms</td><td>100.0%</td><td>342/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-16</td><td>buffer-size</td><td>4,096</td><td>512</td><td>16</td><td>4</td><td>4</td><td>77.7%</td><td>22.3%</td><td>22.3%</td><td>1.59 s</td><td>99.9%</td><td>385/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-16/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-256</td><td>buffer-size</td><td>4,096</td><td>512</td><td>256</td><td>4</td><td>4</td><td>70.2%</td><td>29.8%</td><td>29.8%</td><td>21.76 s</td><td>99.9%</td><td>163/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-256/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-4</td><td>buffer-size</td><td>4,096</td><td>512</td><td>4</td><td>4</td><td>4</td><td>79.0%</td><td>21.0%</td><td>20.9%</td><td>346.4 ms</td><td>99.9%</td><td>450/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-64</td><td>buffer-size</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>75.0%</td><td>25.0%</td><td>25.0%</td><td>8.99 s</td><td>99.9%</td><td>223/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-custom-model-1</td><td>custom-model-weight</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>81.9%</td><td>18.1%</td><td>18.1%</td><td>11.79 s</td><td>99.9%</td><td>185/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/custom-model-weight/weight-custom-model-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-model-capacity</td><td>custom-model-weight</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>68.6%</td><td>31.4%</td><td>31.4%</td><td>8.65 s</td><td>99.9%</td><td>236/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/custom-model-weight/weight-model-capacity/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-16</td><td>llm-capacity</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>8</td><td>33.4%</td><td>66.6%</td><td>30.2%</td><td>1.0 ms</td><td>99.6%</td><td>1,053/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/llm-capacity/capacity-16/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-4</td><td>llm-capacity</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>2</td><td>85.1%</td><td>14.9%</td><td>14.8%</td><td>4.6 ms</td><td>99.8%</td><td>645/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/llm-capacity/capacity-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-8</td><td>llm-capacity</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>57.2%</td><td>42.8%</td><td>24.8%</td><td>2.5 ms</td><td>99.7%</td><td>693/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/llm-capacity/capacity-8/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-1</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>1</td><td>4</td><td>16.7%</td><td>83.3%</td><td>18.2%</td><td>1.1 ms</td><td>99.9%</td><td>319/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-2</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>2</td><td>4</td><td>30.0%</td><td>70.0%</td><td>24.7%</td><td>1.4 ms</td><td>99.8%</td><td>518/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-2/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-4</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>59.9%</td><td>40.1%</td><td>22.9%</td><td>1.4 ms</td><td>99.6%</td><td>919/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-8</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>8</td><td>4</td><td>89.4%</td><td>10.6%</td><td>10.3%</td><td>4.5 ms</td><td>99.6%</td><td>913/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-8/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-1024</td><td>row-scale</td><td>8,192</td><td>1,024</td><td>64</td><td>4</td><td>4</td><td>82.0%</td><td>18.0%</td><td>18.0%</td><td>15.99 s</td><td>100.0%</td><td>144/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-1024/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-128</td><td>row-scale</td><td>1,024</td><td>128</td><td>64</td><td>4</td><td>4</td><td>67.5%</td><td>32.5%</td><td>32.4%</td><td>1.58 s</td><td>99.3%</td><td>567/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-128/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-256</td><td>row-scale</td><td>2,048</td><td>256</td><td>64</td><td>4</td><td>4</td><td>60.0%</td><td>40.0%</td><td>40.0%</td><td>5.16 s</td><td>99.7%</td><td>346/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-256/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-512</td><td>row-scale</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>71.0%</td><td>29.0%</td><td>29.0%</td><td>8.58 s</td><td>99.9%</td><td>234/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-512/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-64</td><td>row-scale</td><td>512</td><td>64</td><td>64</td><td>4</td><td>4</td><td>75.2%</td><td>24.8%</td><td>24.3%</td><td>598.8 ms</td><td>98.8%</td><td>763/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>narrow-frontier-high-cap</td><td>stress-shape</td><td>2,048</td><td>1,024</td><td>1</td><td>1</td><td>8</td><td>8.1%</td><td>91.9%</td><td>21.1%</td><td>2.8 ms</td><td>99.9%</td><td>169/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/stress-shape/narrow-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr><tr><td>wide-frontier-high-cap</td><td>stress-shape</td><td>8,192</td><td>1,024</td><td>64</td><td>16</td><td>8</td><td>70.5%</td><td>29.5%</td><td>29.5%</td><td>70.81 s</td><td>99.8%</td><td>98/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/stress-shape/wide-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr></tbody></table></div>
+<p class="artifact-note">Raw artifacts are under <a href="../artifacts/async-scheduling-idle-analysis">artifacts/async-scheduling-idle-analysis</a>.</p>
+</section>
+<section>
+<h2>Potential Changes</h2>
+<ol class="recommendations"><li><strong>Add an adaptive row-group admission controller.</strong> A subclass or policy layer can watch bottleneck-resource starved idle and admit another row group when model capacity is idle while queued or pending model work remains, bounded by buffer and memory limits. When total idle is high but starved idle is low, the controller should treat that as a frontier problem and prefetch more row groups rather than tuning request AIMD.</li><li><strong>Keep resource-aware idle in the standard benchmark output.</strong> The new <code>llm_wait</code> utilization, starved idle, burstiness, and ready-gap metrics identify whether a vLLM-like resource is genuinely idle or merely waiting for dependencies.</li><li><strong>Document custom-model group identity and weight.</strong> <code>SchedulingMetadata.custom_model(...)</code> controls fairness and per-group admission. Plugins that share one external model should use a shared model identity and a weight that reflects real serving capacity; plugins that represent distinct resources should keep distinct identities.</li><li><strong>Use the report as a regression suite.</strong> The worst case here is <code>rows-256</code>; future scheduler or admission changes should improve that case without increasing ready gaps or violating final zero-lease checks.</li></ol>
+</section>
+</main>
+</body>
+</html>
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+<title>Async Scheduling Idle-Time Analysis</title>
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+</head>
+<body>
+<main>
+<section class="hero">
+<p class="eyebrow">DataDesigner async scheduler</p>
+<h1>Idle-Time Analysis</h1>
+<p class="lede">This report measures where model-capacity idle time appears, which knobs change it, and which remaining idle looks avoidable by scheduler or admission-control changes.</p>
+<div class="summary-grid"><div class="summary-card"><span>Benchmark cases</span><strong>22</strong></div><div class="summary-card"><span>Largest synthetic generation count</span><strong>8,192</strong></div><div class="summary-card"><span>Median llm_wait utilization</span><strong>79.2%</strong></div><div class="summary-card"><span>Worst total llm_wait idle</span><strong>91.1%</strong></div><div class="summary-card"><span>Worst starved llm_wait idle</span><strong>33.7%</strong></div><div class="summary-card"><span>Best llm_wait utilization</span><strong>93.0%</strong></div><div class="summary-card"><span>Validation</span><strong>pass</strong></div></div>
+</section>
+<section>
+<h2>What Counts As Idle</h2>
+<p><strong>General idle</strong> is unused configured capacity. It includes expected idle when dependencies are not ready. <strong>Starved idle</strong> is the actionable subset: a resource has idle slots while the scheduler already has queued work that requests that resource. <strong>Frontier/dependency-horizon idle</strong> is total idle minus starved idle: capacity is unused because dependency work has not yet exposed a ready scheduler task. <strong>Scheduler queue age</strong> measures ready-enqueued to lease-acquired time; <strong>downstream ready gap</strong> measures dependency-complete to downstream-dispatch time. For this refactor, the most important resource is <code>llm_wait</code>, because it maps to model-serving capacity that should stay busy when runnable model work exists.</p>
+</section>
+<section>
+<h2>Implementation Improvements</h2>
+<div class="summary-grid"><div class="summary-card"><span>Classification now explicit</span><strong>5 frontier, 16 queued</strong></div><div class="summary-card"><span>Quick-suite proof gates</span><strong>22 cases, zero validation failures</strong></div><div class="summary-card"><span>Largest refreshed case</span><strong>8,192 generations</strong></div><div class="summary-card"><span>Narrow frontier idle identified</span><strong>75.1%</strong></div><div class="summary-card"><span>Row-groups=1 frontier idle</span><strong>67.8%</strong></div><div class="summary-card"><span>Wide queued starvation age</span><strong>86.97 s</strong></div></div><div class="improvement-grid"><article class="improvement-card"><div><h3>Idle partition metrics</h3><span class="status">Implemented</span></div><p>The report now splits total idle into queued-work starvation and frontier/dependency-horizon idle. This prevents the narrow high-capacity case from being mistaken for a request-admission problem.</p><p class="evidence"><strong>Evidence:</strong> <code>narrow-frontier-high-cap</code>: 75.1% frontier idle, 16.0% starved idle.</p><a href="../scripts/benchmarks/benchmark_async_scheduling.py">source</a></article><article class="improvement-card"><div><h3>Queue age vs downstream delay</h3><span class="status">Implemented</span></div><p>Ready-to-lease queue age is now shown separately from dependency-complete-to-dispatch delay. This distinguishes scheduler backlog from slow dependency propagation.</p><p class="evidence"><strong>Evidence:</strong> <code>rows-256</code>: 5.71 s scheduler queue p95, 13.7 ms downstream p95.</p><a href="../scripts/benchmarks/generate_async_scheduling_idle_report.py">source</a></article><article class="improvement-card"><div><h3>Batched frontier enqueue</h3><span class="status">Implemented</span></div><p>A frontier delta with many ready tasks now enters the fair queue in one enqueue operation, while pre-batch parking, dropped rows, and per-task observability are preserved.</p><p class="evidence"><strong>Evidence:</strong> Focused scheduler tests prove one queue operation for a 5-task frontier and one operation for a pre-batch flush after a dropped row.</p><a href="../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py">source</a></article><article class="improvement-card"><div><h3>Resource-overlap peer pressure</h3><span class="status">Implemented</span></div><p>Task admission now applies group-cap peer pressure only for queued peers that can use the same typed resource and are hard-resource eligible. Local/submission-only peers no longer create false pressure on idle model capacity.</p><p class="evidence"><strong>Evidence:</strong> Unit tests cover non-overlapping peers, overlapping <code>llm_wait</code> peers, and peers blocked by another hard resource.</p><a href="../packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py">source</a></article><article class="improvement-card"><div><h3>Adaptive row-group admission</h3><span class="status">Implemented + benchmarked</span></div><p>The scheduler can now start with one admitted row group, raise the row-group target when model capacity is idle and queued model demand is low, and remain bounded by the configured hard cap.</p><p class="evidence"><strong>Evidence:</strong> <code>adaptive-row-groups-fixed-low</code> to <code>adaptive-row-groups-adaptive</code>: utilization +57.2 pp, frontier idle reduction 66.6%, observed target 8.</p><a href="../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py">source</a></article><article class="improvement-card"><div><h3>Request-pressure advisory</h3><span class="status">Implemented + benchmarked</span></div><p>Task selection can now use request pressure snapshots to prefer an open same-frontier peer before spawning work that would immediately wait on request capacity. It does not yield and reacquire leases inside running generators.</p><p class="evidence"><strong>Evidence:</strong> Leased request wait moved from 49.8 ms to 3.2 ms; first dispatch moved from <code>a_pressured</code> to <code>z_open</code>.</p><a href="../packages/data-designer-engine/src/data_designer/engine/models/request_admission">source</a></article></div>
+</section>
+<section>
+<h2>Findings</h2>
+<ul class="finding-list"><li><strong>Scale helps once enough runnable work exists.</strong> The row-scale sweep moved from 79.6% to 66.2% llm_wait utilization (-13.4 pp). Larger runs amortize startup and drain phases.</li><li><strong>Runnable frontier width is the main controllable idle source.</strong> In the row-group sweep, utilization moved from 17.8% to 58.8% (+41.0 pp), while starved llm_wait idle improved by 0.0% between the narrowest case and the best observed case.</li><li><strong>Total idle and starved idle diagnose different causes.</strong> <code>narrow-frontier-high-cap</code> had 91.1% total llm_wait idle but only 16.0% starved idle. The remaining 75.1% was frontier/dependency-horizon idle: the scheduler often had no ready model work because the admitted frontier was too small.</li><li><strong>Capacity increases expose underfeeding.</strong> In the capacity sweep, modeled llm_wait cap 2 reached 86.8% utilization, while cap 4 reached 54.5% with the same small row frontier.</li><li><strong>Custom model scheduling metadata changes traffic shape.</strong> With synthetic <code>custom_model</code> weight forced to 1, llm_wait utilization was 75.8%; with the harness defaulting the weight to modeled model capacity it was 83.9%. This run did not show weight=1 underfeeding because each synthetic column is its own group, but it does show that group identity and weight are first-order benchmark inputs.</li><li><strong>High capacity magnifies traffic-shape effects.</strong> The stress case with one large row group and high cap reached 8.9% utilization; the wide-frontier shape reached 79.4%. This is the vLLM-server-idle failure mode the scheduler needs to expose and track over time.</li><li><strong>Worst observed actionable idle:</strong> rows-256 had 33.7% starved llm_wait idle and 6.11 s max scheduler queue age.</li></ul>
+</section>
+<section>
+<h2>Idle Classification Board</h2>
+<p>This board is the main analysis surface for optimization work. Frontier/dependency-horizon cases need row-group or scheduling-shard changes; queued-work starvation cases need queue/admission/resource selection changes; request-pressure cases need request-backed evidence before scheduler policy changes.</p><div class="table-wrap"><table><thead><tr><th>case</th><th>dominant idle class</th><th>util</th><th>starved idle</th><th>frontier idle</th><th>queue age p95</th><th>downstream p95</th><th>next lever</th></tr></thead><tbody><tr><td>stress-shape/narrow-frontier-high-cap</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>8.9%</td><td>16.0%</td><td>75.1%</td><td>1.7 ms</td><td>1.9 ms</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>adaptations/adaptive-row-groups-fixed-low</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>10.5%</td><td>8.2%</td><td>81.3%</td><td>630 us</td><td>718 us</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>row-group-concurrency/row-groups-1</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>17.8%</td><td>14.5%</td><td>67.8%</td><td>747 us</td><td>867 us</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>row-group-concurrency/row-groups-2</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>31.4%</td><td>23.0%</td><td>45.6%</td><td>1.1 ms</td><td>1.7 ms</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>adaptations/adaptive-row-groups-fixed-high</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>62.4%</td><td>17.3%</td><td>20.4%</td><td>2.1 ms</td><td>2.7 ms</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>llm-capacity/capacity-8</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>54.5%</td><td>24.6%</td><td>20.9%</td><td>2.0 ms</td><td>3.0 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>row-group-concurrency/row-groups-4</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>58.8%</td><td>22.8%</td><td>18.4%</td><td>1.4 ms</td><td>2.0 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>row-scale/rows-256</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>66.2%</td><td>33.7%</td><td>0.1%</td><td>5.71 s</td><td>13.7 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>adaptations/adaptive-row-groups-adaptive</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>67.7%</td><td>17.6%</td><td>14.7%</td><td>1.7 ms</td><td>3.0 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>custom-model-weight/weight-custom-model-1</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>75.8%</td><td>24.2%</td><td>0.0%</td><td>10.77 s</td><td>16.9 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>buffer-size/buffer-64</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.0%</td><td>20.9%</td><td>0.0%</td><td>10.66 s</td><td>12.9 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>stress-shape/wide-frontier-high-cap</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.4%</td><td>20.6%</td><td>0.0%</td><td>86.97 s</td><td>113.8 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>row-scale/rows-128</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.5%</td><td>20.3%</td><td>0.3%</td><td>1.60 s</td><td>9.9 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>row-scale/rows-64</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.6%</td><td>19.4%</td><td>1.1%</td><td>541.6 ms</td><td>7.6 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>buffer-size/buffer-16</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>80.5%</td><td>19.4%</td><td>0.0%</td><td>3.68 s</td><td>52.4 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>adaptations/adaptive-request-pressure-control</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>82.4%</td><td>15.7%</td><td>1.7%</td><td>3.5 ms</td><td>0 us</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>buffer-size/buffer-1</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>82.6%</td><td>17.4%</td><td>0.0%</td><td>47.4 ms</td><td>44.7 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>custom-model-weight/weight-model-capacity</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>83.9%</td><td>16.1%</td><td>0.0%</td><td>11.60 s</td><td>13.8 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>adaptations/adaptive-request-pressure-combined</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>84.4%</td><td>14.5%</td><td>1.1%</td><td>10.4 ms</td><td>0 us</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>adaptations/request-pressure-advisory</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>85.3%</td><td>13.2%</td><td>1.4%</td><td>124.8 ms</td><td>0 us</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>llm-capacity/capacity-4</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>86.8%</td><td>13.0%</td><td>0.2%</td><td>5.5 ms</td><td>4.6 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>adaptations/request-pressure-control</td><td><span class="idle-class idle-healthy-control">healthy/control</span></td><td>93.0%</td><td>5.6%</td><td>1.4%</td><td>143.7 ms</td><td>0 us</td><td>keep as control case and watch for regressions</td></tr></tbody></table></div>
+</section>
+<section>
+<h2>Adaptation Benchmarks</h2>
+<div class="summary-grid"><div class="summary-card"><span>Adaptive utilization delta</span><strong>+57.2 pp</strong></div><div class="summary-card"><span>Adaptive frontier-idle reduction</span><strong>66.6%</strong></div><div class="summary-card"><span>Adaptive vs fixed-high</span><strong>+5.3 pp</strong></div><div class="summary-card"><span>Adaptive observed target</span><strong>8</strong></div><div class="summary-card"><span>Request leased-wait reduction</span><strong>46.6 ms</strong></div><div class="summary-card"><span>Advisory first dispatch</span><strong>z_open</strong></div><div class="summary-card"><span>Request queue-age p95 reduction</span><strong>18.9 ms</strong></div><div class="summary-card"><span>Combined leased-wait delta</span><strong>-26.8 ms</strong></div><div class="summary-card"><span>Combined request-idle delta</span><strong>+3.7 pp</strong></div></div><p>The row-group adaptation is judged against a fixed-low frontier because that is the failure shape where capacity is idle before enough dependency work exists. It should reduce frontier idle or raise utilization while staying below the hard row-group cap. The fixed-high control isolates how close the adaptive policy gets to simply admitting the full hard-cap frontier up front. The fixed-high ceiling reached 62.4% utilization with 20.4% frontier idle.</p><p>The request-pressure adaptation is judged by whether it avoids dispatching the pressured model first and reduces request-wait time while a scheduler task lease is already held. The combined case keeps adaptive row-group admission enabled while request pressure is present, so it measures whether the scheduler can widen the frontier and still avoid sending the first available leases into a saturated model.</p><div class="table-wrap"><table><thead><tr><th>adaptation</th><th>case</th><th>util</th><th>frontier idle</th><th>observed target</th><th>leased request wait</th><th>first model dispatch</th></tr></thead><tbody><tr><td>adaptive fixed-low control</td><td>adaptive-row-groups-fixed-low</td><td>10.5%</td><td>81.3%</td><td>1</td><td>-</td><td>-</td></tr><tr><td>adaptive enabled</td><td>adaptive-row-groups-adaptive</td><td>67.7%</td><td>14.7%</td><td>8</td><td>-</td><td>-</td></tr><tr><td>adaptive fixed-high control</td><td>adaptive-row-groups-fixed-high</td><td>62.4%</td><td>20.4%</td><td>8</td><td>-</td><td>-</td></tr><tr><td>request control</td><td>request-pressure-control</td><td>93.0%</td><td>1.4%</td><td>-</td><td>49.8 ms</td><td>a_pressured</td></tr><tr><td>request advisory</td><td>request-pressure-advisory</td><td>85.3%</td><td>1.4%</td><td>-</td><td>3.2 ms</td><td>z_open</td></tr><tr><td>combined control</td><td>adaptive-request-pressure-control</td><td>82.4%</td><td>1.7%</td><td>5</td><td>488.3 ms</td><td>a_pressured</td></tr><tr><td>combined enabled</td><td>adaptive-request-pressure-combined</td><td>84.4%</td><td>1.1%</td><td>5</td><td>515.1 ms</td><td>z_open</td></tr></tbody></table></div>
+</section>
+<section>
+<h2>Figures</h2>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Scale: more rows smooth the model-resource flow"><text class="chart-title" x="64" y="24">Scale: more rows smooth the model-resource flow</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">synthetic model generations</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,96.0 328.0,96.2 856.0,131.3" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="96.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7955</title></circle><circle cx="328.0" cy="96.2" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7946</title></circle><circle cx="856.0" cy="131.3" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.6617</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,254.8 328.0,252.5 856.0,216.9" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="254.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1940</title></circle><circle cx="328.0" cy="252.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2026</title></circle><circle cx="856.0" cy="216.9" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3374</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">512</text><text class="tick" x="328.0" y="332" text-anchor="middle">1.0k</text><text class="tick" x="856.0" y="332" text-anchor="middle">2.0k</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Frontier width: row-group concurrency changes starvation"><text class="chart-title" x="64" y="24">Frontier width: row-group concurrency changes starvation</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">row-group concurrency</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,226.2 328.0,164.9 856.0,42.0" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="226.2" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.1776</title></circle><circle cx="328.0" cy="164.9" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.3142</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5878</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,241.0 328.0,202.8 856.0,203.6" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="241.0" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1447</title></circle><circle cx="328.0" cy="202.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2299</title></circle><circle cx="856.0" cy="203.6" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2280</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,305.6 328.0,305.2 856.0,305.1" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="305.6" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0009</title></circle><circle cx="328.0" cy="305.2" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0017</title></circle><circle cx="856.0" cy="305.1" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0020</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">downstream ready p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="328.0" y="332" text-anchor="middle">2</text><text class="tick" x="856.0" y="332" text-anchor="middle">4</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Buffer size: row-group shape affects traffic waves"><text class="chart-title" x="64" y="24">Buffer size: row-group shape affects traffic waves</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">buffer size</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,285.6 252.6,286.1 856.0,286.4" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="285.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8259</title></circle><circle cx="252.6" cy="286.1" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8054</title></circle><circle cx="856.0" cy="286.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7904</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,301.7 252.6,301.2 856.0,300.8" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="301.7" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1738</title></circle><circle cx="252.6" cy="301.2" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1944</title></circle><circle cx="856.0" cy="300.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2094</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,304.8 252.6,214.8 856.0,42.0" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="304.8" r="4" fill="#6c5ce7"><title>scheduler queue age p95: 0.0474</title></circle><circle cx="252.6" cy="214.8" r="4" fill="#6c5ce7"><title>scheduler queue age p95: 3.6832</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#6c5ce7"><title>scheduler queue age p95: 10.6643</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">scheduler queue age p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="252.6" y="332" text-anchor="middle">16</text><text class="tick" x="856.0" y="332" text-anchor="middle">64</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Capacity scaling: underfeeding becomes visible as cap rises"><text class="chart-title" x="64" y="24">Capacity scaling: underfeeding becomes visible as cap rises</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">modeled llm_wait capacity</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / kgen/s</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,42.0 856.0,140.3" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="42.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8679</title></circle><circle cx="856.0" cy="140.3" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5448</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,266.5 856.0,231.1" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="266.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1297</title></circle><circle cx="856.0" cy="231.1" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2462</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,124.7 856.0,72.4" fill="none" stroke="#326273" stroke-width="3"/><circle cx="64.0" cy="124.7" r="4" fill="#326273"><title>throughput / 1000: 0.5960</title></circle><circle cx="856.0" cy="72.4" r="4" fill="#326273"><title>throughput / 1000: 0.7681</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#326273"/><text x="666" y="62" class="legend-text">throughput / 1000</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">2</text><text class="tick" x="856.0" y="332" text-anchor="middle">4</text></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Custom-model metadata changes per-stage fairness"><text class="chart-title" x="64" y="24">Custom-model metadata changes per-stage fairness</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="230.0" y="80.1" width="29.0" height="197.9" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8385</title></rect><rect x="262.0" y="239.9" width="29.0" height="38.1" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1613</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">model cap</text><rect x="626.0" y="99.1" width="29.0" height="178.9" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.7579</title></rect><rect x="658.0" y="220.9" width="29.0" height="57.1" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2419</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">weight 1</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Stress shapes: narrow vs wide runnable horizon"><text class="chart-title" x="64" y="24">Stress shapes: narrow vs wide runnable horizon</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="182.0" y="257.0" width="29.0" height="21.0" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.0890</title></rect><rect x="214.0" y="63.0" width="29.0" height="215.0" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.9110</title></rect><rect x="246.0" y="240.3" width="29.0" height="37.7" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1598</title></rect><rect x="278.0" y="100.7" width="29.0" height="177.3" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.7511</title></rect><rect x="310.0" y="203.0" width="29.0" height="75.0" rx="2" fill="#326273"><title>burstiness / 2: 0.3177</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">narrow frontier high cap</text><rect x="578.0" y="90.5" width="29.0" height="187.5" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.7943</title></rect><rect x="610.0" y="229.5" width="29.0" height="48.5" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.2057</title></rect><rect x="642.0" y="229.5" width="29.0" height="48.5" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2056</title></rect><rect x="674.0" y="278.0" width="29.0" height="0.0" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.0001</title></rect><rect x="706.0" y="252.3" width="29.0" height="25.7" rx="2" fill="#326273"><title>burstiness / 2: 0.1091</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">wide frontier high cap</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#8a6f3d"/><text x="666" y="45" class="legend-text">total llm_wait idle</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">starved llm_wait idle</text></g><g class="legend"><rect x="650" y="69" width="10" height="10" fill="#6c5ce7"/><text x="666" y="79" class="legend-text">frontier/dependency idle</text></g><g class="legend"><rect x="650" y="86" width="10" height="10" fill="#326273"/><text x="666" y="96" class="legend-text">burstiness / 2</text></g></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Adaptation: adaptive row groups target frontier idle"><text class="chart-title" x="64" y="24">Adaptation: adaptive row groups target frontier idle</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="148.0" y="118.3" width="29.0" height="159.7" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.6768</title></rect><rect x="180.0" y="243.2" width="29.0" height="34.8" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.1473</title></rect><rect x="212.0" y="236.5" width="29.0" height="41.5" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1759</title></rect><text class="tick" x="196.0" y="312" text-anchor="middle">adaptive</text><rect x="412.0" y="130.8" width="29.0" height="147.2" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.6236</title></rect><rect x="444.0" y="229.9" width="29.0" height="48.1" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.2038</title></rect><rect x="476.0" y="237.3" width="29.0" height="40.7" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1725</title></rect><text class="tick" x="460.0" y="312" text-anchor="middle">fixed-high</text><rect x="676.0" y="253.3" width="29.0" height="24.7" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.1047</title></rect><rect x="708.0" y="86.0" width="29.0" height="192.0" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.8134</title></rect><rect x="740.0" y="258.7" width="29.0" height="19.3" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.0820</title></rect><text class="tick" x="724.0" y="312" text-anchor="middle">fixed-low</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#6c5ce7"/><text x="666" y="45" class="legend-text">frontier/dependency idle</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">starved llm_wait idle</text></g></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Adaptation: request-pressure advisory avoids leased request wait"><text class="chart-title" x="64" y="24">Adaptation: request-pressure advisory avoids leased request wait</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="214.0" y="272.8" width="29.0" height="5.2" rx="2" fill="#b23a48"><title>request wait while leased: 0.0032</title></rect><rect x="246.0" y="73.0" width="29.0" height="205.0" rx="2" fill="#6c5ce7"><title>scheduler queue age p95: 0.1248</title></rect><rect x="278.0" y="256.0" width="29.0" height="22.0" rx="2" fill="#1f7a8c"><title>wall seconds / 10: 0.0134</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">advisory</text><rect x="610.0" y="196.3" width="29.0" height="81.7" rx="2" fill="#b23a48"><title>request wait while leased: 0.0498</title></rect><rect x="642.0" y="42.0" width="29.0" height="236.0" rx="2" fill="#6c5ce7"><title>scheduler queue age p95: 0.1437</title></rect><rect x="674.0" y="253.1" width="29.0" height="24.9" rx="2" fill="#1f7a8c"><title>wall seconds / 10: 0.0152</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">control</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#b23a48"/><text x="666" y="28" class="legend-text">request wait while leased</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#6c5ce7"/><text x="666" y="45" class="legend-text">scheduler queue age p95</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#1f7a8c"/><text x="666" y="62" class="legend-text">wall seconds / 10</text></g></svg></figure>
+<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Adaptation: combined adaptive frontier and request pressure"><text class="chart-title" x="64" y="24">Adaptation: combined adaptive frontier and request pressure</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="198.0" y="78.9" width="29.0" height="199.1" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8438</title></rect><rect x="230.0" y="67.4" width="29.0" height="210.6" rx="2" fill="#326273"><title>request utilization: 0.8924</title></rect><rect x="262.0" y="273.0" width="29.0" height="5.0" rx="2" fill="#b23a48"><title>request starved idle: 0.0211</title></rect><rect x="294.0" y="275.4" width="29.0" height="2.6" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.0110</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">combined</text><rect x="594.0" y="83.4" width="29.0" height="194.6" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8245</title></rect><rect x="626.0" y="75.9" width="29.0" height="202.1" rx="2" fill="#326273"><title>request utilization: 0.8563</title></rect><rect x="658.0" y="272.9" width="29.0" height="5.1" rx="2" fill="#b23a48"><title>request starved idle: 0.0217</title></rect><rect x="690.0" y="273.9" width="29.0" height="4.1" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.0174</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">control</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#326273"/><text x="666" y="45" class="legend-text">request utilization</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">request starved idle</text></g><g class="legend"><rect x="650" y="69" width="10" height="10" fill="#6c5ce7"/><text x="666" y="79" class="legend-text">frontier/dependency idle</text></g></svg></figure>
+</section>
+<section>
+<h2>Benchmark Matrix</h2>
+<div class="table-wrap"><table><thead><tr><th>case</th><th>sweep</th><th>scenario</th><th>generations</th><th>rows</th><th>buffer</th><th>rg conc</th><th>llm cap</th><th>util</th><th>idle</th><th>starved idle</th><th>frontier idle</th><th>queue age p95</th><th>downstream gap p95</th><th>leased request wait</th><th>request util</th><th>request starved idle</th><th>advisory skips</th><th>first model</th><th>overlap</th><th>throughput</th><th>artifact</th></tr></thead><tbody><tr><td>adaptive-request-pressure-combined</td><td>adaptations</td><td>adaptive-request-pressure</td><td>256</td><td>128</td><td>1</td><td>8</td><td>2</td><td>84.4%</td><td>15.6%</td><td>14.5%</td><td>1.1%</td><td>10.4 ms</td><td>0 us</td><td>515.1 ms</td><td>89.2%</td><td>2.1%</td><td>193</td><td>z_open</td><td>0.0%</td><td>689/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-request-pressure-combined/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-request-pressure-control</td><td>adaptations</td><td>adaptive-request-pressure</td><td>256</td><td>128</td><td>1</td><td>8</td><td>2</td><td>82.4%</td><td>17.4%</td><td>15.7%</td><td>1.7%</td><td>3.5 ms</td><td>0 us</td><td>488.3 ms</td><td>85.6%</td><td>2.2%</td><td>0</td><td>a_pressured</td><td>0.0%</td><td>723/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-request-pressure-control/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-row-groups-adaptive</td><td>adaptations</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>8</td><td>8</td><td>67.7%</td><td>32.3%</td><td>17.6%</td><td>14.7%</td><td>1.7 ms</td><td>3.0 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.3%</td><td>1,108/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-row-groups-adaptive/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-row-groups-fixed-high</td><td>adaptations</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>8</td><td>8</td><td>62.4%</td><td>37.6%</td><td>17.3%</td><td>20.4%</td><td>2.1 ms</td><td>2.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.2%</td><td>1,062/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-row-groups-fixed-high/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-row-groups-fixed-low</td><td>adaptations</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>1</td><td>8</td><td>10.5%</td><td>89.5%</td><td>8.2%</td><td>81.3%</td><td>630 us</td><td>718 us</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.8%</td><td>229/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-row-groups-fixed-low/async_scheduling_benchmark.json">json</a></td></tr><tr><td>request-pressure-advisory</td><td>adaptations</td><td>request-pressure-advisory</td><td>64</td><td>32</td><td>32</td><td>1</td><td>1</td><td>85.3%</td><td>14.7%</td><td>13.2%</td><td>1.4%</td><td>124.8 ms</td><td>0 us</td><td>3.2 ms</td><td>49.0%</td><td>1.3%</td><td>29</td><td>z_open</td><td>0.0%</td><td>478/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/request-pressure-advisory/async_scheduling_benchmark.json">json</a></td></tr><tr><td>request-pressure-control</td><td>adaptations</td><td>request-pressure-advisory</td><td>64</td><td>32</td><td>32</td><td>1</td><td>1</td><td>93.0%</td><td>7.0%</td><td>5.6%</td><td>1.4%</td><td>143.7 ms</td><td>0 us</td><td>49.8 ms</td><td>41.6%</td><td>0.7%</td><td>0</td><td>a_pressured</td><td>0.0%</td><td>422/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/request-pressure-control/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-1</td><td>buffer-size</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>1</td><td>4</td><td>4</td><td>82.6%</td><td>17.4%</td><td>17.4%</td><td>0.0%</td><td>47.4 ms</td><td>44.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>100.0%</td><td>379/s</td><td><a href="../artifacts/async-scheduling-idle-regression/buffer-size/buffer-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-16</td><td>buffer-size</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>16</td><td>4</td><td>4</td><td>80.5%</td><td>19.5%</td><td>19.4%</td><td>0.0%</td><td>3.68 s</td><td>52.4 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>100.0%</td><td>204/s</td><td><a href="../artifacts/async-scheduling-idle-regression/buffer-size/buffer-16/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-64</td><td>buffer-size</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>79.0%</td><td>21.0%</td><td>20.9%</td><td>0.0%</td><td>10.66 s</td><td>12.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>195/s</td><td><a href="../artifacts/async-scheduling-idle-regression/buffer-size/buffer-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-custom-model-1</td><td>custom-model-weight</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>75.8%</td><td>24.2%</td><td>24.2%</td><td>0.0%</td><td>10.77 s</td><td>16.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>192/s</td><td><a href="../artifacts/async-scheduling-idle-regression/custom-model-weight/weight-custom-model-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-model-capacity</td><td>custom-model-weight</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>83.9%</td><td>16.1%</td><td>16.1%</td><td>0.0%</td><td>11.60 s</td><td>13.8 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>185/s</td><td><a href="../artifacts/async-scheduling-idle-regression/custom-model-weight/weight-model-capacity/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-4</td><td>llm-capacity</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>2</td><td>86.8%</td><td>13.2%</td><td>13.0%</td><td>0.2%</td><td>5.5 ms</td><td>4.6 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>596/s</td><td><a href="../artifacts/async-scheduling-idle-regression/llm-capacity/capacity-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-8</td><td>llm-capacity</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>54.5%</td><td>45.5%</td><td>24.6%</td><td>20.9%</td><td>2.0 ms</td><td>3.0 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>768/s</td><td><a href="../artifacts/async-scheduling-idle-regression/llm-capacity/capacity-8/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-1</td><td>row-group-concurrency</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>1</td><td>4</td><td>17.8%</td><td>82.2%</td><td>14.5%</td><td>67.8%</td><td>747 us</td><td>867 us</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>356/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-group-concurrency/row-groups-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-2</td><td>row-group-concurrency</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>2</td><td>4</td><td>31.4%</td><td>68.6%</td><td>23.0%</td><td>45.6%</td><td>1.1 ms</td><td>1.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.8%</td><td>525/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-group-concurrency/row-groups-2/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-4</td><td>row-group-concurrency</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>58.8%</td><td>41.2%</td><td>22.8%</td><td>18.4%</td><td>1.4 ms</td><td>2.0 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>867/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-group-concurrency/row-groups-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-128</td><td>row-scale</td><td>real-pipeline-overlap</td><td>1,024</td><td>128</td><td>64</td><td>4</td><td>4</td><td>79.5%</td><td>20.5%</td><td>20.3%</td><td>0.3%</td><td>1.60 s</td><td>9.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.3%</td><td>578/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-scale/rows-128/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-256</td><td>row-scale</td><td>real-pipeline-overlap</td><td>2,048</td><td>256</td><td>64</td><td>4</td><td>4</td><td>66.2%</td><td>33.8%</td><td>33.7%</td><td>0.1%</td><td>5.71 s</td><td>13.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>332/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-scale/rows-256/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-64</td><td>row-scale</td><td>real-pipeline-overlap</td><td>512</td><td>64</td><td>64</td><td>4</td><td>4</td><td>79.6%</td><td>20.4%</td><td>19.4%</td><td>1.1%</td><td>541.6 ms</td><td>7.6 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>98.6%</td><td>859/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-scale/rows-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>narrow-frontier-high-cap</td><td>stress-shape</td><td>real-pipeline-overlap</td><td>2,048</td><td>1,024</td><td>1</td><td>1</td><td>8</td><td>8.9%</td><td>91.1%</td><td>16.0%</td><td>75.1%</td><td>1.7 ms</td><td>1.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>193/s</td><td><a href="../artifacts/async-scheduling-idle-regression/stress-shape/narrow-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr><tr><td>wide-frontier-high-cap</td><td>stress-shape</td><td>real-pipeline-overlap</td><td>8,192</td><td>1,024</td><td>64</td><td>16</td><td>8</td><td>79.4%</td><td>20.6%</td><td>20.6%</td><td>0.0%</td><td>86.97 s</td><td>113.8 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>88/s</td><td><a href="../artifacts/async-scheduling-idle-regression/stress-shape/wide-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr></tbody></table></div>
+<p class="artifact-note">Raw artifacts are under <a href="../artifacts/async-scheduling-idle-regression">artifacts/async-scheduling-idle-regression</a>.</p>
+</section>
+<section>
+<h2>Potential Changes</h2>
+<ol class="recommendations"><li><strong>Add an adaptive row-group admission controller.</strong> A subclass or policy layer can watch bottleneck-resource starved idle and admit another row group when model capacity is idle while queued or pending model work remains, bounded by buffer and memory limits. When total idle is high but starved idle is low, the controller should treat that as a frontier problem and prefetch more row groups rather than tuning request AIMD.</li><li><strong>Keep resource-aware idle in the standard benchmark output.</strong> The new <code>llm_wait</code> utilization, starved idle, frontier/dependency-horizon idle, scheduler queue-age, downstream ready-gap, and burstiness metrics identify whether a vLLM-like resource is genuinely idle or merely waiting for dependencies.</li><li><strong>Document custom-model group identity and weight.</strong> <code>SchedulingMetadata.custom_model(...)</code> controls fairness and per-group admission. Plugins that share one external model should use a shared model identity and a weight that reflects real serving capacity; plugins that represent distinct resources should keep distinct identities.</li><li><strong>Use the report as a regression suite.</strong> The worst case here is <code>rows-256</code>; future scheduler or admission changes should improve that case without increasing ready gaps or violating final zero-lease checks.</li></ol>
+</section>
+</main>
+</body>
+</html>
diff --git a/scripts/benchmarks/benchmark_async_scheduling.py b/scripts/benchmarks/benchmark_async_scheduling.py
index 964ec5fdb..25f265395 100644
--- a/scripts/benchmarks/benchmark_async_scheduling.py
+++ b/scripts/benchmarks/benchmark_async_scheduling.py
@@ -6,6 +6,7 @@
 from __future__ import annotations
 
 import argparse
+import asyncio
 import csv
 import hashlib
 import json
@@ -14,12 +15,22 @@
 import subprocess
 import sys
 import time
+import uuid
 from collections.abc import Mapping
 from dataclasses import dataclass, fields, is_dataclass
 from enum import Enum
 from pathlib import Path
 from typing import Any
 
+import data_designer.lazy_heavy_imports as lazy
+from data_designer.config.column_configs import (
+    ExpressionColumnConfig,
+    GenerationStrategy,
+    LLMTextColumnConfig,
+    SamplerColumnConfig,
+)
+from data_designer.config.sampler_params import SamplerType
+from data_designer.config.scheduling import SchedulingMetadata
 from data_designer.engine.capacity import (
     AsyncCapacityConfigured,
     AsyncCapacityObservedMaxima,
@@ -29,6 +40,9 @@
     RequestAdmissionConfigSnapshot,
     RowGroupAdmission,
 )
+from data_designer.engine.column_generators.generators.base import ColumnGenerator, FromScratchColumnGenerator
+from data_designer.engine.dataset_builders.async_scheduler import AsyncTaskScheduler
+from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker
 from data_designer.engine.dataset_builders.scheduling.queue import FairTaskQueue
 from data_designer.engine.dataset_builders.scheduling.resources import (
     SchedulableTask,
@@ -38,20 +52,31 @@
 )
 from data_designer.engine.dataset_builders.scheduling.task_admission import TaskAdmissionConfig, TaskAdmissionController
 from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
+from data_designer.engine.dataset_builders.utils.row_group_buffer import RowGroupBufferManager
 from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
-from data_designer.engine.models.request_admission.controller import AdaptiveRequestAdmissionController
+from data_designer.engine.models.request_admission.controller import (
+    AdaptiveRequestAdmissionController,
+    RequestAdmissionLease,
+)
 from data_designer.engine.models.request_admission.outcomes import RequestReleaseOutcome
 from data_designer.engine.models.request_admission.resources import (
     RequestAdmissionItem,
     RequestDomain,
+    RequestEventContext,
     RequestGroupSpec,
     RequestResourceKey,
 )
 from data_designer.engine.models.resources import ProviderModelKey, ProviderModelStaticCap
-from data_designer.engine.observability import InMemoryAdmissionEventSink, SchedulerAdmissionEvent
+from data_designer.engine.observability import (
+    InMemoryAdmissionEventSink,
+    SchedulerAdmissionEvent,
+    runtime_correlation_provider,
+)
 
 ARTIFACT_SCHEMA_VERSION = "async-scheduling-benchmark-v1"
-HARNESS_VERSION = "1.0"
+HARNESS_VERSION = "1.1"
+ASYNC_WAKEUP_GATE_SECONDS = 0.025
 
 
 @dataclass(frozen=True)
@@ -64,6 +89,12 @@ class BenchmarkInputs:
     row_group_concurrency: int
     task_admission_capacity: int
     request_latency_seconds: float
+    upstream_latency_seconds: float
+    downstream_latency_seconds: float
+    fanout_width: int
+    model_stage_weight: int
+    adaptive_row_group_admission: bool
+    request_pressure_advisory: bool
     warmups: int
     iterations: int
     seed: int
@@ -71,6 +102,123 @@ class BenchmarkInputs:
     harness_version: str
 
 
+class _BenchmarkStorage:
+    dataset_name = "async-scheduling-benchmark"
+
+    def __init__(self) -> None:
+        self.write_count = 0
+
+    def get_file_paths(self) -> dict[str, str]:
+        return {}
+
+    def write_batch_to_parquet_file(self, **kwargs: object) -> str:
+        self.write_count += 1
+        return f"/tmp/async-scheduling-benchmark/partial-{self.write_count}.parquet"
+
+    def move_partial_result_to_final_file_path(self, batch_number: int) -> str:
+        return f"/tmp/async-scheduling-benchmark/final-{batch_number}.parquet"
+
+
+class _BenchmarkSeedGenerator(FromScratchColumnGenerator[ExpressionColumnConfig]):
+    @staticmethod
+    def get_generation_strategy() -> GenerationStrategy:
+        return GenerationStrategy.FULL_COLUMN
+
+    def generate(self, data: lazy.pd.DataFrame) -> lazy.pd.DataFrame:
+        return data
+
+    def generate_from_scratch(self, num_records: int) -> lazy.pd.DataFrame:
+        return lazy.pd.DataFrame({self.config.name: list(range(num_records))})
+
+
+class _BenchmarkTimedCellGenerator(ColumnGenerator[ExpressionColumnConfig]):
+    def __init__(
+        self,
+        *args: object,
+        delay_seconds: float,
+        model_stage: bool = False,
+        model_stage_weight: int = 1,
+        **kwargs: object,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self._delay_seconds = delay_seconds
+        self._model_stage = model_stage
+        self._model_stage_weight = model_stage_weight
+
+    @staticmethod
+    def get_generation_strategy() -> GenerationStrategy:
+        return GenerationStrategy.CELL_BY_CELL
+
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        if self._model_stage:
+            return SchedulingMetadata.custom_model(
+                "benchmark-provider",
+                self.config.name,
+                "chat",
+                weight=self._model_stage_weight,
+            )
+        return super().get_scheduling_metadata()
+
+    def generate(self, data: dict) -> dict:
+        data[self.config.name] = f"{self.config.name}_{data.get('seed', '?')}"
+        return data
+
+    async def agenerate(self, data: dict) -> dict:
+        if self._delay_seconds:
+            await asyncio.sleep(self._delay_seconds)
+        return self.generate(data)
+
+
+class _BenchmarkRequestPressureCellGenerator(ColumnGenerator[ExpressionColumnConfig]):
+    def __init__(
+        self,
+        *args: object,
+        request_admission: AdaptiveRequestAdmissionController,
+        provider_name: str,
+        model_id: str,
+        delay_seconds: float,
+        **kwargs: object,
+    ) -> None:
+        super().__init__(*args, **kwargs)
+        self._request_admission = request_admission
+        self._resource = RequestResourceKey(provider_name, model_id, RequestDomain.CHAT)
+        self._delay_seconds = delay_seconds
+
+    @staticmethod
+    def get_generation_strategy() -> GenerationStrategy:
+        return GenerationStrategy.CELL_BY_CELL
+
+    def get_scheduling_metadata(self) -> SchedulingMetadata:
+        return SchedulingMetadata.model(
+            self._resource.provider_name,
+            self._resource.model_id,
+            self._resource.domain.value,
+            weight=1,
+        )
+
+    def generate(self, data: dict) -> dict:
+        data[self.config.name] = f"{self.config.name}_{data.get('seed', '?')}"
+        return data
+
+    async def agenerate(self, data: dict) -> dict:
+        item = RequestAdmissionItem(
+            self._resource,
+            RequestGroupSpec(self._resource),
+            queue_wait_timeout_seconds=30.0,
+            event_context=RequestEventContext(
+                captured_correlation=runtime_correlation_provider.current(),
+                request_attempt_id=f"benchmark-request-{uuid.uuid4().hex}",
+            ),
+        )
+        lease = await self._request_admission.acquire_async(item)
+        try:
+            if self._delay_seconds:
+                await asyncio.sleep(self._delay_seconds)
+            return self.generate(data)
+        finally:
+            self._request_admission.release(lease, RequestReleaseOutcome(kind="success"))
+
+
 def main() -> None:
     args = _parse_args()
     output_dir = Path(args.output_dir)
@@ -85,6 +233,12 @@ def main() -> None:
         row_group_concurrency=args.row_group_concurrency,
         task_admission_capacity=args.task_admission_capacity,
         request_latency_seconds=args.request_latency_seconds,
+        upstream_latency_seconds=args.upstream_latency_seconds,
+        downstream_latency_seconds=args.downstream_latency_seconds,
+        fanout_width=args.fanout_width,
+        model_stage_weight=args.model_stage_weight or max(1, args.task_admission_capacity // 2),
+        adaptive_row_group_admission=args.adaptive_row_group_admission,
+        request_pressure_advisory=args.request_pressure_advisory,
         warmups=args.warmups,
         iterations=args.iterations,
         seed=args.seed,
@@ -97,6 +251,7 @@ def main() -> None:
 
     iterations = [_run_iteration(inputs, measured=True) for _ in range(args.iterations)]
     artifact = _artifact(inputs, iterations)
+    _validate_artifact(artifact)
     json_path = output_dir / "async_scheduling_benchmark.json"
     csv_path = output_dir / "async_scheduling_benchmark.csv"
     md_path = output_dir / "async_scheduling_benchmark.md"
@@ -118,6 +273,17 @@ def _parse_args() -> argparse.Namespace:
     parser.add_argument("--row-group-concurrency", type=int, default=2)
     parser.add_argument("--task-admission-capacity", type=int, default=8)
     parser.add_argument("--request-latency-seconds", type=float, default=0.0)
+    parser.add_argument("--upstream-latency-seconds", type=float, default=0.01)
+    parser.add_argument("--downstream-latency-seconds", type=float, default=0.0)
+    parser.add_argument("--fanout-width", type=int, default=3)
+    parser.add_argument(
+        "--model-stage-weight",
+        type=int,
+        default=0,
+        help="Synthetic custom-model scheduling weight. Defaults to the modeled llm_wait capacity.",
+    )
+    parser.add_argument("--adaptive-row-group-admission", action="store_true")
+    parser.add_argument("--request-pressure-advisory", action="store_true")
     parser.add_argument("--warmups", type=int, default=1)
     parser.add_argument("--iterations", type=int, default=5)
     parser.add_argument("--output-dir", required=True)
@@ -128,6 +294,16 @@ def _parse_args() -> argparse.Namespace:
 
 
 def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
+    if inputs.scenario == "adaptive-request-pressure":
+        return _run_adaptive_request_pressure_iteration(inputs, measured=measured)
+    if inputs.scenario == "request-pressure-advisory":
+        return _run_request_pressure_iteration(inputs, measured=measured)
+    if inputs.scenario == "real-pipeline-overlap":
+        return _run_real_pipeline_iteration(inputs, measured=measured)
+    return _run_queue_admission_iteration(inputs, measured=measured)
+
+
+def _run_queue_admission_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
     sink = InMemoryAdmissionEventSink()
     task_config = TaskAdmissionConfig(
         submission_capacity=inputs.task_admission_capacity,
@@ -162,6 +338,19 @@ def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]
     request_item = RequestAdmissionItem(request_resource, RequestGroupSpec(request_resource))
     started = time.monotonic()
     sequence = 0
+    accepted_by_id = {task.task_id: task for task in tasks if task.task_id in accepted}
+    for task_id in accepted:
+        sequence += 1
+        task = accepted_by_id[task_id]
+        sink.emit_scheduler_event(
+            SchedulerAdmissionEvent.capture(
+                "ready_enqueued",
+                sequence=sequence,
+                task_id=task.task_id,
+                snapshot=task_controller.view(),
+                diagnostics={"resource_request": dict(task.resource_request.amounts)},
+            )
+        )
 
     while queue.has_queued_tasks:
         selection = queue.select_next(lambda item, view: task_controller.is_eligible(item, view))
@@ -174,6 +363,7 @@ def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]
                 sequence=sequence,
                 task_id=selection.item.task_id,
                 snapshot=task_controller.view(),
+                diagnostics={"resource_request": dict(selection.item.resource_request.amounts)},
             )
         )
         decision = task_controller.try_acquire(selection.item, selection.queue_view)
@@ -192,6 +382,7 @@ def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]
                 task_id=committed.task_id,
                 task_lease_id=lease.lease_id,
                 snapshot=task_controller.view(),
+                diagnostics={"resource_request": dict(committed.resource_request.amounts)},
             )
         )
         request_lease = request_controller.acquire_sync(request_item)
@@ -202,10 +393,17 @@ def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]
         selected.append(committed.task_id)
 
     wall_time = time.monotonic() - started
+    async_request_wakeup_seconds = asyncio.run(_measure_async_request_wakeup_seconds())
     task_snapshot = task_controller.view()
     request_snapshots = request_controller.pressure.snapshots()
     global_snapshots = request_controller.pressure.global_snapshots()
     output_hash = hashlib.sha256("\n".join(selected).encode()).hexdigest()
+    max_task_leases = _max_task_leases_by_resource(sink.scheduler_events)
+    max_request_in_flight = _max_request_in_flight_by_resource(sink.request_events)
+    utilization_metrics = {
+        "scheduler_resources": _scheduler_utilization_metrics(sink.scheduler_events),
+        "request_resources": _request_utilization_metrics(sink.request_events),
+    }
     timeline = [{"stream": "scheduler", **_event_payload(event)} for event in sink.scheduler_events] + [
         {"stream": "request", **_event_payload(event)} for event in sink.request_events
     ]
@@ -213,7 +411,9 @@ def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]
     return {
         "measured": measured,
         "wall_time_seconds": wall_time,
+        "async_request_wakeup_seconds": async_request_wakeup_seconds,
         "timeline": timeline,
+        "utilization_metrics": utilization_metrics,
         "final_task_snapshot": task_snapshot,
         "final_request_snapshot": {
             "domains": request_snapshots,
@@ -226,10 +426,312 @@ def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]
         "output_hashes": {"selected_task_ids": output_hash},
         "per_layer_observed_maxima": {
             "selected_tasks": len(selected),
+            "accepted_tasks": len(accepted),
+            "task_leases_by_resource": max_task_leases,
+            "request_in_flight_by_resource": max_request_in_flight,
             "active_task_leases_at_end": sum(task_snapshot.leased_resources.values()),
             "active_request_leases_at_end": sum(snapshot.active_lease_count for snapshot in request_snapshots.values()),
         },
         "accepted_task_count": len(accepted),
+        "selected_task_count": len(selected),
+    }
+
+
+def _run_real_pipeline_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
+    return asyncio.run(_run_real_pipeline_iteration_async(inputs, measured=measured))
+
+
+async def _run_real_pipeline_iteration_async(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
+    sink = InMemoryAdmissionEventSink()
+    fanout_width = max(1, inputs.fanout_width)
+    upstream_cols = [f"heavy_{index}" for index in range(fanout_width)]
+    downstream_cols = [f"fast_{index}" for index in range(fanout_width)]
+    configs = [
+        SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        *[LLMTextColumnConfig(name=col, prompt="{{ seed }}", model_alias="benchmark") for col in upstream_cols],
+        *[
+            LLMTextColumnConfig(name=downstream, prompt=f"{{{{ {upstream} }}}}", model_alias="benchmark")
+            for upstream, downstream in zip(upstream_cols, downstream_cols)
+        ],
+    ]
+    strategies: dict[str, GenerationStrategy] = {"seed": GenerationStrategy.FULL_COLUMN}
+    strategies.update({col: GenerationStrategy.CELL_BY_CELL for col in (*upstream_cols, *downstream_cols)})
+    provider = object()
+    generators: dict[str, ColumnGenerator] = {
+        "seed": _BenchmarkSeedGenerator(
+            config=ExpressionColumnConfig(name="seed", expr="{{ x }}"), resource_provider=provider
+        ),
+        **{
+            col: _BenchmarkTimedCellGenerator(
+                config=ExpressionColumnConfig(name=col, expr="{{ x }}"),
+                resource_provider=provider,
+                delay_seconds=inputs.upstream_latency_seconds,
+                model_stage=True,
+                model_stage_weight=inputs.model_stage_weight,
+            )
+            for col in upstream_cols
+        },
+        **{
+            col: _BenchmarkTimedCellGenerator(
+                config=ExpressionColumnConfig(name=col, expr="{{ x }}"),
+                resource_provider=provider,
+                delay_seconds=inputs.downstream_latency_seconds,
+                model_stage=True,
+                model_stage_weight=inputs.model_stage_weight,
+            )
+            for col in downstream_cols
+        },
+    }
+    row_groups = _row_groups(inputs.record_count, inputs.buffer_size)
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    storage = _BenchmarkStorage()
+    buffer_manager = RowGroupBufferManager(storage)
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        buffer_manager=buffer_manager,
+        max_concurrent_row_groups=inputs.row_group_concurrency,
+        max_submitted_tasks=inputs.task_admission_capacity,
+        max_model_task_admission=max(1, inputs.task_admission_capacity // 2),
+        scheduler_event_sink=sink,
+        trace=True,
+        num_records=inputs.record_count,
+        buffer_size=inputs.buffer_size,
+        adaptive_row_group_admission=inputs.adaptive_row_group_admission,
+    )
+
+    started = time.monotonic()
+    await scheduler.run()
+    wall_time = time.monotonic() - started
+    async_request_wakeup_seconds = await _measure_async_request_wakeup_seconds()
+    traces = [_trace_payload(trace) for trace in scheduler.traces]
+    pipeline_metrics = _pipeline_metrics(
+        traces,
+        sink.scheduler_events,
+        upstream_cols=upstream_cols,
+        downstream_cols=downstream_cols,
+        submission_capacity=inputs.task_admission_capacity,
+        llm_wait_capacity=max(1, inputs.task_admission_capacity // 2),
+        row_group_concurrency=inputs.row_group_concurrency,
+        capacity_plan=scheduler.capacity_plan(),
+        expected_task_count=inputs.record_count * fanout_width,
+    )
+    utilization_metrics = {
+        "scheduler_resources": _scheduler_utilization_metrics(sink.scheduler_events),
+        "request_resources": {},
+    }
+    timeline = [{"stream": "scheduler", **_event_payload(event)} for event in sink.scheduler_events]
+    timeline.sort(key=lambda event: (event["captured_at_monotonic"], event["sequence"]))
+    task_snapshot = scheduler.task_admission_snapshot()
+    return {
+        "measured": measured,
+        "wall_time_seconds": wall_time,
+        "async_request_wakeup_seconds": async_request_wakeup_seconds,
+        "timeline": timeline,
+        "traces": traces,
+        "pipeline_metrics": pipeline_metrics,
+        "utilization_metrics": utilization_metrics,
+        "capacity_plan": scheduler.capacity_plan(),
+        "final_task_snapshot": task_snapshot,
+        "final_request_snapshot": {
+            "domains": {},
+            "provider_models": {},
+            "zero_active_request_leases": True,
+            "zero_request_waiters": True,
+        },
+        "output_hashes": {
+            "completed_task_trace": hashlib.sha256(
+                "\n".join(f"{trace['column']}:{trace['row_group']}:{trace['row_index']}" for trace in traces).encode()
+            ).hexdigest()
+        },
+        "per_layer_observed_maxima": {
+            "selected_tasks": len(traces),
+            "active_task_leases_at_end": sum(task_snapshot.leased_resources.values()),
+            "active_request_leases_at_end": 0,
+            **pipeline_metrics["observed_maxima"],
+        },
+        "accepted_task_count": len(traces),
+        "selected_task_count": len(traces),
+    }
+
+
+def _run_request_pressure_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
+    return asyncio.run(
+        _run_request_pressure_iteration_async(
+            inputs,
+            measured=measured,
+            row_groups=[(0, inputs.record_count)],
+            max_concurrent_row_groups=1,
+            adaptive_row_group_admission=False,
+        )
+    )
+
+
+def _run_adaptive_request_pressure_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
+    return asyncio.run(
+        _run_request_pressure_iteration_async(
+            inputs,
+            measured=measured,
+            row_groups=_row_groups(inputs.record_count, inputs.buffer_size),
+            max_concurrent_row_groups=inputs.row_group_concurrency,
+            adaptive_row_group_admission=inputs.adaptive_row_group_admission,
+        )
+    )
+
+
+async def _run_request_pressure_iteration_async(
+    inputs: BenchmarkInputs,
+    *,
+    measured: bool,
+    row_groups: list[tuple[int, int]],
+    max_concurrent_row_groups: int,
+    adaptive_row_group_admission: bool,
+) -> dict[str, Any]:
+    sink = InMemoryAdmissionEventSink()
+    request_controller = AdaptiveRequestAdmissionController(
+        RequestAdmissionConfig(default_queue_wait_timeout_seconds=30.0),
+        event_sink=sink,
+    )
+    request_controller.register(
+        provider_name="aa-pressured-provider",
+        model_id="pressured-model",
+        alias="pressured",
+        max_parallel_requests=1,
+    )
+    request_controller.register(
+        provider_name="zz-open-provider",
+        model_id="open-model",
+        alias="open",
+        max_parallel_requests=1,
+    )
+    pressured_resource = RequestResourceKey("aa-pressured-provider", "pressured-model", RequestDomain.CHAT)
+    holder_item = RequestAdmissionItem(pressured_resource, RequestGroupSpec(pressured_resource))
+    holder_lease = request_controller.try_acquire(holder_item)
+    if not isinstance(holder_lease, RequestAdmissionLease):
+        raise RuntimeError(f"Expected pressure holder request lease, got {holder_lease.reason}")
+
+    async def release_holder() -> None:
+        await asyncio.sleep(max(inputs.request_latency_seconds, inputs.upstream_latency_seconds))
+        request_controller.release(holder_lease, RequestReleaseOutcome(kind="success"))
+
+    release_task = asyncio.create_task(release_holder())
+    configs = [
+        SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="a_pressured", prompt="{{ seed }}", model_alias="pressured"),
+        LLMTextColumnConfig(name="z_open", prompt="{{ seed }}", model_alias="open"),
+    ]
+    strategies = {
+        "seed": GenerationStrategy.FULL_COLUMN,
+        "a_pressured": GenerationStrategy.CELL_BY_CELL,
+        "z_open": GenerationStrategy.CELL_BY_CELL,
+    }
+    provider = object()
+    generators: dict[str, ColumnGenerator] = {
+        "seed": _BenchmarkSeedGenerator(
+            config=ExpressionColumnConfig(name="seed", expr="{{ x }}"), resource_provider=provider
+        ),
+        "a_pressured": _BenchmarkRequestPressureCellGenerator(
+            config=ExpressionColumnConfig(name="a_pressured", expr="{{ x }}"),
+            resource_provider=provider,
+            request_admission=request_controller,
+            provider_name="aa-pressured-provider",
+            model_id="pressured-model",
+            delay_seconds=inputs.downstream_latency_seconds,
+        ),
+        "z_open": _BenchmarkRequestPressureCellGenerator(
+            config=ExpressionColumnConfig(name="z_open", expr="{{ x }}"),
+            resource_provider=provider,
+            request_admission=request_controller,
+            provider_name="zz-open-provider",
+            model_id="open-model",
+            delay_seconds=inputs.downstream_latency_seconds,
+        ),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    storage = _BenchmarkStorage()
+    buffer_manager = RowGroupBufferManager(storage)
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        buffer_manager=buffer_manager,
+        max_concurrent_row_groups=max_concurrent_row_groups,
+        max_submitted_tasks=max(1, inputs.task_admission_capacity),
+        max_model_task_admission=max(1, inputs.task_admission_capacity),
+        scheduler_event_sink=sink,
+        trace=True,
+        num_records=inputs.record_count,
+        buffer_size=inputs.buffer_size,
+        adaptive_row_group_admission=adaptive_row_group_admission,
+        request_pressure_provider=request_controller.pressure,
+        request_pressure_advisory=inputs.request_pressure_advisory,
+    )
+
+    started = time.monotonic()
+    try:
+        await scheduler.run()
+    finally:
+        await release_task
+    wall_time = time.monotonic() - started
+    async_request_wakeup_seconds = await _measure_async_request_wakeup_seconds()
+    traces = [_trace_payload(trace) for trace in scheduler.traces]
+    utilization_metrics = {
+        "scheduler_resources": _scheduler_utilization_metrics(sink.scheduler_events),
+        "request_resources": _request_utilization_metrics(sink.request_events),
+    }
+    task_snapshot = scheduler.task_admission_snapshot()
+    request_snapshots = request_controller.pressure.snapshots()
+    global_snapshots = request_controller.pressure.global_snapshots()
+    request_wait_while_leased = _request_wait_while_task_leased_seconds(sink.scheduler_events, sink.request_events)
+    timeline = [{"stream": "scheduler", **_event_payload(event)} for event in sink.scheduler_events] + [
+        {"stream": "request", **_event_payload(event)} for event in sink.request_events
+    ]
+    timeline.sort(key=lambda event: (event["captured_at_monotonic"], event["sequence"]))
+    return {
+        "measured": measured,
+        "wall_time_seconds": wall_time,
+        "async_request_wakeup_seconds": async_request_wakeup_seconds,
+        "timeline": timeline,
+        "traces": traces,
+        "request_pressure_metrics": {
+            "request_wait_seconds_while_task_leased": request_wait_while_leased,
+            "request_pressure_advisory_enabled": inputs.request_pressure_advisory,
+            "request_pressure_advisory_skip_count": sum(
+                1 for event in sink.scheduler_events if event.event_kind == "request_pressure_advisory_skipped"
+            ),
+            "first_model_dispatch_column": _first_model_dispatch_column(traces),
+        },
+        "utilization_metrics": utilization_metrics,
+        "capacity_plan": scheduler.capacity_plan(),
+        "final_task_snapshot": task_snapshot,
+        "final_request_snapshot": {
+            "domains": request_snapshots,
+            "provider_models": global_snapshots,
+            "zero_active_request_leases": all(
+                snapshot.active_lease_count == 0 for snapshot in request_snapshots.values()
+            ),
+            "zero_request_waiters": all(snapshot.waiters == 0 for snapshot in request_snapshots.values()),
+        },
+        "output_hashes": {
+            "completed_task_trace": hashlib.sha256(
+                "\n".join(f"{trace['column']}:{trace['row_group']}:{trace['row_index']}" for trace in traces).encode()
+            ).hexdigest()
+        },
+        "per_layer_observed_maxima": {
+            "selected_tasks": len(traces),
+            "accepted_tasks": len(traces),
+            "task_leases_by_resource": _max_task_leases_by_resource(sink.scheduler_events),
+            "request_in_flight_by_resource": _max_request_in_flight_by_resource(sink.request_events),
+            "active_task_leases_at_end": sum(task_snapshot.leased_resources.values()),
+            "active_request_leases_at_end": sum(snapshot.active_lease_count for snapshot in request_snapshots.values()),
+        },
+        "accepted_task_count": len(traces),
+        "selected_task_count": len(traces),
     }
 
 
@@ -255,6 +757,605 @@ def _schedulable_tasks(record_count: int, groups: tuple[TaskGroupSpec, TaskGroup
     return tuple(tasks)
 
 
+def _row_groups(record_count: int, buffer_size: int) -> list[tuple[int, int]]:
+    row_groups: list[tuple[int, int]] = []
+    remaining = record_count
+    row_group = 0
+    while remaining > 0:
+        size = min(buffer_size, remaining)
+        row_groups.append((row_group, size))
+        row_group += 1
+        remaining -= size
+    return row_groups
+
+
+def _trace_payload(trace: Any) -> dict[str, Any]:
+    return {
+        "column": trace.column,
+        "row_group": trace.row_group,
+        "row_index": trace.row_index,
+        "task_type": trace.task_type,
+        "dispatched_at": trace.dispatched_at,
+        "completed_at": trace.completed_at,
+        "status": trace.status,
+        "error": trace.error,
+    }
+
+
+def _first_model_dispatch_column(traces: list[dict[str, Any]]) -> str | None:
+    model_traces = [trace for trace in traces if trace["column"] in {"a_pressured", "z_open"}]
+    if not model_traces:
+        return None
+    return min(model_traces, key=lambda trace: trace["dispatched_at"])["column"]
+
+
+def _request_wait_while_task_leased_seconds(
+    scheduler_events: list[SchedulerAdmissionEvent],
+    request_events: list[Any],
+) -> float:
+    lease_starts_by_lease: dict[str, float] = {}
+    lease_intervals: dict[str, tuple[float, float]] = {}
+    for event in _ordered_events(scheduler_events):
+        lease_id = getattr(event, "task_lease_id", None)
+        if lease_id is None:
+            continue
+        if event.event_kind == "task_lease_acquired":
+            lease_starts_by_lease[lease_id] = float(event.captured_at_monotonic)
+        elif event.event_kind == "task_lease_released":
+            task_execution_id = getattr(event, "task_execution_id", None)
+            started = lease_starts_by_lease.pop(lease_id, None)
+            if started is not None and task_execution_id is not None:
+                lease_intervals[task_execution_id] = (started, float(event.captured_at_monotonic))
+
+    wait_starts: dict[tuple[str, str | None], float] = {}
+    total = 0.0
+    for event in _ordered_events(request_events):
+        correlation = getattr(event, "captured_correlation", None)
+        task_execution_id = getattr(correlation, "task_execution_id", None)
+        if task_execution_id is None:
+            continue
+        attempt_key = (task_execution_id, getattr(event, "request_attempt_id", None))
+        if event.event_kind == "request_wait_started":
+            wait_starts[attempt_key] = float(event.captured_at_monotonic)
+        elif event.event_kind in {
+            "request_wait_completed",
+            "request_wait_timeout",
+            "request_wait_cancelled",
+            "request_acquire_denied",
+        }:
+            wait_started = wait_starts.pop(attempt_key, None)
+            lease_interval = lease_intervals.get(task_execution_id)
+            if wait_started is None or lease_interval is None:
+                continue
+            wait_ended = float(event.captured_at_monotonic)
+            lease_started, lease_ended = lease_interval
+            total += max(0.0, min(wait_ended, lease_ended) - max(wait_started, lease_started))
+    return total
+
+
+def _pipeline_metrics(
+    traces: list[dict[str, Any]],
+    scheduler_events: list[SchedulerAdmissionEvent],
+    *,
+    upstream_cols: list[str],
+    downstream_cols: list[str],
+    submission_capacity: int,
+    llm_wait_capacity: int,
+    row_group_concurrency: int,
+    capacity_plan: AsyncCapacityPlan,
+    expected_task_count: int,
+) -> dict[str, Any]:
+    upstream = [trace for trace in traces if trace["column"] in upstream_cols]
+    downstream = [trace for trace in traces if trace["column"] in downstream_cols]
+    first_upstream_dispatch = min((trace["dispatched_at"] for trace in upstream), default=None)
+    last_upstream_dispatch = max((trace["dispatched_at"] for trace in upstream), default=None)
+    last_upstream_complete = max((trace["completed_at"] for trace in upstream), default=None)
+    first_downstream_dispatch = min((trace["dispatched_at"] for trace in downstream), default=None)
+    ready_gaps = _downstream_ready_gaps(upstream, downstream, upstream_cols, downstream_cols)
+    max_task_leases = _max_task_leases_by_resource(scheduler_events)
+    observed_row_groups = capacity_plan.observed_maxima.row_groups_in_flight
+    observed_queued = dict(capacity_plan.observed_maxima.queued_tasks_by_group)
+    has_overlap_points = (
+        first_downstream_dispatch is not None
+        and last_upstream_dispatch is not None
+        and last_upstream_complete is not None
+        and first_upstream_dispatch is not None
+    )
+    overlap_seconds = max(0.0, last_upstream_complete - first_downstream_dispatch) if has_overlap_points else 0.0
+    upstream_duration = max(0.0, last_upstream_complete - first_upstream_dispatch) if has_overlap_points else 0.0
+    validation = {
+        "expected_upstream_task_count": len(upstream) == expected_task_count,
+        "expected_downstream_task_count": len(downstream) == expected_task_count,
+        "expected_downstream_ready_gap_count": len(ready_gaps) == expected_task_count,
+        "submission_cap_respected": max_task_leases.get("submission", 0) <= submission_capacity,
+        "llm_wait_cap_respected": max_task_leases.get("llm_wait", 0) <= llm_wait_capacity,
+        "row_group_cap_respected": observed_row_groups <= row_group_concurrency,
+        "downstream_interleaved_before_all_upstream_dispatched": has_overlap_points
+        and first_downstream_dispatch < last_upstream_dispatch,
+        "downstream_interleaved_before_all_upstream_completed": has_overlap_points
+        and first_downstream_dispatch < last_upstream_complete,
+    }
+    return {
+        "upstream_columns": tuple(upstream_cols),
+        "downstream_columns": tuple(downstream_cols),
+        "expected_task_count": expected_task_count,
+        "upstream_task_count": len(upstream),
+        "downstream_task_count": len(downstream),
+        "downstream_ready_gap_count": len(ready_gaps),
+        "first_downstream_dispatch_delay_seconds": (
+            max(0.0, first_downstream_dispatch - first_upstream_dispatch) if has_overlap_points else 0.0
+        ),
+        "downstream_ready_gap_mean_seconds": statistics.fmean(ready_gaps) if ready_gaps else 0.0,
+        "downstream_ready_gap_p95_seconds": _percentile(ready_gaps, 0.95),
+        "downstream_ready_gap_max_seconds": max(ready_gaps) if ready_gaps else 0.0,
+        "upstream_downstream_overlap_seconds": overlap_seconds,
+        "upstream_downstream_overlap_ratio": _safe_ratio(overlap_seconds, upstream_duration),
+        "observed_maxima": {
+            "row_groups_in_flight": observed_row_groups,
+            "task_leases_by_resource": max_task_leases,
+            "queued_tasks_by_group": observed_queued,
+        },
+        "validation": validation,
+        "validation_passed": all(validation.values()),
+    }
+
+
+def _downstream_ready_gaps(
+    upstream: list[dict[str, Any]],
+    downstream: list[dict[str, Any]],
+    upstream_cols: list[str],
+    downstream_cols: list[str],
+) -> list[float]:
+    upstream_by_key = {(trace["column"], trace["row_group"], trace["row_index"]): trace for trace in upstream}
+    gaps: list[float] = []
+    for upstream_col, downstream_col in zip(upstream_cols, downstream_cols):
+        for downstream_trace in downstream:
+            if downstream_trace["column"] != downstream_col:
+                continue
+            upstream_trace = upstream_by_key.get(
+                (upstream_col, downstream_trace["row_group"], downstream_trace["row_index"])
+            )
+            if upstream_trace is None:
+                continue
+            gaps.append(max(0.0, downstream_trace["dispatched_at"] - upstream_trace["completed_at"]))
+    return gaps
+
+
+def _max_task_leases_by_resource(events: list[SchedulerAdmissionEvent]) -> dict[str, int]:
+    maxima: dict[str, int] = {}
+    for event in events:
+        snapshot = event.snapshot
+        leased = getattr(snapshot, "leased_resources", {})
+        for resource, count in leased.items():
+            maxima[str(resource)] = max(maxima.get(str(resource), 0), int(count))
+    return maxima
+
+
+def _max_request_in_flight_by_resource(events: list[Any]) -> dict[str, int]:
+    maxima: dict[str, int] = {}
+    for event in events:
+        snapshot = getattr(event, "pressure_snapshot", None)
+        if snapshot is None:
+            continue
+        resource = getattr(snapshot, "resource", None) or getattr(event, "request_resource_key", None)
+        if resource is None:
+            continue
+        count = int(getattr(snapshot, "in_flight_count", 0))
+        maxima[str(resource)] = max(maxima.get(str(resource), 0), count)
+    return maxima
+
+
+def _scheduler_utilization_metrics(events: list[Any]) -> dict[str, dict[str, float | int]]:
+    ordered = _ordered_events(events)
+    if len(ordered) < 2:
+        return {}
+
+    resource_limits: dict[str, int] = {}
+    active_by_resource: dict[str, int] = {}
+    queued_by_task: dict[str, dict[str, int]] = {}
+    queued_at: dict[str, float] = {}
+    queue_ages: dict[str, list[float]] = {}
+    totals = _empty_utilization_totals()
+
+    for index, event in enumerate(ordered):
+        now = float(event.captured_at_monotonic)
+        snapshot = getattr(event, "snapshot", None)
+        if snapshot is not None:
+            for resource, limit in _int_mapping(getattr(snapshot, "resource_limits", {})).items():
+                resource_limits[resource] = max(resource_limits.get(resource, 0), limit)
+            active_by_resource = _int_mapping(getattr(snapshot, "leased_resources", {}))
+
+        task_id = getattr(event, "task_id", None)
+        resource_request = _event_resource_request(event)
+        if event.event_kind == "ready_enqueued" and task_id is not None:
+            queued_by_task[task_id] = resource_request
+            queued_at[task_id] = now
+        elif event.event_kind == "task_lease_acquired" and task_id is not None:
+            task_resources = queued_by_task.pop(task_id, resource_request)
+            started = queued_at.pop(task_id, None)
+            if started is not None:
+                age = max(0.0, now - started)
+                for resource, amount in task_resources.items():
+                    if amount > 0:
+                        queue_ages.setdefault(resource, []).append(age)
+
+        if index == len(ordered) - 1:
+            continue
+        next_time = float(ordered[index + 1].captured_at_monotonic)
+        interval_seconds = max(0.0, next_time - now)
+        queued_demand = _queued_resource_demand(queued_by_task)
+        _record_utilization_interval(
+            totals,
+            resource_limits=resource_limits,
+            active_by_resource=active_by_resource,
+            queued_or_waiting_by_resource=queued_demand,
+            interval_seconds=interval_seconds,
+        )
+
+    return _finalize_utilization_metrics(totals, queue_ages)
+
+
+def _request_utilization_metrics(events: list[Any]) -> dict[str, dict[str, float | int]]:
+    ordered = [event for event in _ordered_events(events) if getattr(event, "pressure_snapshot", None) is not None]
+    if len(ordered) < 2:
+        return {}
+
+    capacities: dict[str, int] = {}
+    active_by_resource: dict[str, int] = {}
+    waiters_by_resource: dict[str, int] = {}
+    wait_started: dict[str, float] = {}
+    wait_ages: dict[str, list[float]] = {}
+    totals = _empty_utilization_totals()
+
+    for index, event in enumerate(ordered):
+        now = float(event.captured_at_monotonic)
+        snapshot = event.pressure_snapshot
+        resource = str(snapshot.resource)
+        capacity = max(1, int(getattr(snapshot, "effective_max", 1)))
+        capacities[resource] = max(capacities.get(resource, 0), capacity)
+        active_by_resource[resource] = int(getattr(snapshot, "in_flight_count", 0))
+        waiters_by_resource[resource] = int(getattr(snapshot, "waiters", 0))
+
+        attempt_id = getattr(event, "request_attempt_id", None)
+        if attempt_id is not None and event.event_kind == "request_wait_started":
+            wait_started[attempt_id] = now
+        elif attempt_id is not None and event.event_kind in {
+            "request_wait_completed",
+            "request_wait_timeout",
+            "request_wait_cancelled",
+            "request_acquire_denied",
+        }:
+            started = wait_started.pop(attempt_id, None)
+            if started is not None:
+                wait_ages.setdefault(resource, []).append(max(0.0, now - started))
+
+        if index == len(ordered) - 1:
+            continue
+        next_time = float(ordered[index + 1].captured_at_monotonic)
+        interval_seconds = max(0.0, next_time - now)
+        _record_utilization_interval(
+            totals,
+            resource_limits=capacities,
+            active_by_resource=active_by_resource,
+            queued_or_waiting_by_resource=waiters_by_resource,
+            interval_seconds=interval_seconds,
+        )
+
+    return _finalize_utilization_metrics(totals, wait_ages)
+
+
+def _ordered_events(events: list[Any]) -> list[Any]:
+    return sorted(events, key=lambda event: (event.captured_at_monotonic, event.sequence))
+
+
+def _empty_utilization_totals() -> dict[str, dict[str, Any]]:
+    return {}
+
+
+def _record_utilization_interval(
+    totals: dict[str, dict[str, Any]],
+    *,
+    resource_limits: dict[str, int],
+    active_by_resource: dict[str, int],
+    queued_or_waiting_by_resource: dict[str, int],
+    interval_seconds: float,
+) -> None:
+    if interval_seconds <= 0.0:
+        return
+    resources = {*resource_limits, *active_by_resource, *queued_or_waiting_by_resource}
+    for resource in resources:
+        active = max(0, active_by_resource.get(resource, 0))
+        capacity = max(1, resource_limits.get(resource, max(active, queued_or_waiting_by_resource.get(resource, 0))))
+        idle_slots = max(0, capacity - active)
+        queued_or_waiting = queued_or_waiting_by_resource.get(resource, 0)
+        resource_totals = totals.setdefault(
+            resource,
+            {
+                "capacity": capacity,
+                "active_window_seconds": 0.0,
+                "capacity_seconds": 0.0,
+                "busy_capacity_seconds": 0.0,
+                "idle_capacity_seconds": 0.0,
+                "starved_idle_seconds": 0.0,
+                "max_in_flight": 0,
+                "max_idle_slots": 0,
+                "samples": [],
+            },
+        )
+        resource_totals["capacity"] = max(int(resource_totals["capacity"]), capacity)
+        resource_totals["active_window_seconds"] += interval_seconds
+        resource_totals["capacity_seconds"] += capacity * interval_seconds
+        resource_totals["busy_capacity_seconds"] += active * interval_seconds
+        resource_totals["idle_capacity_seconds"] += idle_slots * interval_seconds
+        if queued_or_waiting > 0 and idle_slots > 0:
+            resource_totals["starved_idle_seconds"] += idle_slots * interval_seconds
+        resource_totals["max_in_flight"] = max(int(resource_totals["max_in_flight"]), active)
+        resource_totals["max_idle_slots"] = max(int(resource_totals["max_idle_slots"]), idle_slots)
+        resource_totals["samples"].append((interval_seconds, active))
+
+
+def _finalize_utilization_metrics(
+    totals: dict[str, dict[str, Any]],
+    queue_ages: dict[str, list[float]],
+) -> dict[str, dict[str, float | int]]:
+    metrics: dict[str, dict[str, float | int]] = {}
+    for resource, resource_totals in totals.items():
+        capacity_seconds = float(resource_totals["capacity_seconds"])
+        active_window_seconds = float(resource_totals["active_window_seconds"])
+        busy_capacity_seconds = float(resource_totals["busy_capacity_seconds"])
+        idle_capacity_seconds = float(resource_totals["idle_capacity_seconds"])
+        starved_idle_seconds = float(resource_totals["starved_idle_seconds"])
+        dependency_horizon_idle_seconds = max(0.0, idle_capacity_seconds - starved_idle_seconds)
+        ages = queue_ages.get(resource, [])
+        if busy_capacity_seconds == 0.0 and starved_idle_seconds == 0.0 and not ages:
+            continue
+        active_count_mean = _safe_ratio(busy_capacity_seconds, active_window_seconds)
+        active_count_stdev = _weighted_stdev(resource_totals["samples"])
+        queue_age_mean_seconds = statistics.fmean(ages) if ages else 0.0
+        queue_age_p50_seconds = _percentile(ages, 0.50)
+        queue_age_p95_seconds = _percentile(ages, 0.95)
+        queue_age_max_seconds = max(ages) if ages else 0.0
+        metrics[resource] = {
+            "capacity": int(resource_totals["capacity"]),
+            "active_window_seconds": active_window_seconds,
+            "capacity_seconds": capacity_seconds,
+            "busy_capacity_seconds": busy_capacity_seconds,
+            "idle_capacity_seconds": idle_capacity_seconds,
+            "starved_idle_seconds": starved_idle_seconds,
+            "dependency_horizon_idle_seconds": dependency_horizon_idle_seconds,
+            "frontier_dependency_horizon_idle_seconds": dependency_horizon_idle_seconds,
+            "utilization_ratio": _safe_ratio(busy_capacity_seconds, capacity_seconds),
+            "idle_ratio": _safe_ratio(idle_capacity_seconds, capacity_seconds),
+            "starved_idle_ratio": _safe_ratio(starved_idle_seconds, capacity_seconds),
+            "dependency_horizon_idle_ratio": _safe_ratio(dependency_horizon_idle_seconds, capacity_seconds),
+            "frontier_dependency_horizon_idle_ratio": _safe_ratio(
+                dependency_horizon_idle_seconds,
+                capacity_seconds,
+            ),
+            "active_count_mean": active_count_mean,
+            "active_count_stdev": active_count_stdev,
+            "burstiness_coefficient": _safe_ratio(active_count_stdev, active_count_mean),
+            "max_in_flight": int(resource_totals["max_in_flight"]),
+            "max_idle_slots": int(resource_totals["max_idle_slots"]),
+            "scheduler_queue_age_mean_seconds": queue_age_mean_seconds,
+            "scheduler_queue_age_p50_seconds": queue_age_p50_seconds,
+            "scheduler_queue_age_p95_seconds": queue_age_p95_seconds,
+            "scheduler_queue_age_max_seconds": queue_age_max_seconds,
+            "scheduler_queue_age_sample_count": len(ages),
+            "ready_to_dispatch_gap_mean_seconds": queue_age_mean_seconds,
+            "ready_to_dispatch_gap_p50_seconds": queue_age_p50_seconds,
+            "ready_to_dispatch_gap_p95_seconds": queue_age_p95_seconds,
+            "ready_to_dispatch_gap_max_seconds": queue_age_max_seconds,
+            "ready_to_dispatch_sample_count": len(ages),
+        }
+    return metrics
+
+
+def _event_resource_request(event: Any) -> dict[str, int]:
+    diagnostics = getattr(event, "diagnostics", {}) or {}
+    return _int_mapping(diagnostics.get("resource_request", {}))
+
+
+def _queued_resource_demand(queued_by_task: Mapping[str, Mapping[str, int]]) -> dict[str, int]:
+    demand: dict[str, int] = {}
+    for resources in queued_by_task.values():
+        for resource, amount in resources.items():
+            demand[resource] = demand.get(resource, 0) + amount
+    return demand
+
+
+def _int_mapping(value: Any) -> dict[str, int]:
+    if not isinstance(value, Mapping):
+        return {}
+    return {str(resource): int(amount) for resource, amount in value.items()}
+
+
+def _weighted_stdev(samples: list[tuple[float, int]]) -> float:
+    total_weight = sum(weight for weight, _value in samples)
+    if total_weight <= 0.0:
+        return 0.0
+    mean = sum(weight * value for weight, value in samples) / total_weight
+    variance = sum(weight * ((value - mean) ** 2) for weight, value in samples) / total_weight
+    return variance**0.5
+
+
+def _safe_ratio(numerator: float, denominator: float) -> float:
+    if denominator <= 0:
+        return 0.0
+    return numerator / denominator
+
+
+def _pipeline_derived_metrics(metrics: list[dict[str, Any]]) -> dict[str, Any]:
+    if not metrics:
+        return {}
+    ready_gap_p95s = [metric["downstream_ready_gap_p95_seconds"] for metric in metrics]
+    ready_gap_maxes = [metric["downstream_ready_gap_max_seconds"] for metric in metrics]
+    overlap_ratios = [metric["upstream_downstream_overlap_ratio"] for metric in metrics]
+    return {
+        "pipeline_validation_passed": all(metric["validation_passed"] for metric in metrics),
+        "pipeline_mean_overlap_ratio": statistics.fmean(overlap_ratios) if overlap_ratios else 0.0,
+        "pipeline_max_downstream_ready_gap_seconds": max(ready_gap_maxes) if ready_gap_maxes else 0.0,
+        "pipeline_p95_downstream_ready_gap_seconds": _percentile(ready_gap_p95s, 0.95),
+        "pipeline_downstream_interleaved_before_all_upstream_completed": all(
+            metric["validation"]["downstream_interleaved_before_all_upstream_completed"] for metric in metrics
+        ),
+    }
+
+
+def _request_pressure_derived_metrics(iterations: list[dict[str, Any]]) -> dict[str, Any]:
+    metrics = [iteration.get("request_pressure_metrics", {}) for iteration in iterations]
+    if not any(metrics):
+        return {}
+    wait_seconds = [float(metric.get("request_wait_seconds_while_task_leased", 0.0)) for metric in metrics]
+    first_dispatches = [metric.get("first_model_dispatch_column") for metric in metrics]
+    skip_counts = [int(metric.get("request_pressure_advisory_skip_count", 0) or 0) for metric in metrics]
+    return {
+        "request_pressure_advisory_enabled": any(
+            bool(metric.get("request_pressure_advisory_enabled", False)) for metric in metrics
+        ),
+        "request_pressure_advisory_skip_count": sum(skip_counts),
+        "request_wait_seconds_while_task_leased_mean": statistics.fmean(wait_seconds),
+        "request_wait_seconds_while_task_leased_max": max(wait_seconds),
+        "first_model_dispatch_column": first_dispatches[0] if first_dispatches else None,
+    }
+
+
+def _aggregate_utilization_metrics(iterations: list[dict[str, Any]]) -> dict[str, Any]:
+    scheduler = _aggregate_resource_utilization(iterations, "scheduler_resources")
+    request = _aggregate_resource_utilization(iterations, "request_resources")
+    all_scheduler = list(scheduler.values())
+    all_request = list(request.values())
+    return {
+        "scheduler_resource_utilization": scheduler,
+        "request_resource_utilization": request,
+        "scheduler_min_utilization_ratio": min(
+            (metric["mean_utilization_ratio"] for metric in all_scheduler),
+            default=0.0,
+        ),
+        "scheduler_max_starved_idle_seconds": max(
+            (metric["max_starved_idle_seconds"] for metric in all_scheduler),
+            default=0.0,
+        ),
+        "scheduler_max_ready_to_dispatch_gap_seconds": max(
+            (metric["max_ready_to_dispatch_gap_seconds"] for metric in all_scheduler),
+            default=0.0,
+        ),
+        "scheduler_max_scheduler_queue_age_seconds": max(
+            (metric["max_scheduler_queue_age_seconds"] for metric in all_scheduler),
+            default=0.0,
+        ),
+        "scheduler_max_burstiness_coefficient": max(
+            (metric["max_burstiness_coefficient"] for metric in all_scheduler),
+            default=0.0,
+        ),
+        "request_min_utilization_ratio": min(
+            (metric["mean_utilization_ratio"] for metric in all_request),
+            default=0.0,
+        ),
+        "request_max_starved_idle_seconds": max(
+            (metric["max_starved_idle_seconds"] for metric in all_request),
+            default=0.0,
+        ),
+        "request_max_ready_to_dispatch_gap_seconds": max(
+            (metric["max_ready_to_dispatch_gap_seconds"] for metric in all_request),
+            default=0.0,
+        ),
+        "request_max_burstiness_coefficient": max(
+            (metric["max_burstiness_coefficient"] for metric in all_request),
+            default=0.0,
+        ),
+    }
+
+
+def _aggregate_resource_utilization(
+    iterations: list[dict[str, Any]],
+    resource_kind: str,
+) -> dict[str, dict[str, float | int]]:
+    by_resource: dict[str, list[dict[str, float | int]]] = {}
+    for iteration in iterations:
+        for resource, metrics in iteration.get("utilization_metrics", {}).get(resource_kind, {}).items():
+            by_resource.setdefault(resource, []).append(metrics)
+
+    aggregated: dict[str, dict[str, float | int]] = {}
+    for resource, metrics in by_resource.items():
+        utilization_ratios = [float(metric["utilization_ratio"]) for metric in metrics]
+        idle_seconds = [float(metric["idle_capacity_seconds"]) for metric in metrics]
+        starved_idle_seconds = [float(metric["starved_idle_seconds"]) for metric in metrics]
+        dependency_horizon_idle_seconds = [_dependency_horizon_idle_seconds(metric) for metric in metrics]
+        dependency_horizon_idle_ratios = [_dependency_horizon_idle_ratio(metric) for metric in metrics]
+        burstiness = [float(metric["burstiness_coefficient"]) for metric in metrics]
+        scheduler_queue_ages = [
+            float(metric.get("scheduler_queue_age_max_seconds", metric["ready_to_dispatch_gap_max_seconds"]))
+            for metric in metrics
+        ]
+        aggregated[resource] = {
+            "iterations_observed": len(metrics),
+            "mean_utilization_ratio": statistics.fmean(utilization_ratios),
+            "min_utilization_ratio": min(utilization_ratios),
+            "max_utilization_ratio": max(utilization_ratios),
+            "mean_idle_capacity_seconds": statistics.fmean(idle_seconds),
+            "mean_starved_idle_seconds": statistics.fmean(starved_idle_seconds),
+            "mean_dependency_horizon_idle_seconds": statistics.fmean(dependency_horizon_idle_seconds),
+            "max_dependency_horizon_idle_seconds": max(dependency_horizon_idle_seconds),
+            "mean_dependency_horizon_idle_ratio": statistics.fmean(dependency_horizon_idle_ratios),
+            "max_dependency_horizon_idle_ratio": max(dependency_horizon_idle_ratios),
+            "mean_frontier_dependency_horizon_idle_seconds": statistics.fmean(dependency_horizon_idle_seconds),
+            "max_frontier_dependency_horizon_idle_seconds": max(dependency_horizon_idle_seconds),
+            "mean_frontier_dependency_horizon_idle_ratio": statistics.fmean(dependency_horizon_idle_ratios),
+            "max_frontier_dependency_horizon_idle_ratio": max(dependency_horizon_idle_ratios),
+            "max_starved_idle_seconds": max(starved_idle_seconds),
+            "max_burstiness_coefficient": max(burstiness),
+            "max_scheduler_queue_age_seconds": max(scheduler_queue_ages),
+            "max_ready_to_dispatch_gap_seconds": max(scheduler_queue_ages),
+        }
+    return aggregated
+
+
+def _dependency_horizon_idle_seconds(metric: Mapping[str, float | int]) -> float:
+    if "dependency_horizon_idle_seconds" in metric:
+        return float(metric["dependency_horizon_idle_seconds"])
+    return max(0.0, float(metric["idle_capacity_seconds"]) - float(metric["starved_idle_seconds"]))
+
+
+def _dependency_horizon_idle_ratio(metric: Mapping[str, float | int]) -> float:
+    if "dependency_horizon_idle_ratio" in metric:
+        return float(metric["dependency_horizon_idle_ratio"])
+    return _safe_ratio(_dependency_horizon_idle_seconds(metric), float(metric["capacity_seconds"]))
+
+
+async def _measure_async_request_wakeup_seconds() -> float:
+    resource = RequestResourceKey("mock-provider", "mock-model", RequestDomain.CHAT)
+    controller = AdaptiveRequestAdmissionController(
+        RequestAdmissionConfig(default_queue_wait_timeout_seconds=30.0, increase_after_successes=1)
+    )
+    controller.register(
+        provider_name="mock-provider",
+        model_id="mock-model",
+        alias="mock-alias",
+        max_parallel_requests=1,
+    )
+    item = RequestAdmissionItem(resource, RequestGroupSpec(resource), queue_wait_timeout_seconds=30.0)
+    first_lease = controller.try_acquire(item)
+    if not isinstance(first_lease, RequestAdmissionLease):
+        raise RuntimeError(f"Expected initial request lease, got {first_lease.reason}")
+
+    queued_task = asyncio.create_task(controller.acquire_async(item))
+    for _ in range(100):
+        snapshot = controller.pressure.snapshot(resource)
+        if snapshot is not None and snapshot.waiters == 1:
+            break
+        await asyncio.sleep(0)
+    else:
+        raise RuntimeError("Async request waiter did not enqueue.")
+
+    started = time.perf_counter()
+    controller.release(first_lease, RequestReleaseOutcome(kind="success"))
+    queued_lease = await asyncio.wait_for(queued_task, timeout=5.0)
+    elapsed = time.perf_counter() - started
+    controller.release(queued_lease, RequestReleaseOutcome(kind="success"))
+    return elapsed
+
+
 def _artifact(inputs: BenchmarkInputs, iterations: list[dict[str, Any]]) -> dict[str, Any]:
     resource = RequestResourceKey("mock-provider", "mock-model", RequestDomain.CHAT)
     provider_model = ProviderModelKey("mock-provider", "mock-model")
@@ -307,6 +1408,10 @@ def _artifact(inputs: BenchmarkInputs, iterations: list[dict[str, Any]]) -> dict
         ),
     )
     wall_times = [iteration["wall_time_seconds"] for iteration in iterations]
+    async_wakeups = [iteration["async_request_wakeup_seconds"] for iteration in iterations]
+    pipeline_metrics = [iteration["pipeline_metrics"] for iteration in iterations if "pipeline_metrics" in iteration]
+    utilization_metrics = _aggregate_utilization_metrics(iterations)
+    request_pressure_metrics = _request_pressure_derived_metrics(iterations)
     return {
         "scenario_id": inputs.scenario,
         "artifact_schema_version": ARTIFACT_SCHEMA_VERSION,
@@ -315,6 +1420,8 @@ def _artifact(inputs: BenchmarkInputs, iterations: list[dict[str, Any]]) -> dict
         "baseline_sha": _git_rev_parse(inputs.baseline_ref),
         "candidate_sha": _git_rev_parse(inputs.candidate_ref),
         "worktree_dirty": _worktree_dirty(),
+        "worktree_status_short": _worktree_status_short(),
+        "worktree_diff_sha256": _worktree_diff_hash(),
         "command_line": sys.argv,
         "machine": {
             "platform": platform.platform(),
@@ -333,6 +1440,12 @@ def _artifact(inputs: BenchmarkInputs, iterations: list[dict[str, Any]]) -> dict
             "min_wall_time_seconds": min(wall_times) if wall_times else 0.0,
             "max_wall_time_seconds": max(wall_times) if wall_times else 0.0,
             "stdev_wall_time_seconds": statistics.stdev(wall_times) if len(wall_times) > 1 else 0.0,
+            "mean_async_request_wakeup_seconds": statistics.fmean(async_wakeups) if async_wakeups else 0.0,
+            "p95_async_request_wakeup_seconds": _percentile(async_wakeups, 0.95),
+            "max_async_request_wakeup_seconds": max(async_wakeups) if async_wakeups else 0.0,
+            **_pipeline_derived_metrics(pipeline_metrics),
+            **utilization_metrics,
+            **request_pressure_metrics,
             "max_hidden_scheduler_resource_waiters": 0,
             "final_zero_task_leases": all(
                 sum(iteration["final_task_snapshot"].leased_resources.values()) == 0 for iteration in iterations
@@ -361,13 +1474,35 @@ def _event_payload(event: Any) -> dict[str, Any]:
         "scheduler_resource_key": getattr(event, "scheduler_resource_key", None),
         "request_resource_key": getattr(event, "request_resource_key", None),
         "reason_or_outcome": getattr(event, "reason_or_outcome", None) or getattr(event, "reason_or_result", None),
+        "snapshot": getattr(event, "snapshot", None),
+        "pressure_snapshot": getattr(event, "pressure_snapshot", None),
+        "diagnostics": getattr(event, "diagnostics", {}),
     }
 
 
+def _iteration_min_utilization_ratio(iteration: Mapping[str, Any], resource_kind: str) -> float:
+    resources = iteration.get("utilization_metrics", {}).get(resource_kind, {})
+    return min((float(metric["utilization_ratio"]) for metric in resources.values()), default=0.0)
+
+
+def _iteration_starved_idle_seconds(iteration: Mapping[str, Any], resource_kind: str) -> float:
+    resources = iteration.get("utilization_metrics", {}).get(resource_kind, {})
+    return sum(float(metric["starved_idle_seconds"]) for metric in resources.values())
+
+
 def _write_csv(path: Path, artifact: Mapping[str, Any]) -> None:
     with path.open("w", newline="", encoding="utf-8") as handle:
         writer = csv.DictWriter(
-            handle, fieldnames=["iteration", "wall_time_seconds", "accepted_task_count", "output_hash"]
+            handle,
+            fieldnames=[
+                "iteration",
+                "wall_time_seconds",
+                "async_request_wakeup_seconds",
+                "accepted_task_count",
+                "scheduler_min_utilization_ratio",
+                "scheduler_starved_idle_seconds",
+                "output_hash",
+            ],
         )
         writer.writeheader()
         for index, iteration in enumerate(artifact["iterations"]):
@@ -375,8 +1510,17 @@ def _write_csv(path: Path, artifact: Mapping[str, Any]) -> None:
                 {
                     "iteration": index,
                     "wall_time_seconds": iteration["wall_time_seconds"],
+                    "async_request_wakeup_seconds": iteration["async_request_wakeup_seconds"],
                     "accepted_task_count": iteration["accepted_task_count"],
-                    "output_hash": iteration["output_hashes"]["selected_task_ids"],
+                    "scheduler_min_utilization_ratio": _iteration_min_utilization_ratio(
+                        iteration,
+                        "scheduler_resources",
+                    ),
+                    "scheduler_starved_idle_seconds": _iteration_starved_idle_seconds(
+                        iteration,
+                        "scheduler_resources",
+                    ),
+                    "output_hash": next(iter(iteration["output_hashes"].values())),
                 }
             )
 
@@ -392,15 +1536,215 @@ def _write_markdown(path: Path, artifact: Mapping[str, Any]) -> None:
         f"- iterations: `{len(artifact['iterations'])}`",
         f"- mean_wall_time_seconds: `{metrics['mean_wall_time_seconds']:.6f}`",
         f"- p95_wall_time_seconds: `{metrics['p95_wall_time_seconds']:.6f}`",
+        f"- mean_async_request_wakeup_seconds: `{metrics['mean_async_request_wakeup_seconds']:.6f}`",
+        f"- p95_async_request_wakeup_seconds: `{metrics['p95_async_request_wakeup_seconds']:.6f}`",
+        f"- max_async_request_wakeup_seconds: `{metrics['max_async_request_wakeup_seconds']:.6f}`",
+        f"- scheduler_min_utilization_ratio: `{metrics['scheduler_min_utilization_ratio']:.6f}`",
+        f"- scheduler_max_starved_idle_seconds: `{metrics['scheduler_max_starved_idle_seconds']:.6f}`",
+        f"- scheduler_max_scheduler_queue_age_seconds: `{metrics['scheduler_max_scheduler_queue_age_seconds']:.6f}`",
+        "- scheduler_max_ready_to_dispatch_gap_seconds: "
+        f"`{metrics['scheduler_max_ready_to_dispatch_gap_seconds']:.6f}`",
+        f"- scheduler_max_burstiness_coefficient: `{metrics['scheduler_max_burstiness_coefficient']:.6f}`",
         f"- max_hidden_scheduler_resource_waiters: `{metrics['max_hidden_scheduler_resource_waiters']}`",
         f"- final_zero_task_leases: `{metrics['final_zero_task_leases']}`",
         f"- final_zero_request_leases: `{metrics['final_zero_request_leases']}`",
         f"- final_zero_request_waiters: `{metrics['final_zero_request_waiters']}`",
-        "",
     ]
+    if "pipeline_validation_passed" in metrics:
+        lines.extend(
+            [
+                f"- pipeline_validation_passed: `{metrics['pipeline_validation_passed']}`",
+                f"- pipeline_mean_overlap_ratio: `{metrics['pipeline_mean_overlap_ratio']:.6f}`",
+                f"- pipeline_p95_downstream_ready_gap_seconds: `{metrics['pipeline_p95_downstream_ready_gap_seconds']:.6f}`",
+                f"- pipeline_max_downstream_ready_gap_seconds: `{metrics['pipeline_max_downstream_ready_gap_seconds']:.6f}`",
+                "- pipeline_downstream_interleaved_before_all_upstream_completed: "
+                f"`{metrics['pipeline_downstream_interleaved_before_all_upstream_completed']}`",
+            ]
+        )
+    lines.extend(
+        _resource_utilization_markdown("Scheduler Resource Utilization", metrics["scheduler_resource_utilization"])
+    )
+    if metrics["request_resource_utilization"]:
+        lines.extend(
+            _resource_utilization_markdown("Request Resource Utilization", metrics["request_resource_utilization"])
+        )
+    lines.append("")
     path.write_text("\n".join(lines), encoding="utf-8")
 
 
+def _resource_utilization_markdown(title: str, resources: Mapping[str, Mapping[str, Any]]) -> list[str]:
+    if not resources:
+        return []
+    lines = ["", f"## {title}", ""]
+    for resource, metrics in sorted(resources.items()):
+        lines.append(
+            f"- `{resource}`: util=`{float(metrics['mean_utilization_ratio']):.6f}`, "
+            f"idle_s=`{float(metrics['mean_idle_capacity_seconds']):.6f}`, "
+            f"starved_idle_s=`{float(metrics['mean_starved_idle_seconds']):.6f}`, "
+            f"dependency_horizon_idle_s=`{float(metrics['mean_dependency_horizon_idle_seconds']):.6f}`, "
+            f"max_scheduler_queue_age_s=`{float(metrics['max_scheduler_queue_age_seconds']):.6f}`, "
+            f"burstiness=`{float(metrics['max_burstiness_coefficient']):.6f}`"
+        )
+    return lines
+
+
+def _validate_artifact(artifact: Mapping[str, Any]) -> None:
+    metrics = artifact["derived_metrics"]
+    inputs = artifact["inputs"]
+    task_capacity = _input_value(inputs, "task_admission_capacity")
+    failures: list[str] = []
+    if not metrics["final_zero_task_leases"]:
+        failures.append("task leases leaked at end of benchmark")
+    if not metrics["final_zero_request_leases"]:
+        failures.append("request leases leaked at end of benchmark")
+    if not metrics["final_zero_request_waiters"]:
+        failures.append("request waiters leaked at end of benchmark")
+    if metrics["p95_async_request_wakeup_seconds"] > ASYNC_WAKEUP_GATE_SECONDS:
+        failures.append(
+            "async request wakeup p95 exceeded "
+            f"{ASYNC_WAKEUP_GATE_SECONDS:.3f}s: {metrics['p95_async_request_wakeup_seconds']:.6f}s"
+        )
+    if "pipeline_validation_passed" in metrics and not metrics["pipeline_validation_passed"]:
+        failures.append("real-pipeline-overlap validation failed")
+    for index, iteration in enumerate(artifact["iterations"]):
+        selected_count = iteration.get(
+            "selected_task_count",
+            iteration.get("per_layer_observed_maxima", {}).get("selected_tasks", 0),
+        )
+        accepted_count = iteration["accepted_task_count"]
+        if selected_count != accepted_count:
+            failures.append(
+                f"iteration {index} selected {selected_count} tasks but accepted {accepted_count}; queue drained early"
+            )
+        observed = iteration.get("per_layer_observed_maxima", {})
+        task_leases = observed.get("task_leases_by_resource", {})
+        for resource, limit in _task_resource_limits(artifact, iteration, task_capacity).items():
+            observed_count = int(task_leases.get(resource, 0))
+            if observed_count > limit:
+                failures.append(f"iteration {index} exceeded {resource} task cap {limit}: observed {observed_count}")
+        request_limits = _request_resource_limits(iteration, default=max(1, task_capacity // 2))
+        for resource, observed_count in observed.get("request_in_flight_by_resource", {}).items():
+            request_capacity = request_limits.get(str(resource), max(1, task_capacity // 2))
+            if int(observed_count) > request_capacity:
+                failures.append(
+                    f"iteration {index} exceeded request cap {request_capacity} for {resource}: observed {observed_count}"
+                )
+        utilization = iteration.get("utilization_metrics", {})
+        scheduler_utilization = utilization.get("scheduler_resources", {})
+        if not scheduler_utilization:
+            failures.append(f"iteration {index} did not record scheduler utilization metrics")
+        _validate_resource_utilization_metrics(
+            scheduler_utilization,
+            failures=failures,
+            iteration=index,
+            resource_kind="scheduler",
+        )
+        _validate_resource_utilization_metrics(
+            utilization.get("request_resources", {}),
+            failures=failures,
+            iteration=index,
+            resource_kind="request",
+        )
+    if failures:
+        joined = "; ".join(failures)
+        raise RuntimeError(f"Async scheduling benchmark validation failed: {joined}")
+
+
+def _task_resource_limits(
+    artifact: Mapping[str, Any],
+    iteration: Mapping[str, Any],
+    task_capacity: int,
+) -> dict[str, int]:
+    default = {
+        "submission": task_capacity,
+        "llm_wait": max(1, task_capacity // 2),
+        "local": task_capacity,
+    }
+    plan = iteration.get("capacity_plan") or artifact.get("capacity_plan")
+    configured = _field(plan, "configured")
+    capacity_value = _field(configured, "task_resource_limits")
+    value = _field(capacity_value, "value")
+    if not isinstance(value, Mapping):
+        return default
+    return {str(resource): int(limit) for resource, limit in value.items()}
+
+
+def _request_resource_limits(iteration: Mapping[str, Any], *, default: int) -> dict[str, int]:
+    final_request_snapshot = iteration.get("final_request_snapshot", {})
+    domains = _field(final_request_snapshot, "domains")
+    if not isinstance(domains, Mapping):
+        return {}
+    limits: dict[str, int] = {}
+    for resource, snapshot in domains.items():
+        effective_max = _field(snapshot, "effective_max")
+        current_limit = _field(snapshot, "current_limit")
+        limit = effective_max if effective_max is not None else current_limit
+        limits[str(resource)] = int(limit if limit is not None else default)
+    return limits
+
+
+def _field(value: Any, name: str) -> Any:
+    if isinstance(value, Mapping):
+        return value.get(name)
+    return getattr(value, name, None)
+
+
+def _validate_resource_utilization_metrics(
+    resource_metrics: Mapping[str, Mapping[str, Any]],
+    *,
+    failures: list[str],
+    iteration: int,
+    resource_kind: str,
+) -> None:
+    for resource, metrics in resource_metrics.items():
+        capacity_seconds = float(metrics.get("capacity_seconds", 0.0))
+        busy_capacity_seconds = float(metrics.get("busy_capacity_seconds", 0.0))
+        idle_capacity_seconds = float(metrics.get("idle_capacity_seconds", 0.0))
+        starved_idle_seconds = float(metrics.get("starved_idle_seconds", 0.0))
+        dependency_horizon_idle_seconds = float(
+            metrics.get(
+                "dependency_horizon_idle_seconds",
+                max(0.0, idle_capacity_seconds - starved_idle_seconds),
+            )
+        )
+        utilization_ratio = float(metrics.get("utilization_ratio", -1.0))
+        dependency_horizon_idle_ratio = float(
+            metrics.get(
+                "dependency_horizon_idle_ratio",
+                _safe_ratio(dependency_horizon_idle_seconds, capacity_seconds),
+            )
+        )
+        if (
+            min(
+                capacity_seconds,
+                busy_capacity_seconds,
+                idle_capacity_seconds,
+                starved_idle_seconds,
+                dependency_horizon_idle_seconds,
+            )
+            < 0.0
+        ):
+            failures.append(f"iteration {iteration} has negative {resource_kind} utilization metric for {resource}")
+        if busy_capacity_seconds > capacity_seconds + 1e-9:
+            failures.append(f"iteration {iteration} has busy {resource_kind} capacity above total for {resource}")
+        if starved_idle_seconds > idle_capacity_seconds + 1e-9:
+            failures.append(f"iteration {iteration} has starved {resource_kind} idle above total idle for {resource}")
+        if abs((starved_idle_seconds + dependency_horizon_idle_seconds) - idle_capacity_seconds) > 1e-9:
+            failures.append(f"iteration {iteration} has invalid {resource_kind} idle partition for {resource}")
+        if not 0.0 <= utilization_ratio <= 1.0:
+            failures.append(f"iteration {iteration} has invalid {resource_kind} utilization ratio for {resource}")
+        if not 0.0 <= dependency_horizon_idle_ratio <= 1.0:
+            failures.append(
+                f"iteration {iteration} has invalid {resource_kind} dependency-horizon idle ratio for {resource}"
+            )
+
+
+def _input_value(inputs: Any, name: str) -> Any:
+    if isinstance(inputs, Mapping):
+        return inputs[name]
+    return getattr(inputs, name)
+
+
 def _git_rev_parse(ref: str) -> str:
     try:
         return subprocess.check_output(["git", "rev-parse", ref], text=True).strip()
@@ -416,6 +1760,22 @@ def _worktree_dirty() -> bool:
     return bool(result.stdout.strip())
 
 
+def _worktree_status_short() -> str:
+    try:
+        result = subprocess.run(["git", "status", "--short"], check=False, capture_output=True, text=True)
+    except Exception as exc:
+        return f"unavailable:{exc}"
+    return result.stdout
+
+
+def _worktree_diff_hash() -> str:
+    try:
+        diff = subprocess.check_output(["git", "diff", "--binary", "HEAD"], text=False)
+    except Exception as exc:
+        return f"unavailable:{exc}"
+    return hashlib.sha256(diff).hexdigest()
+
+
 def _percentile(values: list[float], pct: float) -> float:
     if not values:
         return 0.0
diff --git a/scripts/benchmarks/export_async_scheduling_perfetto.py b/scripts/benchmarks/export_async_scheduling_perfetto.py
new file mode 100644
index 000000000..ea847f58e
--- /dev/null
+++ b/scripts/benchmarks/export_async_scheduling_perfetto.py
@@ -0,0 +1,431 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Export async scheduling benchmark sink timelines to Perfetto JSON.
+
+The output is Chrome trace-event JSON, which Perfetto can open directly.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+from collections.abc import Mapping
+from pathlib import Path
+from typing import Any
+
+TRACE_SCHEMA = "async-scheduling-perfetto-trace-v1"
+
+PID = 1
+LANES = {
+    "scheduler_events": 1,
+    "row_groups": 2,
+    "task_execution": 3,
+    "task_leases": 4,
+    "request_waits": 5,
+    "request_leases": 6,
+    "model_requests": 7,
+    "counters": 8,
+}
+
+TERMINAL_REQUEST_WAIT_EVENTS = {
+    "request_wait_completed",
+    "request_wait_timeout",
+    "request_wait_cancelled",
+    "request_acquire_denied",
+}
+
+
+def main() -> None:
+    args = _parse_args()
+    artifact_path = Path(args.artifact)
+    artifact = json.loads(artifact_path.read_text(encoding="utf-8"))
+    output_path = Path(args.output) if args.output else artifact_path.with_suffix(".perfetto.json")
+    trace = benchmark_artifact_to_perfetto(artifact, iteration_index=args.iteration)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    output_path.write_text(json.dumps(trace, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+    print(f"Wrote {output_path}")
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("artifact", help="Path to async_scheduling_benchmark.json")
+    parser.add_argument("--output", help="Output .perfetto.json path")
+    parser.add_argument("--iteration", type=int, default=0, help="Benchmark iteration index to export.")
+    return parser.parse_args()
+
+
+def benchmark_artifact_to_perfetto(
+    artifact: Mapping[str, Any],
+    *,
+    iteration_index: int = 0,
+) -> dict[str, Any]:
+    iterations = artifact.get("iterations", [])
+    if not isinstance(iterations, list) or not iterations:
+        raise ValueError("Benchmark artifact has no iterations.")
+    try:
+        iteration = iterations[iteration_index]
+    except IndexError:
+        raise ValueError(f"Iteration {iteration_index} is not present.") from None
+    timeline = sorted(
+        iteration.get("timeline", []),
+        key=lambda event: (
+            float(event.get("captured_at_monotonic", 0.0) or 0.0),
+            int(event.get("sequence", 0) or 0),
+        ),
+    )
+    if not timeline:
+        raise ValueError("Selected iteration has no timeline events.")
+
+    base = min(float(event.get("captured_at_monotonic", 0.0) or 0.0) for event in timeline)
+    trace_events: list[dict[str, Any]] = []
+    trace_events.extend(_metadata_events())
+    trace_events.extend(_counter_events(timeline, base))
+    trace_events.extend(_interval_events(timeline, base))
+    trace_events.extend(_instant_events(timeline, base))
+
+    return {
+        "traceEvents": trace_events,
+        "displayTimeUnit": "ms",
+        "metadata": {
+            "schema": TRACE_SCHEMA,
+            "scenario_id": artifact.get("scenario_id"),
+            "scenario_version": artifact.get("scenario_version"),
+            "harness_version": artifact.get("harness_version"),
+            "iteration_index": iteration_index,
+            "mean_wall_time_seconds": artifact.get("derived_metrics", {}).get("mean_wall_time_seconds"),
+            "candidate_sha": artifact.get("candidate_sha"),
+            "worktree_dirty": artifact.get("worktree_dirty"),
+            "worktree_diff_sha256": artifact.get("worktree_diff_sha256"),
+        },
+    }
+
+
+def _metadata_events() -> list[dict[str, Any]]:
+    return [
+        {
+            "name": "thread_name",
+            "ph": "M",
+            "pid": PID,
+            "tid": tid,
+            "args": {"name": name},
+        }
+        for name, tid in LANES.items()
+    ]
+
+
+def _counter_events(timeline: list[Mapping[str, Any]], base: float) -> list[dict[str, Any]]:
+    events: list[dict[str, Any]] = []
+    for event in timeline:
+        if event.get("event_kind") != "scheduler_health_snapshot":
+            continue
+        diagnostics = _mapping(event.get("diagnostics"))
+        ts = _timestamp_us(event, base)
+        counters = {
+            "active_row_groups": diagnostics.get("active_row_groups"),
+            "target_row_groups": diagnostics.get("target_row_groups"),
+            "active_admitted_rows": diagnostics.get("active_admitted_rows"),
+            "queued_total": diagnostics.get("queued_total"),
+            "in_flight_tasks": diagnostics.get("in_flight_tasks"),
+            "active_workers": diagnostics.get("active_workers"),
+            "deferred_tasks": diagnostics.get("deferred_tasks"),
+            "request_pressure_advisory_skips": diagnostics.get("request_pressure_advisory_skips"),
+        }
+        for name, value in counters.items():
+            if isinstance(value, int | float):
+                events.append(_counter_event(name, ts, value))
+        for resource, value in _mapping(diagnostics.get("leased_resources")).items():
+            if isinstance(value, int | float):
+                events.append(_counter_event(f"leased:{resource}", ts, value))
+        for resource, value in _mapping(diagnostics.get("resources_available")).items():
+            if isinstance(value, int | float):
+                events.append(_counter_event(f"available:{resource}", ts, value))
+        for resource, value in _mapping(diagnostics.get("queued_demand_by_resource")).items():
+            if isinstance(value, int | float):
+                events.append(_counter_event(f"queued_demand:{resource}", ts, value))
+        request_pressure = _mapping(diagnostics.get("request_pressure"))
+        for resource, snapshot in _mapping(request_pressure.get("resources")).items():
+            snapshot_map = _mapping(snapshot)
+            for field in ("in_flight_count", "waiters", "current_limit", "effective_max"):
+                value = snapshot_map.get(field)
+                if isinstance(value, int | float):
+                    events.append(_counter_event(f"request:{resource}:{field}", ts, value))
+    return events
+
+
+def _counter_event(name: str, ts: int, value: int | float) -> dict[str, Any]:
+    return {
+        "name": name,
+        "cat": "scheduler_counters",
+        "ph": "C",
+        "pid": PID,
+        "tid": LANES["counters"],
+        "ts": ts,
+        "args": {"value": value},
+    }
+
+
+def _interval_events(timeline: list[Mapping[str, Any]], base: float) -> list[dict[str, Any]]:
+    starts: dict[tuple[str, str], Mapping[str, Any]] = {}
+    intervals: list[dict[str, Any]] = []
+    for event in timeline:
+        kind = str(event.get("event_kind"))
+        if kind == "row_group_admitted":
+            row_group = _diagnostic(event, "row_group")
+            if row_group is not None:
+                starts[("row_group", str(row_group))] = event
+        elif kind == "row_group_checkpointed":
+            row_group = _diagnostic(event, "row_group")
+            if row_group is not None:
+                intervals.extend(
+                    _finish_interval(
+                        starts,
+                        ("row_group", str(row_group)),
+                        event,
+                        base,
+                        lane="row_groups",
+                        cat="row_group",
+                        name=f"row group {row_group}",
+                    )
+                )
+        elif kind == "worker_spawned":
+            task_execution_id = _event_key(event, "task_execution_id")
+            if task_execution_id is not None:
+                starts[("task_execution", task_execution_id)] = event
+        elif kind == "task_completed":
+            task_execution_id = _event_key(event, "task_execution_id")
+            if task_execution_id is not None:
+                intervals.extend(
+                    _finish_interval(
+                        starts,
+                        ("task_execution", task_execution_id),
+                        event,
+                        base,
+                        lane="task_execution",
+                        cat="scheduler_task",
+                        name=f"task {_task_label(event)}",
+                    )
+                )
+        elif kind == "task_lease_acquired":
+            lease_id = _event_key(event, "task_lease_id")
+            if lease_id is not None:
+                starts[("task_lease", lease_id)] = event
+        elif kind == "task_lease_released":
+            lease_id = _event_key(event, "task_lease_id")
+            if lease_id is not None:
+                intervals.extend(
+                    _finish_interval(
+                        starts,
+                        ("task_lease", lease_id),
+                        event,
+                        base,
+                        lane="task_leases",
+                        cat="scheduler_lease",
+                        name=f"task lease {_task_label(event)}",
+                    )
+                )
+        elif kind == "request_wait_started":
+            request_key = _request_attempt_key(event)
+            if request_key is not None:
+                starts[("request_wait", request_key)] = event
+        elif kind in TERMINAL_REQUEST_WAIT_EVENTS:
+            request_key = _request_attempt_key(event)
+            if request_key is not None:
+                intervals.extend(
+                    _finish_interval(
+                        starts,
+                        ("request_wait", request_key),
+                        event,
+                        base,
+                        lane="request_waits",
+                        cat="request_wait",
+                        name=f"request wait {_request_resource_label(event)}",
+                    )
+                )
+        elif kind == "request_lease_acquired":
+            lease_id = _event_key(event, "request_lease_id")
+            if lease_id is not None:
+                starts[("request_lease", lease_id)] = event
+        elif kind == "request_lease_released":
+            lease_id = _event_key(event, "request_lease_id")
+            if lease_id is not None:
+                intervals.extend(
+                    _finish_interval(
+                        starts,
+                        ("request_lease", lease_id),
+                        event,
+                        base,
+                        lane="request_leases",
+                        cat="request_lease",
+                        name=f"request lease {_request_resource_label(event)}",
+                    )
+                )
+        elif kind == "model_request_started":
+            request_key = _request_attempt_key(event) or _event_key(event, "request_lease_id")
+            if request_key is not None:
+                starts[("model_request", request_key)] = event
+        elif kind == "model_request_completed":
+            request_key = _request_attempt_key(event) or _event_key(event, "request_lease_id")
+            if request_key is not None:
+                intervals.extend(
+                    _finish_interval(
+                        starts,
+                        ("model_request", request_key),
+                        event,
+                        base,
+                        lane="model_requests",
+                        cat="model_request",
+                        name=f"model request {_request_resource_label(event)}",
+                    )
+                )
+    return intervals
+
+
+def _finish_interval(
+    starts: dict[tuple[str, str], Mapping[str, Any]],
+    key: tuple[str, str],
+    end_event: Mapping[str, Any],
+    base: float,
+    *,
+    lane: str,
+    cat: str,
+    name: str,
+) -> list[dict[str, Any]]:
+    start_event = starts.pop(key, None)
+    if start_event is None:
+        return []
+    started = _timestamp_us(start_event, base)
+    ended = _timestamp_us(end_event, base)
+    return [
+        {
+            "name": name,
+            "cat": cat,
+            "ph": "X",
+            "pid": PID,
+            "tid": LANES[lane],
+            "ts": started,
+            "dur": max(0, ended - started),
+            "args": _event_args(end_event) | {"start_event": _event_args(start_event)},
+        }
+    ]
+
+
+def _instant_events(timeline: list[Mapping[str, Any]], base: float) -> list[dict[str, Any]]:
+    events = []
+    for event in timeline:
+        stream = str(event.get("stream", "scheduler"))
+        kind = str(event.get("event_kind"))
+        lane = "scheduler_events"
+        events.append(
+            {
+                "name": kind,
+                "cat": f"{stream}_event",
+                "ph": "i",
+                "s": "t",
+                "pid": PID,
+                "tid": LANES[lane],
+                "ts": _timestamp_us(event, base),
+                "args": _event_args(event),
+            }
+        )
+    return events
+
+
+def _event_args(event: Mapping[str, Any]) -> dict[str, Any]:
+    return {
+        "stream": event.get("stream"),
+        "sequence": event.get("sequence"),
+        "task_id": event.get("task_id"),
+        "task_execution_id": event.get("task_execution_id"),
+        "task_lease_id": event.get("task_lease_id"),
+        "request_attempt_id": event.get("request_attempt_id"),
+        "request_lease_id": event.get("request_lease_id"),
+        "request_resource": _request_resource_label(event),
+        "reason_or_outcome": event.get("reason_or_outcome"),
+        "correlation": _jsonable(event.get("captured_correlation"), max_depth=2),
+        "diagnostics": _jsonable(event.get("diagnostics"), max_depth=3),
+    }
+
+
+def _timestamp_us(event: Mapping[str, Any], base: float) -> int:
+    captured_at = float(event.get("captured_at_monotonic", 0.0) or 0.0)
+    return int(round((captured_at - base) * 1_000_000.0))
+
+
+def _event_key(event: Mapping[str, Any], key: str) -> str | None:
+    value = event.get(key)
+    return None if value is None else str(value)
+
+
+def _request_attempt_key(event: Mapping[str, Any]) -> str | None:
+    attempt_id = _event_key(event, "request_attempt_id")
+    if attempt_id is not None:
+        return attempt_id
+    lease_id = _event_key(event, "request_lease_id")
+    if lease_id is not None:
+        return lease_id
+    correlation = _mapping(event.get("captured_correlation"))
+    task_execution_id = correlation.get("task_execution_id")
+    request_resource = _request_resource_label(event)
+    if task_execution_id is None and request_resource is None:
+        return None
+    return f"{task_execution_id}:{request_resource}"
+
+
+def _task_label(event: Mapping[str, Any]) -> str:
+    correlation = _mapping(event.get("captured_correlation"))
+    column = correlation.get("task_column") or "unknown"
+    row_group = correlation.get("row_group")
+    task_type = correlation.get("task_type") or "task"
+    return f"{column} rg={row_group} {task_type}"
+
+
+def _request_resource_label(event: Mapping[str, Any]) -> str | None:
+    resource = event.get("request_resource_key")
+    if resource is None:
+        resource = _diagnostic(event, "request_resource")
+    if isinstance(resource, Mapping):
+        provider = resource.get("provider_name")
+        model = resource.get("model_id")
+        domain = resource.get("domain")
+        if provider is not None and model is not None and domain is not None:
+            return f"{provider}/{model}/{domain}"
+    return None if resource is None else str(resource)
+
+
+def _diagnostic(event: Mapping[str, Any], key: str) -> Any:
+    diagnostics = event.get("diagnostics", {})
+    if not isinstance(diagnostics, Mapping):
+        return None
+    return diagnostics.get(key)
+
+
+def _mapping(value: Any) -> Mapping[str, Any]:
+    return value if isinstance(value, Mapping) else {}
+
+
+def _jsonable(value: Any, *, max_depth: int, depth: int = 0) -> Any:
+    if value is None or isinstance(value, str | int | float | bool):
+        return value
+    if depth >= max_depth:
+        return str(value)
+    if isinstance(value, Mapping):
+        return {
+            str(_jsonable(key, max_depth=max_depth, depth=depth + 1)): _jsonable(
+                item,
+                max_depth=max_depth,
+                depth=depth + 1,
+            )
+            for key, item in list(value.items())[:50]
+        }
+    if isinstance(value, list | tuple | set):
+        items = list(value)
+        result = [_jsonable(item, max_depth=max_depth, depth=depth + 1) for item in items[:50]]
+        if len(items) > 50:
+            result.append(f"... {len(items) - 50} more")
+        return result
+    return str(value)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/benchmarks/generate_async_scheduling_idle_report.py b/scripts/benchmarks/generate_async_scheduling_idle_report.py
new file mode 100644
index 000000000..707706090
--- /dev/null
+++ b/scripts/benchmarks/generate_async_scheduling_idle_report.py
@@ -0,0 +1,1765 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Generate an HTML idle-time analysis report for async scheduling benchmarks."""
+
+from __future__ import annotations
+
+import argparse
+import html
+import json
+import math
+import subprocess
+import sys
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import asdict, dataclass, replace
+from pathlib import Path
+from typing import Any
+
+DEFAULT_ARTIFACT_DIR = Path("artifacts/async-scheduling-idle-analysis")
+DEFAULT_REPORT_PATH = Path("reports/async-scheduling-idle-analysis.html")
+BENCHMARK_SCRIPT = Path("scripts/benchmarks/benchmark_async_scheduling.py")
+IDLE_SUITE_ID = "async-scheduling-idle-regression"
+IDLE_SUITE_VERSION = "1.1"
+IDLE_SUMMARY_SCHEMA_VERSION = "async-scheduling-idle-summary-v1"
+
+
+@dataclass(frozen=True)
+class IdleBenchmarkCase:
+    name: str
+    sweep: str
+    record_count: int
+    buffer_size: int
+    row_group_concurrency: int
+    task_admission_capacity: int
+    fanout_width: int
+    upstream_latency_seconds: float
+    downstream_latency_seconds: float
+    scenario: str = "real-pipeline-overlap"
+    request_latency_seconds: float = 0.0
+    model_stage_weight: int = 0
+    adaptive_row_group_admission: bool = False
+    request_pressure_advisory: bool = False
+    iterations: int = 1
+    warmups: int = 0
+
+    @property
+    def generation_count(self) -> int:
+        return self.record_count * self.fanout_width * 2
+
+
+@dataclass(frozen=True)
+class IdleBenchmarkResult:
+    case: IdleBenchmarkCase
+    artifact_path: Path
+    mean_wall_time_seconds: float
+    p95_wall_time_seconds: float
+    llm_utilization_ratio: float
+    llm_idle_ratio: float
+    llm_starved_idle_ratio: float
+    llm_frontier_dependency_horizon_idle_ratio: float
+    llm_starved_idle_seconds: float
+    llm_frontier_dependency_horizon_idle_seconds: float
+    llm_scheduler_queue_age_p95_seconds: float
+    llm_scheduler_queue_age_max_seconds: float
+    llm_ready_gap_p95_seconds: float
+    llm_ready_gap_max_seconds: float
+    llm_burstiness_coefficient: float
+    submission_utilization_ratio: float
+    submission_starved_idle_ratio: float
+    submission_frontier_dependency_horizon_idle_ratio: float
+    pipeline_overlap_ratio: float
+    downstream_ready_gap_p95_seconds: float
+    downstream_ready_gap_max_seconds: float
+    throughput_generations_per_second: float
+    request_wait_seconds_while_task_leased_mean: float
+    request_wait_seconds_while_task_leased_max: float
+    request_utilization_ratio: float
+    request_idle_ratio: float
+    request_starved_idle_ratio: float
+    request_frontier_dependency_horizon_idle_ratio: float
+    request_burstiness_coefficient: float
+    request_pressure_advisory_skip_count: int
+    first_model_dispatch_column: str | None
+    request_pressure_advisory_enabled: bool
+    row_group_admission_mode: str
+    row_group_admission_target: int | None
+    row_group_admission_observed_max_target: int | None
+    row_group_admission_max_admitted_rows: int | None
+    validation_passed: bool
+    final_zero_task_leases: bool
+    final_zero_request_leases: bool
+    final_zero_request_waiters: bool
+
+
+def main() -> None:
+    args = _parse_args()
+    artifact_dir = Path(args.artifact_dir)
+    report_path = Path(args.report_path)
+    artifact_dir.mkdir(parents=True, exist_ok=True)
+    report_path.parent.mkdir(parents=True, exist_ok=True)
+
+    results = run_idle_benchmark_suite(artifact_dir, quick=args.quick, skip_run=args.skip_run)
+    render_idle_report(results, report_path, artifact_dir)
+    if args.summary_path:
+        write_idle_results_summary(Path(args.summary_path), results, quick=args.quick)
+
+    print(f"Wrote {report_path}")
+    print(f"Wrote benchmark artifacts under {artifact_dir}")
+    if args.summary_path:
+        print(f"Wrote {args.summary_path}")
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--artifact-dir", default=str(DEFAULT_ARTIFACT_DIR))
+    parser.add_argument("--report-path", default=str(DEFAULT_REPORT_PATH))
+    parser.add_argument("--summary-path")
+    parser.add_argument("--skip-run", action="store_true", help="Reuse existing benchmark JSON files.")
+    parser.add_argument(
+        "--quick",
+        action="store_true",
+        help="Run a shorter smoke suite while preserving every sweep dimension.",
+    )
+    return parser.parse_args()
+
+
+def build_idle_benchmark_cases(*, quick: bool) -> list[IdleBenchmarkCase]:
+    return _build_cases(quick)
+
+
+def run_idle_benchmark_suite(
+    artifact_dir: Path,
+    *,
+    quick: bool,
+    skip_run: bool,
+) -> list[IdleBenchmarkResult]:
+    cases = build_idle_benchmark_cases(quick=quick)
+    return [_run_or_load_case(case, artifact_dir, skip_run=skip_run) for case in cases]
+
+
+def render_idle_report(
+    results: Sequence[IdleBenchmarkResult],
+    report_path: Path,
+    artifact_dir: Path,
+) -> None:
+    report_path.parent.mkdir(parents=True, exist_ok=True)
+    report_path.write_text(_render_report(results, report_path, artifact_dir), encoding="utf-8")
+
+
+def write_idle_results_summary(
+    summary_path: Path,
+    results: Sequence[IdleBenchmarkResult],
+    *,
+    quick: bool,
+) -> None:
+    summary_path.parent.mkdir(parents=True, exist_ok=True)
+    summary = idle_results_summary(results, quick=quick)
+    summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+
+
+def idle_results_summary(results: Sequence[IdleBenchmarkResult], *, quick: bool) -> dict[str, Any]:
+    cases = {_case_key(result): _result_summary(result) for result in results}
+    return {
+        "summary_schema_version": IDLE_SUMMARY_SCHEMA_VERSION,
+        "suite_id": IDLE_SUITE_ID,
+        "suite_version": IDLE_SUITE_VERSION,
+        "mode": "quick" if quick else "full",
+        "case_count": len(results),
+        "largest_generation_count": max((result.case.generation_count for result in results), default=0),
+        "cases": cases,
+    }
+
+
+def _result_summary(result: IdleBenchmarkResult) -> dict[str, Any]:
+    return {
+        "case": asdict(result.case),
+        "artifact_path": str(result.artifact_path),
+        "generation_count": result.case.generation_count,
+        "mean_wall_time_seconds": result.mean_wall_time_seconds,
+        "p95_wall_time_seconds": result.p95_wall_time_seconds,
+        "llm_wait_utilization_ratio": result.llm_utilization_ratio,
+        "llm_wait_idle_ratio": result.llm_idle_ratio,
+        "llm_wait_starved_idle_ratio": result.llm_starved_idle_ratio,
+        "llm_wait_frontier_dependency_horizon_idle_ratio": result.llm_frontier_dependency_horizon_idle_ratio,
+        "llm_wait_dependency_horizon_idle_ratio": result.llm_frontier_dependency_horizon_idle_ratio,
+        "llm_wait_starved_idle_seconds": result.llm_starved_idle_seconds,
+        "llm_wait_frontier_dependency_horizon_idle_seconds": result.llm_frontier_dependency_horizon_idle_seconds,
+        "llm_wait_dependency_horizon_idle_seconds": result.llm_frontier_dependency_horizon_idle_seconds,
+        "llm_wait_scheduler_queue_age_p95_seconds": result.llm_scheduler_queue_age_p95_seconds,
+        "llm_wait_scheduler_queue_age_max_seconds": result.llm_scheduler_queue_age_max_seconds,
+        "llm_wait_ready_gap_p95_seconds": result.llm_ready_gap_p95_seconds,
+        "llm_wait_ready_gap_max_seconds": result.llm_ready_gap_max_seconds,
+        "llm_wait_burstiness_coefficient": result.llm_burstiness_coefficient,
+        "submission_utilization_ratio": result.submission_utilization_ratio,
+        "submission_starved_idle_ratio": result.submission_starved_idle_ratio,
+        "submission_frontier_dependency_horizon_idle_ratio": (result.submission_frontier_dependency_horizon_idle_ratio),
+        "pipeline_overlap_ratio": result.pipeline_overlap_ratio,
+        "downstream_ready_gap_p95_seconds": result.downstream_ready_gap_p95_seconds,
+        "downstream_ready_gap_max_seconds": result.downstream_ready_gap_max_seconds,
+        "throughput_generations_per_second": result.throughput_generations_per_second,
+        "request_wait_seconds_while_task_leased_mean": result.request_wait_seconds_while_task_leased_mean,
+        "request_wait_seconds_while_task_leased_max": result.request_wait_seconds_while_task_leased_max,
+        "request_utilization_ratio": result.request_utilization_ratio,
+        "request_idle_ratio": result.request_idle_ratio,
+        "request_starved_idle_ratio": result.request_starved_idle_ratio,
+        "request_frontier_dependency_horizon_idle_ratio": result.request_frontier_dependency_horizon_idle_ratio,
+        "request_burstiness_coefficient": result.request_burstiness_coefficient,
+        "request_pressure_advisory_skip_count": result.request_pressure_advisory_skip_count,
+        "first_model_dispatch_column": result.first_model_dispatch_column,
+        "request_pressure_advisory_enabled": result.request_pressure_advisory_enabled,
+        "row_group_admission_mode": result.row_group_admission_mode,
+        "row_group_admission_target": result.row_group_admission_target,
+        "row_group_admission_observed_max_target": result.row_group_admission_observed_max_target,
+        "row_group_admission_max_admitted_rows": result.row_group_admission_max_admitted_rows,
+        "validation_passed": result.validation_passed,
+        "final_zero_task_leases": result.final_zero_task_leases,
+        "final_zero_request_leases": result.final_zero_request_leases,
+        "final_zero_request_waiters": result.final_zero_request_waiters,
+        "dominant_idle_class": _dominant_idle_class(result),
+        "recommended_next_lever": _recommended_next_lever(result),
+    }
+
+
+def _case_key(result: IdleBenchmarkResult) -> str:
+    return f"{result.case.sweep}/{result.case.name}"
+
+
+def _build_cases(quick: bool) -> list[IdleBenchmarkCase]:
+    base = IdleBenchmarkCase(
+        name="baseline-frontier",
+        sweep="baseline",
+        record_count=512,
+        buffer_size=64,
+        row_group_concurrency=4,
+        task_admission_capacity=8,
+        fanout_width=4,
+        upstream_latency_seconds=0.003,
+        downstream_latency_seconds=0.0003,
+    )
+    rows = [64, 128, 256] if quick else [64, 128, 256, 512, 1024]
+    row_group_concurrency = [1, 2, 4] if quick else [1, 2, 4, 8]
+    buffers = [1, 16, 64] if quick else [1, 4, 16, 64, 256]
+    capacities = [4, 8] if quick else [4, 8, 16]
+    adaptation_rows = 512 if quick else 1024
+    combined_adaptation_rows = 128 if quick else 512
+
+    cases = [
+        replace(base, name=f"rows-{record_count}", sweep="row-scale", record_count=record_count)
+        for record_count in rows
+    ]
+    cases.extend(
+        replace(
+            base,
+            name=f"row-groups-{concurrency}",
+            sweep="row-group-concurrency",
+            record_count=512,
+            buffer_size=1,
+            row_group_concurrency=concurrency,
+            fanout_width=1,
+        )
+        for concurrency in row_group_concurrency
+    )
+    cases.extend(
+        replace(base, name=f"buffer-{buffer_size}", sweep="buffer-size", record_count=512, buffer_size=buffer_size)
+        for buffer_size in buffers
+    )
+    cases.extend(
+        replace(
+            base,
+            name=f"capacity-{capacity}",
+            sweep="llm-capacity",
+            record_count=512,
+            buffer_size=1,
+            task_admission_capacity=capacity,
+            row_group_concurrency=4,
+            fanout_width=1,
+        )
+        for capacity in capacities
+    )
+    cases.extend(
+        (
+            replace(
+                base,
+                name="weight-custom-model-1",
+                sweep="custom-model-weight",
+                record_count=512,
+                model_stage_weight=1,
+            ),
+            replace(
+                base,
+                name="weight-model-capacity",
+                sweep="custom-model-weight",
+                record_count=512,
+                model_stage_weight=0,
+            ),
+        )
+    )
+    cases.extend(
+        (
+            replace(
+                base,
+                name="narrow-frontier-high-cap",
+                sweep="stress-shape",
+                record_count=1024,
+                buffer_size=1,
+                row_group_concurrency=1,
+                task_admission_capacity=16,
+                fanout_width=1,
+                upstream_latency_seconds=0.006,
+                downstream_latency_seconds=0.0003,
+            ),
+            replace(
+                base,
+                name="wide-frontier-high-cap",
+                sweep="stress-shape",
+                record_count=1024,
+                buffer_size=64,
+                row_group_concurrency=16,
+                task_admission_capacity=16,
+                fanout_width=4,
+                upstream_latency_seconds=0.006,
+                downstream_latency_seconds=0.0003,
+            ),
+        )
+    )
+    cases.extend(
+        (
+            replace(
+                base,
+                name="adaptive-row-groups-fixed-low",
+                sweep="adaptations",
+                record_count=adaptation_rows,
+                buffer_size=1,
+                row_group_concurrency=1,
+                task_admission_capacity=16,
+                fanout_width=1,
+                upstream_latency_seconds=0.006,
+                downstream_latency_seconds=0.0003,
+            ),
+            replace(
+                base,
+                name="adaptive-row-groups-adaptive",
+                sweep="adaptations",
+                record_count=adaptation_rows,
+                buffer_size=1,
+                row_group_concurrency=8,
+                task_admission_capacity=16,
+                fanout_width=1,
+                upstream_latency_seconds=0.006,
+                downstream_latency_seconds=0.0003,
+                adaptive_row_group_admission=True,
+            ),
+            replace(
+                base,
+                name="adaptive-row-groups-fixed-high",
+                sweep="adaptations",
+                record_count=adaptation_rows,
+                buffer_size=1,
+                row_group_concurrency=8,
+                task_admission_capacity=16,
+                fanout_width=1,
+                upstream_latency_seconds=0.006,
+                downstream_latency_seconds=0.0003,
+            ),
+            replace(
+                base,
+                name="request-pressure-control",
+                sweep="adaptations",
+                scenario="request-pressure-advisory",
+                record_count=32,
+                buffer_size=32,
+                row_group_concurrency=1,
+                task_admission_capacity=1,
+                fanout_width=1,
+                request_latency_seconds=0.05,
+                upstream_latency_seconds=0.0,
+                downstream_latency_seconds=0.001,
+            ),
+            replace(
+                base,
+                name="request-pressure-advisory",
+                sweep="adaptations",
+                scenario="request-pressure-advisory",
+                record_count=32,
+                buffer_size=32,
+                row_group_concurrency=1,
+                task_admission_capacity=1,
+                fanout_width=1,
+                request_latency_seconds=0.05,
+                upstream_latency_seconds=0.0,
+                downstream_latency_seconds=0.001,
+                request_pressure_advisory=True,
+            ),
+            replace(
+                base,
+                name="adaptive-request-pressure-control",
+                sweep="adaptations",
+                scenario="adaptive-request-pressure",
+                record_count=combined_adaptation_rows,
+                buffer_size=1,
+                row_group_concurrency=8,
+                task_admission_capacity=4,
+                fanout_width=1,
+                request_latency_seconds=0.05,
+                upstream_latency_seconds=0.0,
+                downstream_latency_seconds=0.001,
+                adaptive_row_group_admission=True,
+                iterations=3,
+            ),
+            replace(
+                base,
+                name="adaptive-request-pressure-combined",
+                sweep="adaptations",
+                scenario="adaptive-request-pressure",
+                record_count=combined_adaptation_rows,
+                buffer_size=1,
+                row_group_concurrency=8,
+                task_admission_capacity=4,
+                fanout_width=1,
+                request_latency_seconds=0.05,
+                upstream_latency_seconds=0.0,
+                downstream_latency_seconds=0.001,
+                adaptive_row_group_admission=True,
+                request_pressure_advisory=True,
+                iterations=3,
+            ),
+        )
+    )
+    return cases
+
+
+def _run_or_load_case(case: IdleBenchmarkCase, artifact_dir: Path, *, skip_run: bool) -> IdleBenchmarkResult:
+    output_dir = artifact_dir / case.sweep / case.name
+    json_path = output_dir / "async_scheduling_benchmark.json"
+    if not skip_run or not json_path.exists():
+        output_dir.mkdir(parents=True, exist_ok=True)
+        command = [
+            sys.executable,
+            str(BENCHMARK_SCRIPT),
+            "--scenario",
+            case.scenario,
+            "--record-count",
+            str(case.record_count),
+            "--buffer-size",
+            str(case.buffer_size),
+            "--row-group-concurrency",
+            str(case.row_group_concurrency),
+            "--task-admission-capacity",
+            str(case.task_admission_capacity),
+            "--fanout-width",
+            str(case.fanout_width),
+            "--upstream-latency-seconds",
+            _format_float(case.upstream_latency_seconds),
+            "--downstream-latency-seconds",
+            _format_float(case.downstream_latency_seconds),
+            "--request-latency-seconds",
+            _format_float(case.request_latency_seconds),
+            "--model-stage-weight",
+            str(case.model_stage_weight),
+            "--warmups",
+            str(case.warmups),
+            "--iterations",
+            str(case.iterations),
+            "--output-dir",
+            str(output_dir),
+        ]
+        if case.adaptive_row_group_admission:
+            command.append("--adaptive-row-group-admission")
+        if case.request_pressure_advisory:
+            command.append("--request-pressure-advisory")
+        subprocess.run(command, check=True)
+    artifact = json.loads(json_path.read_text(encoding="utf-8"))
+    return _extract_result(case, json_path, artifact)
+
+
+def _extract_result(case: IdleBenchmarkCase, artifact_path: Path, artifact: Mapping[str, Any]) -> IdleBenchmarkResult:
+    metrics = artifact["derived_metrics"]
+    scheduler_resources = metrics.get("scheduler_resource_utilization", {})
+    request_resources = metrics.get("request_resource_utilization", {})
+    llm = scheduler_resources.get("llm_wait", {})
+    submission = scheduler_resources.get("submission", {})
+    mean_wall = _float(metrics, "mean_wall_time_seconds")
+    row_group_admission = _row_group_admission_snapshot(artifact)
+    return IdleBenchmarkResult(
+        case=case,
+        artifact_path=artifact_path,
+        mean_wall_time_seconds=mean_wall,
+        p95_wall_time_seconds=_float(metrics, "p95_wall_time_seconds"),
+        llm_utilization_ratio=_float(llm, "mean_utilization_ratio"),
+        llm_idle_ratio=_safe_ratio(
+            _float(llm, "mean_idle_capacity_seconds"),
+            _mean_capacity_seconds(artifact, "llm_wait"),
+        ),
+        llm_starved_idle_ratio=_safe_ratio(
+            _float(llm, "mean_starved_idle_seconds"),
+            _mean_capacity_seconds(artifact, "llm_wait"),
+        ),
+        llm_frontier_dependency_horizon_idle_ratio=_idle_ratio(
+            llm,
+            artifact,
+            "llm_wait",
+            "mean_frontier_dependency_horizon_idle_ratio",
+        ),
+        llm_starved_idle_seconds=_float(llm, "mean_starved_idle_seconds"),
+        llm_frontier_dependency_horizon_idle_seconds=_float(
+            llm,
+            "mean_frontier_dependency_horizon_idle_seconds",
+            fallback_key="mean_dependency_horizon_idle_seconds",
+        ),
+        llm_scheduler_queue_age_p95_seconds=max(
+            _max_iteration_metric(
+                artifact,
+                "llm_wait",
+                "scheduler_queue_age_p95_seconds",
+            ),
+            _max_iteration_metric(
+                artifact,
+                "llm_wait",
+                "ready_to_dispatch_gap_p95_seconds",
+            ),
+        ),
+        llm_scheduler_queue_age_max_seconds=_float(
+            llm,
+            "max_scheduler_queue_age_seconds",
+            fallback_key="max_ready_to_dispatch_gap_seconds",
+        ),
+        llm_ready_gap_p95_seconds=_max_iteration_metric(
+            artifact,
+            "llm_wait",
+            "ready_to_dispatch_gap_p95_seconds",
+        ),
+        llm_ready_gap_max_seconds=_float(llm, "max_ready_to_dispatch_gap_seconds"),
+        llm_burstiness_coefficient=_float(llm, "max_burstiness_coefficient"),
+        submission_utilization_ratio=_float(submission, "mean_utilization_ratio"),
+        submission_starved_idle_ratio=_safe_ratio(
+            _float(submission, "mean_starved_idle_seconds"),
+            _mean_capacity_seconds(artifact, "submission"),
+        ),
+        submission_frontier_dependency_horizon_idle_ratio=_idle_ratio(
+            submission,
+            artifact,
+            "submission",
+            "mean_frontier_dependency_horizon_idle_ratio",
+        ),
+        pipeline_overlap_ratio=_float(metrics, "pipeline_mean_overlap_ratio"),
+        downstream_ready_gap_p95_seconds=_float(metrics, "pipeline_p95_downstream_ready_gap_seconds"),
+        downstream_ready_gap_max_seconds=_float(metrics, "pipeline_max_downstream_ready_gap_seconds"),
+        throughput_generations_per_second=_safe_ratio(case.generation_count, mean_wall),
+        request_wait_seconds_while_task_leased_mean=_float(metrics, "request_wait_seconds_while_task_leased_mean"),
+        request_wait_seconds_while_task_leased_max=_float(metrics, "request_wait_seconds_while_task_leased_max"),
+        request_utilization_ratio=_mean_resource_metric(request_resources, "mean_utilization_ratio"),
+        request_idle_ratio=_safe_ratio(
+            _sum_resource_metric(request_resources, "mean_idle_capacity_seconds"),
+            _sum_request_capacity_seconds(artifact),
+        ),
+        request_starved_idle_ratio=_safe_ratio(
+            _sum_resource_metric(request_resources, "mean_starved_idle_seconds"),
+            _sum_request_capacity_seconds(artifact),
+        ),
+        request_frontier_dependency_horizon_idle_ratio=_safe_ratio(
+            _sum_resource_metric(
+                request_resources,
+                "mean_frontier_dependency_horizon_idle_seconds",
+                fallback_key="mean_dependency_horizon_idle_seconds",
+            ),
+            _sum_request_capacity_seconds(artifact),
+        ),
+        request_burstiness_coefficient=_max_resource_metric(request_resources, "max_burstiness_coefficient"),
+        request_pressure_advisory_skip_count=int(metrics.get("request_pressure_advisory_skip_count", 0) or 0),
+        first_model_dispatch_column=_optional_str(metrics.get("first_model_dispatch_column")),
+        request_pressure_advisory_enabled=bool(metrics.get("request_pressure_advisory_enabled", False)),
+        row_group_admission_mode=str(row_group_admission.get("mode", "fixed")),
+        row_group_admission_target=_optional_int(row_group_admission.get("target_in_flight")),
+        row_group_admission_observed_max_target=_optional_int(row_group_admission.get("observed_max_target")),
+        row_group_admission_max_admitted_rows=_optional_int(row_group_admission.get("max_admitted_rows")),
+        validation_passed=bool(metrics.get("pipeline_validation_passed", True)),
+        final_zero_task_leases=bool(metrics.get("final_zero_task_leases", False)),
+        final_zero_request_leases=bool(metrics.get("final_zero_request_leases", False)),
+        final_zero_request_waiters=bool(metrics.get("final_zero_request_waiters", False)),
+    )
+
+
+def _render_report(
+    results: Sequence[IdleBenchmarkResult],
+    report_path: Path,
+    artifact_dir: Path,
+) -> str:
+    by_sweep = _group_results(results)
+    all_valid = all(
+        result.validation_passed
+        and result.final_zero_task_leases
+        and result.final_zero_request_leases
+        and result.final_zero_request_waiters
+        for result in results
+    )
+    max_generations = max((result.case.generation_count for result in results), default=0)
+    worst_idle = max(results, key=lambda result: result.llm_starved_idle_ratio)
+    worst_total_idle = max(results, key=lambda result: result.llm_idle_ratio)
+    best_util = max(results, key=lambda result: result.llm_utilization_ratio)
+    median_util = _median(result.llm_utilization_ratio for result in results)
+    artifact_link = _relative_href(report_path, artifact_dir)
+
+    return "\n".join(
+        [
+            "<!doctype html>",
+            '<html lang="en">',
+            "<head>",
+            '<meta charset="utf-8">',
+            '<meta name="viewport" content="width=device-width, initial-scale=1">',
+            "<title>Async Scheduling Idle-Time Analysis</title>",
+            _style_block(),
+            "</head>",
+            "<body>",
+            "<main>",
+            '<section class="hero">',
+            '<p class="eyebrow">DataDesigner async scheduler</p>',
+            "<h1>Idle-Time Analysis</h1>",
+            '<p class="lede">This report measures where model-capacity idle time appears, '
+            "which knobs change it, and which remaining idle looks avoidable by scheduler or "
+            "admission-control changes.</p>",
+            _summary_grid(
+                (
+                    ("Benchmark cases", f"{len(results)}"),
+                    ("Largest synthetic generation count", f"{max_generations:,}"),
+                    ("Median llm_wait utilization", _format_percent(median_util)),
+                    ("Worst total llm_wait idle", _format_percent(worst_total_idle.llm_idle_ratio)),
+                    ("Worst starved llm_wait idle", _format_percent(worst_idle.llm_starved_idle_ratio)),
+                    ("Best llm_wait utilization", _format_percent(best_util.llm_utilization_ratio)),
+                    ("Validation", "pass" if all_valid else "check failures"),
+                )
+            ),
+            "</section>",
+            "<section>",
+            "<h2>What Counts As Idle</h2>",
+            "<p><strong>General idle</strong> is unused configured capacity. It includes expected idle when "
+            "dependencies are not ready. <strong>Starved idle</strong> is the actionable subset: a resource "
+            "has idle slots while the scheduler already has queued work that requests that resource. "
+            "<strong>Frontier/dependency-horizon idle</strong> is total idle minus starved idle: capacity "
+            "is unused because dependency work has not yet exposed a ready scheduler task. "
+            "<strong>Scheduler queue age</strong> measures ready-enqueued to lease-acquired time; "
+            "<strong>downstream ready gap</strong> measures dependency-complete to downstream-dispatch time. "
+            "For this refactor, the most important resource is <code>llm_wait</code>, because it maps to "
+            "model-serving capacity that should stay busy when runnable model work exists.</p>",
+            "</section>",
+            "<section>",
+            "<h2>Implementation Improvements</h2>",
+            _implementation_improvements_html(results),
+            "</section>",
+            "<section>",
+            "<h2>Findings</h2>",
+            _findings_html(results, by_sweep),
+            "</section>",
+            "<section>",
+            "<h2>Idle Classification Board</h2>",
+            _idle_classification_table(results),
+            "</section>",
+            "<section>",
+            "<h2>Adaptation Benchmarks</h2>",
+            _adaptation_benchmarks_html(results),
+            "</section>",
+            "<section>",
+            "<h2>Figures</h2>",
+            _line_chart(
+                "Scale: more rows smooth the model-resource flow",
+                _sort(by_sweep["row-scale"], "generation_count"),
+                x_getter=lambda result: result.case.generation_count,
+                y_series=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
+                ),
+                y_max=1.0,
+                x_label="synthetic model generations",
+                y_label="ratio",
+            ),
+            _line_chart(
+                "Frontier width: row-group concurrency changes starvation",
+                _sort(by_sweep["row-group-concurrency"], "row_group_concurrency"),
+                x_getter=lambda result: result.case.row_group_concurrency,
+                y_series=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
+                    ("downstream ready p95", lambda result: result.downstream_ready_gap_p95_seconds, "#6c5ce7"),
+                ),
+                x_label="row-group concurrency",
+                y_label="ratio / seconds",
+            ),
+            _line_chart(
+                "Buffer size: row-group shape affects traffic waves",
+                _sort(by_sweep["buffer-size"], "buffer_size"),
+                x_getter=lambda result: result.case.buffer_size,
+                y_series=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
+                    ("scheduler queue age p95", lambda result: result.llm_scheduler_queue_age_p95_seconds, "#6c5ce7"),
+                ),
+                x_label="buffer size",
+                y_label="ratio / seconds",
+            ),
+            _line_chart(
+                "Capacity scaling: underfeeding becomes visible as cap rises",
+                _sort(by_sweep["llm-capacity"], "task_admission_capacity"),
+                x_getter=lambda result: result.case.task_admission_capacity // 2,
+                y_series=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
+                    ("throughput / 1000", lambda result: result.throughput_generations_per_second / 1000.0, "#326273"),
+                ),
+                x_label="modeled llm_wait capacity",
+                y_label="ratio / kgen/s",
+            ),
+            _bar_chart(
+                "Custom-model metadata changes per-stage fairness",
+                _sort(by_sweep["custom-model-weight"], "model_stage_weight"),
+                label_getter=_weight_label,
+                bars=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
+                ),
+                y_max=1.0,
+            ),
+            _bar_chart(
+                "Stress shapes: narrow vs wide runnable horizon",
+                _sort(by_sweep["stress-shape"], "name"),
+                label_getter=lambda result: result.case.name.replace("-", " "),
+                bars=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    ("total llm_wait idle", lambda result: result.llm_idle_ratio, "#8a6f3d"),
+                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
+                    (
+                        "frontier/dependency idle",
+                        lambda result: result.llm_frontier_dependency_horizon_idle_ratio,
+                        "#6c5ce7",
+                    ),
+                    ("burstiness / 2", lambda result: result.llm_burstiness_coefficient / 2.0, "#326273"),
+                ),
+                y_max=1.0,
+            ),
+            _bar_chart(
+                "Adaptation: adaptive row groups target frontier idle",
+                [
+                    result
+                    for result in _sort(by_sweep.get("adaptations", []), "name")
+                    if result.case.name.startswith("adaptive-row-groups")
+                ],
+                label_getter=lambda result: result.case.name.replace("adaptive-row-groups-", ""),
+                bars=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    (
+                        "frontier/dependency idle",
+                        lambda result: result.llm_frontier_dependency_horizon_idle_ratio,
+                        "#6c5ce7",
+                    ),
+                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
+                ),
+                y_max=1.0,
+            ),
+            _bar_chart(
+                "Adaptation: request-pressure advisory avoids leased request wait",
+                [
+                    result
+                    for result in _sort(by_sweep.get("adaptations", []), "name")
+                    if result.case.name.startswith("request-pressure")
+                ],
+                label_getter=lambda result: result.case.name.replace("request-pressure-", ""),
+                bars=(
+                    (
+                        "request wait while leased",
+                        lambda result: result.request_wait_seconds_while_task_leased_mean,
+                        "#b23a48",
+                    ),
+                    ("scheduler queue age p95", lambda result: result.llm_scheduler_queue_age_p95_seconds, "#6c5ce7"),
+                    ("wall seconds / 10", lambda result: result.mean_wall_time_seconds / 10.0, "#1f7a8c"),
+                ),
+            ),
+            _bar_chart(
+                "Adaptation: combined adaptive frontier and request pressure",
+                [
+                    result
+                    for result in _sort(by_sweep.get("adaptations", []), "name")
+                    if result.case.name.startswith("adaptive-request-pressure")
+                ],
+                label_getter=lambda result: result.case.name.replace("adaptive-request-pressure-", ""),
+                bars=(
+                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
+                    ("request utilization", lambda result: result.request_utilization_ratio, "#326273"),
+                    ("request starved idle", lambda result: result.request_starved_idle_ratio, "#b23a48"),
+                    (
+                        "frontier/dependency idle",
+                        lambda result: result.llm_frontier_dependency_horizon_idle_ratio,
+                        "#6c5ce7",
+                    ),
+                ),
+                y_max=1.0,
+            ),
+            "</section>",
+            "<section>",
+            "<h2>Benchmark Matrix</h2>",
+            _results_table(results, report_path),
+            f'<p class="artifact-note">Raw artifacts are under <a href="{html.escape(artifact_link)}">'
+            f"{html.escape(str(artifact_dir))}</a>.</p>",
+            "</section>",
+            "<section>",
+            "<h2>Potential Changes</h2>",
+            _recommendations_html(results),
+            "</section>",
+            "</main>",
+            "</body>",
+            "</html>",
+            "",
+        ]
+    )
+
+
+def _findings_html(
+    results: Sequence[IdleBenchmarkResult],
+    by_sweep: Mapping[str, list[IdleBenchmarkResult]],
+) -> str:
+    row_scale = _sort(by_sweep["row-scale"], "generation_count")
+    row_gain = row_scale[-1].llm_utilization_ratio - row_scale[0].llm_utilization_ratio
+    rg_results = _sort(by_sweep["row-group-concurrency"], "row_group_concurrency")
+    rg_util_gain = rg_results[-1].llm_utilization_ratio - rg_results[0].llm_utilization_ratio
+    rg_idle_delta = rg_results[0].llm_starved_idle_ratio - min(result.llm_starved_idle_ratio for result in rg_results)
+    capacity_results = _sort(by_sweep["llm-capacity"], "task_admission_capacity")
+    weight_results = by_sweep["custom-model-weight"]
+    weight_one = next(result for result in weight_results if result.case.model_stage_weight == 1)
+    weight_auto = next(result for result in weight_results if result.case.model_stage_weight == 0)
+    stress = {result.case.name: result for result in by_sweep["stress-shape"]}
+    narrow = stress["narrow-frontier-high-cap"]
+    wide = stress["wide-frontier-high-cap"]
+    worst = max(results, key=lambda result: result.llm_starved_idle_ratio)
+
+    return (
+        '<ul class="finding-list">'
+        f"<li><strong>Scale helps once enough runnable work exists.</strong> The row-scale sweep moved "
+        f"from {_format_percent(row_scale[0].llm_utilization_ratio)} to "
+        f"{_format_percent(row_scale[-1].llm_utilization_ratio)} llm_wait utilization "
+        f"({_format_signed_percent(row_gain)}). Larger runs amortize startup and drain phases.</li>"
+        f"<li><strong>Runnable frontier width is the main controllable idle source.</strong> In the "
+        f"row-group sweep, utilization moved from {_format_percent(rg_results[0].llm_utilization_ratio)} "
+        f"to {_format_percent(rg_results[-1].llm_utilization_ratio)} "
+        f"({_format_signed_percent(rg_util_gain)}), while starved llm_wait idle improved by "
+        f"{_format_percent(max(0.0, rg_idle_delta))} between the narrowest case and the best observed case.</li>"
+        f"<li><strong>Total idle and starved idle diagnose different causes.</strong> "
+        f"<code>{html.escape(narrow.case.name)}</code> had {_format_percent(narrow.llm_idle_ratio)} total "
+        f"llm_wait idle but only {_format_percent(narrow.llm_starved_idle_ratio)} starved idle. The remaining "
+        f"{_format_percent(narrow.llm_frontier_dependency_horizon_idle_ratio)} was frontier/dependency-horizon "
+        "idle: the scheduler often had no ready model work because the admitted frontier was too small.</li>"
+        f"<li><strong>Capacity increases expose underfeeding.</strong> In the capacity sweep, modeled "
+        f"llm_wait cap {max(1, capacity_results[0].case.task_admission_capacity // 2)} reached "
+        f"{_format_percent(capacity_results[0].llm_utilization_ratio)} utilization, while cap "
+        f"{max(1, capacity_results[-1].case.task_admission_capacity // 2)} reached "
+        f"{_format_percent(capacity_results[-1].llm_utilization_ratio)} with the same small row frontier.</li>"
+        f"<li><strong>Custom model scheduling metadata changes traffic shape.</strong> With synthetic "
+        f"<code>custom_model</code> weight forced to 1, llm_wait utilization was "
+        f"{_format_percent(weight_one.llm_utilization_ratio)}; with the harness defaulting the weight "
+        f"to modeled model capacity it was {_format_percent(weight_auto.llm_utilization_ratio)}. "
+        "This run did not show weight=1 underfeeding because each synthetic column is its own group, "
+        "but it does show that group identity and weight are first-order benchmark inputs.</li>"
+        f"<li><strong>High capacity magnifies traffic-shape effects.</strong> The stress case with one "
+        f"large row group and high cap reached {_format_percent(narrow.llm_utilization_ratio)} utilization; "
+        f"the wide-frontier shape reached {_format_percent(wide.llm_utilization_ratio)}. "
+        "This is the vLLM-server-idle failure mode the scheduler needs to expose and track over time.</li>"
+        f"<li><strong>Worst observed actionable idle:</strong> {html.escape(worst.case.name)} had "
+        f"{_format_percent(worst.llm_starved_idle_ratio)} starved llm_wait idle and "
+        f"{_format_seconds(worst.llm_scheduler_queue_age_max_seconds)} max scheduler queue age.</li>"
+        "</ul>"
+    )
+
+
+def _find_result(
+    results: Sequence[IdleBenchmarkResult],
+    sweep: str,
+    name: str,
+) -> IdleBenchmarkResult | None:
+    return next((result for result in results if result.case.sweep == sweep and result.case.name == name), None)
+
+
+def _adaptive_row_group_card_evidence(
+    control: IdleBenchmarkResult | None,
+    adaptive: IdleBenchmarkResult | None,
+    row_groups_one: IdleBenchmarkResult,
+    narrow: IdleBenchmarkResult,
+) -> str:
+    if control is None or adaptive is None:
+        return (
+            f"<code>{html.escape(row_groups_one.case.name)}</code> and "
+            f"<code>{html.escape(narrow.case.name)}</code> remain the target proof cases; adaptation "
+            "benchmark artifacts were not available in this run."
+        )
+    return (
+        f"<code>{html.escape(control.case.name)}</code> to <code>{html.escape(adaptive.case.name)}</code>: "
+        f"utilization {_format_signed_percent(adaptive.llm_utilization_ratio - control.llm_utilization_ratio)}, "
+        f"frontier idle reduction "
+        f"{_format_percent(max(0.0, control.llm_frontier_dependency_horizon_idle_ratio - adaptive.llm_frontier_dependency_horizon_idle_ratio))}, "
+        f"observed target {_format_optional_int(adaptive.row_group_admission_observed_max_target)}."
+    )
+
+
+def _request_pressure_card_evidence(
+    control: IdleBenchmarkResult | None,
+    advisory: IdleBenchmarkResult | None,
+) -> str:
+    if control is None or advisory is None:
+        return "Needs adaptation benchmark artifacts from a non-skip idle-regression run."
+    return (
+        f"Leased request wait moved from {_format_seconds(control.request_wait_seconds_while_task_leased_mean)} "
+        f"to {_format_seconds(advisory.request_wait_seconds_while_task_leased_mean)}; first dispatch moved "
+        f"from <code>{html.escape(control.first_model_dispatch_column or 'unknown')}</code> to "
+        f"<code>{html.escape(advisory.first_model_dispatch_column or 'unknown')}</code>."
+    )
+
+
+def _implementation_improvements_html(results: Sequence[IdleBenchmarkResult]) -> str:
+    narrow = next(result for result in results if result.case.name == "narrow-frontier-high-cap")
+    row_groups_one = next(result for result in results if result.case.name == "row-groups-1")
+    rows_256 = next(result for result in results if result.case.name == "rows-256")
+    wide = next(result for result in results if result.case.name == "wide-frontier-high-cap")
+    adaptive_control = _find_result(results, "adaptations", "adaptive-row-groups-fixed-low")
+    adaptive = _find_result(results, "adaptations", "adaptive-row-groups-adaptive")
+    pressure_control = _find_result(results, "adaptations", "request-pressure-control")
+    pressure_advisory = _find_result(results, "adaptations", "request-pressure-advisory")
+    classified = _class_counts(results)
+    return (
+        _summary_grid(
+            (
+                (
+                    "Classification now explicit",
+                    f"{classified.get('frontier/dependency-horizon', 0)} frontier, "
+                    f"{classified.get('queued-work starvation', 0)} queued",
+                ),
+                ("Quick-suite proof gates", f"{len(results)} cases, zero validation failures"),
+                ("Largest refreshed case", f"{max(result.case.generation_count for result in results):,} generations"),
+                ("Narrow frontier idle identified", _format_percent(narrow.llm_frontier_dependency_horizon_idle_ratio)),
+                (
+                    "Row-groups=1 frontier idle",
+                    _format_percent(row_groups_one.llm_frontier_dependency_horizon_idle_ratio),
+                ),
+                ("Wide queued starvation age", _format_seconds(wide.llm_scheduler_queue_age_p95_seconds)),
+            )
+        )
+        + '<div class="improvement-grid">'
+        + _improvement_card(
+            "Idle partition metrics",
+            "Implemented",
+            "The report now splits total idle into queued-work starvation and frontier/dependency-horizon idle. "
+            "This prevents the narrow high-capacity case from being mistaken for a request-admission problem.",
+            (
+                f"<code>{html.escape(narrow.case.name)}</code>: "
+                f"{_format_percent(narrow.llm_frontier_dependency_horizon_idle_ratio)} frontier idle, "
+                f"{_format_percent(narrow.llm_starved_idle_ratio)} starved idle."
+            ),
+            "../scripts/benchmarks/benchmark_async_scheduling.py",
+        )
+        + _improvement_card(
+            "Queue age vs downstream delay",
+            "Implemented",
+            "Ready-to-lease queue age is now shown separately from dependency-complete-to-dispatch delay. "
+            "This distinguishes scheduler backlog from slow dependency propagation.",
+            (
+                f"<code>{html.escape(rows_256.case.name)}</code>: "
+                f"{_format_seconds(rows_256.llm_scheduler_queue_age_p95_seconds)} scheduler queue p95, "
+                f"{_format_seconds(rows_256.downstream_ready_gap_p95_seconds)} downstream p95."
+            ),
+            "../scripts/benchmarks/generate_async_scheduling_idle_report.py",
+        )
+        + _improvement_card(
+            "Batched frontier enqueue",
+            "Implemented",
+            "A frontier delta with many ready tasks now enters the fair queue in one enqueue operation, while "
+            "pre-batch parking, dropped rows, and per-task observability are preserved.",
+            "Focused scheduler tests prove one queue operation for a 5-task frontier and one operation for a "
+            "pre-batch flush after a dropped row.",
+            "../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py",
+        )
+        + _improvement_card(
+            "Resource-overlap peer pressure",
+            "Implemented",
+            "Task admission now applies group-cap peer pressure only for queued peers that can use the same "
+            "typed resource and are hard-resource eligible. Local/submission-only peers no longer create "
+            "false pressure on idle model capacity.",
+            "Unit tests cover non-overlapping peers, overlapping <code>llm_wait</code> peers, and peers blocked "
+            "by another hard resource.",
+            "../packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py",
+        )
+        + _improvement_card(
+            "Adaptive row-group admission",
+            "Implemented + benchmarked",
+            "The scheduler can now start with one admitted row group, raise the row-group target when "
+            "model capacity is idle and queued model demand is low, and remain bounded by the configured "
+            "hard cap.",
+            _adaptive_row_group_card_evidence(adaptive_control, adaptive, row_groups_one, narrow),
+            "../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py",
+        )
+        + _improvement_card(
+            "Request-pressure advisory",
+            "Implemented + benchmarked",
+            "Task selection can now use request pressure snapshots to prefer an open same-frontier peer before "
+            "spawning work that would immediately wait on request capacity. It does not yield and reacquire "
+            "leases inside running generators.",
+            _request_pressure_card_evidence(pressure_control, pressure_advisory),
+            "../packages/data-designer-engine/src/data_designer/engine/models/request_admission",
+        )
+        + "</div>"
+    )
+
+
+def _improvement_card(
+    title: str,
+    status: str,
+    body: str,
+    evidence: str,
+    href: str,
+) -> str:
+    return (
+        '<article class="improvement-card">'
+        f'<div><h3>{html.escape(title)}</h3><span class="status">{html.escape(status)}</span></div>'
+        f"<p>{body}</p>"
+        f'<p class="evidence"><strong>Evidence:</strong> {evidence}</p>'
+        f'<a href="{html.escape(href)}">source</a>'
+        "</article>"
+    )
+
+
+def _idle_classification_table(results: Sequence[IdleBenchmarkResult]) -> str:
+    headers = [
+        "case",
+        "dominant idle class",
+        "util",
+        "starved idle",
+        "frontier idle",
+        "queue age p95",
+        "downstream p95",
+        "next lever",
+    ]
+    rows = []
+    for result in sorted(
+        results,
+        key=lambda item: (
+            _idle_class_priority(_dominant_idle_class(item)),
+            -(item.llm_starved_idle_ratio + item.llm_frontier_dependency_horizon_idle_ratio),
+            item.case.sweep,
+            item.case.name,
+        ),
+    ):
+        idle_class = _dominant_idle_class(result)
+        rows.append(
+            [
+                html.escape(f"{result.case.sweep}/{result.case.name}"),
+                f'<span class="idle-class idle-{html.escape(_class_slug(idle_class))}">{html.escape(idle_class)}</span>',
+                _format_percent(result.llm_utilization_ratio),
+                _format_percent(result.llm_starved_idle_ratio),
+                _format_percent(result.llm_frontier_dependency_horizon_idle_ratio),
+                _format_seconds(result.llm_scheduler_queue_age_p95_seconds),
+                _format_seconds(result.downstream_ready_gap_p95_seconds),
+                html.escape(_recommended_next_lever(result)),
+            ]
+        )
+    return (
+        "<p>This board is the main analysis surface for optimization work. Frontier/dependency-horizon cases "
+        "need row-group or scheduling-shard changes; queued-work starvation cases need queue/admission/resource "
+        "selection changes; request-pressure cases need request-backed evidence before scheduler policy changes.</p>"
+        + _html_table(headers, rows)
+    )
+
+
+def _adaptation_benchmarks_html(results: Sequence[IdleBenchmarkResult]) -> str:
+    adaptive_control = _find_result(results, "adaptations", "adaptive-row-groups-fixed-low")
+    adaptive = _find_result(results, "adaptations", "adaptive-row-groups-adaptive")
+    adaptive_high = _find_result(results, "adaptations", "adaptive-row-groups-fixed-high")
+    pressure_control = _find_result(results, "adaptations", "request-pressure-control")
+    pressure_advisory = _find_result(results, "adaptations", "request-pressure-advisory")
+    combined_control = _find_result(results, "adaptations", "adaptive-request-pressure-control")
+    combined = _find_result(results, "adaptations", "adaptive-request-pressure-combined")
+    if adaptive_control is None or adaptive is None or pressure_control is None or pressure_advisory is None:
+        return (
+            "<p>The adaptation benchmark cases are not present in this artifact set. Re-run the idle "
+            "regression suite without <code>--skip-run</code> to collect them.</p>"
+        )
+
+    adaptive_util_delta = adaptive.llm_utilization_ratio - adaptive_control.llm_utilization_ratio
+    adaptive_frontier_delta = (
+        adaptive_control.llm_frontier_dependency_horizon_idle_ratio
+        - adaptive.llm_frontier_dependency_horizon_idle_ratio
+    )
+    adaptive_high_delta = adaptive.llm_utilization_ratio - adaptive_high.llm_utilization_ratio if adaptive_high else 0.0
+    request_wait_delta = (
+        pressure_control.request_wait_seconds_while_task_leased_mean
+        - pressure_advisory.request_wait_seconds_while_task_leased_mean
+    )
+    queue_age_delta = (
+        pressure_control.llm_scheduler_queue_age_p95_seconds - pressure_advisory.llm_scheduler_queue_age_p95_seconds
+    )
+    combined_request_wait_delta = 0.0
+    combined_request_idle_delta = 0.0
+    if combined_control is not None and combined is not None:
+        combined_request_wait_delta = (
+            combined_control.request_wait_seconds_while_task_leased_mean
+            - combined.request_wait_seconds_while_task_leased_mean
+        )
+        combined_request_idle_delta = combined_control.request_idle_ratio - combined.request_idle_ratio
+    high_context = ""
+    if adaptive_high is not None:
+        high_context = (
+            f" The fixed-high ceiling reached {_format_percent(adaptive_high.llm_utilization_ratio)} utilization "
+            f"with {_format_percent(adaptive_high.llm_frontier_dependency_horizon_idle_ratio)} frontier idle."
+        )
+
+    summary = _summary_grid(
+        (
+            ("Adaptive utilization delta", _format_signed_percent(adaptive_util_delta)),
+            ("Adaptive frontier-idle reduction", _format_percent(max(0.0, adaptive_frontier_delta))),
+            ("Adaptive vs fixed-high", _format_signed_percent(adaptive_high_delta)),
+            (
+                "Adaptive observed target",
+                _format_optional_int(adaptive.row_group_admission_observed_max_target),
+            ),
+            ("Request leased-wait reduction", _format_seconds(max(0.0, request_wait_delta))),
+            (
+                "Advisory first dispatch",
+                pressure_advisory.first_model_dispatch_column or "unknown",
+            ),
+            ("Request queue-age p95 reduction", _format_seconds(max(0.0, queue_age_delta))),
+            ("Combined leased-wait delta", _format_signed_seconds(combined_request_wait_delta)),
+            ("Combined request-idle delta", _format_signed_percent(combined_request_idle_delta)),
+        )
+    )
+    rows = [
+        [
+            "adaptive fixed-low control",
+            html.escape(adaptive_control.case.name),
+            _format_percent(adaptive_control.llm_utilization_ratio),
+            _format_percent(adaptive_control.llm_frontier_dependency_horizon_idle_ratio),
+            _format_optional_int(adaptive_control.row_group_admission_observed_max_target),
+            "-",
+            "-",
+        ],
+        [
+            "adaptive enabled",
+            html.escape(adaptive.case.name),
+            _format_percent(adaptive.llm_utilization_ratio),
+            _format_percent(adaptive.llm_frontier_dependency_horizon_idle_ratio),
+            _format_optional_int(adaptive.row_group_admission_observed_max_target),
+            "-",
+            "-",
+        ],
+        [
+            "request control",
+            html.escape(pressure_control.case.name),
+            _format_percent(pressure_control.llm_utilization_ratio),
+            _format_percent(pressure_control.llm_frontier_dependency_horizon_idle_ratio),
+            "-",
+            _format_seconds(pressure_control.request_wait_seconds_while_task_leased_mean),
+            html.escape(pressure_control.first_model_dispatch_column or "unknown"),
+        ],
+        [
+            "request advisory",
+            html.escape(pressure_advisory.case.name),
+            _format_percent(pressure_advisory.llm_utilization_ratio),
+            _format_percent(pressure_advisory.llm_frontier_dependency_horizon_idle_ratio),
+            "-",
+            _format_seconds(pressure_advisory.request_wait_seconds_while_task_leased_mean),
+            html.escape(pressure_advisory.first_model_dispatch_column or "unknown"),
+        ],
+    ]
+    if combined_control is not None:
+        rows.append(
+            [
+                "combined control",
+                html.escape(combined_control.case.name),
+                _format_percent(combined_control.llm_utilization_ratio),
+                _format_percent(combined_control.llm_frontier_dependency_horizon_idle_ratio),
+                _format_optional_int(combined_control.row_group_admission_observed_max_target),
+                _format_seconds(combined_control.request_wait_seconds_while_task_leased_mean),
+                html.escape(combined_control.first_model_dispatch_column or "unknown"),
+            ]
+        )
+    if combined is not None:
+        rows.append(
+            [
+                "combined enabled",
+                html.escape(combined.case.name),
+                _format_percent(combined.llm_utilization_ratio),
+                _format_percent(combined.llm_frontier_dependency_horizon_idle_ratio),
+                _format_optional_int(combined.row_group_admission_observed_max_target),
+                _format_seconds(combined.request_wait_seconds_while_task_leased_mean),
+                html.escape(combined.first_model_dispatch_column or "unknown"),
+            ]
+        )
+    if adaptive_high is not None:
+        rows.insert(
+            2,
+            [
+                "adaptive fixed-high control",
+                html.escape(adaptive_high.case.name),
+                _format_percent(adaptive_high.llm_utilization_ratio),
+                _format_percent(adaptive_high.llm_frontier_dependency_horizon_idle_ratio),
+                _format_optional_int(adaptive_high.row_group_admission_observed_max_target),
+                "-",
+                "-",
+            ],
+        )
+    return (
+        summary + "<p>The row-group adaptation is judged against a fixed-low frontier because that is the failure "
+        "shape where capacity is idle before enough dependency work exists. It should reduce frontier idle "
+        "or raise utilization while staying below the hard row-group cap. The fixed-high control isolates "
+        "how close the adaptive policy gets to simply admitting the full hard-cap frontier up front."
+        f"{high_context}</p>"
+        "<p>The request-pressure adaptation is judged by whether it avoids dispatching the pressured model "
+        "first and reduces request-wait time while a scheduler task lease is already held. The combined "
+        "case keeps adaptive row-group admission enabled while request pressure is present, so it measures "
+        "whether the scheduler can widen the frontier and still avoid sending the first available leases "
+        "into a saturated model.</p>"
+        + _html_table(
+            [
+                "adaptation",
+                "case",
+                "util",
+                "frontier idle",
+                "observed target",
+                "leased request wait",
+                "first model dispatch",
+            ],
+            rows,
+        )
+    )
+
+
+def _recommendations_html(results: Sequence[IdleBenchmarkResult]) -> str:
+    worst = max(results, key=lambda result: result.llm_starved_idle_ratio)
+    return (
+        '<ol class="recommendations">'
+        "<li><strong>Add an adaptive row-group admission controller.</strong> A subclass or policy layer "
+        "can watch bottleneck-resource starved idle and admit another row group when model capacity is "
+        "idle while queued or pending model work remains, bounded by buffer and memory limits. When total "
+        "idle is high but starved idle is low, the controller should treat that as a frontier problem and "
+        "prefetch more row groups rather than tuning request AIMD.</li>"
+        "<li><strong>Keep resource-aware idle in the standard benchmark output.</strong> The new "
+        "<code>llm_wait</code> utilization, starved idle, frontier/dependency-horizon idle, "
+        "scheduler queue-age, downstream ready-gap, and burstiness metrics identify "
+        "whether a vLLM-like resource is genuinely idle or merely waiting for dependencies.</li>"
+        "<li><strong>Document custom-model group identity and weight.</strong> "
+        "<code>SchedulingMetadata.custom_model(...)</code> controls fairness and per-group admission. "
+        "Plugins that share one external model should use a shared model identity and a weight that reflects "
+        "real serving capacity; plugins that represent distinct resources should keep distinct identities.</li>"
+        "<li><strong>Use the report as a regression suite.</strong> The worst case here is "
+        f"<code>{html.escape(worst.case.name)}</code>; future scheduler or admission changes should improve "
+        "that case without increasing ready gaps or violating final zero-lease checks.</li>"
+        "</ol>"
+    )
+
+
+def _results_table(results: Sequence[IdleBenchmarkResult], report_path: Path) -> str:
+    headers = [
+        "case",
+        "sweep",
+        "scenario",
+        "generations",
+        "rows",
+        "buffer",
+        "rg conc",
+        "llm cap",
+        "util",
+        "idle",
+        "starved idle",
+        "frontier idle",
+        "queue age p95",
+        "downstream gap p95",
+        "leased request wait",
+        "request util",
+        "request starved idle",
+        "advisory skips",
+        "first model",
+        "overlap",
+        "throughput",
+        "artifact",
+    ]
+    rows = []
+    for result in sorted(results, key=lambda item: (item.case.sweep, item.case.name)):
+        rows.append(
+            [
+                html.escape(result.case.name),
+                html.escape(result.case.sweep),
+                html.escape(result.case.scenario),
+                f"{result.case.generation_count:,}",
+                f"{result.case.record_count:,}",
+                f"{result.case.buffer_size:,}",
+                f"{result.case.row_group_concurrency}",
+                f"{max(1, result.case.task_admission_capacity // 2)}",
+                _format_percent(result.llm_utilization_ratio),
+                _format_percent(result.llm_idle_ratio),
+                _format_percent(result.llm_starved_idle_ratio),
+                _format_percent(result.llm_frontier_dependency_horizon_idle_ratio),
+                _format_seconds(result.llm_scheduler_queue_age_p95_seconds),
+                _format_seconds(result.downstream_ready_gap_p95_seconds),
+                _format_seconds(result.request_wait_seconds_while_task_leased_mean),
+                _format_percent(result.request_utilization_ratio),
+                _format_percent(result.request_starved_idle_ratio),
+                f"{result.request_pressure_advisory_skip_count:,}",
+                html.escape(result.first_model_dispatch_column or "-"),
+                _format_percent(result.pipeline_overlap_ratio),
+                f"{result.throughput_generations_per_second:,.0f}/s",
+                f'<a href="{html.escape(_relative_href(report_path, result.artifact_path))}">json</a>',
+            ]
+        )
+    return _html_table(headers, rows)
+
+
+def _line_chart(
+    title: str,
+    results: Sequence[IdleBenchmarkResult],
+    *,
+    x_getter: Any,
+    y_series: Sequence[tuple[str, Any, str]],
+    x_label: str,
+    y_label: str,
+    y_max: float | None = None,
+) -> str:
+    width = 880
+    height = 360
+    left = 64
+    right = 24
+    top = 42
+    bottom = 54
+    plot_w = width - left - right
+    plot_h = height - top - bottom
+    xs = [float(x_getter(result)) for result in results]
+    y_values = [float(getter(result)) for _label, getter, _color in y_series for result in results]
+    min_x = min(xs, default=0.0)
+    max_x = max(xs, default=1.0)
+    if math.isclose(min_x, max_x):
+        max_x = min_x + 1.0
+    max_y = y_max if y_max is not None else max(y_values, default=1.0)
+    max_y = max(max_y, 0.001)
+
+    elements = [_chart_frame(width, height, left, top, plot_w, plot_h, title, x_label, y_label)]
+    for series_index, (label, getter, color) in enumerate(y_series):
+        points = []
+        for result in results:
+            x_value = float(x_getter(result))
+            y_value = min(float(getter(result)), max_y)
+            x = left + ((x_value - min_x) / (max_x - min_x)) * plot_w
+            y = top + plot_h - (y_value / max_y) * plot_h
+            points.append((x, y, y_value))
+        point_attr = " ".join(f"{x:.1f},{y:.1f}" for x, y, _value in points)
+        elements.append(f'<polyline points="{point_attr}" fill="none" stroke="{color}" stroke-width="3"/>')
+        for x, y, value in points:
+            elements.append(
+                f'<circle cx="{x:.1f}" cy="{y:.1f}" r="4" fill="{color}"><title>{label}: {value:.4f}</title></circle>'
+            )
+        elements.append(_legend_item(label, color, series_index))
+
+    for result in results:
+        x_value = float(x_getter(result))
+        x = left + ((x_value - min_x) / (max_x - min_x)) * plot_w
+        elements.append(
+            f'<text class="tick" x="{x:.1f}" y="{height - 28}" text-anchor="middle">{_compact_number(x_value)}</text>'
+        )
+    return f'<figure class="chart">{"".join(elements)}</svg></figure>'
+
+
+def _bar_chart(
+    title: str,
+    results: Sequence[IdleBenchmarkResult],
+    *,
+    label_getter: Any,
+    bars: Sequence[tuple[str, Any, str]],
+    y_max: float | None = None,
+) -> str:
+    width = 880
+    height = 360
+    left = 64
+    top = 42
+    bottom = 82
+    plot_w = width - left - 24
+    plot_h = height - top - bottom
+    y_values = [float(getter(result)) for _label, getter, _color in bars for result in results]
+    max_y = max(y_max or max(y_values, default=1.0), 0.001)
+    group_w = plot_w / max(1, len(results))
+    bar_w = min(32.0, (group_w - 16.0) / max(1, len(bars)))
+
+    elements = [_chart_frame(width, height, left, top, plot_w, plot_h, title, "case", "ratio")]
+    for result_index, result in enumerate(results):
+        group_x = left + result_index * group_w + group_w / 2
+        for bar_index, (label, getter, color) in enumerate(bars):
+            value = min(float(getter(result)), max_y)
+            x = group_x - (bar_w * len(bars)) / 2 + bar_index * bar_w
+            bar_h = (value / max_y) * plot_h
+            y = top + plot_h - bar_h
+            elements.append(
+                f'<rect x="{x:.1f}" y="{y:.1f}" width="{bar_w - 3:.1f}" height="{bar_h:.1f}" '
+                f'rx="2" fill="{color}"><title>{label}: {value:.4f}</title></rect>'
+            )
+        elements.append(
+            f'<text class="tick" x="{group_x:.1f}" y="{height - 48}" text-anchor="middle">'
+            f"{html.escape(label_getter(result))}</text>"
+        )
+    for series_index, (label, _getter, color) in enumerate(bars):
+        elements.append(_legend_item(label, color, series_index))
+    return f'<figure class="chart">{"".join(elements)}</svg></figure>'
+
+
+def _chart_frame(
+    width: int,
+    height: int,
+    left: int,
+    top: int,
+    plot_w: int,
+    plot_h: int,
+    title: str,
+    x_label: str,
+    y_label: str,
+) -> str:
+    bottom = top + plot_h
+    right = left + plot_w
+    return (
+        f'<svg viewBox="0 0 {width} {height}" role="img" aria-label="{html.escape(title)}">'
+        f'<text class="chart-title" x="{left}" y="24">{html.escape(title)}</text>'
+        f'<line x1="{left}" y1="{bottom}" x2="{right}" y2="{bottom}" class="axis"/>'
+        f'<line x1="{left}" y1="{top}" x2="{left}" y2="{bottom}" class="axis"/>'
+        f'<text class="axis-label" x="{left + plot_w / 2:.1f}" y="{height - 8}" text-anchor="middle">{html.escape(x_label)}</text>'
+        f'<text class="axis-label" transform="translate(16 {top + plot_h / 2:.1f}) rotate(-90)" text-anchor="middle">{html.escape(y_label)}</text>'
+        f'<text class="tick" x="{left - 10}" y="{bottom}" text-anchor="end">0</text>'
+        f'<text class="tick" x="{left - 10}" y="{top + 4}" text-anchor="end">max</text>'
+        f'<line x1="{left}" y1="{top + plot_h / 2:.1f}" x2="{right}" y2="{top + plot_h / 2:.1f}" class="grid"/>'
+    )
+
+
+def _legend_item(label: str, color: str, index: int) -> str:
+    x = 650
+    y = 28 + (index % 5) * 17
+    return (
+        f'<g class="legend"><rect x="{x}" y="{y - 10}" width="10" height="10" fill="{color}"/>'
+        f'<text x="{x + 16}" y="{y}" class="legend-text">{html.escape(label)}</text></g>'
+    )
+
+
+def _summary_grid(items: Iterable[tuple[str, str]]) -> str:
+    cards = []
+    for label, value in items:
+        cards.append(
+            f'<div class="summary-card"><span>{html.escape(label)}</span><strong>{html.escape(value)}</strong></div>'
+        )
+    return f'<div class="summary-grid">{"".join(cards)}</div>'
+
+
+def _html_table(headers: Sequence[str], rows: Sequence[Sequence[str]]) -> str:
+    header_html = "".join(f"<th>{html.escape(header)}</th>" for header in headers)
+    row_html = []
+    for row in rows:
+        row_html.append("<tr>" + "".join(f"<td>{cell}</td>" for cell in row) + "</tr>")
+    return f'<div class="table-wrap"><table><thead><tr>{header_html}</tr></thead><tbody>{"".join(row_html)}</tbody></table></div>'
+
+
+def _style_block() -> str:
+    return """
+<style>
+:root {
+  color-scheme: light;
+  --ink: #17212b;
+  --muted: #526070;
+  --line: #d8dee6;
+  --panel: #f7f9fb;
+  --accent: #1f7a8c;
+  --warn: #b23a48;
+}
+* { box-sizing: border-box; }
+body {
+  margin: 0;
+  font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
+  color: var(--ink);
+  background: #ffffff;
+}
+main { max-width: 1120px; margin: 0 auto; padding: 44px 28px 72px; }
+.hero { border-bottom: 1px solid var(--line); padding-bottom: 28px; }
+.eyebrow { color: var(--accent); font-weight: 700; text-transform: uppercase; letter-spacing: .08em; font-size: 12px; }
+h1 { font-size: 44px; line-height: 1.05; margin: 8px 0 14px; letter-spacing: 0; }
+h2 { font-size: 24px; margin: 34px 0 12px; letter-spacing: 0; }
+p, li { color: var(--muted); line-height: 1.6; }
+code { font-family: "SFMono-Regular", Consolas, monospace; font-size: .94em; background: #eef2f6; padding: 1px 5px; border-radius: 4px; }
+.lede { max-width: 820px; font-size: 18px; }
+.summary-grid { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 12px; margin-top: 26px; }
+.summary-card { background: var(--panel); border: 1px solid var(--line); border-radius: 8px; padding: 16px; }
+.summary-card span { display: block; color: var(--muted); font-size: 13px; margin-bottom: 8px; }
+.summary-card strong { display: block; font-size: 24px; }
+.finding-list, .recommendations { padding-left: 22px; }
+.finding-list li, .recommendations li { margin: 10px 0; }
+.improvement-grid { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 14px; margin-top: 18px; }
+.improvement-card { border: 1px solid var(--line); border-radius: 8px; padding: 16px; background: #fff; }
+.improvement-card div { display: flex; align-items: baseline; justify-content: space-between; gap: 12px; margin-bottom: 8px; }
+.improvement-card h3 { margin: 0; font-size: 17px; letter-spacing: 0; }
+.improvement-card p { margin: 8px 0; }
+.improvement-card a { font-size: 13px; font-weight: 700; }
+.status { border: 1px solid var(--line); border-radius: 999px; padding: 3px 8px; color: var(--accent); font-size: 12px; font-weight: 700; white-space: nowrap; }
+.evidence { font-size: 13px; }
+.idle-class { display: inline-block; border-radius: 999px; padding: 3px 9px; font-weight: 700; font-size: 12px; }
+.idle-frontier-dependency-horizon { color: #46348a; background: #eeeafd; }
+.idle-queued-work-starvation { color: #8a2430; background: #fdebed; }
+.idle-healthy-control { color: #1d5f35; background: #e8f5ec; }
+.chart { border: 1px solid var(--line); border-radius: 8px; background: #fff; margin: 18px 0; padding: 10px; overflow-x: auto; }
+svg { width: 100%; min-width: 760px; height: auto; }
+.axis { stroke: #7c8794; stroke-width: 1; }
+.grid { stroke: #e5e9ef; stroke-width: 1; }
+.chart-title { font-size: 17px; font-weight: 700; fill: var(--ink); }
+.axis-label, .tick, .legend-text { fill: var(--muted); font-size: 12px; }
+.legend-text { font-weight: 600; }
+.table-wrap { overflow-x: auto; border: 1px solid var(--line); border-radius: 8px; }
+table { border-collapse: collapse; min-width: 1080px; width: 100%; font-size: 13px; }
+th, td { padding: 10px 12px; border-bottom: 1px solid var(--line); text-align: left; white-space: nowrap; }
+th { background: var(--panel); color: var(--ink); font-size: 12px; text-transform: uppercase; letter-spacing: .04em; }
+td { color: var(--muted); }
+.artifact-note { font-size: 13px; }
+a { color: var(--accent); }
+@media (max-width: 760px) {
+  main { padding: 28px 18px 52px; }
+  h1 { font-size: 34px; }
+  .summary-grid { grid-template-columns: 1fr; }
+  .improvement-grid { grid-template-columns: 1fr; }
+}
+</style>
+"""
+
+
+def _group_results(results: Sequence[IdleBenchmarkResult]) -> dict[str, list[IdleBenchmarkResult]]:
+    grouped: dict[str, list[IdleBenchmarkResult]] = {}
+    for result in results:
+        grouped.setdefault(result.case.sweep, []).append(result)
+    return grouped
+
+
+def _sort(results: Sequence[IdleBenchmarkResult], attr: str) -> list[IdleBenchmarkResult]:
+    return sorted(results, key=lambda result: getattr(result.case, attr))
+
+
+def _float(values: Mapping[str, Any], key: str, *, fallback_key: str | None = None) -> float:
+    value = values.get(key)
+    if value is None and fallback_key is not None:
+        value = values.get(fallback_key)
+    return float(value or 0.0)
+
+
+def _idle_ratio(
+    values: Mapping[str, Any],
+    artifact: Mapping[str, Any],
+    resource: str,
+    key: str,
+) -> float:
+    del key
+    capacity_seconds = _mean_capacity_seconds(artifact, resource)
+    idle_seconds = _float(values, "mean_idle_capacity_seconds")
+    starved_seconds = _float(values, "mean_starved_idle_seconds")
+    return _safe_ratio(max(0.0, idle_seconds - starved_seconds), capacity_seconds)
+
+
+def _mean_capacity_seconds(artifact: Mapping[str, Any], resource: str) -> float:
+    values = []
+    for iteration in artifact.get("iterations", []):
+        metrics = iteration.get("utilization_metrics", {}).get("scheduler_resources", {}).get(resource, {})
+        values.append(float(metrics.get("capacity_seconds", 0.0) or 0.0))
+    if not values:
+        return 0.0
+    return sum(values) / len(values)
+
+
+def _sum_request_capacity_seconds(artifact: Mapping[str, Any]) -> float:
+    values = []
+    for iteration in artifact.get("iterations", []):
+        resources = iteration.get("utilization_metrics", {}).get("request_resources", {})
+        if not isinstance(resources, Mapping):
+            continue
+        values.append(sum(float(metrics.get("capacity_seconds", 0.0) or 0.0) for metrics in resources.values()))
+    if not values:
+        return 0.0
+    return sum(values) / len(values)
+
+
+def _mean_resource_metric(resources: Mapping[str, Mapping[str, Any]], key: str) -> float:
+    values = [float(metrics.get(key, 0.0) or 0.0) for metrics in resources.values()]
+    if not values:
+        return 0.0
+    return sum(values) / len(values)
+
+
+def _sum_resource_metric(
+    resources: Mapping[str, Mapping[str, Any]],
+    key: str,
+    *,
+    fallback_key: str | None = None,
+) -> float:
+    total = 0.0
+    for metrics in resources.values():
+        value = metrics.get(key)
+        if value is None and fallback_key is not None:
+            value = metrics.get(fallback_key)
+        total += float(value or 0.0)
+    return total
+
+
+def _max_resource_metric(resources: Mapping[str, Mapping[str, Any]], key: str) -> float:
+    return max((float(metrics.get(key, 0.0) or 0.0) for metrics in resources.values()), default=0.0)
+
+
+def _max_iteration_metric(artifact: Mapping[str, Any], resource: str, metric: str) -> float:
+    values = []
+    for iteration in artifact.get("iterations", []):
+        resource_metrics = iteration.get("utilization_metrics", {}).get("scheduler_resources", {}).get(resource, {})
+        values.append(float(resource_metrics.get(metric, 0.0) or 0.0))
+    return max(values, default=0.0)
+
+
+def _row_group_admission_snapshot(artifact: Mapping[str, Any]) -> Mapping[str, Any]:
+    iteration_snapshots = [
+        _nested_mapping(
+            iteration,
+            ("capacity_plan", "configured", "row_group_admission"),
+        )
+        for iteration in artifact.get("iterations", [])
+    ]
+    for snapshot in reversed(iteration_snapshots):
+        if snapshot:
+            return snapshot
+    return _nested_mapping(artifact, ("capacity_plan", "configured", "row_group_admission"))
+
+
+def _nested_mapping(values: Mapping[str, Any], keys: Sequence[str]) -> Mapping[str, Any]:
+    current: Any = values
+    for key in keys:
+        if not isinstance(current, Mapping):
+            return {}
+        current = current.get(key)
+    return current if isinstance(current, Mapping) else {}
+
+
+def _safe_ratio(numerator: float, denominator: float) -> float:
+    if denominator <= 0.0:
+        return 0.0
+    return numerator / denominator
+
+
+def _safe_subtract(left: float, right: float) -> float:
+    return max(0.0, left - right)
+
+
+def _median(values: Iterable[float]) -> float:
+    sorted_values = sorted(values)
+    if not sorted_values:
+        return 0.0
+    midpoint = len(sorted_values) // 2
+    if len(sorted_values) % 2:
+        return sorted_values[midpoint]
+    return (sorted_values[midpoint - 1] + sorted_values[midpoint]) / 2.0
+
+
+def _format_float(value: float) -> str:
+    return f"{value:.8f}".rstrip("0").rstrip(".")
+
+
+def _format_percent(value: float) -> str:
+    return f"{value * 100.0:.1f}%"
+
+
+def _format_signed_percent(value: float) -> str:
+    sign = "+" if value >= 0.0 else ""
+    return f"{sign}{value * 100.0:.1f} pp"
+
+
+def _format_signed_seconds(value: float) -> str:
+    sign = "+" if value >= 0.0 else "-"
+    return f"{sign}{_format_seconds(abs(value))}"
+
+
+def _format_seconds(value: float) -> str:
+    if value < 0.001:
+        return f"{value * 1_000_000.0:.0f} us"
+    if value < 1.0:
+        return f"{value * 1000.0:.1f} ms"
+    return f"{value:.2f} s"
+
+
+def _compact_number(value: float) -> str:
+    if value >= 1000:
+        return f"{value / 1000.0:.1f}k"
+    if value.is_integer():
+        return str(int(value))
+    return f"{value:.2f}"
+
+
+def _format_optional_int(value: int | None) -> str:
+    return "n/a" if value is None else f"{value:,}"
+
+
+def _optional_int(value: Any) -> int | None:
+    if value is None:
+        return None
+    try:
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _optional_str(value: Any) -> str | None:
+    if value is None:
+        return None
+    return str(value)
+
+
+def _weight_label(result: IdleBenchmarkResult) -> str:
+    if result.case.model_stage_weight == 0:
+        return "model cap"
+    return f"weight {result.case.model_stage_weight}"
+
+
+def _dominant_idle_class(result: IdleBenchmarkResult) -> str:
+    if (
+        result.llm_frontier_dependency_horizon_idle_ratio >= 0.10
+        and result.llm_frontier_dependency_horizon_idle_ratio > result.llm_starved_idle_ratio
+    ):
+        return "frontier/dependency-horizon"
+    if result.llm_starved_idle_ratio >= 0.10:
+        return "queued-work starvation"
+    if result.llm_scheduler_queue_age_p95_seconds > max(1.0, result.downstream_ready_gap_p95_seconds * 10.0):
+        return "queued-work starvation"
+    return "healthy/control"
+
+
+def _recommended_next_lever(result: IdleBenchmarkResult) -> str:
+    idle_class = _dominant_idle_class(result)
+    if idle_class == "frontier/dependency-horizon":
+        return "adaptive row-group admission with memory and queue guardrails"
+    if idle_class == "queued-work starvation" and result.llm_scheduler_queue_age_p95_seconds > 1.0:
+        return "resource-aware task admission, candidate selection, or smaller scheduling shards"
+    if idle_class == "queued-work starvation":
+        return "task admission/fairness policy and eligible-starved-idle metrics"
+    return "keep as control case and watch for regressions"
+
+
+def _class_counts(results: Sequence[IdleBenchmarkResult]) -> dict[str, int]:
+    counts: dict[str, int] = {}
+    for result in results:
+        idle_class = _dominant_idle_class(result)
+        counts[idle_class] = counts.get(idle_class, 0) + 1
+    return counts
+
+
+def _idle_class_priority(idle_class: str) -> int:
+    priorities = {
+        "frontier/dependency-horizon": 0,
+        "queued-work starvation": 1,
+        "healthy/control": 2,
+    }
+    return priorities.get(idle_class, 99)
+
+
+def _class_slug(idle_class: str) -> str:
+    return idle_class.replace("/", "-").replace(" ", "-")
+
+
+def _relative_href(report_path: Path, target_path: Path) -> str:
+    try:
+        return Path("../" + str(target_path)).as_posix() if not target_path.is_absolute() else target_path.as_uri()
+    except ValueError:
+        return str(target_path)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/benchmarks/run_async_scheduling_idle_regression.py b/scripts/benchmarks/run_async_scheduling_idle_regression.py
new file mode 100644
index 000000000..482f6a8e1
--- /dev/null
+++ b/scripts/benchmarks/run_async_scheduling_idle_regression.py
@@ -0,0 +1,653 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+"""Run the canonical async scheduling idle-time regression suite."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import sys
+from collections.abc import Mapping, Sequence
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Any
+
+from generate_async_scheduling_idle_report import (
+    IDLE_SUITE_ID,
+    IDLE_SUITE_VERSION,
+    idle_results_summary,
+    render_idle_report,
+    run_idle_benchmark_suite,
+    write_idle_results_summary,
+)
+
+DEFAULT_ARTIFACT_DIR = Path("artifacts/async-scheduling-idle-regression")
+DEFAULT_REPORT_PATH = Path("reports/async-scheduling-idle-regression.html")
+DEFAULT_SUMMARY_PATH = DEFAULT_ARTIFACT_DIR / "idle_regression_summary.json"
+DEFAULT_CHECKS_PATH = DEFAULT_ARTIFACT_DIR / "idle_regression_checks.json"
+CHECKS_SCHEMA_VERSION = "async-scheduling-idle-checks-v1"
+
+
+@dataclass(frozen=True)
+class RegressionCheck:
+    name: str
+    category: str
+    severity: str
+    passed: bool
+    observed: float | int | str | bool
+    expected: str
+    detail: str = ""
+
+
+def main() -> None:
+    args = _parse_args()
+    artifact_dir = Path(args.artifact_dir)
+    report_path = Path(args.report_path)
+    summary_path = Path(args.summary_path)
+    checks_path = Path(args.checks_path)
+
+    results = run_idle_benchmark_suite(artifact_dir, quick=args.quick, skip_run=args.skip_run)
+    render_idle_report(results, report_path, artifact_dir)
+    write_idle_results_summary(summary_path, results, quick=args.quick)
+
+    summary = idle_results_summary(results, quick=args.quick)
+    baseline_summary = _load_json(Path(args.baseline_summary)) if args.baseline_summary else None
+    checks = evaluate_idle_regression_summary(
+        summary,
+        baseline_summary=baseline_summary,
+        utilization_tolerance=args.utilization_tolerance,
+        idle_tolerance=args.idle_tolerance,
+    )
+    checks_payload = _checks_payload(checks)
+    checks_path.parent.mkdir(parents=True, exist_ok=True)
+    checks_path.write_text(json.dumps(checks_payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+
+    _print_check_summary(checks, report_path, summary_path, checks_path)
+    if _has_error(checks) and not args.allow_failures:
+        sys.exit(1)
+
+
+def _parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--artifact-dir", default=str(DEFAULT_ARTIFACT_DIR))
+    parser.add_argument("--report-path", default=str(DEFAULT_REPORT_PATH))
+    parser.add_argument("--summary-path", default=str(DEFAULT_SUMMARY_PATH))
+    parser.add_argument("--checks-path", default=str(DEFAULT_CHECKS_PATH))
+    parser.add_argument("--baseline-summary")
+    parser.add_argument("--skip-run", action="store_true", help="Reuse existing benchmark JSON files.")
+    parser.add_argument("--quick", action="store_true", help="Run the shorter suite while preserving coverage shape.")
+    parser.add_argument(
+        "--utilization-tolerance",
+        type=float,
+        default=0.05,
+        help="Allowed absolute llm_wait utilization drop when comparing to --baseline-summary.",
+    )
+    parser.add_argument(
+        "--idle-tolerance",
+        type=float,
+        default=0.05,
+        help="Allowed absolute llm_wait idle increase when comparing to --baseline-summary.",
+    )
+    parser.add_argument("--allow-failures", action="store_true", help="Write artifacts but exit zero on failed checks.")
+    return parser.parse_args()
+
+
+def evaluate_idle_regression_summary(
+    summary: Mapping[str, Any],
+    *,
+    baseline_summary: Mapping[str, Any] | None = None,
+    utilization_tolerance: float = 0.05,
+    idle_tolerance: float = 0.05,
+) -> list[RegressionCheck]:
+    cases = _cases(summary)
+    mode = str(summary.get("mode", "full"))
+    checks: list[RegressionCheck] = [
+        _check(
+            "suite id",
+            category="shape",
+            passed=summary.get("suite_id") == IDLE_SUITE_ID,
+            observed=str(summary.get("suite_id")),
+            expected=IDLE_SUITE_ID,
+        ),
+        _check(
+            "suite version",
+            category="shape",
+            passed=summary.get("suite_version") == IDLE_SUITE_VERSION,
+            observed=str(summary.get("suite_version")),
+            expected=IDLE_SUITE_VERSION,
+        ),
+        _check(
+            "case count",
+            category="shape",
+            passed=len(cases) >= _minimum_case_count(mode),
+            observed=len(cases),
+            expected=f">= {_minimum_case_count(mode)}",
+        ),
+        _check(
+            "large generation case present",
+            category="shape",
+            passed=int(summary.get("largest_generation_count", 0)) >= 8192,
+            observed=int(summary.get("largest_generation_count", 0)),
+            expected=">= 8192 generations",
+        ),
+    ]
+
+    checks.extend(_required_case_checks(cases, mode=mode))
+    checks.extend(_per_case_metric_checks(cases))
+    checks.extend(_suite_behavior_checks(cases, mode=mode))
+    if baseline_summary is not None:
+        checks.extend(
+            _baseline_comparison_checks(
+                cases,
+                _cases(baseline_summary),
+                utilization_tolerance=utilization_tolerance,
+                idle_tolerance=idle_tolerance,
+            )
+        )
+    return checks
+
+
+def _required_case_checks(cases: Mapping[str, Mapping[str, Any]], *, mode: str) -> list[RegressionCheck]:
+    required = {
+        "adaptations/adaptive-row-groups-adaptive",
+        "adaptations/adaptive-row-groups-fixed-high",
+        "adaptations/adaptive-row-groups-fixed-low",
+        "adaptations/adaptive-request-pressure-combined",
+        "adaptations/adaptive-request-pressure-control",
+        "adaptations/request-pressure-advisory",
+        "adaptations/request-pressure-control",
+        "row-scale/rows-64",
+        "row-group-concurrency/row-groups-1",
+        "buffer-size/buffer-1",
+        "stress-shape/narrow-frontier-high-cap",
+        "stress-shape/wide-frontier-high-cap",
+        "custom-model-weight/weight-model-capacity",
+    }
+    if mode != "quick":
+        required.update(
+            {
+                "row-scale/rows-1024",
+                "row-group-concurrency/row-groups-8",
+                "buffer-size/buffer-256",
+                "llm-capacity/capacity-16",
+            }
+        )
+    else:
+        required.add("row-scale/rows-256")
+    return [
+        _check(
+            f"required case {case_key}",
+            category="shape",
+            passed=case_key in cases,
+            observed=case_key in cases,
+            expected="present",
+        )
+        for case_key in sorted(required)
+    ]
+
+
+def _per_case_metric_checks(cases: Mapping[str, Mapping[str, Any]]) -> list[RegressionCheck]:
+    checks: list[RegressionCheck] = []
+    for case_key, case in sorted(cases.items()):
+        checks.append(
+            _check(
+                f"{case_key} validation",
+                category="correctness",
+                passed=bool(case.get("validation_passed")),
+                observed=bool(case.get("validation_passed")),
+                expected="true",
+            )
+        )
+        for cleanup_metric in (
+            "final_zero_task_leases",
+            "final_zero_request_leases",
+            "final_zero_request_waiters",
+        ):
+            checks.append(
+                _check(
+                    f"{case_key} {cleanup_metric}",
+                    category="correctness",
+                    passed=bool(case.get(cleanup_metric)),
+                    observed=bool(case.get(cleanup_metric)),
+                    expected="true",
+                )
+            )
+        for metric in (
+            "llm_wait_utilization_ratio",
+            "llm_wait_idle_ratio",
+            "llm_wait_starved_idle_ratio",
+            "llm_wait_frontier_dependency_horizon_idle_ratio",
+            "request_utilization_ratio",
+            "request_idle_ratio",
+            "request_starved_idle_ratio",
+            "request_frontier_dependency_horizon_idle_ratio",
+        ):
+            value = _metric(case, metric)
+            checks.append(
+                _check(
+                    f"{case_key} {metric}",
+                    category="metrics",
+                    passed=math.isfinite(value) and 0.0 <= value <= 1.0,
+                    observed=value,
+                    expected="finite ratio in [0, 1]",
+                )
+            )
+        queue_age_p95 = _metric(case, "llm_wait_scheduler_queue_age_p95_seconds")
+        checks.append(
+            _check(
+                f"{case_key} llm_wait_scheduler_queue_age_p95_seconds",
+                category="metrics",
+                passed=math.isfinite(queue_age_p95) and queue_age_p95 >= 0.0,
+                observed=queue_age_p95,
+                expected="finite seconds >= 0",
+            )
+        )
+        checks.append(
+            _check(
+                f"{case_key} idle dominates starved idle",
+                category="metrics",
+                passed=_metric(case, "llm_wait_idle_ratio") + 1e-9 >= _metric(case, "llm_wait_starved_idle_ratio"),
+                observed=_metric(case, "llm_wait_idle_ratio") - _metric(case, "llm_wait_starved_idle_ratio"),
+                expected="idle ratio >= starved idle ratio",
+            )
+        )
+        checks.append(
+            _check(
+                f"{case_key} idle partition",
+                category="metrics",
+                passed=abs(
+                    _metric(case, "llm_wait_idle_ratio")
+                    - _metric(case, "llm_wait_starved_idle_ratio")
+                    - _metric(case, "llm_wait_frontier_dependency_horizon_idle_ratio")
+                )
+                <= 1e-6,
+                observed=(
+                    _metric(case, "llm_wait_starved_idle_ratio")
+                    + _metric(case, "llm_wait_frontier_dependency_horizon_idle_ratio")
+                    - _metric(case, "llm_wait_idle_ratio")
+                ),
+                expected="starved + frontier/dependency-horizon idle == total idle",
+            )
+        )
+    return checks
+
+
+def _suite_behavior_checks(cases: Mapping[str, Mapping[str, Any]], *, mode: str) -> list[RegressionCheck]:
+    checks: list[RegressionCheck] = []
+    row_group_cases = [case for key, case in cases.items() if key.startswith("row-group-concurrency/")]
+    if row_group_cases:
+        low = min(row_group_cases, key=lambda case: int(case["case"]["row_group_concurrency"]))
+        high = max(row_group_cases, key=lambda case: int(case["case"]["row_group_concurrency"]))
+        low_util = _metric(low, "llm_wait_utilization_ratio")
+        high_util = _metric(high, "llm_wait_utilization_ratio")
+        checks.append(
+            _check(
+                "row-group frontier response",
+                category="optimization",
+                passed=high_util >= low_util + 0.20 or low_util >= 0.60,
+                observed=high_util - low_util,
+                expected="highest row-group concurrency improves utilization by >= 20pp unless low case is already >= 60%",
+            )
+        )
+        checks.append(
+            _check(
+                "wide row-group frontier utilization",
+                category="optimization",
+                passed=high_util >= _wide_row_group_utilization_floor(mode),
+                observed=high_util,
+                expected=f">= {_wide_row_group_utilization_floor(mode):.2f} llm_wait utilization",
+            )
+        )
+
+    if "buffer-size/buffer-1" in cases:
+        checks.append(
+            _check(
+                "small-buffer model utilization",
+                category="optimization",
+                passed=_metric(cases["buffer-size/buffer-1"], "llm_wait_utilization_ratio") >= 0.65,
+                observed=_metric(cases["buffer-size/buffer-1"], "llm_wait_utilization_ratio"),
+                expected=">= 0.65 llm_wait utilization",
+            )
+        )
+    if "llm-capacity/capacity-16" in cases:
+        checks.append(
+            _check(
+                "high-capacity underfeeding floor",
+                category="optimization",
+                passed=_metric(cases["llm-capacity/capacity-16"], "llm_wait_utilization_ratio") >= 0.25,
+                observed=_metric(cases["llm-capacity/capacity-16"], "llm_wait_utilization_ratio"),
+                expected=">= 0.25 llm_wait utilization",
+            )
+        )
+    if "stress-shape/wide-frontier-high-cap" in cases:
+        checks.append(
+            _check(
+                "wide high-capacity stress utilization",
+                category="optimization",
+                passed=_metric(cases["stress-shape/wide-frontier-high-cap"], "llm_wait_utilization_ratio") >= 0.55,
+                observed=_metric(cases["stress-shape/wide-frontier-high-cap"], "llm_wait_utilization_ratio"),
+                expected=">= 0.55 llm_wait utilization",
+            )
+        )
+    if {
+        "stress-shape/narrow-frontier-high-cap",
+        "stress-shape/wide-frontier-high-cap",
+    }.issubset(cases):
+        narrow_dependency_idle = _metric(
+            cases["stress-shape/narrow-frontier-high-cap"],
+            "llm_wait_frontier_dependency_horizon_idle_ratio",
+        )
+        wide_dependency_idle = _metric(
+            cases["stress-shape/wide-frontier-high-cap"],
+            "llm_wait_frontier_dependency_horizon_idle_ratio",
+        )
+        checks.append(
+            _check(
+                "wide frontier dependency-horizon idle response",
+                category="optimization",
+                passed=wide_dependency_idle <= narrow_dependency_idle + 0.10,
+                observed=wide_dependency_idle - narrow_dependency_idle,
+                expected="wide frontier dependency-horizon idle does not exceed narrow by > 10pp",
+            )
+        )
+    if {
+        "adaptations/adaptive-row-groups-fixed-low",
+        "adaptations/adaptive-row-groups-fixed-high",
+        "adaptations/adaptive-row-groups-adaptive",
+    }.issubset(cases):
+        control = cases["adaptations/adaptive-row-groups-fixed-low"]
+        fixed_high = cases["adaptations/adaptive-row-groups-fixed-high"]
+        adaptive = cases["adaptations/adaptive-row-groups-adaptive"]
+        control_util = _metric(control, "llm_wait_utilization_ratio")
+        fixed_high_util = _metric(fixed_high, "llm_wait_utilization_ratio")
+        adaptive_util = _metric(adaptive, "llm_wait_utilization_ratio")
+        control_frontier = _metric(control, "llm_wait_frontier_dependency_horizon_idle_ratio")
+        fixed_high_frontier = _metric(fixed_high, "llm_wait_frontier_dependency_horizon_idle_ratio")
+        adaptive_frontier = _metric(adaptive, "llm_wait_frontier_dependency_horizon_idle_ratio")
+        checks.append(
+            _check(
+                "adaptive row-group utilization response",
+                category="optimization",
+                passed=adaptive_util >= control_util + 0.10 or control_util >= 0.70,
+                observed=adaptive_util - control_util,
+                expected="adaptive row groups improve low-frontier utilization by >= 10pp unless control is >= 70%",
+            )
+        )
+        checks.append(
+            _check(
+                "adaptive row-group fixed-high isolation",
+                category="optimization",
+                passed=adaptive_util + 0.10 >= fixed_high_util,
+                observed=adaptive_util - fixed_high_util,
+                expected="adaptive row groups stay within 10pp utilization of fixed-high hard-cap control",
+            )
+        )
+        checks.append(
+            _check(
+                "adaptive row-group frontier-idle response",
+                category="optimization",
+                passed=adaptive_frontier <= min(control_frontier, fixed_high_frontier) + 0.05,
+                observed=adaptive_frontier - min(control_frontier, fixed_high_frontier),
+                expected="adaptive row groups do not exceed the better fixed-control frontier idle by > 5pp",
+            )
+        )
+        checks.append(
+            _check(
+                "adaptive row-group target grew",
+                category="optimization",
+                passed=int(adaptive.get("row_group_admission_observed_max_target", 0) or 0) > 1,
+                observed=int(adaptive.get("row_group_admission_observed_max_target", 0) or 0),
+                expected="observed adaptive target > 1",
+            )
+        )
+    if {
+        "adaptations/request-pressure-control",
+        "adaptations/request-pressure-advisory",
+    }.issubset(cases):
+        control = cases["adaptations/request-pressure-control"]
+        advisory = cases["adaptations/request-pressure-advisory"]
+        control_wait = _metric(control, "request_wait_seconds_while_task_leased_mean")
+        advisory_wait = _metric(advisory, "request_wait_seconds_while_task_leased_mean")
+        checks.append(
+            _check(
+                "request-pressure control dispatch choice",
+                category="optimization",
+                passed=control.get("first_model_dispatch_column") == "a_pressured",
+                observed=str(control.get("first_model_dispatch_column")),
+                expected="a_pressured",
+            )
+        )
+        checks.append(
+            _check(
+                "request-pressure control leased wait present",
+                category="optimization",
+                passed=control_wait >= 0.01,
+                observed=control_wait,
+                expected=">= 0.01 seconds",
+            )
+        )
+        checks.append(
+            _check(
+                "request-pressure advisory leased-wait response",
+                category="optimization",
+                passed=advisory_wait <= control_wait - 0.005,
+                observed=advisory_wait - control_wait,
+                expected="advisory leased request wait at least 5ms lower than control",
+            )
+        )
+        checks.append(
+            _check(
+                "request-pressure advisory dispatch choice",
+                category="optimization",
+                passed=advisory.get("first_model_dispatch_column") == "z_open",
+                observed=str(advisory.get("first_model_dispatch_column")),
+                expected="z_open",
+            )
+        )
+    if {
+        "adaptations/adaptive-request-pressure-control",
+        "adaptations/adaptive-request-pressure-combined",
+    }.issubset(cases):
+        control = cases["adaptations/adaptive-request-pressure-control"]
+        combined = cases["adaptations/adaptive-request-pressure-combined"]
+        control_wait = _metric(control, "request_wait_seconds_while_task_leased_mean")
+        combined_wait = _metric(combined, "request_wait_seconds_while_task_leased_mean")
+        control_llm_util = _metric(control, "llm_wait_utilization_ratio")
+        combined_llm_util = _metric(combined, "llm_wait_utilization_ratio")
+        control_request_idle = _metric(control, "request_idle_ratio")
+        combined_request_idle = _metric(combined, "request_idle_ratio")
+        checks.extend(
+            [
+                _check(
+                    "combined adaptive/request target grew",
+                    category="optimization",
+                    passed=int(combined.get("row_group_admission_observed_max_target", 0) or 0) > 1,
+                    observed=int(combined.get("row_group_admission_observed_max_target", 0) or 0),
+                    expected="observed adaptive target > 1",
+                ),
+                _check(
+                    "combined adaptive/request advisory enabled",
+                    category="optimization",
+                    passed=bool(combined.get("request_pressure_advisory_enabled")),
+                    observed=bool(combined.get("request_pressure_advisory_enabled")),
+                    expected="true",
+                ),
+                _check(
+                    "combined adaptive/request advisory skipped pressured work",
+                    category="optimization",
+                    passed=int(combined.get("request_pressure_advisory_skip_count", 0) or 0) > 0,
+                    observed=int(combined.get("request_pressure_advisory_skip_count", 0) or 0),
+                    expected="> 0 skip events",
+                ),
+                _check(
+                    "combined adaptive/request leased wait captured",
+                    category="optimization",
+                    passed=math.isfinite(combined_wait) and combined_wait >= 0.0 and control_wait >= 0.0,
+                    observed=combined_wait - control_wait,
+                    expected="finite leased request wait delta",
+                ),
+                _check(
+                    "combined adaptive/request llm utilization response",
+                    category="optimization",
+                    passed=combined_llm_util + 0.05 >= control_llm_util,
+                    observed=combined_llm_util - control_llm_util,
+                    expected="combined llm utilization stays within 5pp of control or improves",
+                ),
+                _check(
+                    "combined adaptive/request request-idle response",
+                    category="optimization",
+                    passed=combined_request_idle <= control_request_idle + 0.05,
+                    observed=combined_request_idle - control_request_idle,
+                    expected="combined request idle does not exceed control by > 5pp",
+                ),
+            ]
+        )
+    return checks
+
+
+def _baseline_comparison_checks(
+    cases: Mapping[str, Mapping[str, Any]],
+    baseline_cases: Mapping[str, Mapping[str, Any]],
+    *,
+    utilization_tolerance: float,
+    idle_tolerance: float,
+) -> list[RegressionCheck]:
+    checks: list[RegressionCheck] = []
+    for case_key, baseline in sorted(baseline_cases.items()):
+        current = cases.get(case_key)
+        if current is None:
+            checks.append(
+                _check(
+                    f"{case_key} baseline case present",
+                    category="baseline",
+                    passed=False,
+                    observed="missing",
+                    expected="present",
+                )
+            )
+            continue
+        baseline_util = _metric(baseline, "llm_wait_utilization_ratio")
+        current_util = _metric(current, "llm_wait_utilization_ratio")
+        checks.append(
+            _check(
+                f"{case_key} utilization regression",
+                category="baseline",
+                passed=current_util + utilization_tolerance >= baseline_util,
+                observed=current_util - baseline_util,
+                expected=f">= -{utilization_tolerance:.3f}",
+            )
+        )
+        for metric in (
+            "llm_wait_idle_ratio",
+            "llm_wait_starved_idle_ratio",
+            "llm_wait_frontier_dependency_horizon_idle_ratio",
+        ):
+            baseline_idle = _metric(baseline, metric)
+            current_idle = _metric(current, metric)
+            checks.append(
+                _check(
+                    f"{case_key} {metric} regression",
+                    category="baseline",
+                    passed=current_idle <= baseline_idle + idle_tolerance,
+                    observed=current_idle - baseline_idle,
+                    expected=f"<= +{idle_tolerance:.3f}",
+                )
+            )
+        baseline_throughput = _metric(baseline, "throughput_generations_per_second")
+        current_throughput = _metric(current, "throughput_generations_per_second")
+        checks.append(
+            _check(
+                f"{case_key} throughput smoke",
+                category="baseline",
+                severity="warning",
+                passed=current_throughput >= baseline_throughput * 0.75,
+                observed=current_throughput - baseline_throughput,
+                expected=">= 75% of baseline throughput",
+            )
+        )
+    return checks
+
+
+def _wide_row_group_utilization_floor(mode: str) -> float:
+    return 0.55 if mode == "quick" else 0.70
+
+
+def _checks_payload(checks: Sequence[RegressionCheck]) -> dict[str, Any]:
+    return {
+        "checks_schema_version": CHECKS_SCHEMA_VERSION,
+        "suite_id": IDLE_SUITE_ID,
+        "suite_version": IDLE_SUITE_VERSION,
+        "passed": not _has_error(checks),
+        "error_count": sum(1 for check in checks if check.severity == "error" and not check.passed),
+        "warning_count": sum(1 for check in checks if check.severity == "warning" and not check.passed),
+        "checks": [asdict(check) for check in checks],
+    }
+
+
+def _print_check_summary(
+    checks: Sequence[RegressionCheck],
+    report_path: Path,
+    summary_path: Path,
+    checks_path: Path,
+) -> None:
+    errors = [check for check in checks if check.severity == "error" and not check.passed]
+    warnings = [check for check in checks if check.severity == "warning" and not check.passed]
+    status = "PASS" if not errors else "FAIL"
+    print(f"Idle regression suite: {status} ({len(errors)} errors, {len(warnings)} warnings)")
+    print(f"Wrote {report_path}")
+    print(f"Wrote {summary_path}")
+    print(f"Wrote {checks_path}")
+    for check in errors[:10]:
+        print(f"ERROR {check.name}: observed {check.observed}; expected {check.expected}")
+    for check in warnings[:5]:
+        print(f"WARN {check.name}: observed {check.observed}; expected {check.expected}")
+
+
+def _has_error(checks: Sequence[RegressionCheck]) -> bool:
+    return any(check.severity == "error" and not check.passed for check in checks)
+
+
+def _check(
+    name: str,
+    *,
+    category: str,
+    passed: bool,
+    observed: float | int | str | bool,
+    expected: str,
+    severity: str = "error",
+    detail: str = "",
+) -> RegressionCheck:
+    return RegressionCheck(
+        name=name,
+        category=category,
+        severity=severity,
+        passed=passed,
+        observed=observed,
+        expected=expected,
+        detail=detail,
+    )
+
+
+def _cases(summary: Mapping[str, Any]) -> Mapping[str, Mapping[str, Any]]:
+    cases = summary.get("cases", {})
+    if not isinstance(cases, Mapping):
+        return {}
+    return cases
+
+
+def _minimum_case_count(mode: str) -> int:
+    return 15 if mode == "quick" else 21
+
+
+def _metric(case: Mapping[str, Any], name: str) -> float:
+    return float(case.get(name, 0.0) or 0.0)
+
+
+def _load_json(path: Path) -> Mapping[str, Any]:
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+if __name__ == "__main__":
+    main()

From 9a8312a964d6f4ceec8a193bb696c51c3a5649be Mon Sep 17 00:00:00 2001
From: Andre Manoel <165937436+andreatgretel@users.noreply.github.com>
Date: Tue, 19 May 2026 16:27:48 -0300
Subject: [PATCH 13/33] fix request pressure domain metadata (#679)

---
 .../engine/column_generators/generators/base.py    | 13 ++++++++++++-
 .../dataset_builders/scheduling/test_resolver.py   | 14 ++++++++++----
 2 files changed, 22 insertions(+), 5 deletions(-)

diff --git a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
index 66d902a46..e2a333c69 100644
--- a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
+++ b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
@@ -35,6 +35,13 @@ class _EndpointBucket:
     caps: list[int] = field(default_factory=list)
 
 
+def _scheduling_generation_kind(generation_type: object) -> str:
+    value = getattr(generation_type, "value", generation_type)
+    if value == "chat-completion":
+        return "chat"
+    return str(value)
+
+
 if TYPE_CHECKING:
     import pandas as pd
 
@@ -192,7 +199,11 @@ def get_scheduling_metadata(self) -> SchedulingMetadata:
                     diagnostics={"alias": alias, "generator_type": type(self).__name__},
                 ) from exc
 
-            endpoint = (provider_name, str(model_config.model), str(model_config.generation_type))
+            endpoint = (
+                provider_name,
+                str(model_config.model),
+                _scheduling_generation_kind(model_config.generation_type),
+            )
             max_parallel = getattr(model_config.inference_parameters, "max_parallel_requests", 1)
             cap = max_parallel if isinstance(max_parallel, int) and max_parallel > 0 else 1
             bucket = endpoints.setdefault(endpoint, _EndpointBucket())
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
index ab8ce0a2c..1804dd272 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
@@ -9,9 +9,12 @@
 import pytest
 
 from data_designer.config.column_configs import ExpressionColumnConfig
+from data_designer.config.models import GenerationType
 from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
+from data_designer.engine.column_generators.generators.base import ColumnGeneratorWithModelRegistry
 from data_designer.engine.dataset_builders.scheduling.resolver import TaskSchedulingResolver
 from data_designer.engine.dataset_builders.scheduling.task_model import Task
+from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
 
 
 class _LocalGenerator:
@@ -65,6 +68,7 @@ def test_task_scheduling_resolver_maps_model_metadata_to_model_resource() -> Non
     assert schedulable.group.weight == 3.0
     assert schedulable.group.admitted_limit == 6
     assert schedulable.resource_request.amounts == {"submission": 1, "llm_wait": 1}
+    assert schedulable.request_resource_key == RequestResourceKey("nvidia", "nemotron", RequestDomain.CHAT)
 
 
 def test_task_scheduling_resolver_records_safe_fallback_diagnostics() -> None:
@@ -82,8 +86,6 @@ def test_task_scheduling_resolver_raises_fatal_metadata_error() -> None:
 
 
 def test_model_registry_generator_metadata_deduplicates_same_endpoint_aliases() -> None:
-    from data_designer.engine.column_generators.generators.base import ColumnGeneratorWithModelRegistry
-
     class _RegistryGenerator(ColumnGeneratorWithModelRegistry[ExpressionColumnConfig]):
         @staticmethod
         def get_generation_strategy() -> object:
@@ -98,12 +100,12 @@ def generate(self, data: object) -> object:
     configs = {
         "primary": SimpleNamespace(
             model="endpoint",
-            generation_type="chat",
+            generation_type=GenerationType.CHAT_COMPLETION,
             inference_parameters=SimpleNamespace(max_parallel_requests=4),
         ),
         "secondary": SimpleNamespace(
             model="endpoint",
-            generation_type="chat",
+            generation_type=GenerationType.CHAT_COMPLETION,
             inference_parameters=SimpleNamespace(max_parallel_requests=2),
         ),
     }
@@ -119,3 +121,7 @@ def generate(self, data: object) -> object:
     assert metadata.identity == ("model", "nvidia", "endpoint", "chat")
     assert metadata.weight == 2
     assert metadata.diagnostics["merge_rule"] == "min_same_endpoint"
+
+    resolver = TaskSchedulingResolver({"answer": generator})  # type: ignore[arg-type]
+    schedulable = resolver.schedulable_task(_task(), ("answer",))
+    assert schedulable.request_resource_key == RequestResourceKey("nvidia", "endpoint", RequestDomain.CHAT)

From 2c4379ad078f4f2120f0e19632f836952da2880b Mon Sep 17 00:00:00 2001
From: Nabin Mulepati <nmulepati@nvidia.com>
Date: Tue, 19 May 2026 14:30:10 -0600
Subject: [PATCH 14/33] fix: harden async scheduling admission follow-ups
 (#680)

---
 ...request-keying.png => throttle-keying.png} | Bin
 .../concepts/architecture-and-performance.mdx |  58 +++++++--
 fern/versions/v0.5.8/pages/devnotes/index.mdx |   2 +-
 .../devnotes/posts/async-all-the-way-down.mdx |  47 +++----
 .../devnotes/posts/owning-the-model-stack.mdx |  58 ++++++---
 fern/versions/v0.5.9/pages/devnotes/index.mdx |   2 +-
 .../dataset_builders/async_scheduler.py       | 122 ++++++++++++++++--
 .../dataset_builders/dataset_builder.py       |   1 +
 .../scheduling/task_admission.py              |  15 ++-
 .../engine/models/clients/factory.py          |   7 +-
 .../models/clients/model_request_executor.py  |  54 ++++++++
 .../models/request_admission/controller.py    |  32 ++++-
 .../models/request_admission/pressure.py      |   4 +
 .../scheduling/test_task_admission.py         |  18 +++
 .../dataset_builders/test_async_scheduler.py  |  70 +++++++++-
 .../engine/models/clients/test_factory.py     |   5 +-
 .../clients/test_model_request_executor.py    | 105 +++++++++++++++
 .../request_admission/test_controller.py      |  47 +++++--
 .../engine/test_async_scheduling_benchmark.py |  23 ++++
 .../generate_async_scheduling_idle_report.py  |   8 +-
 .../run_async_scheduling_idle_regression.py   |   9 +-
 tests_e2e/tests/test_mcp_demo.py              |  14 +-
 22 files changed, 602 insertions(+), 99 deletions(-)
 rename fern/assets/owning-the-model-stack/{request-keying.png => throttle-keying.png} (100%)

diff --git a/fern/assets/owning-the-model-stack/request-keying.png b/fern/assets/owning-the-model-stack/throttle-keying.png
similarity index 100%
rename from fern/assets/owning-the-model-stack/request-keying.png
rename to fern/assets/owning-the-model-stack/throttle-keying.png
diff --git a/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx b/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx
index cc9bd0638..fc0704cda 100644
--- a/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx
+++ b/fern/versions/v0.5.8/pages/concepts/architecture-and-performance.mdx
@@ -51,7 +51,7 @@ This guide explains the architecture, execution model, and how to tune performan
 ## Execution Model
 
 <Note title="Two execution engines">
-  The default execution path is the **async engine**, which dispatches work at the cell level and overlaps independent columns — see [Async Engine](#async-engine) below for its semantics. The legacy **sync engine** is still available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0` and is what this section describes. The public configuration knobs documented below (`buffer_size`, `max_parallel_requests`, error handling) apply to both engines; the differences are flagged inline.
+  The default execution path is the **async engine**, which dispatches work at the cell level and overlaps independent columns — see [Async Engine](#async-engine) below for its semantics. The legacy **sync engine** is still available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0` and is what this section describes. The configuration knobs documented below (`buffer_size`, `max_parallel_requests`, AIMD throttle config, error handling) apply to both engines; the differences are flagged inline.
 </Note>
 
 The sync engine processes datasets in **batches**, with **parallel** operations within each batch.
@@ -108,12 +108,12 @@ At any moment, the number of concurrent LLM requests is:
 ```python
 concurrent_requests = min(
     buffer_size,                # Records in current batch
-    current_request_limit,      # AIMD-managed limit (≤ max_parallel_requests)
+    current_throttle_limit,     # AIMD-managed limit (≤ max_parallel_requests)
     remaining_cells_in_column   # Cells left to generate
 )
 ```
 
-`max_parallel_requests` sets the **ceiling**. The actual limit (`current_request_limit`) is managed at runtime by adaptive request admission that reacts to rate-limit signals from the inference server:
+`max_parallel_requests` sets the **ceiling**. The actual limit (`current_throttle_limit`) is managed at runtime by an AIMD (Additive Increase / Multiplicative Decrease) controller that reacts to rate-limit signals from the inference server:
 
 - **On the first 429 in a burst**: the limit is reduced by a configurable factor (default: 25% reduction) and a cooldown is applied. Further 429s from already in-flight requests in the same burst do not reduce the limit again — they release their permits and hold the limit steady.
 - **After consecutive successes**: the limit increases by 1 (by default) until it reaches the ceiling or a stabilized rate-limit threshold.
@@ -121,7 +121,7 @@ concurrent_requests = min(
 This means Data Designer automatically finds the right concurrency level for your server without manual tuning.
 
 <Note title="Engine paths">
-  Adaptive request admission is fully active on the default **async engine**. The legacy **sync engine** is available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0`; on that path 429s are first retried at the HTTP transport layer and AIMD only engages as a fallback. See [Async engine](#async-engine) below.
+  AIMD adaptive concurrency is fully active on the default **async engine**. The legacy **sync engine** is available for one transitional release via `DATA_DESIGNER_ASYNC_ENGINE=0`; on that path 429s are first retried at the HTTP transport layer and AIMD only engages as a fallback. See [Async engine](#async-engine) below.
 </Note>
 
 **Example**: With `buffer_size=100` and `max_parallel_requests=32`, Data Designer starts sending up to 32 requests in parallel. If the server returns 429s, concurrency drops automatically (e.g., to 24, then 18) and recovers once the server catches up.
@@ -198,7 +198,7 @@ Only resume datasets from trusted artifact directories. Resume reads local `meta
 
 ### `max_parallel_requests` (InferenceParams)
 
-Sets the **maximum** concurrent LLM API calls **per model**. This is the ceiling that adaptive request admission can ramp up to — the actual concurrency at runtime may be lower if the server signals rate limits.
+Sets the **maximum** concurrent LLM API calls **per model**. This is the ceiling that the AIMD throttle controller can ramp up to — the actual concurrency at runtime may be lower if the server signals rate limits.
 
 ```python
 import data_designer.config as dd
@@ -215,7 +215,7 @@ model = dd.ModelConfig(
 
 **Default**: 4
 
-**When to increase**: Your inference backend has high throughput capacity, you're using a cloud API with generous rate limits, or you're running vLLM/TensorRT-LLM with multiple GPUs. With adaptive request admission, setting an aggressively high value is safer than before — the system will self-correct downward if the server can't keep up. The salvage queue on the async engine (default) reclaims failed rows; on the sync engine the initial burst of 429s before AIMD stabilizes can drop rows, so start with a more conservative ceiling if you've opted into sync.
+**When to increase**: Your inference backend has high throughput capacity, you're using a cloud API with generous rate limits, or you're running vLLM/TensorRT-LLM with multiple GPUs. With AIMD, setting an aggressively high value is safer than before — the system will self-correct downward if the server can't keep up. The salvage queue on the async engine (default) reclaims failed rows; on the sync engine the initial burst of 429s before AIMD stabilizes can drop rows, so start with a more conservative ceiling if you've opted into sync.
 
 **When to decrease**: You want to cap resource usage to a known safe level, or you want more predictable/debuggable execution.
 
@@ -223,7 +223,7 @@ model = dd.ModelConfig(
 Finding the optimal value
 The right value depends on your inference stack and model. Self-hosted vLLM servers can often handle values as high as 256, 512, or even 1024 depending on your hardware.
 
-With adaptive request admission, a practical approach is to set `max_parallel_requests` to the **upper bound** you're comfortable with and let the request controller find the sustainable level automatically. If you see frequent 429 → recovery cycles in the logs, your ceiling is above the server's true capacity but the system is handling it. If you never see any request-admission activity, you may have room to increase the ceiling further.
+With AIMD, a practical approach is to set `max_parallel_requests` to the **upper bound** you're comfortable with and let the throttle controller find the sustainable level automatically. If you see frequent 429 → recovery cycles in the logs, your ceiling is above the server's true capacity but the system is handling it. If you never see any throttle activity, you may have room to increase the ceiling further.
 
 **Benchmark approach**: Run a small dataset (e.g., 100 records) with increasing `max_parallel_requests` values (4 → 8 → 16 → 32 → ...) and measure generation time. Stop increasing when the runtime stops decreasing—that's when your inference server is saturated.
 </Tip>
@@ -245,9 +245,39 @@ designer.set_run_config(run_config)
 
 ---
 
-### Adaptive Request Admission
+### Adaptive Throttling (RunConfig)
 
-Data Designer uses AIMD (Additive Increase / Multiplicative Decrease) request admission to automatically adjust concurrency per provider/model/domain based on rate-limit feedback from the inference server. This is an internal runtime controller, not a public `RunConfig` knob. Set `max_parallel_requests` as the user-facing ceiling and inspect `AsyncCapacityPlan`/logs to understand the effective runtime limits.
+Data Designer uses an AIMD (Additive Increase / Multiplicative Decrease) controller to automatically adjust concurrency per model based on rate-limit feedback from the inference server. The defaults work well for most workloads. Override them via `ThrottleConfig` only when you understand the trade-offs.
+
+<Note title="Engine paths">
+  Adaptive throttling is fully active on the default **async engine**, where 429 responses propagate directly to the AIMD controller. On the legacy **sync engine** (`DATA_DESIGNER_ASYNC_ENGINE=0`), 429s are first retried at the HTTP transport layer; `ThrottleConfig` settings only take effect as a fallback if transport retries are exhausted.
+</Note>
+
+```python
+import data_designer.config as dd
+from data_designer.interface import DataDesigner
+
+run_config = dd.RunConfig(
+    throttle=dd.ThrottleConfig(
+        reduce_factor=0.75,       # Multiply limit by this on a 429 (default: 0.75)
+        additive_increase=1,      # Add this many slots after success_window successes (default: 1)
+        success_window=25,        # Consecutive successes before increasing (default: 25)
+        cooldown_seconds=2.0,     # Pause after a 429 when no Retry-After header (default: 2.0)
+        ceiling_overshoot=0.10,   # Probe 10% above observed server limit (default: 0.10)
+    ),
+)
+
+designer = DataDesigner()
+designer.set_run_config(run_config)
+```
+
+| Parameter | Default | Effect |
+|-----------|---------|--------|
+| `reduce_factor` | 0.75 | How aggressively to cut concurrency on a 429. Lower = more aggressive. |
+| `additive_increase` | 1 | Slots added per recovery step. Higher = faster ramp-up, but riskier. |
+| `success_window` | 25 | Consecutive successes required before each increase step. |
+| `cooldown_seconds` | 2.0 | Pause duration after a 429 (used when the server doesn't send `Retry-After`). |
+| `ceiling_overshoot` | 0.10 | Fraction above the observed rate-limit ceiling the controller is allowed to probe. |
 
 <Tip>
 How it works in practice
@@ -283,11 +313,11 @@ designer.set_run_config(run_config)
 
 ## Async Engine
 
-The async engine is the default execution path. It dispatches work at the cell level rather than the column level, so independent columns overlap in time and provider/model/domain request resources tune themselves independently. See the [Async All the Way Down](/dev-notes/async-all-the-way-down) dev note for the full architecture.
+The async engine is the default execution path. It dispatches work at the cell level rather than the column level, so independent columns overlap in time and per-(provider, model) AIMD pools tune themselves independently. See the [Async All the Way Down](/dev-notes/async-all-the-way-down) dev note for the full architecture.
 
 ### Per-model timeouts drive every deadline
 
-The `inference_parameters.timeout` field on a `ModelConfig` sets the per-request HTTP timeout. The same value also drives the sync→async bridge that custom columns use when they call `model.generate()`. There is no separate queue-wait deadline — waits scale with provider speed and adaptive request admission. Slow self-hosted endpoints (e.g. large models on a single GPU) only need this one knob raised:
+The `inference_parameters.timeout` field on a `ModelConfig` sets the per-request HTTP timeout. The same value also drives the sync→async bridge that custom columns use when they call `model.generate()`. There is no separate queue-wait deadline — waits scale with provider speed and AIMD's adaptive concurrency. Slow self-hosted endpoints (e.g. large models on a single GPU) only need this one knob raised:
 
 ```python
 import data_designer.config as dd
@@ -336,8 +366,8 @@ DATA_DESIGNER_ASYNC_ENGINE=0 python my_pipeline.py
 
 | Problem | Symptom | Solution |
 |---------|---------|----------|
-| **Low throughput** | Low GPU utilization | Increase `max_parallel_requests` and/or `buffer_size`. If request admission has self-reduced due to earlier 429s (check logs for "concurrency reduced" messages), the server may need more capacity or you can wait for AIMD recovery. |
-| **Frequent 429 → recovery cycles** | Logs show repeated concurrency drops and ramp-ups | The `max_parallel_requests` ceiling is above the server's sustained capacity. This is handled automatically, but you can lower the ceiling to reduce the sawtooth. |
+| **Low throughput** | Low GPU utilization | Increase `max_parallel_requests` and/or `buffer_size`. If the throttle has self-reduced due to earlier 429s (check logs for "concurrency reduced" messages), the server may need more capacity or you can wait for AIMD recovery. |
+| **Frequent 429 → recovery cycles** | Logs show repeated concurrency drops and ramp-ups | The `max_parallel_requests` ceiling is above the server's sustained capacity. This is handled automatically, but you can lower the ceiling to reduce the sawtooth or tune `reduce_factor` / `success_window`. |
 | **Long tail of slow generations** | Most records fast, few very slow | Reduce `max_conversation_restarts`, simplify schemas, improve prompts |
 | **Multi-model idle periods** | One model busy, others idle | Reduce `buffer_size` for faster cycling, or consolidate models |
 | **Memory errors** | OOM crashes | Reduce `buffer_size` and `max_parallel_requests` |
@@ -350,7 +380,7 @@ DATA_DESIGNER_ASYNC_ENGINE=0 python my_pipeline.py
 1. **Start with defaults** for initial development — AIMD handles rate-limit adaptation automatically
 2. **Profile your workload**: How many LLM columns? How many records? What models?
 3. **Identify bottleneck**: Low GPU util → increase `max_parallel_requests` (AIMD will self-correct if you overshoot). Memory issues → decrease `buffer_size`. Long tails → tune retry settings.
-4. **Check request-admission logs**: Look for "concurrency reduced" / "concurrency increased" messages to understand whether rate limits are the bottleneck
+4. **Check throttle logs**: Look for "concurrency reduced" / "concurrency increased" messages to understand whether rate limits are the bottleneck
 5. **Iterate**: Make one change at a time, measure impact before next change
 
 ---
diff --git a/fern/versions/v0.5.8/pages/devnotes/index.mdx b/fern/versions/v0.5.8/pages/devnotes/index.mdx
index fa5545d2c..2bab4a5fd 100644
--- a/fern/versions/v0.5.8/pages/devnotes/index.mdx
+++ b/fern/versions/v0.5.8/pages/devnotes/index.mdx
@@ -44,7 +44,7 @@ Welcome to NeMo Data Designer Dev Notes — in-depth guides, benchmark write-ups
   <BlogCard
     href="/dev-notes/owning-the-model-stack"
     title="Owning the Model Stack"
-    description="Adaptive concurrency, request-resource keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
+    description="Adaptive concurrency, throttle keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
     date="Mar 25, 2026"
     authors={["nmulepati"]}
     image={<img src="/assets/owning-the-model-stack/native-model-client-hero.png" alt="" loading="lazy" />}
diff --git a/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx b/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx
index c81f1551d..631d1ab18 100644
--- a/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx
+++ b/fern/versions/v0.5.8/pages/devnotes/posts/async-all-the-way-down.mdx
@@ -58,7 +58,7 @@ Getting this right required solving three problems at different levels of the st
 
 <div style="text-align: center;" markdown>
 
-![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, and request admission for provider capacity](/assets/async-all-the-way-down/architecture-layers.svg)
+![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, ThrottleManager for rate limits](/assets/async-all-the-way-down/architecture-layers.svg)
 
 </div>
 
@@ -66,42 +66,43 @@ Getting this right required solving three problems at different levels of the st
 
 At the top sits the `AsyncTaskScheduler`. It builds an `ExecutionGraph` from your column configs using [Kahn's algorithm](https://en.wikipedia.org/wiki/Topological_sorting#Kahn's_algorithm) for topological ordering, then tracks per-cell completion via a `CompletionTracker`. When a cell completes, the tracker determines which downstream cells are now ready and pushes them onto the dispatch queue.
 
-The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: enqueue ready tasks, ask `FairTaskQueue` to select the next eligible task, acquire a scheduler-resource lease from `TaskAdmissionController`, commit the queue selection, then spawn a worker. When the worker completes, mark the cell done, release the lease, and make any newly-ready downstream tasks visible. The loop runs until every cell in every row group has completed or been dropped.
+The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: pull ready tasks from the frontier, acquire a [semaphore](https://en.wikipedia.org/wiki/Semaphore_(programming)) slot, spawn a worker. When the worker completes, mark the cell done, which may add new tasks to the frontier. The loop runs until every cell in every row group has completed or been dropped.
 
-There's a subtlety in how the scheduler manages task-stage pressure. Scheduler resources and provider request resources are separate. The scheduler decides which dependency-ready cell may become a running worker; `ModelRequestExecutor` and request admission decide when an individual provider call may execute. Keeping those ledgers separate prevents provider cooldown or request backpressure from masquerading as DAG readiness.
+There's a subtlety in how the scheduler manages its task slots, and getting it right required a delicate dance between two semaphores. A naïve approach would hold a submission slot for the entire lifetime of a task. That's fine for the outbound HTTP call — the slot is released before the request goes out. But the `ThrottleManager` can impose an internal timeout while waiting for a permit during AIMD cooldown, and *that* wait would hold the submission slot hostage. If enough tasks are blocked waiting for throttle permits, the scheduler can't dispatch new work even when the frontier has ready tasks.
 
-The durable scheduler contract is selection plus admission plus commit:
+The fix is a one-way semaphore handoff. The scheduler maintains two pools: a *submission* semaphore that caps how many tasks can be dispatched, and an *LLM-wait* semaphore (sized larger) for tasks that are blocked on a model call. When a task is about to call the model, it acquires an LLM-wait slot and releases its submission slot in the same atomic operation — stepping from one pool to the other mid-flight. The dispatch loop immediately sees a free submission slot and can send another task. When the LLM responds, the LLM-wait slot is released. Non-LLM generators (samplers, Jinja expressions) skip the handoff and hold their submission slot for the full duration, which is fine because they complete quickly.
 
 ```py
-selection = queue.select_next(lambda item, view: admission.is_eligible(item, view))
-decision = admission.try_acquire(selection.item, selection.queue_view)
-committed = queue.commit(selection)
-spawn_worker(committed, decision)
+if is_llm_bound:
+    await self._llm_wait_semaphore.acquire()
+    holds_llm_wait = True
+    self._submission_semaphore.release()
+    holds_submission = False
 ```
 
-This keeps ready-work ordering, scheduler-resource accounting, and request-capacity adaptation explicit. `SchedulingMetadata` describes static generator pressure, `TaskSchedulingResolver` turns it into scheduler inputs, `FairTaskQueue` handles ready ordering, and `TaskAdmissionController` owns leases for scheduler resources.
+This keeps the dispatch loop saturated without unbounded coroutine growth — the submission semaphore controls how fast tasks enter, and the LLM-wait semaphore controls how many are waiting on the network.
 
 ### **Layer 2: Row-group admission**
 
-Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. Row-group admission limits how many groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
+Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. A semaphore limits how many row groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
 
 When a row group completes — all columns generated for all its rows — the buffer manager flushes it to disk and releases the memory. Partial results appear on disk during generation. If something fails, you keep everything that already checkpointed. This is also the basis for fault tolerance, discussed later — the unit of loss is a row group, not the entire run.
 
 ### **Layer 3: Adaptive rate limiting**
 
-At the bottom, each provider/model/request-domain resource gets [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) request admission. When the provider returns a 429, `AdaptiveRequestAdmissionController` reduces admitted concurrency for that request resource. On streaks of successful requests, it gradually increases. Because this happens per request resource, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](/dev-notes/owning-the-model-stack) dev note covers this layer in depth.
+At the bottom, each (provider, model) pair gets an independent concurrency pool with [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) rate adaptation. When the provider returns a 429, the pool cuts its concurrency. On streaks of successful requests, it gradually increases. Because this happens per-model, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](/dev-notes/owning-the-model-stack) dev note covers this layer in depth.
 
 ### **How they compose**
 
-The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The request-admission layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
+The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The throttle layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
 
 A single task's lifecycle makes the composition concrete:
 
 <div style="text-align: center;">
-  <img src="/assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier to fair queue to task admission to generator to request admission to LLM call to complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
+  <img src="/assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier → Dispatch → Generator → Throttle → LLM Call → Complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
 </div>
 
-A cell enters the frontier when its upstream dependencies are satisfied. The scheduler enqueues it, selects it through `FairTaskQueue`, acquires a `TaskAdmissionLease`, commits the selection, and hands the task to a worker. The worker runs the generator; any model call goes through `ModelRequestExecutor`, which acquires and releases the request-admission lease around provider execution. On completion, leases are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
+A cell enters the frontier when its upstream dependencies are satisfied. The dispatch loop acquires a submission slot and hands it to a worker. The worker runs the generator, which acquires a throttle permit before making the LLM call. On completion, permits are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
 
 ---
 
@@ -136,13 +137,13 @@ The pattern is clear: speedup scales with the amount of parallelism available in
 
 The **narrow** workload is a sequential chain with no cross-column parallelism. The async engine still ekes out a small gain from overlapping row-level dispatch, but there's no structural parallelism to exploit. This is expected: async can't speed up a fundamentally serial pipeline.
 
-The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model maps to its own request-admission resources. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
+The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model gets its own ThrottleManager pool. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
 
 ### **At higher record counts**
 
-The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, adaptive request admission backs off, and the backoff can cascade through downstream columns that were waiting on the rate-limited model's output.
+The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, the AIMD controller backs off, and the backoff can cascade through downstream columns that were waiting on the throttled model's output.
 
-This is where per-model request resources become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same provider capacity. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
+This is where the per-model throttle pools become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same pool. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
 
 The primary tuning lever is `max_parallel_requests` per model. Set it to a generous upper bound and let AIMD find the real ceiling. See the [Owning the Model Stack](/dev-notes/owning-the-model-stack) dev note for the full story on adaptive concurrency.
 
@@ -178,7 +179,7 @@ The performance numbers are satisfying, but raw throughput is only part of the p
 
 ### **Progress you can see**
 
-Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and request-admission layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
+Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and throttle layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
 
 ```
   column 'topic'    ████████████████████████████████████░░░░  89% | 890/1000 |  148.3 rec/s | eta 1s  | 0 failed
@@ -187,13 +188,13 @@ Because rows complete out of order and row groups checkpoint independently, resu
   column 'analysis' ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░  35% | 350/1000 |   87.5 rec/s | eta 7s  | 1 failed
 ```
 
-When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired scheduler resources, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
+When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired a semaphore slot, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
 
 ### **Fault tolerance**
 
 Failures in a long-running pipeline are not exceptional — they're expected. Model endpoints return 429s, connections time out, prompts produce unparseable output. The scheduler classifies errors into two buckets.
 
-Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding row-group admission capacity forever and blocking admission of new row groups.
+Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding its semaphore slot forever and blocking admission of new row groups.
 
 Non-retryable errors (malformed output, validation failures) drop the row immediately. The `CompletionTracker` knows which downstream tasks depended on that row and removes them from the frontier, so no work is wasted on a row that's already lost.
 
@@ -201,7 +202,7 @@ In both cases, completed row groups are already on disk. The unit of loss is at
 
 ### **Multi-model concurrency**
 
-Multi-model pipelines are where the architecture pays for itself. With independent request resources per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
+Multi-model pipelines are where the architecture pays for itself. With independent throttle pools per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
 
 ### **Adoption**
 
@@ -213,7 +214,7 @@ Adoption is opt-in. Set `DATA_DESIGNER_ASYNC_ENGINE=1` in your environment. Your
 
 This was a ground-up rebuild of the execution layer, delivered across six PRs over four weeks.
 
-It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The historical predecessor to request admission and task-stage capacity control landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
+It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The `ThrottledModelClient` and dual-semaphore scheduler landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
 
 The symmetric bridging was critical for adoption. Every `ColumnGenerator` has both a `generate()` and an `agenerate()` method. Implement one, and the base class synthesizes the other:
 
@@ -276,7 +277,7 @@ Key Resources:
 
 1. [NeMo Data Designer on GitHub](https://github.com/NVIDIA-NeMo/DataDesigner)
 2. [Data Designer Documentation](/getting-started/welcome)
-3. [Owning the Model Stack: Adaptive Concurrency](/dev-notes/owning-the-model-stack) — companion dev note on the native client layer and AIMD request admission
+3. [Owning the Model Stack: Adaptive Concurrency](/dev-notes/owning-the-model-stack) — companion dev note on the native client layer and AIMD throttling
 4. [Async Engine Plan (#346)](https://github.com/NVIDIA-NeMo/DataDesigner/issues/346) — original issue and architecture plan
 
 *Want to learn more about NeMo Data Designer? Check out our [documentation](/getting-started/welcome) and start building your own synthetic data pipelines today.*
diff --git a/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx b/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx
index 0eec884c5..ad9136362 100644
--- a/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx
+++ b/fern/versions/v0.5.8/pages/devnotes/posts/owning-the-model-stack.mdx
@@ -10,11 +10,11 @@ import { Authors } from "@/components/Authors";
 
 Picture this: you're generating a million-record dataset. Thirty two concurrent requests per model, three models in the pipeline, two providers. Everything hums along for the first ten minutes — then one provider starts returning 429s, your retry logic kicks in, and suddenly you're in a feedback loop where retries *cause* more 429s. The run stalls. You restart with lower concurrency, waste throughput for hours, and wonder if there's a better way.
 
-There is. This post is about the native model client layer we built with adaptive request admission (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
+There is. This post is about the native model client layer we built with adaptive throttling (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
 
 {/* more */}
 
-![From chaotic request flow to calibrated concurrency via adaptive request admission](/assets/owning-the-model-stack/native-model-client-hero.png)
+![From chaotic request flow to calibrated concurrency via adaptive throttling](/assets/owning-the-model-stack/native-model-client-hero.png)
 
 ## **Why We Made the Move**
 
@@ -40,9 +40,9 @@ From top to bottom:
 
 1. **ModelFacade**: orchestrates correction loops, MCP tool-calling, and usage tracking. This is the public API. Column generators talk to this layer, and it was untouched during the migration. If you've written a Data Designer pipeline, nothing about your code changes.
 
-2. **ModelRequestExecutor**: the request execution layer. It maps every outbound model call to a provider/model/domain request resource, acquires a request-admission lease before provider execution, releases it on every terminal path, and feeds the outcome (success, 429, or error) back to request admission.
+2. **ThrottledModelClient**: the new layer. It's a decorator around `HttpModelClient` — same `ModelClient` protocol, but every outbound call is wrapped with a throttle permit: acquire a concurrency slot before the call, release it after, and feed the outcome (success, 429, or error) back to `ThrottleManager`. This is where adaptive throttling lives.
 
-3. **AdaptiveRequestAdmissionController**: the Additive Increase / Multiplicative Decrease (AIMD) controller used by `ModelRequestExecutor`. A single shared controller is created at pipeline startup and shared across all model clients. It owns the mutable request-admission state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
+3. **ThrottleManager**: the Additive Increase / Multiplicative Decrease (AIMD) controller that `ThrottledModelClient` delegates to. A single instance is created at pipeline startup and shared across all model clients. It owns all the mutable concurrency state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
 
 4. **HttpModelClient**: an abstract base class that defines the interface for all provider adapters. It owns the shared `httpx` transport lifecycle — connection pooling, timeouts, and transport-level retries for transient failures (502, 503, 504). Boring but important.
 
@@ -52,7 +52,7 @@ From top to bottom:
 
 The boundary between `ModelFacade` and the client layer is defined by canonical types. `ChatCompletionRequest`, `ChatCompletionResponse`, `EmbeddingRequest`, `EmbeddingResponse`, `ImageGenerationRequest`, `ImageGenerationResponse`, and `ProviderError`. These are plain dataclasses. No provider SDK objects cross this line. A `ModelClient` protocol defines the contract that all adapters implement, and that's the only interface the rest of the system sees.
 
-## **Adaptive Request Admission: The Centerpiece**
+## **Adaptive Throttling: The Centerpiece**
 
 With this client stack in place, we had the foundation to build something that wasn't possible before. Adaptive concurrency control. Let's start with the problem.
 
@@ -97,23 +97,23 @@ Here's a subtlety that bit us during testing. When the system is running at capa
 
 Real pipelines aren't simple. A single provider+model combination might serve chat completions, embeddings, and image generation, potentially on different rate-limit budgets. And multiple [model aliases](/concepts/models/model-configs) in your pipeline might point to the same underlying provider and model (say, one alias for generation and another for judging, both hitting the same NVIDIA endpoint).
 
-Request admission handles this with two-level keying:
+The throttle manager handles this with two-level keying:
 
 <div style="text-align: center;" markdown>
 
-![Two-level request-resource keying: global cap per provider+model, independent domain states for chat, embedding, image](/assets/owning-the-model-stack/request-keying.png)
+![Two-level throttle keying: global cap per provider+model, independent domain states for chat, embedding, image](/assets/owning-the-model-stack/throttle-keying.png)
 
 </div>
 
 - **Global cap**: keyed by `(provider_name, model_id)`. When multiple model aliases target the same provider and model, the effective max is `min()` of their configured `max_parallel_requests`. This enforces the most conservative limit for shared upstream capacity, because the provider doesn't care what you *call* the model, it sees the same API key.
 
-- **Domain state**: keyed by `(provider_name, model_id, request_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
+- **Domain state**: keyed by `(provider_name, model_id, throttle_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
 
 The practical effect is that a burst of 429s on the chat route doesn't starve embedding requests, and vice versa. Each route adapts to its own capacity independently while respecting the shared upstream limit.
 
 ## **The Retry Boundary**
 
-There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire adaptive request-admission system.
+There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire throttling system.
 
 The transport layer (via `httpx` with `RetryTransport`) handles transient server failures like 502, 503, 504, and connection errors. These are hiccups. The server is temporarily broken. Retry with exponential backoff and jitter, and move on.
 
@@ -121,25 +121,37 @@ But **429 is explicitly excluded from transport retries**.
 
 <div style="text-align: center;" markdown>
 
-![Retry boundary: 502/503/504 retried at transport, 429 passed through to ModelRequestExecutor for AIMD feedback](/assets/owning-the-model-stack/retry-boundary.png)
+![Retry boundary: 502/503/504 retried at transport, 429 passed through to ThrottledModelClient for AIMD feedback](/assets/owning-the-model-stack/retry-boundary.png)
 
 </div>
 
-Why? Because if the retry layer swallows 429s, request admission never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ModelRequestExecutor` so it can release the request lease as rate-limited, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the request-admission path before making another HTTP call.
+Why? Because if the retry layer swallows 429s, the throttle manager never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ThrottledModelClient` so it can call `release_rate_limited()`, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the throttle acquire path, waiting for a permit, before making another HTTP call.
 
-The split is clean and worth remembering. Transport retries handle *server problems*. Request admission handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
+The split is clean and worth remembering. Transport retries handle *server problems*. Throttle adaptation handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
 
-One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ModelRequestExecutor` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](/dev-notes/async-all-the-way-down) for the full story on the async engine.
+One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ThrottledModelClient` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](/dev-notes/async-all-the-way-down) for the full story on the async engine.
 
 ## **Configuration**
 
-Adaptive request admission is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
+The throttle system is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
+
+For workloads where you want to fine-tune the adaptation behavior, `ThrottleConfig` is available on `RunConfig`:
 
 ```python
 import data_designer.config as dd
 from data_designer.interface import DataDesigner
 
 data_designer = DataDesigner()
+data_designer.set_run_config(
+    dd.RunConfig(
+        throttle=dd.ThrottleConfig(
+            reduce_factor=0.75,
+            success_window=25,
+            cooldown_seconds=2.0,
+            ceiling_overshoot=0.10,
+        )
+    )
+)
 config_builder = dd.DataDesignerConfigBuilder(
     model_configs=[
         dd.ModelConfig(
@@ -161,11 +173,21 @@ create_result = data_designer.create(
 )
 ```
 
-Most users will never need more than `max_parallel_requests`. The system adapts automatically, and capacity diagnostics are exposed through runtime logs and `AsyncCapacityPlan` rather than public controller tuning knobs.
+| Parameter | Default | What it does |
+|---|---|---|
+| `reduce_factor` | 0.75 | Multiplicative decrease on 429 (0.75 = reduce by 25%) |
+| `additive_increase` | 1 | How much to increase the limit after a success window |
+| `success_window` | 25 | Consecutive successes before additive increase |
+| `cooldown_seconds` | 2.0 | Default cooldown when no `Retry-After` header |
+| `ceiling_overshoot` | 0.10 | How far above the observed ceiling to probe (10%) |
+
+In practice, the parameter most worth adjusting is `success_window`. A smaller window (say, 10) makes the system more aggressive about reclaiming throughput after a pullback, useful when you know the provider's capacity fluctuates quickly. A larger window (say, 50) makes it more conservative, better for providers with strict, stable rate limits where you'd rather not probe at all.
+
+Most users will never need to touch any of these. The system adapts automatically.
 
 ## **What It Looks Like in the Logs**
 
-Request admission logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
+`ThrottleManager` logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
 
 ```
 # When the system hits a 429 and cuts concurrency:
@@ -188,9 +210,9 @@ Reading these lines in sequence tells you exactly what happened: where the syste
 
 ## **Where This Leaves Us**
 
-This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates request state per route, and separates retry logic from rate-limit adaptation. Adaptive request admission is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](/dev-notes/async-all-the-way-down) for details.
+This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates throttle state per route, and separates retry logic from rate-limit adaptation. Adaptive throttling is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](/dev-notes/async-all-the-way-down) for details.
 
-For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without public controller tuning.
+For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without any tuning. If you want finer control, `ThrottleConfig` is there — but the goal is that you spend your time designing datasets, not tuning concurrency knobs.
 
 Key Resources:
 
diff --git a/fern/versions/v0.5.9/pages/devnotes/index.mdx b/fern/versions/v0.5.9/pages/devnotes/index.mdx
index 504c4b6f7..1d5fe68da 100644
--- a/fern/versions/v0.5.9/pages/devnotes/index.mdx
+++ b/fern/versions/v0.5.9/pages/devnotes/index.mdx
@@ -44,7 +44,7 @@ Welcome to NeMo Data Designer Dev Notes — in-depth guides, benchmark write-ups
   <BlogCard
     href="/dev-notes/owning-the-model-stack"
     title="Owning the Model Stack"
-    description="Adaptive concurrency, request-resource keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
+    description="Adaptive concurrency, throttle keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
     date="Mar 25, 2026"
     authors={["nmulepati"]}
     image={<img src="/assets/owning-the-model-stack/native-model-client-hero.png" alt="" loading="lazy" />}
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index 231aec159..df19c5716 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -22,6 +22,7 @@
     AsyncCapacityPlan,
     AsyncCapacityRuntimeSnapshot,
     CapacityValue,
+    RequestAdmissionConfigSnapshot,
     RowGroupAdmission,
 )
 from data_designer.engine.context import current_row_group
@@ -57,6 +58,9 @@
 from data_designer.engine.errors import DataDesignerError
 from data_designer.engine.models.clients.errors import ProviderError
 from data_designer.engine.models.errors import RETRYABLE_MODEL_ERRORS, GenerationValidationFailureError
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
+from data_designer.engine.models.request_admission.resources import RequestResourceKey
+from data_designer.engine.models.resources import ProviderModelKey, ProviderModelStaticCap
 from data_designer.engine.observability import (
     RuntimeCorrelation,
     SchedulerAdmissionEvent,
@@ -280,6 +284,10 @@ def __init__(
         self._observed_max_row_groups_in_flight = 0
         self._observed_max_task_leases_by_resource: dict[str, int] = {}
         self._observed_max_queued_by_group: dict[str, int] = {}
+        self._observed_max_request_waiters_by_resource: dict[RequestResourceKey, int] = {}
+        self._observed_max_request_in_flight_by_resource: dict[RequestResourceKey, int] = {}
+        self._observed_max_provider_model_aggregate_in_flight: dict[ProviderModelKey, int] = {}
+        self._observed_max_request_domain_current_limits: dict[RequestResourceKey, int] = {}
         self._adaptive_row_group_admission = adaptive_row_group_admission
         self._row_group_admission_hard_cap = max(1, max_concurrent_row_groups)
         self._row_group_admission_target = (
@@ -438,6 +446,26 @@ def _record_observed_task_state(self) -> None:
         for group, count in queue_view.queued_by_group.items():
             label = f"{group.kind}:{'/'.join(group.identity)}"
             self._observed_max_queued_by_group[label] = max(self._observed_max_queued_by_group.get(label, 0), count)
+        if self._request_pressure_provider is None:
+            return
+        for resource, snapshot in self._request_pressure_provider.snapshots().items():
+            self._observed_max_request_waiters_by_resource[resource] = max(
+                self._observed_max_request_waiters_by_resource.get(resource, 0),
+                snapshot.waiters,
+            )
+            self._observed_max_request_in_flight_by_resource[resource] = max(
+                self._observed_max_request_in_flight_by_resource.get(resource, 0),
+                snapshot.in_flight_count,
+            )
+            self._observed_max_request_domain_current_limits[resource] = max(
+                self._observed_max_request_domain_current_limits.get(resource, 0),
+                snapshot.current_limit,
+            )
+        for provider_model, snapshot in self._request_pressure_provider.global_snapshots().items():
+            self._observed_max_provider_model_aggregate_in_flight[provider_model] = max(
+                self._observed_max_provider_model_aggregate_in_flight.get(provider_model, 0),
+                snapshot.aggregate_in_flight,
+            )
 
     def _emit_scheduler_health_snapshot(self, reason: str) -> None:
         self._emit_scheduler_event(
@@ -1758,6 +1786,53 @@ def task_admission_snapshot(self) -> object:
     def capacity_plan(self) -> AsyncCapacityPlan:
         """Return the scheduler-side async capacity explanation for this run."""
         task_view = self._task_admission.view()
+        request_snapshots = (
+            dict(self._request_pressure_provider.snapshots()) if self._request_pressure_provider is not None else {}
+        )
+        provider_snapshots = (
+            dict(self._request_pressure_provider.global_snapshots())
+            if self._request_pressure_provider is not None
+            else {}
+        )
+        request_resources = tuple(sorted(request_snapshots))
+        provider_model_static_caps = {
+            provider_model: ProviderModelStaticCap(
+                cap=snapshot.static_cap,
+                aliases=snapshot.aliases,
+                raw_caps=snapshot.raw_caps,
+            )
+            for provider_model, snapshot in provider_snapshots.items()
+        }
+        request_config = self._request_pressure_provider.config if self._request_pressure_provider is not None else None
+        request_config_snapshot = (
+            RequestAdmissionConfigSnapshot.from_config(request_config)
+            if isinstance(request_config, RequestAdmissionConfig)
+            else None
+        )
+        request_domain_initial_limits: dict[RequestResourceKey, int] = {}
+        if request_config_snapshot is not None:
+            request_domain_initial_limits.update(request_config_snapshot.initial_limits)
+        for resource, snapshot in request_snapshots.items():
+            configured_initial = (
+                request_config_snapshot.initial_limits.get(resource) if request_config_snapshot is not None else None
+            )
+            request_domain_initial_limits[resource] = (
+                max(1, min(configured_initial, snapshot.effective_max))
+                if configured_initial is not None
+                else snapshot.effective_max
+            )
+        request_domain_current_limits = {
+            resource: snapshot.current_limit for resource, snapshot in request_snapshots.items()
+        }
+        request_domain_effective_max = {
+            resource: snapshot.effective_max for resource, snapshot in request_snapshots.items()
+        }
+        request_domain_blocked_until = {
+            resource: snapshot.blocked_until_monotonic for resource, snapshot in request_snapshots.items()
+        }
+        provider_model_aggregate_in_flight = {
+            provider_model: snapshot.aggregate_in_flight for provider_model, snapshot in provider_snapshots.items()
+        }
         return AsyncCapacityPlan(
             configured=AsyncCapacityConfigured(
                 buffer_size=CapacityValue(value=self._buffer_size, source="run_config"),
@@ -1779,24 +1854,28 @@ def capacity_plan(self) -> AsyncCapacityPlan:
                     source="engine_internal_config",
                 ),
                 request_resources=CapacityValue(
-                    value=(),
+                    value=request_resources,
                     source="runtime_snapshot",
-                    missing_reason="request admission resources are reported by the model registry request controller",
+                    missing_reason=None if request_resources else "request admission has not observed any resources",
                 ),
                 provider_model_static_caps=CapacityValue(
-                    value={},
-                    source="runtime_snapshot",
-                    missing_reason="request admission caps are reported by the model registry request controller",
+                    value=provider_model_static_caps,
+                    source="model_metadata",
+                    missing_reason=None if provider_model_static_caps else "request admission has no registered models",
                 ),
                 request_domain_initial_limits=CapacityValue(
-                    value={},
-                    source="runtime_snapshot",
-                    missing_reason="request admission limits are reported by the model registry request controller",
+                    value=request_domain_initial_limits,
+                    source="engine_internal_config" if request_config_snapshot is not None else "runtime_snapshot",
+                    missing_reason=None
+                    if request_domain_initial_limits
+                    else "request admission has not observed any domain limits",
                 ),
                 request_admission_config=CapacityValue(
-                    value=None,
-                    source="runtime_snapshot",
-                    missing_reason="request admission config is owned by the model registry request controller",
+                    value=request_config_snapshot,
+                    source="engine_internal_config",
+                    missing_reason=None
+                    if request_config_snapshot is not None
+                    else "request admission config is not exposed by the pressure provider",
                 ),
                 transport_pool_limits=CapacityValue(
                     value={},
@@ -1804,11 +1883,30 @@ def capacity_plan(self) -> AsyncCapacityPlan:
                     missing_reason="transport pool utilization is adapter-specific",
                 ),
             ),
-            runtime_snapshot=AsyncCapacityRuntimeSnapshot(),
+            runtime_snapshot=AsyncCapacityRuntimeSnapshot(
+                request_domain_current_limits=request_domain_current_limits,
+                request_domain_effective_max=request_domain_effective_max,
+                request_domain_blocked_until=request_domain_blocked_until,
+                provider_model_aggregate_in_flight=provider_model_aggregate_in_flight,
+            ),
             observed_maxima=AsyncCapacityObservedMaxima(
                 row_groups_in_flight=self._observed_max_row_groups_in_flight,
                 queued_tasks_by_group=dict(self._observed_max_queued_by_group),
                 task_leases_by_resource=dict(self._observed_max_task_leases_by_resource or task_view.leased_resources),
+                request_waiters_by_resource=dict(
+                    self._observed_max_request_waiters_by_resource
+                    or {resource: snapshot.waiters for resource, snapshot in request_snapshots.items()}
+                ),
+                request_in_flight_by_resource=dict(
+                    self._observed_max_request_in_flight_by_resource
+                    or {resource: snapshot.in_flight_count for resource, snapshot in request_snapshots.items()}
+                ),
+                provider_model_aggregate_in_flight=dict(
+                    self._observed_max_provider_model_aggregate_in_flight or provider_model_aggregate_in_flight
+                ),
+                request_domain_current_limits=dict(
+                    self._observed_max_request_domain_current_limits or request_domain_current_limits
+                ),
                 transport_pool_utilization=None,
             ),
         )
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
index 73737629e..b19871319 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
@@ -1045,6 +1045,7 @@ def on_before_checkpoint(rg_id: int, rg_size: int) -> None:
             buffer_size=buffer_size,
             progress_interval=self._resource_provider.run_config.progress_interval,
             progress_bar=self._resource_provider.run_config.progress_bar,
+            request_pressure_provider=self._resource_provider.model_registry.request_admission,
         )
         return scheduler, buffer_manager
 
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_admission.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_admission.py
index 4dffb47f7..89fb3e280 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_admission.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_admission.py
@@ -5,7 +5,7 @@
 
 import time
 import uuid
-from collections import Counter, defaultdict
+from collections import Counter, defaultdict, deque
 from collections.abc import Mapping
 from dataclasses import dataclass, field
 from typing import Literal
@@ -35,6 +35,7 @@
     "wrong_controller_generation",
     "unknown_lease",
 ]
+RELEASED_TASK_LEASE_HISTORY_LIMIT = 8192
 
 
 @dataclass(frozen=True)
@@ -113,6 +114,7 @@ def __init__(
         self._generation = uuid.uuid4().hex
         self._leases: dict[str, TaskAdmissionLease] = {}
         self._released: set[str] = set()
+        self._released_order: deque[str] = deque(maxlen=RELEASED_TASK_LEASE_HISTORY_LIMIT)
         self._leased_by_resource: Counter[SchedulerResourceKey] = Counter()
         self._leased_by_group: dict[TaskGroupKey, Counter[SchedulerResourceKey]] = defaultdict(Counter)
         self._running_by_group: Counter[TaskGroupKey] = Counter()
@@ -184,7 +186,7 @@ def release(self, lease: TaskAdmissionLease) -> ReleaseResult:
             self._release_diagnostics["stale_lease"] += 1
             return ReleaseResult(released=False, reason="stale_lease")
 
-        self._released.add(lease.lease_id)
+        self._remember_released(lease.lease_id)
         for resource, amount in active.resources.items():
             self._leased_by_resource[resource] = max(0, self._leased_by_resource[resource] - amount)
             self._leased_by_group[active.item.group.key][resource] = max(
@@ -259,3 +261,12 @@ def _missing_resources(
     def _apply_delta(self, delta: PolicyStateDelta) -> None:
         for key, change in delta.debt_changes.items():
             self._policy_debt[key] = max(0, self._policy_debt[key] + change)
+
+    def _remember_released(self, lease_id: str) -> None:
+        if lease_id in self._released:
+            return
+        maxlen = self._released_order.maxlen
+        if maxlen is not None and len(self._released_order) >= maxlen:
+            self._released.discard(self._released_order[0])
+        self._released.add(lease_id)
+        self._released_order.append(lease_id)
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
index 57483ec01..398d151a4 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/factory.py
@@ -18,6 +18,7 @@
 from data_designer.engine.secret_resolver import SecretResolver
 
 _SUPPORTED_PROVIDER_TYPES = ("openai", "anthropic")
+_NO_TRANSPORT_RETRY_CONFIG = RetryConfig(max_retries=0, retryable_status_codes=frozenset())
 
 
 def create_model_client(
@@ -71,13 +72,14 @@ def create_model_client(
     max_parallel = model_config.inference_parameters.max_parallel_requests
     raw_timeout = model_config.inference_parameters.timeout
     timeout_s = float(raw_timeout if raw_timeout is not None else 60)
+    adapter_retry_config = _NO_TRANSPORT_RETRY_CONFIG if request_admission is not None else retry_config
 
     if provider.provider_type == "openai":
         client: ModelClient = OpenAICompatibleClient(
             provider_name=provider.name,
             endpoint=provider.endpoint,
             api_key=api_key,
-            retry_config=retry_config,
+            retry_config=adapter_retry_config,
             max_parallel_requests=max_parallel,
             timeout_s=timeout_s,
             concurrency_mode=client_concurrency_mode,
@@ -87,7 +89,7 @@ def create_model_client(
             provider_name=provider.name,
             endpoint=provider.endpoint,
             api_key=api_key,
-            retry_config=retry_config,
+            retry_config=adapter_retry_config,
             max_parallel_requests=max_parallel,
             timeout_s=timeout_s,
             concurrency_mode=client_concurrency_mode,
@@ -111,6 +113,7 @@ def create_model_client(
             provider_name=provider.name,
             model_id=model_config.model,
             event_sink=request_event_sink,
+            retry_config=retry_config,
         )
 
     return client
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
index 01f917585..6782c16bc 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
@@ -5,11 +5,13 @@
 
 import asyncio
 import logging
+import time
 import uuid
 from typing import TYPE_CHECKING, TypeVar
 
 from data_designer.engine.models.clients.base import ModelClient
 from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
+from data_designer.engine.models.clients.retry import RetryConfig
 from data_designer.engine.models.clients.types import (
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -56,6 +58,7 @@ def __init__(
         model_id: str,
         event_sink: RequestAdmissionEventSink | None = None,
         resource_resolver: RequestResourceResolver | None = None,
+        retry_config: RetryConfig | None = None,
     ) -> None:
         self._inner = inner
         self._request_admission = request_admission
@@ -63,6 +66,7 @@ def __init__(
         self._model_id = model_id
         self._event_sink = event_sink
         self._resource_resolver = resource_resolver or RequestResourceResolver()
+        self._retry_config = retry_config or RetryConfig()
         self._event_sequence = 0
 
     @property
@@ -103,6 +107,16 @@ async def agenerate_image(self, request: ImageGenerationRequest) -> ImageGenerat
         return await self._execute_async(self._image_domain(request), lambda: self._inner.agenerate_image(request))
 
     def _execute_sync(self, domain: RequestDomain, call: Callable[[], _T]) -> _T:
+        for attempt in range(self._max_attempts()):
+            try:
+                return self._execute_sync_attempt(domain, call)
+            except ProviderError as exc:
+                if not self._should_retry(exc, attempt):
+                    raise
+                self._sleep_before_retry(attempt)
+        raise RuntimeError("unreachable request retry state")
+
+    def _execute_sync_attempt(self, domain: RequestDomain, call: Callable[[], _T]) -> _T:
         item = self._item(domain)
         try:
             lease = self._request_admission.acquire_sync(item)
@@ -141,6 +155,16 @@ def _execute_sync(self, domain: RequestDomain, call: Callable[[], _T]) -> _T:
             return result
 
     async def _execute_async(self, domain: RequestDomain, call: Callable[[], Awaitable[_T]]) -> _T:
+        for attempt in range(self._max_attempts()):
+            try:
+                return await self._execute_async_attempt(domain, call)
+            except ProviderError as exc:
+                if not self._should_retry(exc, attempt):
+                    raise
+                await self._async_sleep_before_retry(attempt)
+        raise RuntimeError("unreachable request retry state")
+
+    async def _execute_async_attempt(self, domain: RequestDomain, call: Callable[[], Awaitable[_T]]) -> _T:
         item = self._item(domain)
         try:
             lease = await self._request_admission.acquire_async(item)
@@ -187,6 +211,36 @@ async def _execute_async(self, domain: RequestDomain, call: Callable[[], Awaitab
             )
             return result
 
+    def _max_attempts(self) -> int:
+        return max(1, self._retry_config.max_retries + 1)
+
+    def _should_retry(self, exc: ProviderError, attempt: int) -> bool:
+        if attempt >= self._max_attempts() - 1:
+            return False
+        if isinstance(exc.__cause__, RequestAdmissionError):
+            return False
+        if exc.kind == ProviderErrorKind.RATE_LIMIT:
+            return False
+        if exc.status_code is not None:
+            return exc.status_code in self._retry_config.retryable_status_codes
+        return exc.kind == ProviderErrorKind.API_CONNECTION
+
+    def _sleep_before_retry(self, attempt: int) -> None:
+        delay = self._retry_delay_seconds(attempt)
+        if delay > 0.0:
+            time.sleep(delay)
+
+    async def _async_sleep_before_retry(self, attempt: int) -> None:
+        delay = self._retry_delay_seconds(attempt)
+        if delay > 0.0:
+            await asyncio.sleep(delay)
+
+    def _retry_delay_seconds(self, attempt: int) -> float:
+        if self._retry_config.backoff_factor <= 0.0:
+            return 0.0
+        delay = self._retry_config.backoff_factor * (2**attempt)
+        return min(delay, self._retry_config.max_backoff_wait)
+
     def _release_provider_error(self, lease: RequestAdmissionLease, exc: ProviderError) -> None:
         if exc.kind == ProviderErrorKind.RATE_LIMIT:
             outcome = RequestReleaseOutcome(kind="rate_limited", retry_after_seconds=exc.retry_after)
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index 9d63858fe..09e4dd512 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -9,7 +9,7 @@
 import threading
 import time
 import uuid
-from collections import Counter
+from collections import Counter, deque
 from collections.abc import Mapping
 from dataclasses import dataclass, field
 from typing import Literal, Protocol
@@ -46,6 +46,8 @@
     "shutdown",
     "hard_policy_denial",
 ]
+RELEASED_LEASE_HISTORY_LIMIT = 8192
+_TERMINAL_DENIAL_REASONS: frozenset[RequestDenyReason] = frozenset({"hard_policy_denial", "shutdown"})
 
 
 @dataclass(frozen=True)
@@ -119,6 +121,7 @@ def __init__(
         self._domains: dict[RequestResourceKey, AdaptiveRequestLimitState] = {}
         self._active_leases: dict[str, RequestAdmissionLease] = {}
         self._released: set[str] = set()
+        self._released_order: deque[str] = deque(maxlen=RELEASED_LEASE_HISTORY_LIMIT)
         self._aggregate_in_flight: Counter[ProviderModelKey] = Counter()
         self._aggregate_active_leases: Counter[ProviderModelKey] = Counter()
         self._sequence = 0
@@ -223,6 +226,11 @@ def acquire_sync(self, item: RequestAdmissionItem) -> RequestAdmissionLease:
                     if waiter.assigned_lease is not None:
                         return waiter.assigned_lease
                     now = time.monotonic()
+                    if (denied := self._terminal_denial_for(item, now)) is not None:
+                        self._remove_waiter_locked(waiter)
+                        events.append(self._request_event_locked("request_acquire_denied", item=item, decision=denied))
+                        self._condition.notify_all()
+                        raise RequestAdmissionError(denied)
                     if deadline is not None and now >= deadline:
                         self._remove_waiter_locked(waiter)
                         denied = RequestAdmissionDenied(
@@ -264,6 +272,11 @@ async def acquire_async(self, item: RequestAdmissionItem) -> RequestAdmissionLea
                     if waiter.assigned_lease is not None:
                         return waiter.assigned_lease
                     now = time.monotonic()
+                    if (denied := self._terminal_denial_for(item, now)) is not None:
+                        self._remove_waiter_locked(waiter)
+                        events.append(self._request_event_locked("request_acquire_denied", item=item, decision=denied))
+                        self._condition.notify_all()
+                        raise RequestAdmissionError(denied)
                     if deadline is not None and now >= deadline:
                         self._remove_waiter_locked(waiter)
                         denied = RequestAdmissionDenied(
@@ -335,7 +348,7 @@ def release(self, lease: RequestAdmissionLease, outcome: RequestReleaseOutcome)
                     )
                 )
             else:
-                self._released.add(lease.lease_id)
+                self._remember_released_locked(lease.lease_id)
                 resource = active.item.resource
                 provider_model = resource.provider_model_key
                 state = self._get_or_create_state(resource)
@@ -472,6 +485,21 @@ def _denial_for(self, item: RequestAdmissionItem, now: float) -> RequestAdmissio
             )
         return None
 
+    def _terminal_denial_for(self, item: RequestAdmissionItem, now: float) -> RequestAdmissionDenied | None:
+        denied = self._denial_for(item, now)
+        if denied is None or denied.reason not in _TERMINAL_DENIAL_REASONS:
+            return None
+        return denied
+
+    def _remember_released_locked(self, lease_id: str) -> None:
+        if lease_id in self._released:
+            return
+        maxlen = self._released_order.maxlen
+        if maxlen is not None and len(self._released_order) >= maxlen:
+            self._released.discard(self._released_order[0])
+        self._released.add(lease_id)
+        self._released_order.append(lease_id)
+
     def _acquire_locked(self, item: RequestAdmissionItem, now: float) -> RequestAdmissionLease:
         resource = item.resource
         provider_model = resource.provider_model_key
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py
index 9f0386dc7..a268f8898 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/pressure.py
@@ -7,6 +7,7 @@
 from dataclasses import dataclass
 from typing import Protocol
 
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
 from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
 from data_designer.engine.models.resources import ProviderModelKey
 
@@ -43,6 +44,9 @@ class ProviderModelPressureSnapshot:
 
 
 class RequestPressureSnapshotProvider(Protocol):
+    @property
+    def config(self) -> RequestAdmissionConfig | None: ...
+
     def snapshot(self, resource: RequestResourceKey) -> RequestPressureSnapshot | None: ...
 
     def snapshots(self) -> Mapping[RequestResourceKey, RequestPressureSnapshot]: ...
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
index e1b6382a4..fbb2fd469 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_task_admission.py
@@ -12,6 +12,7 @@
     stable_task_id,
 )
 from data_designer.engine.dataset_builders.scheduling.task_admission import (
+    RELEASED_TASK_LEASE_HISTORY_LIMIT,
     TaskAdmissionConfig,
     TaskAdmissionController,
     TaskAdmissionDenied,
@@ -85,6 +86,23 @@ def test_task_admission_duplicate_release_does_not_increase_capacity() -> None:
     assert controller.view().resources_available["submission"] == 1
 
 
+def test_task_admission_released_history_is_bounded() -> None:
+    controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=1))
+    first_lease: TaskAdmissionLease | None = None
+    for index in range(RELEASED_TASK_LEASE_HISTORY_LIMIT + 5):
+        item = _item(f"task-{index}")
+        lease = controller.try_acquire(item, _queue_view(item))
+        assert isinstance(lease, TaskAdmissionLease)
+        first_lease = first_lease or lease
+        controller.release(lease)
+
+    assert len(controller._released) == RELEASED_TASK_LEASE_HISTORY_LIMIT
+    assert len(controller._released_order) == RELEASED_TASK_LEASE_HISTORY_LIMIT
+    assert controller._released_order.maxlen == RELEASED_TASK_LEASE_HISTORY_LIMIT
+    assert first_lease is not None
+    assert controller.release(first_lease).reason == "unknown_lease"
+
+
 def test_task_admission_group_cap_yields_to_peer_pressure() -> None:
     group = TaskGroupSpec(TaskGroupKey(kind="model", identity=("provider", "model")), admitted_limit=1)
     controller = TaskAdmissionController(TaskAdmissionConfig(submission_capacity=2))
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index a3fe5f902..83791e276 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -46,8 +46,19 @@
     ModelRateLimitError,
     ModelTimeoutError,
 )
+from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
+from data_designer.engine.models.request_admission.controller import (
+    AdaptiveRequestAdmissionController,
+    RequestAdmissionLease,
+)
+from data_designer.engine.models.request_admission.outcomes import RequestReleaseOutcome
 from data_designer.engine.models.request_admission.pressure import RequestPressureSnapshot
-from data_designer.engine.models.request_admission.resources import RequestDomain, RequestResourceKey
+from data_designer.engine.models.request_admission.resources import (
+    RequestAdmissionItem,
+    RequestDomain,
+    RequestGroupSpec,
+    RequestResourceKey,
+)
 from data_designer.engine.models.resources import ProviderModelKey
 from data_designer.engine.observability import InMemoryAdmissionEventSink
 from data_designer.engine.resources.resource_provider import ResourceProvider
@@ -2321,6 +2332,10 @@ class _StaticRequestPressureProvider:
     def __init__(self, snapshots: dict[RequestResourceKey, RequestPressureSnapshot]) -> None:
         self._snapshots = snapshots
 
+    @property
+    def config(self) -> RequestAdmissionConfig | None:
+        return None
+
     def snapshot(self, resource: RequestResourceKey) -> RequestPressureSnapshot | None:
         return self._snapshots.get(resource)
 
@@ -2645,6 +2660,59 @@ async def test_scheduler_capacity_plan_observes_buffer_backpressure() -> None:
     assert max(plan.observed_maxima.task_leases_by_resource.values()) <= 2
 
 
+def test_scheduler_capacity_plan_reports_request_admission_state() -> None:
+    resource = RequestResourceKey("provider", "model", RequestDomain.CHAT)
+    request_admission = AdaptiveRequestAdmissionController(
+        RequestAdmissionConfig(initial_limits={resource: 2}, max_limit_clamps={resource: 3})
+    )
+    request_admission.register(
+        provider_name="provider",
+        model_id="model",
+        alias="primary",
+        max_parallel_requests=4,
+    )
+    lease = request_admission.try_acquire(RequestAdmissionItem(resource, RequestGroupSpec(resource)))
+    assert isinstance(lease, RequestAdmissionLease)
+
+    scheduler, _tracker = _build_simple_pipeline()
+    scheduler._request_pressure_provider = request_admission
+    scheduler._record_observed_task_state()
+    plan = scheduler.capacity_plan()
+
+    assert plan.configured.request_resources.value == (resource,)
+    assert plan.configured.request_domain_initial_limits.value[resource] == 2
+    assert plan.configured.request_admission_config.value is not None
+    assert plan.configured.provider_model_static_caps.value[ProviderModelKey("provider", "model")].cap == 4
+    assert plan.runtime_snapshot.request_domain_current_limits[resource] == 2
+    assert plan.runtime_snapshot.request_domain_effective_max[resource] == 3
+    assert plan.runtime_snapshot.provider_model_aggregate_in_flight[ProviderModelKey("provider", "model")] == 1
+    assert plan.observed_maxima.request_in_flight_by_resource[resource] == 1
+    assert plan.observed_maxima.provider_model_aggregate_in_flight[ProviderModelKey("provider", "model")] == 1
+    request_admission.release(lease, RequestReleaseOutcome(kind="success"))
+
+
+def test_scheduler_capacity_plan_reports_default_request_initial_limit_after_aimd_drop() -> None:
+    resource = RequestResourceKey("provider", "model", RequestDomain.CHAT)
+    request_admission = AdaptiveRequestAdmissionController()
+    request_admission.register(
+        provider_name="provider",
+        model_id="model",
+        alias="primary",
+        max_parallel_requests=4,
+    )
+    lease = request_admission.try_acquire(RequestAdmissionItem(resource, RequestGroupSpec(resource)))
+    assert isinstance(lease, RequestAdmissionLease)
+    request_admission.release(lease, RequestReleaseOutcome(kind="rate_limited"))
+
+    scheduler, _tracker = _build_simple_pipeline()
+    scheduler._request_pressure_provider = request_admission
+    plan = scheduler.capacity_plan()
+
+    assert plan.configured.request_domain_initial_limits.value[resource] == 4
+    assert plan.runtime_snapshot.request_domain_effective_max[resource] == 4
+    assert plan.runtime_snapshot.request_domain_current_limits[resource] == 3
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_emits_job_health_and_row_group_telemetry() -> None:
     provider = _mock_provider()
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_factory.py b/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
index df86e12d8..f809db8be 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_factory.py
@@ -187,15 +187,18 @@ def test_request_admission_wraps_openai_client(
     openai_registry: ModelProviderRegistry,
 ) -> None:
     controller = AdaptiveRequestAdmissionController()
+    retry_config = RetryConfig(max_retries=5)
     client = create_model_client(
         openai_model_config,
         secret_resolver,
         openai_registry,
-        retry_config=RetryConfig(),
+        retry_config=retry_config,
         request_admission=controller,
     )
     assert isinstance(client, ModelRequestExecutor)
     assert isinstance(client._inner, OpenAICompatibleClient)
+    assert client._retry_config is retry_config
+    assert client._inner._retry_config.max_retries == 0
 
 
 def test_request_admission_wraps_anthropic_client(
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
index 9eb310edd..2c44a0c00 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
@@ -10,6 +10,7 @@
 
 from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
 from data_designer.engine.models.clients.model_request_executor import ModelRequestExecutor
+from data_designer.engine.models.clients.retry import RetryConfig
 from data_designer.engine.models.clients.types import (
     AssistantMessage,
     ChatCompletionRequest,
@@ -100,6 +101,40 @@ async def agenerate_image(self, request: ImageGenerationRequest) -> ImageGenerat
         return ImageGenerationResponse(images=[ImagePayload("image")])
 
 
+class _FlakyClient(_Client):
+    def __init__(
+        self,
+        *,
+        failures: int,
+        kind: ProviderErrorKind = ProviderErrorKind.INTERNAL_SERVER,
+        status_code: int | None = 503,
+    ) -> None:
+        super().__init__()
+        self.failures = failures
+        self.calls = 0
+        self.kind = kind
+        self.status_code = status_code
+
+    def _maybe_fail(self) -> None:
+        self.calls += 1
+        if self.calls <= self.failures:
+            raise ProviderError(
+                kind=self.kind,
+                message="temporarily unavailable",
+                status_code=self.status_code,
+                provider_name="nvidia",
+                model_name="nemotron",
+            )
+
+    def completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        self._maybe_fail()
+        return ChatCompletionResponse(AssistantMessage(content="ok"))
+
+    async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+        self._maybe_fail()
+        return ChatCompletionResponse(AssistantMessage(content="ok"))
+
+
 def _executor() -> tuple[ModelRequestExecutor, AdaptiveRequestAdmissionController, _Client]:
     controller = AdaptiveRequestAdmissionController()
     controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
@@ -138,6 +173,76 @@ def test_model_request_executor_classifies_rate_limit() -> None:
     assert snapshot.cooldown_remaining_seconds > 0
 
 
+def test_model_request_executor_retries_provider_503_with_fresh_leases() -> None:
+    sink = InMemoryAdmissionEventSink()
+    controller = AdaptiveRequestAdmissionController(event_sink=sink)
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    client = _FlakyClient(failures=1)
+    executor = ModelRequestExecutor(
+        client,
+        controller,
+        "nvidia",
+        "nemotron",
+        event_sink=sink,
+        retry_config=RetryConfig(max_retries=1, backoff_factor=0.0),
+    )
+
+    response = executor.completion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    assert response.message.content == "ok"
+    assert client.calls == 2
+    acquired = [event for event in sink.request_events if event.event_kind == "request_lease_acquired"]
+    released = [event for event in sink.request_events if event.event_kind == "request_lease_released"]
+    assert len(acquired) == 2
+    assert len(released) == 2
+    assert {event.request_lease_id for event in acquired} == {event.request_lease_id for event in released}
+
+
+def test_model_request_executor_does_not_retry_provider_timeout_without_status() -> None:
+    controller = AdaptiveRequestAdmissionController()
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    client = _FlakyClient(failures=2, kind=ProviderErrorKind.TIMEOUT, status_code=None)
+    executor = ModelRequestExecutor(
+        client,
+        controller,
+        "nvidia",
+        "nemotron",
+        retry_config=RetryConfig(max_retries=2, backoff_factor=0.0),
+    )
+
+    with pytest.raises(ProviderError) as exc_info:
+        executor.completion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    assert exc_info.value.kind == ProviderErrorKind.TIMEOUT
+    assert client.calls == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_model_request_executor_retries_async_provider_503_with_fresh_leases() -> None:
+    sink = InMemoryAdmissionEventSink()
+    controller = AdaptiveRequestAdmissionController(event_sink=sink)
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    client = _FlakyClient(failures=1)
+    executor = ModelRequestExecutor(
+        client,
+        controller,
+        "nvidia",
+        "nemotron",
+        event_sink=sink,
+        retry_config=RetryConfig(max_retries=1, backoff_factor=0.0),
+    )
+
+    response = await executor.acompletion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    assert response.message.content == "ok"
+    assert client.calls == 2
+    acquired = [event for event in sink.request_events if event.event_kind == "request_lease_acquired"]
+    released = [event for event in sink.request_events if event.event_kind == "request_lease_released"]
+    assert len(acquired) == 2
+    assert len(released) == 2
+    assert {event.request_lease_id for event in acquired} == {event.request_lease_id for event in released}
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_model_request_executor_releases_async_cancellation() -> None:
     class _SlowClient(_Client):
diff --git a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index b62237a48..6fc65d227 100644
--- a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -10,6 +10,7 @@
 
 from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
 from data_designer.engine.models.request_admission.controller import (
+    RELEASED_LEASE_HISTORY_LIMIT,
     AdaptiveRequestAdmissionController,
     RequestAdmissionDenied,
     RequestAdmissionError,
@@ -154,6 +155,18 @@ def test_request_admission_zero_sync_timeout_is_immediate() -> None:
     controller.release(lease, RequestReleaseOutcome(kind="success"))
 
 
+def test_request_admission_sync_unregistered_provider_raises_hard_denial() -> None:
+    controller = AdaptiveRequestAdmissionController()
+
+    with pytest.raises(RequestAdmissionError) as exc_info:
+        controller.acquire_sync(_item())
+
+    assert exc_info.value.decision.reason == "hard_policy_denial"
+    snapshot = controller.pressure.snapshot(_item().resource)
+    assert snapshot is not None
+    assert snapshot.waiters == 0
+
+
 def test_request_admission_logs_sink_failures(caplog: pytest.LogCaptureFixture) -> None:
     caplog.set_level(logging.WARNING, logger="data_designer.engine.models.request_admission.controller")
     controller = AdaptiveRequestAdmissionController(event_sink=_BrokenRequestSink())
@@ -235,24 +248,34 @@ async def test_acquire_async_wakes_when_release_assigns_lease(monkeypatch: pytes
 
 
 @pytest.mark.asyncio(loop_scope="session")
-async def test_register_wakes_unregistered_async_waiter(monkeypatch: pytest.MonkeyPatch) -> None:
+async def test_acquire_async_unregistered_provider_raises_hard_denial(monkeypatch: pytest.MonkeyPatch) -> None:
     controller = AdaptiveRequestAdmissionController()
     monkeypatch.setattr(controller, "_wait_seconds_locked", lambda _item, _now, _deadline: 10.0)
     queued = _item(RequestDomain.CHAT, timeout=30.0)
 
-    queued_task = asyncio.create_task(controller.acquire_async(queued))
-    for _ in range(20):
-        snapshot = controller.pressure.snapshot(queued.resource)
-        if snapshot is not None and snapshot.waiters == 1:
-            break
-        await asyncio.sleep(0)
-    else:
-        raise AssertionError("async waiter did not enqueue")
+    with pytest.raises(RequestAdmissionError) as exc_info:
+        await asyncio.wait_for(controller.acquire_async(queued), timeout=0.5)
 
-    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
-    queued_lease = await asyncio.wait_for(queued_task, timeout=0.5)
+    assert exc_info.value.decision.reason == "hard_policy_denial"
+    snapshot = controller.pressure.snapshot(queued.resource)
+    assert snapshot is not None
+    assert snapshot.waiters == 0
 
-    controller.release(queued_lease, RequestReleaseOutcome(kind="success"))
+
+def test_request_admission_released_history_is_bounded() -> None:
+    controller = _controller(cap=1)
+    first_lease: RequestAdmissionLease | None = None
+    for _ in range(RELEASED_LEASE_HISTORY_LIMIT + 5):
+        lease = controller.try_acquire(_item())
+        assert isinstance(lease, RequestAdmissionLease)
+        first_lease = first_lease or lease
+        controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+    assert len(controller._released) == RELEASED_LEASE_HISTORY_LIMIT
+    assert len(controller._released_order) == RELEASED_LEASE_HISTORY_LIMIT
+    assert controller._released_order.maxlen == RELEASED_LEASE_HISTORY_LIMIT
+    assert first_lease is not None
+    assert controller.release(first_lease, RequestReleaseOutcome(kind="success")).reason == "unknown_lease"
 
 
 @pytest.mark.asyncio(loop_scope="session")
diff --git a/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py b/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
index 595977464..f3088293c 100644
--- a/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
+++ b/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
@@ -39,6 +39,11 @@ def _load_idle_regression_module() -> ModuleType:
     return module
 
 
+def _load_idle_report_module() -> ModuleType:
+    _load_idle_regression_module()
+    return sys.modules["generate_async_scheduling_idle_report"]
+
+
 def _capacity_plan() -> SimpleNamespace:
     return SimpleNamespace(
         observed_maxima=SimpleNamespace(
@@ -264,6 +269,24 @@ def test_idle_regression_detects_bad_idle_partition() -> None:
     assert any(not check.passed and check.name == "row-scale/rows-64 idle partition" for check in checks)
 
 
+def test_idle_regression_skip_run_does_not_generate_missing_artifacts(tmp_path: Path) -> None:
+    report = _load_idle_report_module()
+    case = report.IdleBenchmarkCase(
+        name="missing",
+        sweep="reuse",
+        record_count=1,
+        buffer_size=1,
+        row_group_concurrency=1,
+        task_admission_capacity=1,
+        fanout_width=1,
+        upstream_latency_seconds=0.0,
+        downstream_latency_seconds=0.0,
+    )
+
+    with pytest.raises(FileNotFoundError, match="Cannot reuse benchmark artifact"):
+        report._run_or_load_case(case, tmp_path, skip_run=True)
+
+
 def test_idle_regression_requires_adaptation_controls() -> None:
     regression = _load_idle_regression_module()
     summary = _idle_regression_summary()
diff --git a/scripts/benchmarks/generate_async_scheduling_idle_report.py b/scripts/benchmarks/generate_async_scheduling_idle_report.py
index 707706090..cfdb98feb 100644
--- a/scripts/benchmarks/generate_async_scheduling_idle_report.py
+++ b/scripts/benchmarks/generate_async_scheduling_idle_report.py
@@ -308,6 +308,7 @@ def _build_cases(quick: bool) -> list[IdleBenchmarkCase]:
                 fanout_width=1,
                 upstream_latency_seconds=0.006,
                 downstream_latency_seconds=0.0003,
+                iterations=3,
             ),
             replace(
                 base,
@@ -320,6 +321,7 @@ def _build_cases(quick: bool) -> list[IdleBenchmarkCase]:
                 fanout_width=4,
                 upstream_latency_seconds=0.006,
                 downstream_latency_seconds=0.0003,
+                iterations=3,
             ),
         )
     )
@@ -432,7 +434,11 @@ def _build_cases(quick: bool) -> list[IdleBenchmarkCase]:
 def _run_or_load_case(case: IdleBenchmarkCase, artifact_dir: Path, *, skip_run: bool) -> IdleBenchmarkResult:
     output_dir = artifact_dir / case.sweep / case.name
     json_path = output_dir / "async_scheduling_benchmark.json"
-    if not skip_run or not json_path.exists():
+    if skip_run and not json_path.exists():
+        raise FileNotFoundError(
+            f"Cannot reuse benchmark artifact for {case.sweep}/{case.name}: {json_path} does not exist."
+        )
+    if not skip_run:
         output_dir.mkdir(parents=True, exist_ok=True)
         command = [
             sys.executable,
diff --git a/scripts/benchmarks/run_async_scheduling_idle_regression.py b/scripts/benchmarks/run_async_scheduling_idle_regression.py
index 482f6a8e1..164459b55 100644
--- a/scripts/benchmarks/run_async_scheduling_idle_regression.py
+++ b/scripts/benchmarks/run_async_scheduling_idle_regression.py
@@ -322,13 +322,14 @@ def _suite_behavior_checks(cases: Mapping[str, Mapping[str, Any]], *, mode: str)
             )
         )
     if "stress-shape/wide-frontier-high-cap" in cases:
+        floor = _wide_high_capacity_stress_utilization_floor(mode)
         checks.append(
             _check(
                 "wide high-capacity stress utilization",
                 category="optimization",
-                passed=_metric(cases["stress-shape/wide-frontier-high-cap"], "llm_wait_utilization_ratio") >= 0.55,
+                passed=_metric(cases["stress-shape/wide-frontier-high-cap"], "llm_wait_utilization_ratio") >= floor,
                 observed=_metric(cases["stress-shape/wide-frontier-high-cap"], "llm_wait_utilization_ratio"),
-                expected=">= 0.55 llm_wait utilization",
+                expected=f">= {floor:.2f} llm_wait utilization",
             )
         )
     if {
@@ -574,6 +575,10 @@ def _wide_row_group_utilization_floor(mode: str) -> float:
     return 0.55 if mode == "quick" else 0.70
 
 
+def _wide_high_capacity_stress_utilization_floor(mode: str) -> float:
+    return 0.53 if mode == "quick" else 0.55
+
+
 def _checks_payload(checks: Sequence[RegressionCheck]) -> dict[str, Any]:
     return {
         "checks_schema_version": CHECKS_SCHEMA_VERSION,
diff --git a/tests_e2e/tests/test_mcp_demo.py b/tests_e2e/tests/test_mcp_demo.py
index 163e904cb..d7bca2e9e 100644
--- a/tests_e2e/tests/test_mcp_demo.py
+++ b/tests_e2e/tests/test_mcp_demo.py
@@ -101,25 +101,25 @@ def test_mcp_server_tool_usage_with_nvidia_text(tmp_path: Path) -> None:
         assert tool_call_messages
 
         tool_calls: list[dict[str, object]] = []
-        tool_call_indices: dict[str, int] = {}
+        tool_call_positions: dict[str, tuple[int, int]] = {}
         for msg_index, msg in enumerate(trace):
             if not isinstance(msg, dict):
                 continue
             if msg.get("role") != "assistant":
                 continue
-            for tool_call in msg.get("tool_calls") or []:
+            for tool_call_index, tool_call in enumerate(msg.get("tool_calls") or []):
                 if not isinstance(tool_call, dict):
                     continue
                 tool_calls.append(tool_call)
                 function = tool_call.get("function") or {}
                 if isinstance(function, dict):
                     name = function.get("name")
-                    if isinstance(name, str) and name not in tool_call_indices:
-                        tool_call_indices[name] = msg_index
+                    if isinstance(name, str) and name not in tool_call_positions:
+                        tool_call_positions[name] = (msg_index, tool_call_index)
 
-        assert tool_call_indices.get("get_fact") is not None
-        assert tool_call_indices.get("add_numbers") is not None
-        assert tool_call_indices["get_fact"] < tool_call_indices["add_numbers"]
+        assert tool_call_positions.get("get_fact") is not None
+        assert tool_call_positions.get("add_numbers") is not None
+        assert tool_call_positions["get_fact"] < tool_call_positions["add_numbers"]
 
         def _tool_call_to_name_args(tool_call: dict[str, object]) -> tuple[str | None, dict[str, object]]:
             function = tool_call.get("function")

From 387d5a5205587d32e8056104d6e5dab27c75b3d1 Mon Sep 17 00:00:00 2001
From: Andre Manoel <165937436+andreatgretel@users.noreply.github.com>
Date: Tue, 19 May 2026 17:38:44 -0300
Subject: [PATCH 15/33] fix request waiter deadline admission (#681)

---
 .../models/request_admission/controller.py    | 34 +++++++++++++++
 .../engine/models/request_admission/queue.py  |  3 ++
 .../request_admission/test_controller.py      | 42 +++++++++++++++++++
 3 files changed, 79 insertions(+)

diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index 09e4dd512..c34cdd012 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -220,6 +220,18 @@ def acquire_sync(self, item: RequestAdmissionItem) -> RequestAdmissionLease:
         try:
             while True:
                 with self._lock:
+                    if waiter.assigned_lease is not None:
+                        return waiter.assigned_lease
+                    now = time.monotonic()
+                    if deadline is not None and now >= deadline:
+                        self._remove_waiter_locked(waiter)
+                        denied = RequestAdmissionDenied(
+                            item=item,
+                            reason="queue_timeout",
+                            snapshot=self._snapshot_locked(item.resource, now),
+                        )
+                        events.append(self._request_event_locked("request_wait_timeout", item=item, decision=denied))
+                        raise RequestAdmissionError(denied)
                     if not self._queue.contains(waiter.waiter_id) and waiter.assigned_lease is None:
                         self._enqueue_waiter_locked(waiter, events)
                     self._admit_waiters_locked(events)
@@ -266,6 +278,18 @@ async def acquire_async(self, item: RequestAdmissionItem) -> RequestAdmissionLea
         try:
             while True:
                 with self._lock:
+                    if waiter.assigned_lease is not None:
+                        return waiter.assigned_lease
+                    now = time.monotonic()
+                    if deadline is not None and now >= deadline:
+                        self._remove_waiter_locked(waiter)
+                        denied = RequestAdmissionDenied(
+                            item=item,
+                            reason="queue_timeout",
+                            snapshot=self._snapshot_locked(item.resource, now),
+                        )
+                        events.append(self._request_event_locked("request_wait_timeout", item=item, decision=denied))
+                        raise RequestAdmissionError(denied)
                     if not self._queue.contains(waiter.waiter_id) and waiter.assigned_lease is None:
                         self._enqueue_waiter_locked(waiter, events)
                     self._admit_waiters_locked(events)
@@ -399,6 +423,7 @@ def global_snapshots(self) -> Mapping[ProviderModelKey, ProviderModelPressureSna
     def _queued_waiter_ahead_locked(self, item: RequestAdmissionItem, now: float) -> bool:
         if not self._queue.has_waiters:
             return False
+        self._expire_waiters_locked(now)
         selection = self._queue.select_next(lambda waiter, _view: self._denial_for(waiter.item, now) is None)
         if selection is None:
             return False
@@ -421,9 +446,18 @@ def _remove_waiter_locked(self, waiter: RequestWaiter) -> None:
         state.waiters = max(0, state.waiters - 1)
         self._sequence += 1
 
+    def _expire_waiters_locked(self, now: float) -> None:
+        for waiter in self._queue.waiters():
+            if waiter.deadline_monotonic is not None and now >= waiter.deadline_monotonic:
+                self._remove_waiter_locked(waiter)
+                self._wake_waiter_locked(waiter)
+
     def _admit_waiters_locked(self, events: list[RequestAdmissionEvent]) -> None:
         while self._queue.has_waiters:
             now = time.monotonic()
+            self._expire_waiters_locked(now)
+            if not self._queue.has_waiters:
+                return
             selection = self._queue.select_next(lambda waiter, _view: self._denial_for(waiter.item, now) is None)
             if selection is None:
                 return
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
index 98610e5ee..cdca7027b 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/queue.py
@@ -64,6 +64,9 @@ def has_waiters(self) -> bool:
     def contains(self, waiter_id: str) -> bool:
         return waiter_id in self._queued
 
+    def waiters(self) -> tuple[RequestWaiter, ...]:
+        return tuple(self._queued.values())
+
     def enqueue(self, waiter: RequestWaiter) -> bool:
         if waiter.waiter_id in self._queued:
             return False
diff --git a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index 6fc65d227..4a18d9a5c 100644
--- a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -5,6 +5,8 @@
 
 import asyncio
 import logging
+import threading
+import time
 
 import pytest
 
@@ -224,6 +226,46 @@ async def test_request_admission_zero_async_timeout_is_immediate() -> None:
     controller.release(lease, RequestReleaseOutcome(kind="success"))
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_acquire_async_does_not_assign_expired_waiter_after_release(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    controller = _controller(cap=1)
+    monkeypatch.setattr(controller, "_wait_seconds_locked", lambda _item, _now, _deadline: 10.0)
+    lease = controller.try_acquire(_item(RequestDomain.CHAT))
+    assert isinstance(lease, RequestAdmissionLease)
+    queued = _item(RequestDomain.EMBEDDING, timeout=0.01)
+
+    queued_task = asyncio.create_task(controller.acquire_async(queued))
+    for _ in range(20):
+        snapshot = controller.pressure.snapshot(queued.resource)
+        if snapshot is not None and snapshot.waiters == 1:
+            break
+        await asyncio.sleep(0)
+    else:
+        raise AssertionError("async waiter did not enqueue")
+
+    def release_after_deadline() -> None:
+        time.sleep(0.03)
+        controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+    release_thread = threading.Thread(target=release_after_deadline)
+    release_thread.start()
+    try:
+        time.sleep(0.06)
+        with pytest.raises(RequestAdmissionError) as exc_info:
+            await asyncio.wait_for(queued_task, timeout=0.5)
+    finally:
+        release_thread.join()
+
+    assert exc_info.value.decision.reason == "queue_timeout"
+    snapshot = controller.pressure.snapshot(queued.resource)
+    assert snapshot is not None
+    assert snapshot.waiters == 0
+    assert snapshot.active_lease_count == 0
+    assert snapshot.in_flight_count == 0
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_acquire_async_wakes_when_release_assigns_lease(monkeypatch: pytest.MonkeyPatch) -> None:
     controller = _controller(cap=1)

From 0ee8f6dd02749b3712dc0961f8dcc414cdc4ff1f Mon Sep 17 00:00:00 2001
From: Andre Manoel <165937436+andreatgretel@users.noreply.github.com>
Date: Tue, 19 May 2026 18:20:22 -0300
Subject: [PATCH 16/33] fix: keep async idle benchmark artifacts in scratch
 (#683)

---
 .../engine/test_async_scheduling_benchmark.py | 27 +++++++++++++++++++
 .../generate_async_scheduling_idle_report.py  | 10 ++++---
 .../run_async_scheduling_idle_regression.py   |  5 ++--
 3 files changed, 37 insertions(+), 5 deletions(-)

diff --git a/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py b/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
index f3088293c..868e7d589 100644
--- a/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
+++ b/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
@@ -287,6 +287,33 @@ def test_idle_regression_skip_run_does_not_generate_missing_artifacts(tmp_path:
         report._run_or_load_case(case, tmp_path, skip_run=True)
 
 
+def test_idle_benchmark_defaults_write_to_scratch() -> None:
+    report = _load_idle_report_module()
+    regression = _load_idle_regression_module()
+
+    assert report.DEFAULT_ARTIFACT_DIR == Path(".scratch/async-scheduling-idle-analysis/artifacts")
+    assert report.DEFAULT_REPORT_PATH == Path(
+        ".scratch/async-scheduling-idle-analysis/async-scheduling-idle-analysis.html"
+    )
+    assert regression.DEFAULT_ARTIFACT_DIR == Path(".scratch/async-scheduling-idle-regression/artifacts")
+    assert regression.DEFAULT_REPORT_PATH == Path(
+        ".scratch/async-scheduling-idle-regression/async-scheduling-idle-regression.html"
+    )
+
+
+def test_idle_report_links_artifacts_relative_to_report() -> None:
+    report = _load_idle_report_module()
+
+    assert (
+        report._relative_href(
+            Path(".scratch/async-scheduling-idle-analysis/async-scheduling-idle-analysis.html"),
+            Path(".scratch/async-scheduling-idle-analysis/artifacts/case.json"),
+        )
+        == "artifacts/case.json"
+    )
+    assert report._relative_href(Path("reports/idle.html"), Path("artifacts/case.json")) == "../artifacts/case.json"
+
+
 def test_idle_regression_requires_adaptation_controls() -> None:
     regression = _load_idle_regression_module()
     summary = _idle_regression_summary()
diff --git a/scripts/benchmarks/generate_async_scheduling_idle_report.py b/scripts/benchmarks/generate_async_scheduling_idle_report.py
index cfdb98feb..ad17e1b87 100644
--- a/scripts/benchmarks/generate_async_scheduling_idle_report.py
+++ b/scripts/benchmarks/generate_async_scheduling_idle_report.py
@@ -9,6 +9,7 @@
 import html
 import json
 import math
+import os
 import subprocess
 import sys
 from collections.abc import Iterable, Mapping, Sequence
@@ -16,8 +17,9 @@
 from pathlib import Path
 from typing import Any
 
-DEFAULT_ARTIFACT_DIR = Path("artifacts/async-scheduling-idle-analysis")
-DEFAULT_REPORT_PATH = Path("reports/async-scheduling-idle-analysis.html")
+DEFAULT_OUTPUT_ROOT = Path(".scratch/async-scheduling-idle-analysis")
+DEFAULT_ARTIFACT_DIR = DEFAULT_OUTPUT_ROOT / "artifacts"
+DEFAULT_REPORT_PATH = DEFAULT_OUTPUT_ROOT / "async-scheduling-idle-analysis.html"
 BENCHMARK_SCRIPT = Path("scripts/benchmarks/benchmark_async_scheduling.py")
 IDLE_SUITE_ID = "async-scheduling-idle-regression"
 IDLE_SUITE_VERSION = "1.1"
@@ -1762,7 +1764,9 @@ def _class_slug(idle_class: str) -> str:
 
 def _relative_href(report_path: Path, target_path: Path) -> str:
     try:
-        return Path("../" + str(target_path)).as_posix() if not target_path.is_absolute() else target_path.as_uri()
+        if target_path.is_absolute():
+            return target_path.as_uri()
+        return Path(os.path.relpath(target_path, start=report_path.parent)).as_posix()
     except ValueError:
         return str(target_path)
 
diff --git a/scripts/benchmarks/run_async_scheduling_idle_regression.py b/scripts/benchmarks/run_async_scheduling_idle_regression.py
index 164459b55..f182f08f9 100644
--- a/scripts/benchmarks/run_async_scheduling_idle_regression.py
+++ b/scripts/benchmarks/run_async_scheduling_idle_regression.py
@@ -23,8 +23,9 @@
     write_idle_results_summary,
 )
 
-DEFAULT_ARTIFACT_DIR = Path("artifacts/async-scheduling-idle-regression")
-DEFAULT_REPORT_PATH = Path("reports/async-scheduling-idle-regression.html")
+DEFAULT_OUTPUT_ROOT = Path(".scratch/async-scheduling-idle-regression")
+DEFAULT_ARTIFACT_DIR = DEFAULT_OUTPUT_ROOT / "artifacts"
+DEFAULT_REPORT_PATH = DEFAULT_OUTPUT_ROOT / "async-scheduling-idle-regression.html"
 DEFAULT_SUMMARY_PATH = DEFAULT_ARTIFACT_DIR / "idle_regression_summary.json"
 DEFAULT_CHECKS_PATH = DEFAULT_ARTIFACT_DIR / "idle_regression_checks.json"
 CHECKS_SCHEMA_VERSION = "async-scheduling-idle-checks-v1"

From 0d2050b1920abebe8ff16f350136ec03a0728663 Mon Sep 17 00:00:00 2001
From: Andre Manoel <165937436+andreatgretel@users.noreply.github.com>
Date: Tue, 19 May 2026 18:22:06 -0300
Subject: [PATCH 17/33] test: pin custom generator error boundary (#684)

---
 .../dataset_builders/test_async_scheduler.py  | 34 +++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index 83791e276..ce78e3141 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -595,6 +595,40 @@ async def test_scheduler_internal_bug_failure_aborts_instead_of_dropping_row(
     assert error_records[0].exc_info is not None
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_custom_generator_key_error_drops_row_without_fatal_abort(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    @custom_column_generator()
+    def failing_custom(row: dict) -> dict:
+        raise KeyError("missing user field")
+
+    provider = _mock_provider()
+    custom_config = CustomColumnConfig(name="custom_col", generator_function=failing_custom)
+    scheduler, tracker = _build_simple_pipeline(
+        num_records=1,
+        configs=[
+            SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+            custom_config,
+        ],
+        strategies={
+            "seed": GenerationStrategy.FULL_COLUMN,
+            "custom_col": GenerationStrategy.CELL_BY_CELL,
+        },
+        generators={
+            "seed": MockSeedGenerator(config=_expr_config("seed"), resource_provider=provider),
+            "custom_col": CustomColumnGenerator(config=custom_config, resource_provider=provider),
+        },
+    )
+
+    with caplog.at_level(logging.WARNING):
+        await scheduler.run()
+
+    assert tracker.is_dropped(0, 0)
+    assert "This record will be skipped" in caplog.text
+    assert "Unexpected fatal Non-retryable failure" not in caplog.text
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_logs_sink_failures(caplog: pytest.LogCaptureFixture) -> None:
     caplog.set_level(logging.WARNING, logger="data_designer.engine.dataset_builders.async_scheduler")

From 70974fd7caa8db49169965bb98f129b68c0a8c93 Mon Sep 17 00:00:00 2001
From: Nabin Mulepati <nmulepati@nvidia.com>
Date: Tue, 19 May 2026 15:44:18 -0600
Subject: [PATCH 18/33] fix: request admission edge cases (#685)

---
 .../dataset_builders/dataset_builder.py       |  1 +
 .../models/request_admission/controller.py    | 12 +--
 .../test_async_builder_integration.py         | 35 +++++++++
 .../request_admission/test_controller.py      | 77 +++++++++++++++++++
 4 files changed, 120 insertions(+), 5 deletions(-)

diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
index b19871319..93ca2ea25 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/dataset_builder.py
@@ -1046,6 +1046,7 @@ def on_before_checkpoint(rg_id: int, rg_size: int) -> None:
             progress_interval=self._resource_provider.run_config.progress_interval,
             progress_bar=self._resource_provider.run_config.progress_bar,
             request_pressure_provider=self._resource_provider.model_registry.request_admission,
+            request_pressure_advisory=True,
         )
         return scheduler, buffer_manager
 
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index c34cdd012..56071ed9b 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -362,7 +362,7 @@ def release(self, lease: RequestAdmissionLease, outcome: RequestReleaseOutcome)
                         "request_release_diagnostic", item=lease.item, lease=lease, result=result
                     )
                 )
-            elif active.item.resource != lease.item.resource:
+            elif active != lease:
                 self._active_leases[lease.lease_id] = active
                 self._release_diagnostics["stale_lease"] += 1
                 result = ReleaseResult(released=False, reason="stale_lease")
@@ -384,7 +384,7 @@ def release(self, lease: RequestAdmissionLease, outcome: RequestReleaseOutcome)
                     0,
                     self._aggregate_active_leases[provider_model] - 1,
                 )
-                self._apply_outcome(state, resource, outcome, now, events)
+                self._apply_outcome(state, resource, active.current_adaptive_limit, outcome, now, events)
                 self._sequence += 1
                 result = ReleaseResult(released=True, reason="released")
                 if outcome.kind == "rate_limited":
@@ -558,6 +558,7 @@ def _apply_outcome(
         self,
         state: AdaptiveRequestLimitState,
         resource: RequestResourceKey,
+        admitted_adaptive_limit: int,
         outcome: RequestReleaseOutcome,
         now: float,
         events: list[RequestAdmissionEvent],
@@ -565,7 +566,7 @@ def _apply_outcome(
         effective_max = self._effective_max_for_resource(resource)
         if outcome.kind == "rate_limited":
             prev_limit = state.current_limit
-            first_in_cascade = state.consecutive_rate_limits == 0
+            should_decrease = admitted_adaptive_limit <= prev_limit
             state.consecutive_rate_limits += 1
             cooldown = (
                 outcome.retry_after_seconds
@@ -574,12 +575,13 @@ def _apply_outcome(
             )
             state.blocked_until = now + cooldown
             state.success_streak = 0
-            if first_in_cascade:
+            if should_decrease:
                 state.current_limit = max(
                     1, math.floor(state.current_limit * self._config.multiplicative_decrease_factor)
                 )
+                observed_limit = max(1, admitted_adaptive_limit)
                 state.rate_limit_ceiling = (
-                    prev_limit if state.rate_limit_ceiling == 0 else min(state.rate_limit_ceiling, prev_limit)
+                    observed_limit if state.rate_limit_ceiling == 0 else min(state.rate_limit_ceiling, observed_limit)
                 )
                 if state.current_limit != prev_limit:
                     events.append(
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py
index 8f75877f4..f01dc1d91 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_builder_integration.py
@@ -5,10 +5,12 @@
 
 import math
 import warnings
+from types import SimpleNamespace
 from unittest.mock import MagicMock, Mock
 
 import pytest
 
+import data_designer.engine.dataset_builders.dataset_builder as builder_mod
 import data_designer.lazy_heavy_imports as lazy
 from data_designer.config.column_configs import (
     ExpressionColumnConfig,
@@ -189,6 +191,39 @@ def finalize_row_group(rg_id: int) -> None:
     assert tracker.is_row_group_complete(1, 2, all_cols)
 
 
+def test_prepare_async_run_enables_request_pressure_advisory(monkeypatch: pytest.MonkeyPatch) -> None:
+    captured_kwargs: dict[str, object] = {}
+
+    class _SpyScheduler:
+        def __init__(self, **kwargs: object) -> None:
+            captured_kwargs.update(kwargs)
+
+    monkeypatch.setattr(builder_mod, "AsyncTaskScheduler", _SpyScheduler)
+    request_admission = object()
+    model_registry = MagicMock()
+    model_registry.get_aggregate_max_parallel_requests.return_value = 2
+    model_registry.request_admission = request_admission
+    provider = SimpleNamespace(
+        model_registry=model_registry,
+        run_config=SimpleNamespace(progress_interval=5.0, progress_bar=False),
+    )
+    processor_runner = MagicMock()
+    processor_runner.has_processors_for.return_value = False
+    config = SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]})
+    builder = SimpleNamespace(
+        _column_configs=[config],
+        _processor_runner=processor_runner,
+        artifact_storage=MagicMock(),
+        _resource_provider=provider,
+    )
+    generator = MockSeed(config=_expr_config("seed"), resource_provider=provider)
+
+    DatasetBuilder._prepare_async_run(builder, [generator], num_records=1, buffer_size=1)
+
+    assert captured_kwargs["request_pressure_provider"] is request_admission
+    assert captured_kwargs["request_pressure_advisory"] is True
+
+
 # -- Test that existing sync path is unaffected --------------------------------
 
 
diff --git a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index 4a18d9a5c..7de678c3e 100644
--- a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -7,6 +7,7 @@
 import logging
 import threading
 import time
+from dataclasses import replace
 
 import pytest
 
@@ -88,6 +89,28 @@ def test_request_admission_duplicate_release_does_not_corrupt_counts() -> None:
     assert controller.pressure.snapshot(item.resource).active_lease_count == 0  # type: ignore[union-attr]
 
 
+def test_request_admission_stale_release_requires_exact_lease() -> None:
+    controller = _controller(cap=1)
+    item = _item()
+    lease = controller.try_acquire(item)
+    assert isinstance(lease, RequestAdmissionLease)
+    stale = replace(lease, current_adaptive_limit=lease.current_adaptive_limit + 1)
+
+    stale_result = controller.release(stale, RequestReleaseOutcome(kind="provider_failure"))
+    snapshot = controller.pressure.snapshot(item.resource)
+
+    assert stale_result.released is False
+    assert stale_result.reason == "stale_lease"
+    assert snapshot is not None
+    assert snapshot.in_flight_count == 1
+    assert snapshot.active_lease_count == 1
+
+    released = controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+    assert released.released is True
+    assert controller.pressure.snapshot(item.resource).active_lease_count == 0  # type: ignore[union-attr]
+
+
 def test_request_admission_rate_limit_decreases_and_sets_cooldown() -> None:
     controller = _controller(
         cap=4,
@@ -111,6 +134,60 @@ def test_request_admission_rate_limit_decreases_and_sets_cooldown() -> None:
     assert snapshot.cooldown_remaining_seconds > 0
 
 
+def test_request_admission_rate_limit_burst_decreases_once_per_cascade() -> None:
+    controller = _controller(
+        cap=8,
+        config=RequestAdmissionConfig(
+            multiplicative_decrease_factor=0.5,
+            cooldown_seconds=10,
+        ),
+    )
+    item = _item()
+    leases = [controller.try_acquire(item) for _ in range(8)]
+    assert all(isinstance(lease, RequestAdmissionLease) for lease in leases)
+
+    for lease in leases:
+        controller.release(lease, RequestReleaseOutcome(kind="rate_limited"))
+    snapshot = controller.pressure.snapshot(item.resource)
+
+    assert snapshot is not None
+    assert snapshot.current_limit == 4
+    assert snapshot.rate_limit_ceiling == 8
+    assert snapshot.consecutive_rate_limits == 8
+
+
+def test_request_admission_fresh_rate_limit_after_burst_decreases_again() -> None:
+    controller = _controller(
+        cap=8,
+        config=RequestAdmissionConfig(
+            multiplicative_decrease_factor=0.5,
+            cooldown_seconds=0,
+        ),
+    )
+    item = _item()
+    leases = [controller.try_acquire(item) for _ in range(8)]
+    assert all(isinstance(lease, RequestAdmissionLease) for lease in leases)
+
+    for lease in leases:
+        controller.release(lease, RequestReleaseOutcome(kind="rate_limited"))
+    snapshot = controller.pressure.snapshot(item.resource)
+    assert snapshot is not None
+    assert snapshot.current_limit == 4
+    assert snapshot.rate_limit_ceiling == 8
+
+    fresh_lease = controller.try_acquire(item)
+    assert isinstance(fresh_lease, RequestAdmissionLease)
+    assert fresh_lease.current_adaptive_limit == 4
+
+    controller.release(fresh_lease, RequestReleaseOutcome(kind="rate_limited"))
+    snapshot = controller.pressure.snapshot(item.resource)
+
+    assert snapshot is not None
+    assert snapshot.current_limit == 2
+    assert snapshot.rate_limit_ceiling == 4
+    assert snapshot.consecutive_rate_limits == 9
+
+
 def test_request_admission_additive_recovery_after_successes() -> None:
     item = _item()
     controller = _controller(

From 05852e4f9cf5dff421b425c491d1089721c74d78 Mon Sep 17 00:00:00 2001
From: Andre Manoel <165937436+andreatgretel@users.noreply.github.com>
Date: Tue, 19 May 2026 21:07:21 -0300
Subject: [PATCH 19/33] fix: tighten request controller release semantics
 (#682)

---
 .../models/request_admission/controller.py    | 19 +++++++++----
 .../request_admission/test_controller.py      | 28 +++++++++++++++++--
 2 files changed, 38 insertions(+), 9 deletions(-)

diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index 56071ed9b..8494d7d69 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -388,9 +388,15 @@ def release(self, lease: RequestAdmissionLease, outcome: RequestReleaseOutcome)
                 self._sequence += 1
                 result = ReleaseResult(released=True, reason="released")
                 if outcome.kind == "rate_limited":
-                    events.append(self._request_event_locked("request_rate_limited", item=lease.item, lease=lease))
+                    events.append(self._request_event_locked("request_rate_limited", item=active.item, lease=active))
                 events.append(
-                    self._request_event_locked("request_lease_released", item=lease.item, lease=lease, result=result)
+                    self._request_event_locked(
+                        "request_lease_released",
+                        item=active.item,
+                        lease=active,
+                        result=result,
+                        outcome=outcome,
+                    )
                 )
                 self._admit_waiters_locked(events)
             self._condition.notify_all()
@@ -579,10 +585,8 @@ def _apply_outcome(
                 state.current_limit = max(
                     1, math.floor(state.current_limit * self._config.multiplicative_decrease_factor)
                 )
-                observed_limit = max(1, admitted_adaptive_limit)
-                state.rate_limit_ceiling = (
-                    observed_limit if state.rate_limit_ceiling == 0 else min(state.rate_limit_ceiling, observed_limit)
-                )
+                if state.rate_limit_ceiling == 0:
+                    state.rate_limit_ceiling = max(1, admitted_adaptive_limit)
                 if state.current_limit != prev_limit:
                     events.append(
                         self._request_event_locked(
@@ -696,6 +700,7 @@ def _request_event_locked(
         lease: RequestAdmissionLease | None = None,
         decision: RequestAdmissionDenied | None = None,
         result: ReleaseResult | None = None,
+        outcome: RequestReleaseOutcome | None = None,
         request_resource_key: RequestResourceKey | None = None,
         diagnostics: Mapping[str, object] | None = None,
     ) -> RequestAdmissionEvent:
@@ -706,6 +711,8 @@ def _request_event_locked(
         reason_or_outcome = None
         if decision is not None:
             reason_or_outcome = decision.reason
+        elif outcome is not None:
+            reason_or_outcome = outcome.kind
         elif result is not None:
             reason_or_outcome = result.reason
         return RequestAdmissionEvent.capture(
diff --git a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index 7de678c3e..c0c9a5801 100644
--- a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -7,7 +7,6 @@
 import logging
 import threading
 import time
-from dataclasses import replace
 
 import pytest
 
@@ -26,6 +25,7 @@
     RequestGroupSpec,
     RequestResourceKey,
 )
+from data_designer.engine.observability import InMemoryAdmissionEventSink
 
 
 def _item(domain: RequestDomain = RequestDomain.CHAT, timeout: float | None = None) -> RequestAdmissionItem:
@@ -94,7 +94,14 @@ def test_request_admission_stale_release_requires_exact_lease() -> None:
     item = _item()
     lease = controller.try_acquire(item)
     assert isinstance(lease, RequestAdmissionLease)
-    stale = replace(lease, current_adaptive_limit=lease.current_adaptive_limit + 1)
+    stale = RequestAdmissionLease(
+        lease_id=lease.lease_id,
+        item=lease.item,
+        acquired_at=lease.acquired_at,
+        current_adaptive_limit=lease.current_adaptive_limit + 1,
+        effective_max=lease.effective_max,
+        controller_generation=lease.controller_generation,
+    )
 
     stale_result = controller.release(stale, RequestReleaseOutcome(kind="provider_failure"))
     snapshot = controller.pressure.snapshot(item.resource)
@@ -184,7 +191,7 @@ def test_request_admission_fresh_rate_limit_after_burst_decreases_again() -> Non
 
     assert snapshot is not None
     assert snapshot.current_limit == 2
-    assert snapshot.rate_limit_ceiling == 4
+    assert snapshot.rate_limit_ceiling == 8
     assert snapshot.consecutive_rate_limits == 9
 
 
@@ -255,6 +262,21 @@ def test_request_admission_logs_sink_failures(caplog: pytest.LogCaptureFixture)
     assert "Request admission event sink raised; dropping event." in caplog.text
 
 
+def test_request_lease_released_event_records_release_outcome() -> None:
+    sink = InMemoryAdmissionEventSink()
+    controller = AdaptiveRequestAdmissionController(event_sink=sink)
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    item = _item()
+    lease = controller.try_acquire(item)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    controller.release(lease, RequestReleaseOutcome(kind="provider_failure"))
+
+    release_events = [event for event in sink.request_events if event.event_kind == "request_lease_released"]
+    assert release_events
+    assert release_events[-1].reason_or_outcome == "provider_failure"
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_acquire_sync_rejects_running_event_loop() -> None:
     controller = _controller(cap=1)

From 1fcc6b5871f2a14a6fe755df1d28adccf50bddb4 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Tue, 19 May 2026 20:34:24 -0400
Subject: [PATCH 20/33] chore: remove generated benchmark artifacts

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 .gitignore                                    |    4 +
 .../agent-aimd-scale/README.md                |   48 -
 .../agent-aimd-scale/combined_summary.json    | 1224 -----------------
 .../645-live-bench-nano/agent-aimd/README.md  |   28 -
 .../agent-aimd/combined_summary.json          |  906 ------------
 .../agent-bottleneck/README.md                |   32 -
 .../agent-bottleneck/README_mixed.md          |   27 -
 .../combined_mixed_summary.json               |  369 -----
 .../agent-bottleneck/combined_summary.json    |  297 ----
 .../agent-cap-scale/README.md                 |   33 -
 .../agent-cap-scale/combined_summary.json     |   93 --
 .../agent-cap-sweep/README.md                 |   13 -
 .../agent-cap-sweep/README_final1024.md       |   13 -
 .../agent-cap-sweep/combined_summary.json     |  269 ----
 .../combined_summary_final1024.json           |  493 -------
 .../agent-fan-scale/README.md                 |   37 -
 .../agent-fan-scale/combined_summary.json     |  279 ----
 .../645-live-bench-nano/agent-fan/README.md   |    4 -
 .../agent-fan/combined_summary.json           |  161 ---
 .../agent-mixed-pipeline/README.md            |   12 -
 .../combined_summary.json                     | 1206 ----------------
 .../agent-mixed-pipeline/smoke/README.md      |   10 -
 .../smoke/combined_summary.json               |  176 ---
 artifacts/645-live-bench/agent-fan/README.md  |   21 -
 .../agent-fan/combined_summary.json           |  452 ------
 .../maxparallel_live_benchmark.json           |  507 -------
 .../maxparallel_live_benchmark.md             |   22 -
 ...sync-scheduling-epic-benchmark-report.html |  791 -----------
 reports/async-scheduling-idle-analysis.html   |   97 --
 reports/async-scheduling-idle-regression.html |  125 --
 30 files changed, 4 insertions(+), 7745 deletions(-)
 delete mode 100644 artifacts/645-live-bench-nano/agent-aimd-scale/README.md
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 delete mode 100644 artifacts/645-live-bench-nano/agent-cap-sweep/README.md
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diff --git a/.gitignore b/.gitignore
index e7f8718b7..c65a30138 100644
--- a/.gitignore
+++ b/.gitignore
@@ -85,6 +85,10 @@ venv.bak/
 # Local scratch space
 .scratch/
 
+# Generated benchmark/report output
+/artifacts/
+/reports/
+
 docs/notebooks/
 docs/notebook_source/*.ipynb
 docs/notebook_source/*.csv
diff --git a/artifacts/645-live-bench-nano/agent-aimd-scale/README.md b/artifacts/645-live-bench-nano/agent-aimd-scale/README.md
deleted file mode 100644
index 39c5c1826..000000000
--- a/artifacts/645-live-bench-nano/agent-aimd-scale/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# 645 live bench nano AIMD scale lane
-
-Ran in `/Users/etramel/src/DataDesigner` on branch `scheduling-yolo` with only `/tmp/dd_live_aimd_bench.py` and artifacts under this directory. No tracked repo files were edited.
-
-## Configuration
-
-- Model: `openai/openai/gpt-5-nano`
-- Provider: `nvidia-internal`
-- Temperature: omitted
-- `skip_health_check=True`
-- `DATA_DESIGNER_ASYNC_ENGINE=1`
-- `DATA_DESIGNER_ASYNC_TRACE=1`
-- `max_parallel_requests=16`
-- AIMD initial limit: 1
-- AIMD `increase_after_successes=16`
-- Shape for final scenarios: 512 rows x 2 independent `LLMTextColumnConfig` columns = 1024 model generations
-- Event instrumentation: `InMemoryAdmissionEventSink`, patched request admission init, model-client factory `request_event_sink`, and scheduler init `scheduler_event_sink`
-
-## Scenarios
-
-| Scenario | Buffer | Requests | Success | Failures | Wall s | Time to cap s | Max in-flight | Max waiters | Request wait p50 / p95 / max s |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| diagnostic-4x2-buffer32 | 32 | 8 | 8 | 0 | 5.586 | n/a | 2 | 7 | 2.638 / 3.871 / 3.887 |
-| final-512x2-buffer32 | 32 | 1024 | 1024 | 0 | 104.823 | 50.332 | 16 | 63 | 3.180 / 19.371 / 29.990 |
-| final-512x2-buffer512 | 512 | 1024 | 1024 | 0 | 114.522 | 57.062 | 16 | 63 | 3.283 / 22.949 / 35.324 |
-
-## Observations
-
-- Both final scenarios completed exactly 1024 `model_request_started` and 1024 `model_request_completed` events, with 0 failed model requests and no fallback model.
-- AIMD limit increased monotonically from 1 through 16 in both final scenarios. There were 15 `request_limit_increased` events, 0 decreases, and 0 rate-limit events in each final scenario.
-- Cap enforcement held: observed request in-flight max was 16 in both final scenarios, matching `max_parallel_requests=16`.
-- `buffer_size=32` reached cap faster (50.332s) and completed faster (104.823s) than `buffer_size=512` (57.062s to cap, 114.522s wall).
-- Request wait p95 was lower for `buffer_size=32` (19.371s) than `buffer_size=512` (22.949s).
-- Traffic became steady after the initial AIMD ramp in both final scenarios; see each `flow_buckets.json` for per-second starts/completions and `monitor_samples.jsonl` for sampled pressure snapshots.
-
-## Artifacts
-
-Each scenario directory contains:
-
-- `timeline.jsonl`
-- `request_events.jsonl`
-- `monitor_samples.jsonl`
-- `task_traces.csv`
-- `task_traces.json`
-- `flow_buckets.json`
-- `summary.json`
-
-Combined summary: `combined_summary.json`.
diff --git a/artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json b/artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json
deleted file mode 100644
index ff9154390..000000000
--- a/artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json
+++ /dev/null
@@ -1,1224 +0,0 @@
-{
-  "artifact_schema_version": "645-live-bench-nano-combined-v1",
-  "comparison": {
-    "both_cap_enforced": true,
-    "both_zero_failures": true,
-    "buffer32_max_waiters": 63,
-    "buffer32_time_to_cap_seconds": 50.33227308301139,
-    "buffer32_wait_p95_seconds": 19.37087424368073,
-    "buffer32_wall_time_seconds": 104.82342862500809,
-    "buffer512_max_waiters": 63,
-    "buffer512_minus_buffer32_seconds": 9.698241957987193,
-    "buffer512_minus_buffer32_time_to_cap_seconds": 6.729556832986418,
-    "buffer512_time_to_cap_seconds": 57.061829915997805,
-    "buffer512_wait_p95_seconds": 22.94936933576682,
-    "buffer512_wall_time_seconds": 114.52167058299528,
-    "same_shape": true
-  },
-  "env": {
-    "DATA_DESIGNER_ASYNC_ENGINE": "1",
-    "DATA_DESIGNER_ASYNC_TRACE": "1"
-  },
-  "fallback": null,
-  "harness": "/tmp/dd_live_aimd_bench.py",
-  "root": "artifacts/645-live-bench-nano/agent-aimd-scale",
-  "scenarios": {
-    "diagnostic-4x2-buffer32": {
-      "artifacts": {
-        "flow_buckets": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/flow_buckets.json",
-        "monitor_samples": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/monitor_samples.jsonl",
-        "request_events": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/request_events.jsonl",
-        "summary": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/summary.json",
-        "task_traces_csv": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/task_traces.csv",
-        "task_traces_json": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/task_traces.json",
-        "timeline": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/timeline.jsonl"
-      },
-      "buffer_size": 32,
-      "increase_after_successes": 4,
-      "initial_limit": 1,
-      "llm_columns": 2,
-      "max_parallel_requests": 16,
-      "model_generations_expected": 8,
-      "model_request_completed_events": 8,
-      "model_request_started_events": 8,
-      "request_summary": {
-        "cap_enforced": true,
-        "event_counts": {
-          "model_request_completed": 8,
-          "model_request_started": 8,
-          "request_effective_cap_changed": 1,
-          "request_lease_acquired": 8,
-          "request_lease_released": 8,
-          "request_limit_increased": 2,
-          "request_queue_drained": 2,
-          "request_queue_formed": 2,
-          "request_resource_registered": 1,
-          "request_wait_completed": 8,
-          "request_wait_started": 8
-        },
-        "final_pressure": {
-          "active_lease_count": 0,
-          "blocked_until_monotonic": null,
-          "captured_at": 173980.1628715,
-          "consecutive_rate_limits": 0,
-          "cooldown_remaining_seconds": 0.0,
-          "current_limit": 3,
-          "effective_max": 16,
-          "in_flight_count": 0,
-          "last_outcome": "success",
-          "leak_diagnostics": {},
-          "rate_limit_ceiling": 0,
-          "resource": {
-            "domain": "chat",
-            "model_id": "openai/openai/gpt-5-nano",
-            "provider_name": "nvidia-internal"
-          },
-          "sequence": 65,
-          "waiters": 0
-        },
-        "limit_path": [
-          {
-            "current_limit": 1,
-            "diagnostics": {
-              "alias": "aimd-nano",
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-              "provider_model": {
-                "model_id": "openai/openai/gpt-5-nano",
-                "provider_name": "nvidia-internal"
-              }
-            },
-            "effective_max": 16,
-            "elapsed_seconds": 0.010773582995170727,
-            "event_kind": "request_resource_registered"
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-          {
-            "current_limit": 1,
-            "diagnostics": {
-              "current": 16,
-              "previous": 0,
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-            "event_kind": "request_effective_cap_changed"
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-            "current_limit": 2,
-            "diagnostics": {
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-            },
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-          {
-            "current_limit": 3,
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-          "p50": 2.6384192499972414,
-          "p95": 3.8706564271909882
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-      "rows": 4,
-      "scheduler_event_counts": {
-        "dependency_ready": 9,
-        "queue_drained": 1,
-        "ready_enqueued": 9,
-        "selected": 9,
-        "task_completed": 9,
-        "task_lease_acquired": 9,
-        "task_lease_released": 9,
-        "worker_spawned": 9
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-      "success": true,
-      "summary_path": "artifacts/645-live-bench-nano/agent-aimd-scale/diagnostic-4x2-buffer32/summary.json",
-      "task_trace_count": 9,
-      "task_trace_status_counts": {
-        "ok": 9
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-      "traffic_regularity": {
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-        "first_15_buckets": [
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diff --git a/artifacts/645-live-bench-nano/agent-aimd/README.md b/artifacts/645-live-bench-nano/agent-aimd/README.md
deleted file mode 100644
index 8db50cc8e..000000000
--- a/artifacts/645-live-bench-nano/agent-aimd/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# Live AIMD benchmark: gpt-5-nano
-
-Runtime patches were applied only inside `/tmp/live_aimd_bench.py`; no tracked repository files were edited.
-
-Common settings:
-- provider: `nvidia-internal`
-- model: `openai/openai/gpt-5-nano`
-- `skip_health_check=True`
-- temperature omitted from `ChatCompletionInferenceParams`
-- `DATA_DESIGNER_ASYNC_ENGINE=1` and `DATA_DESIGNER_ASYNC_TRACE=1`
-- shared `InMemoryAdmissionEventSink` passed to request admission, model request executor, and async scheduler
-
-| Scenario | Rows | LLM cols | Expected requests | Wall time (s) | Success/failure | Limit ramp | Max in-flight | Max waiters | Cap enforced | Rate limits |
-|---|---:|---:|---:|---:|---|---|---:|---:|---|---:|
-| `aimd_rows48_cols3_cap6_initial2` | 48 | 3 | 144 | 39.863 | 144/0 | [2, 3, 4, 5, 6] | 6 | 34 | True | 0 |
-| `aimd_rows64_cols2_cap8_initial1` | 64 | 2 | 128 | 38.998 | 128/0 | [1, 2, 3, 4, 5, 6, 7, 8] | 8 | 31 | True | 0 |
-
-Artifacts per scenario:
-- `timeline.jsonl`: merged scheduler and request timeline, sorted by monotonic capture time and event sequence
-- `request_events.jsonl`: request-admission and model-request events using `event_kind` and `pressure_snapshot`
-- `monitor_samples.jsonl`: periodic pressure snapshots while the preview ran
-- `task_traces.json` and `task_traces.csv`: async scheduler task traces from `DATA_DESIGNER_ASYNC_TRACE=1`
-- `summary.json`: scenario-level rollup including limit changes, final pressure, event counts, and cap validation
-
-Notes:
-- Both scenarios completed without provider failures or rate limits.
-- Observed request in-flight counts never exceeded `max_parallel_requests`.
-- Console model-usage logs reported 128 successful requests / 4608 total tokens for the 64x2 run and 144 successful requests / 5184 total tokens for the 48x3 run.
diff --git a/artifacts/645-live-bench-nano/agent-aimd/combined_summary.json b/artifacts/645-live-bench-nano/agent-aimd/combined_summary.json
deleted file mode 100644
index 00ae89034..000000000
--- a/artifacts/645-live-bench-nano/agent-aimd/combined_summary.json
+++ /dev/null
@@ -1,906 +0,0 @@
-{
-  "benchmark_lane": "agent-aimd",
-  "env": {
-    "DATA_DESIGNER_ASYNC_ENGINE": "1",
-    "DATA_DESIGNER_ASYNC_TRACE": "1"
-  },
-  "model_name": "openai/openai/gpt-5-nano",
-  "provider": "nvidia-internal",
-  "scenario_count": 2,
-  "scenarios": [
-    {
-      "artifacts": {
-        "monitor_samples_jsonl": "artifacts/645-live-bench-nano/agent-aimd/aimd_rows48_cols3_cap6_initial2/monitor_samples.jsonl",
-        "request_events_jsonl": "artifacts/645-live-bench-nano/agent-aimd/aimd_rows48_cols3_cap6_initial2/request_events.jsonl",
-        "summary_json": "artifacts/645-live-bench-nano/agent-aimd/aimd_rows48_cols3_cap6_initial2/summary.json",
-        "task_traces_csv": "artifacts/645-live-bench-nano/agent-aimd/aimd_rows48_cols3_cap6_initial2/task_traces.csv",
-        "task_traces_json": "artifacts/645-live-bench-nano/agent-aimd/aimd_rows48_cols3_cap6_initial2/task_traces.json",
-        "timeline_jsonl": "artifacts/645-live-bench-nano/agent-aimd/aimd_rows48_cols3_cap6_initial2/timeline.jsonl"
-      },
-      "cap_enforced": true,
-      "columns": 3,
-      "env": {
-        "DATA_DESIGNER_ASYNC_ENGINE": "1",
-        "DATA_DESIGNER_ASYNC_TRACE": "1"
-      },
-      "error": null,
-      "expected_requests": 144,
-      "final_pressure": {
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-        "captured_at": 173652.671785458,
-        "consecutive_rate_limits": 0,
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-        "current_limit": 6,
-        "effective_max": 6,
-        "in_flight_count": 0,
-        "last_outcome": "success",
-        "leak_diagnostics": {},
-        "rate_limit_ceiling": 0,
-        "resource": {
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-          "model_id": "openai/openai/gpt-5-nano",
-          "provider_name": "nvidia-internal"
-        },
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-        "waiters": 0
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diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/README.md b/artifacts/645-live-bench-nano/agent-bottleneck/README.md
deleted file mode 100644
index 2422dcc2a..000000000
--- a/artifacts/645-live-bench-nano/agent-bottleneck/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# Agent Bottleneck Live Benchmark
-
-Model: `openai/openai/gpt-5-nano`
-Provider: `nvidia-internal`
-Temperature: omitted
-Health check: skipped
-
-Note: exact model returned `not_found` for every model request in these runs; artifacts still capture admission behavior up to the provider failure boundary.
-
-## request_bottleneck_rows64_cap2
-- Rows requested/result: 64 / 0
-- max_parallel_requests: 2; observed max in-flight: 2; cap enforced: True
-- Wait p50/p95/max: 1.823871 / 3.294706 / 3.412188 sec
-- Max waiters: 62; scheduler group_capped/admission_blocked: 0 / 0
-- Wall time: 4.241 sec; request successes/failed: 0 / 64; outcomes: `{'not_found': 64}`
-- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/request_bottleneck_rows64_cap2/summary.json`
-
-## narrow_downstream_rows40_cap1
-- Rows requested/result: 40 / 0
-- max_parallel_requests: 1; observed max in-flight: 1; cap enforced: True
-- Wait p50/p95/max: 2.060097 / 3.858477 / 4.058885 sec
-- Max waiters: 39; scheduler group_capped/admission_blocked: 0 / 0
-- Wall time: 4.667 sec; request successes/failed: 0 / 40; outcomes: `{'not_found': 40}`
-- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/narrow_downstream_rows40_cap1/summary.json`
-
-## heavy_fast_rows32_cap8
-- Rows requested/result: 32 / 0
-- max_parallel_requests: 8; observed max in-flight: 8; cap enforced: True
-- Wait p50/p95/max: 0.242845 / 0.434496 / 0.473193 sec
-- Max waiters: 24; scheduler group_capped/admission_blocked: 0 / 0
-- Wall time: 1.336 sec; request successes/failed: 0 / 32; outcomes: `{'not_found': 32}`
-- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/heavy_fast_rows32_cap8/summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md b/artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md
deleted file mode 100644
index 96904bcdc..000000000
--- a/artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md
+++ /dev/null
@@ -1,27 +0,0 @@
-# Mixed Buffer Live Benchmarks
-
-No model fallback was used. Initial integrate-endpoint attempt failed with gpt-5.5 not_found; final runs used configured nvidia-internal endpoint.
-
-Previous small-run summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json`
-
-## mixed_rows512_buf256_1024gens
-- Rows / expected model generations / observed completions: 512 / 1024 / 1024
-- Buffer size: 256; wall time: 264.457 sec; result rows: 512
-- Outcomes: `{'openai/openai/gpt-5-nano': {'success': 512}, 'openai/openai/gpt-5.5': {'success': 512}}`
-- Max in-flight: `{'openai/openai/gpt-5-nano': 7, 'openai/openai/gpt-5.5': 8}`
-- Max waiters: `{'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 248}`
-- Wait p95/max by model: `{'openai/openai/gpt-5-nano': {'p50': 0.000496, 'p95': 0.00113, 'max': 0.005292}, 'openai/openai/gpt-5.5': {'p50': 119.00833, 'p95': 129.106918, 'max': 131.423565}}`
-- Upstream-to-downstream idle p95/max: 0.111947 / 0.356167 sec
-- Traffic shape: `{'openai/openai/gpt-5.5': {'active_seconds': 224, 'zero_bins_inside_active_window': 33, 'max_started_per_second': 8, 'mean_started_per_second': 1.992, 'burst_ratio': 4.016, 'interpretation': 'waves'}, 'openai/openai/gpt-5-nano': {'active_seconds': 232, 'zero_bins_inside_active_window': 27, 'max_started_per_second': 7, 'mean_started_per_second': 1.977, 'burst_ratio': 3.541, 'interpretation': 'waves'}}`
-- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf256_1024gens/summary.json`
-
-## mixed_rows512_buf32_1024gens
-- Rows / expected model generations / observed completions: 512 / 1024 / 1024
-- Buffer size: 32; wall time: 259.315 sec; result rows: 512
-- Outcomes: `{'openai/openai/gpt-5-nano': {'success': 512}, 'openai/openai/gpt-5.5': {'success': 512}}`
-- Max in-flight: `{'openai/openai/gpt-5-nano': 9, 'openai/openai/gpt-5.5': 8}`
-- Max waiters: `{'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 88}`
-- Wait p95/max by model: `{'openai/openai/gpt-5-nano': {'max': 0.001512, 'p50': 0.000276, 'p95': 0.000618}, 'openai/openai/gpt-5.5': {'max': 45.718323, 'p50': 31.02182, 'p95': 39.684837}}`
-- Upstream-to-downstream idle p95/max: 0.002748 / 0.028367 sec
-- Traffic shape: `{'openai/openai/gpt-5-nano': {'active_seconds': 220, 'burst_ratio': 2.412, 'interpretation': 'waves', 'max_started_per_second': 5, 'mean_started_per_second': 2.073, 'zero_bins_inside_active_window': 27}, 'openai/openai/gpt-5.5': {'active_seconds': 217, 'burst_ratio': 3.828, 'interpretation': 'waves', 'max_started_per_second': 8, 'mean_started_per_second': 2.09, 'zero_bins_inside_active_window': 28}}`
-- Summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/mixed_rows512_buf32_1024gens/summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json b/artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json
deleted file mode 100644
index 8fc843a3a..000000000
--- a/artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json
+++ /dev/null
@@ -1,369 +0,0 @@
-{
-  "created_at_epoch": 1778787927.324943,
-  "notes": [
-    "Previous smaller bottleneck runs are preserved separately in combined_summary.json.",
-    "Mixed runs use exact upstream openai/openai/gpt-5.5 and downstream openai/openai/gpt-5-nano with temperature omitted and skip_health_check=True.",
-    "Initial mixed attempt against https://integrate.api.nvidia.com/v1 returned not_found for gpt-5.5; rerun used configured nvidia-internal endpoint https://inference-api.nvidia.com/v1 and exact requested models succeeded. No model fallback was used."
-  ],
-  "previous_small_runs_location": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json",
-  "run_class": "final mixed 1024+ generation scale",
-  "scenarios": [
-    {
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-        "openai/openai/gpt-5.5": true
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-      "dataset_path": "None",
-      "expected_model_generations": 1024,
-      "failures": [],
-      "final_pressure_by_model": {
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-          "blocked_until_monotonic": null,
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-          "current_limit": 16,
-          "effective_max": 16,
-          "in_flight_count": 0,
-          "last_outcome": "success",
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-          "resource": {
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-          "waiters": 0
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-        "openai/openai/gpt-5.5": {
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-          "current_limit": 8,
-          "effective_max": 8,
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-          "last_outcome": "success",
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-          "sequence": 7199,
-          "waiters": 0
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-      "max_inflight_by_model": {
-        "openai/openai/gpt-5-nano": 7,
-        "openai/openai/gpt-5.5": 8
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-      "max_waiters_by_model": {
-        "openai/openai/gpt-5-nano": 1,
-        "openai/openai/gpt-5.5": 248
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-      "model_request_outcomes": {
-        "openai/openai/gpt-5-nano": {
-          "success": 512
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-        "openai/openai/gpt-5.5": {
-          "success": 512
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-      "models": {
-        "downstream_fast": {
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-          "max_parallel_requests": 16,
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diff --git a/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json b/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json
deleted file mode 100644
index 9935f8154..000000000
--- a/artifacts/645-live-bench-nano/agent-bottleneck/combined_summary.json
+++ /dev/null
@@ -1,297 +0,0 @@
-{
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diff --git a/artifacts/645-live-bench-nano/agent-cap-scale/README.md b/artifacts/645-live-bench-nano/agent-cap-scale/README.md
deleted file mode 100644
index ebc8e094f..000000000
--- a/artifacts/645-live-bench-nano/agent-cap-scale/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Live async scheduling benchmark: cap scale
-
-Ran two required live DataDesigner async scheduling scenarios on `openai/openai/gpt-5-nano` via `nvidia-internal` with `temperature` omitted, `skip_health_check=True`, `DATA_DESIGNER_ASYNC_ENGINE=1`, and `DATA_DESIGNER_ASYNC_TRACE=1`.
-
-Instrumentation used one shared `InMemoryAdmissionEventSink` and runtime monkeypatches for `AdaptiveRequestAdmissionController.__init__`, `data_designer.engine.models.clients.factory.create_model_client`, and `AsyncTaskScheduler.__init__`.
-
-| Scenario | Rows x cols | Buffer | Requests | Wall s | Max in-flight | Max waiters | Start mean/s | Start CV | Max start gap s | Wave score | Failures |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| cap8_rows512_cols2_buffer32 | 512x2 | 32 | 1024 | 160.30 | 8 | 30 | 6.38 | 0.252 | 1.086 | 0.816 | 0 |
-| cap8_rows512_cols2_buffer512 | 512x2 | 512 | 1024 | 167.08 | 8 | 31 | 6.14 | 0.246 | 1.091 | 0.872 | 0 |
-
-## Buffer comparison
-
-- `buffer_size=32`: 16 row groups, repeated dispatch/checkpoint waves, request start CV 0.252, wave score 0.816.
-- `buffer_size=512`: 1 row group, no repeated row-group dispatch waves, request start CV 0.246, wave score 0.872.
-- Cap enforcement held in both scenarios: max observed request in-flight was 8 and 8 for cap 8.
-- No fallback model was used. No request rate-limit events were observed. Total failures: 0.
-
-## Artifacts
-
-- `cap8_rows512_cols2_buffer32/timeline.jsonl`
-- `cap8_rows512_cols2_buffer32/request_events.jsonl`
-- `cap8_rows512_cols2_buffer32/scheduler_events.jsonl`
-- `cap8_rows512_cols2_buffer32/task_traces.json` and `cap8_rows512_cols2_buffer32/task_traces.csv`
-- `cap8_rows512_cols2_buffer32/flow_buckets.json`
-- `cap8_rows512_cols2_buffer32/summary.json`
-- `cap8_rows512_cols2_buffer512/timeline.jsonl`
-- `cap8_rows512_cols2_buffer512/request_events.jsonl`
-- `cap8_rows512_cols2_buffer512/scheduler_events.jsonl`
-- `cap8_rows512_cols2_buffer512/task_traces.json` and `cap8_rows512_cols2_buffer512/task_traces.csv`
-- `cap8_rows512_cols2_buffer512/flow_buckets.json`
-- `cap8_rows512_cols2_buffer512/summary.json`
-- `combined_summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json b/artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json
deleted file mode 100644
index cc03cd844..000000000
--- a/artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json
+++ /dev/null
@@ -1,93 +0,0 @@
-{
-  "all_caps_enforced": true,
-  "comparison": {
-    "buffer32_minus_buffer512_max_waiters": -1,
-    "buffer32_minus_buffer512_start_rate_cv": 0.005909466834193605,
-    "buffer32_minus_buffer512_wall_time_seconds": -6.777404916996602,
-    "buffer32_minus_buffer512_wave_score": -0.05615488622945386
-  },
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-    "DATA_DESIGNER_ASYNC_ENGINE": "1",
-    "DATA_DESIGNER_ASYNC_TRACE": "1"
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-  "fallback_used": false,
-  "model_requested": "openai/openai/gpt-5-nano",
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-      "buffer_size": 32,
-      "cap_enforced": true,
-      "expected_model_generations": 1024,
-      "failure_count": 0,
-      "llm_columns": 2,
-      "max_observed_request_in_flight": 8,
-      "max_observed_request_waiters": 30,
-      "max_parallel_requests": 8,
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-      "model_request_completed": 1024,
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-      "request_rate_limited_events": 0,
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-      "scenario": "cap8_rows512_cols2_buffer32",
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-      "start_rate_mean_after_warmup": 6.380645161290323,
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-      "status": "success",
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-  "temperature_omitted": true,
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-  "total_model_request_started": 2048
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diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/README.md b/artifacts/645-live-bench-nano/agent-cap-sweep/README.md
deleted file mode 100644
index f174fc90a..000000000
--- a/artifacts/645-live-bench-nano/agent-cap-sweep/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# GPT-5 Nano Live Cap Sweep
-
-Model: `openai/openai/gpt-5-nano`
-Provider: `nvidia-internal`
-Max tokens: `32`
-Temperature: omitted
-Async env: `DATA_DESIGNER_ASYNC_ENGINE=1`, `DATA_DESIGNER_ASYNC_TRACE=1`
-
-| Scenario | Status | Rows | Requests completed | Wall s | Max in-flight | Cap | Enforced |
-|---|---:|---:|---:|---:|---:|---:|---:|
-| cap2_rows40_cols2 | success | 40/40 | 80/80 | 54.33 | 2 | 2 | True |
-| cap4_rows40_cols2 | success | 40/40 | 80/80 | 27.41 | 4 | 4 | True |
-| cap8_rows40_cols2 | success | 40/40 | 80/80 | 16.11 | 8 | 8 | True |
diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md b/artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md
deleted file mode 100644
index cbc4dee68..000000000
--- a/artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# GPT-5 Nano Live 1024+ Buffer Sweep
-
-Model: `openai/openai/gpt-5-nano`
-Provider: `nvidia-internal`
-Max tokens: `32`
-Temperature: omitted
-Async env: `DATA_DESIGNER_ASYNC_ENGINE=1`, `DATA_DESIGNER_ASYNC_TRACE=1`
-
-| Scenario | Status | Buffer | Rows | Requests completed | Wall s | Max in-flight | Cap | Traffic | Enforced |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
-| final1024_cap8_buffer32_rows512_cols2 | success | 32 | 512/512 | 1024/1024 | 155.37 | 8 | 8 | steady | True |
-| final1024_cap8_buffer128_rows512_cols2 | success | 128 | 512/512 | 1024/1024 | 153.96 | 8 | 8 | steady | True |
-| final1024_cap8_buffer512_rows512_cols2 | success | 512 | 512/512 | 1024/1024 | 150.27 | 8 | 8 | steady | True |
diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary.json b/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary.json
deleted file mode 100644
index 917a3ac11..000000000
--- a/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary.json
+++ /dev/null
@@ -1,269 +0,0 @@
-[
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diff --git a/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json b/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json
deleted file mode 100644
index 76bd8c86b..000000000
--- a/artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json
+++ /dev/null
@@ -1,493 +0,0 @@
-[
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diff --git a/artifacts/645-live-bench-nano/agent-fan-scale/README.md b/artifacts/645-live-bench-nano/agent-fan-scale/README.md
deleted file mode 100644
index 868b2c69d..000000000
--- a/artifacts/645-live-bench-nano/agent-fan-scale/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Live async fan scale benchmark
-
-Second-wave large-scale live benchmark lane for async scheduling fan-out/fan-in and buffer-size effects.
-
-## Configuration
-
-- Model: `openai/openai/gpt-5-nano`
-- Provider: `nvidia-internal`
-- Fallback used: `false`
-- Temperature: omitted
-- `skip_health_check=True`
-- `DATA_DESIGNER_ASYNC_ENGINE=1`
-- `DATA_DESIGNER_ASYNC_TRACE=1`
-- Shared sink: `InMemoryAdmissionEventSink` injected into request admission, model client factory, and scheduler
-
-## Scenario Summary
-
-| Scenario | Topology | Buffer | Requests | Wall s | Max in-flight | Max waiters | Wait p50/p95/max s | Failures |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|
-| `fanout_buffer32` | fanout | 32 | 1024/1024 | 132.24 | 8 | 56 | 6.79/8.97/9.80 | 0 |
-| `fanout_buffer256` | fanout | 256 | 1024/1024 | 125.94 | 8 | 57 | 6.58/8.09/9.83 | 0 |
-| `fanin_buffer256` | fanin | 256 | 1024/1024 | 141.64 | 8 | 57 | 7.18/8.85/9.62 | 0 |
-
-## Observations
-
-- Fan-out buffer 32 and 256 both maintained balanced traffic across all four independent branches.
-- Buffer 256 was modestly faster than buffer 32 in this run, while wait distributions were similar.
-- Fan-in used 3 upstream LLM columns plus 1 downstream synthesis LLM column for 1024 total model generations.
-- Fan-in showed row-level fan-in: downstream synthesis began almost immediately after the first row became ready and interleaved with upstream traffic.
-- Request in-flight never exceeded the configured cap of 8 in any scenario.
-
-## Artifacts
-
-- `fanout_buffer32`: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer32`
-- `fanout_buffer256`: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer256`
-- `fanin_buffer256`: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256`
-- Combined summary: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json`
diff --git a/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json b/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json
deleted file mode 100644
index cab97c802..000000000
--- a/artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json
+++ /dev/null
@@ -1,279 +0,0 @@
-{
-  "all_caps_enforced": true,
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-    "DATA_DESIGNER_ASYNC_TRACE": "1"
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-  "max_parallel_requests": 8,
-  "model_attempted": "openai/openai/gpt-5-nano",
-  "provider": "nvidia-internal",
-  "scenario_count": 3,
-  "skip_health_check": true,
-  "summaries": [
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-        "request_events": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer32/request_events.jsonl",
-        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer32/summary.json",
-        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer32/task_traces.csv",
-        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer32/task_traces.json",
-        "timeline": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer32/timeline.jsonl"
-      },
-      "buffer_size": 32,
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-        "DATA_DESIGNER_ASYNC_TRACE": "1"
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-      "error": null,
-      "expected_model_generations": 1024,
-      "fanin_delay_stats": null,
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-        "model_request_starts": 1024,
-        "starts_by_column": {
-          "branch_0": 256,
-          "branch_1": 256,
-          "branch_2": 256,
-          "branch_3": 256
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-        "success_completions": 1024,
-        "wait_summary": {
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-      "topology": "fanout",
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-        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer256/summary.json",
-        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer256/task_traces.csv",
-        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer256/task_traces.json",
-        "timeline": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanout_buffer256/timeline.jsonl"
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-      "buffer_size": 256,
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-        "DATA_DESIGNER_ASYNC_TRACE": "1"
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-      "error": null,
-      "expected_model_generations": 1024,
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-      "max_parallel_requests": 8,
-      "model": "openai/openai/gpt-5-nano",
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-      "name": "fanout_buffer256",
-      "num_records": 256,
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-          "branch_0": 256,
-          "branch_1": 256,
-          "branch_2": 256,
-          "branch_3": 256
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-        "success_completions": 1024,
-        "wait_summary": {
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-      "status": "success",
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-    {
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-        "flow_buckets": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256/flow_buckets.json",
-        "request_events": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256/request_events.jsonl",
-        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256/summary.json",
-        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256/task_traces.csv",
-        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256/task_traces.json",
-        "timeline": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-fan-scale/fanin_buffer256/timeline.jsonl"
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-      "name": "fanin_buffer256",
-      "num_records": 256,
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-      "records": 256,
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-          "up_1": 256,
-          "up_2": 256
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diff --git a/artifacts/645-live-bench-nano/agent-fan/README.md b/artifacts/645-live-bench-nano/agent-fan/README.md
deleted file mode 100644
index 458e05ead..000000000
--- a/artifacts/645-live-bench-nano/agent-fan/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-# Live async fan benchmark
-
-- fanout_rows32_cols4_cap6: success=True, rows=32, wall_time_s=33.839, requests=128, max_in_flight=6/6, queue_formed=7, rate_limits=0
-- fanin_rows24_up3_down1_cap4: success=True, rows=24, wall_time_s=43.434, requests=96, max_in_flight=4/4, queue_formed=5, rate_limits=0
diff --git a/artifacts/645-live-bench-nano/agent-fan/combined_summary.json b/artifacts/645-live-bench-nano/agent-fan/combined_summary.json
deleted file mode 100644
index d3eb8ab24..000000000
--- a/artifacts/645-live-bench-nano/agent-fan/combined_summary.json
+++ /dev/null
@@ -1,161 +0,0 @@
-{
-  "endpoint": "https://inference-api.nvidia.com/v1",
-  "env": {
-    "DATA_DESIGNER_ASYNC_ENGINE": "1",
-    "DATA_DESIGNER_ASYNC_TRACE": "1"
-  },
-  "model": "openai/openai/gpt-5-nano",
-  "provider": "nvidia-internal",
-  "summaries": [
-    {
-      "cap_enforced": true,
-      "error": null,
-      "failure_count": 0,
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-        "current_limit": 6,
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-        "last_outcome": "success",
-        "leak_diagnostics": {},
-        "rate_limit_ceiling": 0,
-        "resource": {
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-          "upstream_2": 24,
-          "upstream_3": 24
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-        "request_wait_started": 96
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-      "rows_requested": 24,
-      "scenario": "fanin_rows24_up3_down1_cap4",
-      "scheduler_event_counts": {
-        "dependency_ready": 97,
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-        "queue_drained": 11,
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diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md b/artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md
deleted file mode 100644
index 4f59d0939..000000000
--- a/artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Mixed-model async scheduling live benchmark
-
-Rows: 512; model generations per scenario: 1024.
-Provider: `nvidia-internal`.
-Upstream: `openai/openai/gpt-5.5` cap 4, max_tokens 96.
-Downstream: `openai/openai/gpt-5-nano` cap 8, max_tokens 32.
-Temperature omitted for both model configs. Health checks skipped.
-
-| Scenario | Buffer | Success | Wall s | Heavy starts | Nano starts | Overlap s | Idle p50/p95/max s | Max in-flight | Max waiters | Diagnosis | Cap violation |
-|---|---:|---|---:|---:|---:|---:|---|---|---|---|---|
-| mixed_rows512_buf256_heavy4_nano8 | 256 | True | 366.44 | 512 | 512 | 329.37 | 0.037/0.071/0.133 | {'openai/openai/gpt-5-nano': 6, 'openai/openai/gpt-5.5': 4} | {'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 252} | steady_interleaving | {'openai/openai/gpt-5-nano': False, 'openai/openai/gpt-5.5': False} |
-| mixed_rows512_buf32_heavy4_nano8 | 32 | True | 368.46 | 512 | 512 | 321.33 | 0.001/0.005/0.053 | {'openai/openai/gpt-5-nano': 6, 'openai/openai/gpt-5.5': 4} | {'openai/openai/gpt-5-nano': 1, 'openai/openai/gpt-5.5': 92} | steady_interleaving | {'openai/openai/gpt-5-nano': False, 'openai/openai/gpt-5.5': False} |
diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json b/artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json
deleted file mode 100644
index 2c14d75ac..000000000
--- a/artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json
+++ /dev/null
@@ -1,1206 +0,0 @@
-{
-  "buffers": [
-    32,
-    256
-  ],
-  "models": {
-    "downstream": "openai/openai/gpt-5-nano",
-    "upstream": "openai/openai/gpt-5.5"
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-  "provider": "nvidia-internal",
-  "rows": 512,
-  "summaries": [
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-        "idle_times": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/mixed_rows512_buf256_heavy4_nano8/idle_times.json",
-        "request_events": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/mixed_rows512_buf256_heavy4_nano8/request_events.jsonl",
-        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/mixed_rows512_buf256_heavy4_nano8/summary.json",
-        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/mixed_rows512_buf256_heavy4_nano8/task_traces.csv",
-        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/mixed_rows512_buf256_heavy4_nano8/task_traces.json",
-        "timeline": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/mixed_rows512_buf256_heavy4_nano8/timeline.jsonl"
-      },
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-      "start_regularities_by_model_column": {
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diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/README.md b/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/README.md
deleted file mode 100644
index d28937cbd..000000000
--- a/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Mixed-model async scheduling live benchmark
-
-Rows: 2; model generations per scenario: 4.
-Provider: `nvidia-internal`.
-Upstream: `openai/openai/gpt-5.5` cap 1, max_tokens 32.
-Downstream: `openai/openai/gpt-5-nano` cap 1, max_tokens 16.
-
-| Scenario | Buffer | Success | Wall s | Heavy starts | Nano starts | Overlap s | Idle p50/p95/max s | Diagnosis | Cap violation |
-|---|---:|---|---:|---:|---:|---:|---|---|---|
-| mixed_rows2_buf2_heavy1_nano1 | 2 | True | 4.67 | 2 | 2 | 1.37 | None/None/None | wave_or_serialized | {'openai/openai/gpt-5.5': False, 'openai/openai/gpt-5-nano': False} |
diff --git a/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/combined_summary.json b/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/combined_summary.json
deleted file mode 100644
index 6c0de069b..000000000
--- a/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/combined_summary.json
+++ /dev/null
@@ -1,176 +0,0 @@
-{
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-    "upstream": "openai/openai/gpt-5.5"
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-        "request_events": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/request_events.jsonl",
-        "summary": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/summary.json",
-        "task_traces_csv": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/task_traces.csv",
-        "task_traces_json": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/task_traces.json",
-        "timeline": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench-nano/agent-mixed-pipeline/smoke/mixed_rows2_buf2_heavy1_nano1/timeline.jsonl"
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diff --git a/artifacts/645-live-bench/agent-fan/README.md b/artifacts/645-live-bench/agent-fan/README.md
deleted file mode 100644
index 80381379c..000000000
--- a/artifacts/645-live-bench/agent-fan/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Agent Fan Live Benchmark
-
-Output: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan`
-
-## fan_out_mp2_short
-- DAG shape: fan-out
-- Wall time: 6.556s
-- Rows / columns: 2 / 6
-- max_parallel_requests: 2; observed max request in-flight: 2; waiters: 4
-- Scheduler events: `{'dependency_ready': 7, 'ready_enqueued': 7, 'selected': 7, 'task_lease_acquired': 7, 'worker_spawned': 7, 'queue_drained': 2, 'task_completed': 7, 'task_lease_released': 7}`
-- Request events: `{'request_resource_registered': 1, 'request_effective_cap_changed': 1, 'request_queue_formed': 3, 'request_wait_started': 6, 'request_wait_completed': 6, 'request_lease_acquired': 6, 'request_queue_drained': 3, 'model_request_started': 6, 'request_lease_released': 6, 'model_request_completed': 6}`
-- Timeline: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/timeline.jsonl`
-
-## fan_in_mp2_mixed
-- DAG shape: fan-in
-- Wall time: 10.622s
-- Rows / columns: 2 / 7
-- max_parallel_requests: 2; observed max request in-flight: 2; waiters: 4
-- Scheduler events: `{'dependency_ready': 9, 'ready_enqueued': 9, 'selected': 9, 'task_lease_acquired': 9, 'worker_spawned': 9, 'queue_drained': 4, 'task_completed': 9, 'task_lease_released': 9}`
-- Request events: `{'request_resource_registered': 1, 'request_effective_cap_changed': 1, 'request_queue_formed': 5, 'request_wait_started': 8, 'request_wait_completed': 8, 'request_lease_acquired': 8, 'request_queue_drained': 5, 'model_request_started': 8, 'request_lease_released': 8, 'model_request_completed': 8}`
-- Timeline: `/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/timeline.jsonl`
diff --git a/artifacts/645-live-bench/agent-fan/combined_summary.json b/artifacts/645-live-bench/agent-fan/combined_summary.json
deleted file mode 100644
index c237e82ec..000000000
--- a/artifacts/645-live-bench/agent-fan/combined_summary.json
+++ /dev/null
@@ -1,452 +0,0 @@
-{
-  "created_at_epoch": 1778786197.816855,
-  "notes": [
-    "All live model calls used alias gpt-5.5.",
-    "temperature was omitted because gpt-5.5 rejects temperature=0.",
-    "AdaptiveRequestAdmissionController.__init__, create_model_client, and AsyncTaskScheduler.__init__ were patched only in-process for observability injection."
-  ],
-  "output_dir": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan",
-  "scenarios": [
-    {
-      "artifact_dataset_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/artifacts/fan_out_mp2_short_05-14-2026_151620/parquet-files",
-      "capacity_plan": {
-        "buffer_size": 2,
-        "observed_maxima": {
-          "queued_tasks_by_group": {
-            "local:local/default/topic/audience/constraint": 1,
-            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/action_brief": 2,
-            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/metric_brief": 2,
-            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/risk_brief": 2
-          },
-          "row_groups_in_flight": 1,
-          "task_leases_by_resource": {
-            "llm_wait": 6,
-            "submission": 6
-          }
-        },
-        "row_group_concurrency": 3,
-        "submission_capacity": 256,
-        "task_resource_limits": {
-          "llm_wait": 256,
-          "local": 256,
-          "submission": 256
-        }
-      },
-      "dag_definition": [
-        {
-          "allow_resize": false,
-          "column_type": "llm-text",
-          "drop": false,
-          "extract_reasoning_content": false,
-          "model_alias": "gpt-5.5",
-          "multi_modal_context": null,
-          "name": "action_brief",
-          "prompt": "For {{ audience }}, write one sentence under 18 words about the best immediate action for: {{ topic }}. Constraint: {{ constraint }}.",
-          "propagate_skip": true,
-          "skip": null,
-          "system_prompt": null,
-          "tool_alias": null,
-          "with_trace": "none"
-        },
-        {
-          "allow_resize": false,
-          "column_type": "llm-text",
-          "drop": false,
-          "extract_reasoning_content": false,
-          "model_alias": "gpt-5.5",
-          "multi_modal_context": null,
-          "name": "risk_brief",
-          "prompt": "For {{ audience }}, write one sentence under 18 words about the highest operational risk for: {{ topic }}. Constraint: {{ constraint }}.",
-          "propagate_skip": true,
-          "skip": null,
-          "system_prompt": null,
-          "tool_alias": null,
-          "with_trace": "none"
-        },
-        {
-          "allow_resize": false,
-          "column_type": "llm-text",
-          "drop": false,
-          "extract_reasoning_content": false,
-          "model_alias": "gpt-5.5",
-          "multi_modal_context": null,
-          "name": "metric_brief",
-          "prompt": "For {{ audience }}, write one sentence under 18 words about one useful measurement for: {{ topic }}. Constraint: {{ constraint }}.",
-          "propagate_skip": true,
-          "skip": null,
-          "system_prompt": null,
-          "tool_alias": null,
-          "with_trace": "none"
-        }
-      ],
-      "dag_shape": "fan-out",
-      "dataset_preview_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/dataset_preview.json",
-      "failures": [],
-      "files_changed_by_benchmark": [
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/seed.csv",
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/timeline.jsonl",
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/dataset_preview.json",
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/summary.json"
-      ],
-      "max_parallel_requests": 2,
-      "max_tokens": 24,
-      "model_alias": "gpt-5.5",
-      "num_rows": 2,
-      "observed_max_active_request_leases": 2,
-      "observed_max_request_current_limit": 2,
-      "observed_max_request_in_flight": 2,
-      "observed_max_request_waiters": 4,
-      "observed_max_task_llm_wait_leases": 6,
-      "observed_max_task_submission_leases": 6,
-      "ready_frontier_by_column": {
-        "action_brief": {
-          "dependency_ready": 2,
-          "ready_enqueued": 2,
-          "selected": 2,
-          "task_completed": 2,
-          "task_lease_acquired": 2,
-          "task_lease_released": 2,
-          "worker_spawned": 2
-        },
-        "metric_brief": {
-          "dependency_ready": 2,
-          "ready_enqueued": 2,
-          "selected": 2,
-          "task_completed": 2,
-          "task_lease_acquired": 2,
-          "task_lease_released": 2,
-          "worker_spawned": 2
-        },
-        "risk_brief": {
-          "dependency_ready": 2,
-          "ready_enqueued": 2,
-          "selected": 2,
-          "task_completed": 2,
-          "task_lease_acquired": 2,
-          "task_lease_released": 2,
-          "worker_spawned": 2
-        },
-        "topic": {
-          "dependency_ready": 1,
-          "ready_enqueued": 1,
-          "selected": 1,
-          "task_completed": 1,
-          "task_lease_acquired": 1,
-          "task_lease_released": 1,
-          "worker_spawned": 1
-        }
-      },
-      "request_event_counts": {
-        "model_request_completed": 6,
-        "model_request_started": 6,
-        "request_effective_cap_changed": 1,
-        "request_lease_acquired": 6,
-        "request_lease_released": 6,
-        "request_queue_drained": 3,
-        "request_queue_formed": 3,
-        "request_resource_registered": 1,
-        "request_wait_completed": 6,
-        "request_wait_started": 6
-      },
-      "request_pressure_final": {
-        "domains": {
-          "nvidia-internal:openai/openai/gpt-5.5:chat": {
-            "active_lease_count": 0,
-            "current_limit": 2,
-            "effective_max": 2,
-            "in_flight_count": 0,
-            "last_outcome": "success",
-            "waiters": 0
-          }
-        },
-        "provider_models": {
-          "nvidia-internal:openai/openai/gpt-5.5:None": {
-            "aggregate_active_lease_count": 0,
-            "aggregate_in_flight": 0,
-            "aliases": [
-              "gpt-5.5"
-            ],
-            "domains": {
-              "chat": 2
-            },
-            "raw_caps": {
-              "gpt-5.5": 2
-            },
-            "static_cap": 2
-          }
-        }
-      },
-      "result_columns": [
-        "topic",
-        "audience",
-        "constraint",
-        "metric_brief",
-        "action_brief",
-        "risk_brief"
-      ],
-      "result_rows": 2,
-      "scenario": "fan_out_mp2_short",
-      "scheduler_event_counts": {
-        "dependency_ready": 7,
-        "queue_drained": 2,
-        "ready_enqueued": 7,
-        "selected": 7,
-        "task_completed": 7,
-        "task_lease_acquired": 7,
-        "task_lease_released": 7,
-        "worker_spawned": 7
-      },
-      "skip_health_check": true,
-      "task_traces": {
-        "by_column": {
-          "action_brief": 2,
-          "metric_brief": 2,
-          "risk_brief": 2,
-          "topic": 1
-        },
-        "count": 7,
-        "duration_seconds_max": null,
-        "duration_seconds_mean": null,
-        "duration_seconds_min": null
-      },
-      "temperature_omitted": true,
-      "timeline_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_out_mp2_short/timeline.jsonl",
-      "wall_time_seconds": 6.556
-    },
-    {
-      "artifact_dataset_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/artifacts/fan_in_mp2_mixed_05-14-2026_151627/parquet-files",
-      "capacity_plan": {
-        "buffer_size": 2,
-        "observed_maxima": {
-          "queued_tasks_by_group": {
-            "local:local/default/topic/audience/constraint": 1,
-            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/customer_view": 2,
-            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/measurement_view": 2,
-            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/operator_view": 2,
-            "model:model/nvidia-internal/openai/openai/gpt-5.5/chat-completion/synthesis_judge": 1
-          },
-          "row_groups_in_flight": 1,
-          "task_leases_by_resource": {
-            "llm_wait": 6,
-            "submission": 6
-          }
-        },
-        "row_group_concurrency": 3,
-        "submission_capacity": 256,
-        "task_resource_limits": {
-          "llm_wait": 256,
-          "local": 256,
-          "submission": 256
-        }
-      },
-      "dag_definition": [
-        {
-          "allow_resize": false,
-          "column_type": "llm-text",
-          "drop": false,
-          "extract_reasoning_content": false,
-          "model_alias": "gpt-5.5",
-          "multi_modal_context": null,
-          "name": "operator_view",
-          "prompt": "In one sentence under 16 words, state an operator's most practical concern for {{ topic }}. Audience: {{ audience }}.",
-          "propagate_skip": true,
-          "skip": null,
-          "system_prompt": null,
-          "tool_alias": null,
-          "with_trace": "none"
-        },
-        {
-          "allow_resize": false,
-          "column_type": "llm-text",
-          "drop": false,
-          "extract_reasoning_content": false,
-          "model_alias": "gpt-5.5",
-          "multi_modal_context": null,
-          "name": "customer_view",
-          "prompt": "In one sentence under 16 words, state the customer-visible consequence for {{ topic }}. Audience: {{ audience }}.",
-          "propagate_skip": true,
-          "skip": null,
-          "system_prompt": null,
-          "tool_alias": null,
-          "with_trace": "none"
-        },
-        {
-          "allow_resize": false,
-          "column_type": "llm-text",
-          "drop": false,
-          "extract_reasoning_content": false,
-          "model_alias": "gpt-5.5",
-          "multi_modal_context": null,
-          "name": "measurement_view",
-          "prompt": "In one sentence under 16 words, state the measurement that would reveal success or failure for {{ topic }}. Audience: {{ audience }}.",
-          "propagate_skip": true,
-          "skip": null,
-          "system_prompt": null,
-          "tool_alias": null,
-          "with_trace": "none"
-        },
-        {
-          "allow_resize": false,
-          "column_type": "llm-text",
-          "drop": false,
-          "extract_reasoning_content": false,
-          "model_alias": "gpt-5.5",
-          "multi_modal_context": null,
-          "name": "synthesis_judge",
-          "prompt": "Synthesize these three notes into a concise recommendation under 45 words. Topic: {{ topic }}\nOperator: {{ operator_view }}\nCustomer: {{ customer_view }}\nMeasurement: {{ measurement_view }}\nEnd with a short confidence phrase.",
-          "propagate_skip": true,
-          "skip": null,
-          "system_prompt": null,
-          "tool_alias": null,
-          "with_trace": "none"
-        }
-      ],
-      "dag_shape": "fan-in",
-      "dataset_preview_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/dataset_preview.json",
-      "failures": [],
-      "files_changed_by_benchmark": [
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/seed.csv",
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/timeline.jsonl",
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/dataset_preview.json",
-        "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/summary.json"
-      ],
-      "max_parallel_requests": 2,
-      "max_tokens": 64,
-      "model_alias": "gpt-5.5",
-      "num_rows": 2,
-      "observed_max_active_request_leases": 2,
-      "observed_max_request_current_limit": 2,
-      "observed_max_request_in_flight": 2,
-      "observed_max_request_waiters": 4,
-      "observed_max_task_llm_wait_leases": 6,
-      "observed_max_task_submission_leases": 6,
-      "ready_frontier_by_column": {
-        "customer_view": {
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-          "ready_enqueued": 2,
-          "selected": 2,
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-          "task_lease_acquired": 2,
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-        "measurement_view": {
-          "dependency_ready": 2,
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-          "selected": 2,
-          "task_completed": 2,
-          "task_lease_acquired": 2,
-          "task_lease_released": 2,
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-        },
-        "operator_view": {
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-          "ready_enqueued": 2,
-          "selected": 2,
-          "task_completed": 2,
-          "task_lease_acquired": 2,
-          "task_lease_released": 2,
-          "worker_spawned": 2
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-        "synthesis_judge": {
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-          "selected": 2,
-          "task_completed": 2,
-          "task_lease_acquired": 2,
-          "task_lease_released": 2,
-          "worker_spawned": 2
-        },
-        "topic": {
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-          "ready_enqueued": 1,
-          "selected": 1,
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-          "task_lease_acquired": 1,
-          "task_lease_released": 1,
-          "worker_spawned": 1
-        }
-      },
-      "request_event_counts": {
-        "model_request_completed": 8,
-        "model_request_started": 8,
-        "request_effective_cap_changed": 1,
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-        "request_lease_released": 8,
-        "request_queue_drained": 5,
-        "request_queue_formed": 5,
-        "request_resource_registered": 1,
-        "request_wait_completed": 8,
-        "request_wait_started": 8
-      },
-      "request_pressure_final": {
-        "domains": {
-          "nvidia-internal:openai/openai/gpt-5.5:chat": {
-            "active_lease_count": 0,
-            "current_limit": 2,
-            "effective_max": 2,
-            "in_flight_count": 0,
-            "last_outcome": "success",
-            "waiters": 0
-          }
-        },
-        "provider_models": {
-          "nvidia-internal:openai/openai/gpt-5.5:None": {
-            "aggregate_active_lease_count": 0,
-            "aggregate_in_flight": 0,
-            "aliases": [
-              "gpt-5.5"
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-            "domains": {
-              "chat": 2
-            },
-            "raw_caps": {
-              "gpt-5.5": 2
-            },
-            "static_cap": 2
-          }
-        }
-      },
-      "result_columns": [
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-        "audience",
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-        "operator_view",
-        "measurement_view",
-        "customer_view",
-        "synthesis_judge"
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-      "result_rows": 2,
-      "scenario": "fan_in_mp2_mixed",
-      "scheduler_event_counts": {
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-        "task_lease_released": 9,
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-      "task_traces": {
-        "by_column": {
-          "customer_view": 2,
-          "measurement_view": 2,
-          "operator_view": 2,
-          "synthesis_judge": 2,
-          "topic": 1
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-        "count": 9,
-        "duration_seconds_max": null,
-        "duration_seconds_mean": null,
-        "duration_seconds_min": null
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-      "temperature_omitted": true,
-      "timeline_path": "/Users/etramel/src/DataDesigner/artifacts/645-live-bench/agent-fan/fan_in_mp2_mixed/timeline.jsonl",
-      "wall_time_seconds": 10.622
-    }
-  ],
-  "schema": "agent-fan-live-benchmark-v1",
-  "workspace": "/Users/etramel/src/DataDesigner"
-}
diff --git a/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.json b/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.json
deleted file mode 100644
index 2d2d47cb5..000000000
--- a/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.json
+++ /dev/null
@@ -1,507 +0,0 @@
-{
-  "agent": "agent-maxparallel",
-  "all_cap_checks_passed": true,
-  "branch": "scheduling-yolo",
-  "command": "DATA_DESIGNER_ASYNC_ENGINE=1 DATA_DESIGNER_ASYNC_TRACE=1 PYTHONUNBUFFERED=1 .venv/bin/python - <<'PY'  # benchmark driver",
-  "files_written": [
-    "artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.json",
-    "artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md",
-    "artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/request_events.jsonl",
-    "artifacts/645-live-bench/agent-maxparallel/stress_cap2_3rows_2cols/request_events.jsonl",
-    "artifacts/645-live-bench/agent-maxparallel/fanout_cap3_2rows_3cols/request_events.jsonl",
-    "artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/task_traces.csv",
-    "artifacts/645-live-bench/agent-maxparallel/stress_cap2_3rows_2cols/task_traces.csv",
-    "artifacts/645-live-bench/agent-maxparallel/fanout_cap3_2rows_3cols/task_traces.csv",
-    "artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/summary.json",
-    "artifacts/645-live-bench/agent-maxparallel/stress_cap2_3rows_2cols/summary.json",
-    "artifacts/645-live-bench/agent-maxparallel/fanout_cap3_2rows_3cols/summary.json"
-  ],
-  "health_check_policy": "skip_health_check=True in in-memory ModelConfig",
-  "model_alias": "gpt-5.5",
-  "output_dir": "artifacts/645-live-bench/agent-maxparallel",
-  "results": [
-    {
-      "dataset_sha256": "6836d3cddee8039113ad6a8548d8a34013e2005328ddfa4d500432f3c3d981e3",
-      "dataset_shape": [
-        2,
-        3
-      ],
-      "event_path": "artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/request_events.jsonl",
-      "expected_requests": 4,
-      "failure": null,
-      "final_pressure": {
-        "domains": {
-          "{'provider_name': 'nvidia-internal', 'model_id': 'openai/openai/gpt-5.5', 'domain': 'chat'}": {
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-            "blocked_until_monotonic": null,
-            "captured_at": 171960.385970541,
-            "consecutive_rate_limits": 0,
-            "cooldown_remaining_seconds": 0.0,
-            "current_limit": 1,
-            "effective_max": 1,
-            "in_flight_count": 0,
-            "last_outcome": "success",
-            "leak_diagnostics": {},
-            "rate_limit_ceiling": 0,
-            "resource": {
-              "domain": "chat",
-              "model_id": "openai/openai/gpt-5.5",
-              "provider_name": "nvidia-internal"
-            },
-            "sequence": 35,
-            "waiters": 0
-          }
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-        "provider_models": {
-          "{'provider_name': 'nvidia-internal', 'model_id': 'openai/openai/gpt-5.5'}": {
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-            "aggregate_in_flight": 0,
-            "aliases": [
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-            "captured_at": 171960.386059333,
-            "domains": {
-              "chat": 1
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-            "provider_model": {
-              "model_id": "openai/openai/gpt-5.5",
-              "provider_name": "nvidia-internal"
-            },
-            "raw_caps": {
-              "gpt-5.5": 1
-            },
-            "sequence": 35,
-            "static_cap": 1
-          }
-        }
-      },
-      "observed": {
-        "event_counts": {
-          "model_request_completed": 4,
-          "model_request_started": 4,
-          "request_effective_cap_changed": 1,
-          "request_lease_acquired": 4,
-          "request_lease_released": 4,
-          "request_queue_drained": 2,
-          "request_queue_formed": 2,
-          "request_resource_registered": 1,
-          "request_wait_completed": 4,
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diff --git a/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md b/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md
deleted file mode 100644
index 0fa710894..000000000
--- a/artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md
+++ /dev/null
@@ -1,22 +0,0 @@
-# Live Max Parallel Benchmark
-
-- model alias: `gpt-5.5`
-- temperature: omitted
-- health checks: skipped with in-memory `ModelConfig(skip_health_check=True)`
-- total wall time: `17.351s`
-- cap checks passed: `True`
-
-| scenario | cap | requests | wall s | max snapshot in-flight | max lease overlap | max model overlap | max wait s | cap ok | failure |
-|---|---:|---:|---:|---:|---:|---:|---:|---|---|
-| serialish_cap1_2rows_2cols | 1 | 4 | 9.210 | 1 | 1 | 1 | 5.408 | True |  |
-| stress_cap2_3rows_2cols | 2 | 6 | 4.566 | 2 | 2 | 2 | 3.076 | True |  |
-| fanout_cap3_2rows_3cols | 3 | 6 | 3.523 | 3 | 3 | 3 | 2.133 | True |  |
-
-## Artifacts
-
-- `serialish_cap1_2rows_2cols` events: `artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/request_events.jsonl`
-- `serialish_cap1_2rows_2cols` traces: `artifacts/645-live-bench/agent-maxparallel/serialish_cap1_2rows_2cols/task_traces.csv`
-- `stress_cap2_3rows_2cols` events: `artifacts/645-live-bench/agent-maxparallel/stress_cap2_3rows_2cols/request_events.jsonl`
-- `stress_cap2_3rows_2cols` traces: `artifacts/645-live-bench/agent-maxparallel/stress_cap2_3rows_2cols/task_traces.csv`
-- `fanout_cap3_2rows_3cols` events: `artifacts/645-live-bench/agent-maxparallel/fanout_cap3_2rows_3cols/request_events.jsonl`
-- `fanout_cap3_2rows_3cols` traces: `artifacts/645-live-bench/agent-maxparallel/fanout_cap3_2rows_3cols/task_traces.csv`
diff --git a/reports/async-scheduling-epic-benchmark-report.html b/reports/async-scheduling-epic-benchmark-report.html
deleted file mode 100644
index 433f8a021..000000000
--- a/reports/async-scheduling-epic-benchmark-report.html
+++ /dev/null
@@ -1,791 +0,0 @@
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-      border-left-color: var(--amber);
-      background: #fff9e8;
-    }
-
-    .two-col {
-      display: grid;
-      grid-template-columns: minmax(0, 1fr) minmax(0, 1fr);
-      gap: 18px;
-    }
-
-    .artifact-list {
-      columns: 2 360px;
-      padding-left: 18px;
-    }
-
-    footer {
-      width: min(1240px, calc(100vw - 40px));
-      margin: 0 auto 48px;
-      color: var(--muted);
-      font-size: 13px;
-    }
-
-    @media (max-width: 960px) {
-      header {
-        padding: 32px 20px 26px;
-      }
-
-      main {
-        width: min(100vw - 24px, 1240px);
-      }
-
-      section {
-        padding: 18px;
-      }
-
-      .flow,
-      .two-col {
-        grid-template-columns: 1fr;
-      }
-    }
-  </style>
-</head>
-<body>
-  <header>
-    <h1>Async Scheduling Epic QA and Benchmark Report</h1>
-    <p>
-      Status report for issue 645 on branch <code>scheduling-yolo</code>: implementation shape,
-      verification gates, and live benchmark evidence gathered from the GPT-5.5 and GPT-5 Nano
-      traffic runs.
-    </p>
-    <div class="meta">
-      <span class="pill">Generated: 2026-05-14</span>
-      <span class="pill">Workspace: /Users/etramel/src/DataDesigner</span>
-      <span class="pill">Primary spec: plans/645/README.md</span>
-      <span class="pill">Live artifacts: artifacts/645-live-bench*</span>
-    </div>
-  </header>
-
-  <main>
-    <section>
-      <h2>Executive Summary</h2>
-      <p>
-        The epic implementation is now shaped around explicit two-stage admission control:
-        scheduler-level task admission decides when ready dataset work may run, and request-level
-        admission controls each concrete provider/model call. Observability has enough layer
-        separation to explain whether a run is limited by readiness, task scheduling, request
-        queueing, provider latency, AIMD ramp, or downstream fan-in dependencies.
-      </p>
-      <div class="grid cards">
-        <div class="card">
-          <span class="metric">17,408</span>
-          <span class="label">completed model requests in final 1024+ live scenarios, excluding small smoke runs</span>
-        </div>
-        <div class="card">
-          <span class="metric">0</span>
-          <span class="label">rate-limit events or provider failures in final Nano scale lanes</span>
-        </div>
-        <div class="card">
-          <span class="metric">Held</span>
-          <span class="label">all observed <code>max_parallel_requests</code> caps in completed scale scenarios</span>
-        </div>
-        <div class="card">
-          <span class="metric">&lt; 0.071s</span>
-          <span class="label">p95 downstream idle in the dedicated GPT-5.5 -> Nano mixed pipeline runs</span>
-        </div>
-      </div>
-      <div class="callout">
-        <strong>Current read:</strong> the scheduler is interleaving downstream work at row granularity
-        rather than running all upstream work first. Buffer size is still a real operations knob:
-        larger buffers can deepen upstream queues and amplify waiters, especially with slow GPT-5.5
-        upstream work.
-      </div>
-    </section>
-
-    <section>
-      <h2>Epic Implementation Shape</h2>
-      <p>
-        The implementation follows the plan in <a href="../plans/645/README.md">plans/645</a>.
-        The core move is from implicit scheduler behavior and transport-local throttling toward
-        typed resource metadata, fair ready-work selection, task leases, request leases, and
-        correlated event streams.
-      </p>
-
-      <div class="flow" aria-label="Async scheduling runtime flow">
-        <div class="node">
-          <strong>Column Generator</strong>
-          <span>Declares static <code>SchedulingMetadata</code>; plugins do not see runtime queues.</span>
-        </div>
-        <div class="node">
-          <strong>Task Resolver</strong>
-          <span>Maps metadata to <code>TaskGroupSpec</code> and scheduler resource requests.</span>
-        </div>
-        <div class="node">
-          <strong>Completion Tracker</strong>
-          <span>Owns dependency readiness and ready frontier state.</span>
-        </div>
-        <div class="node">
-          <strong>Fair Task Queue</strong>
-          <span>Orders ready work with non-mutating selection and explicit commit.</span>
-        </div>
-        <div class="node">
-          <strong>Task Admission</strong>
-          <span>Grants scheduler task leases before workers spawn.</span>
-        </div>
-        <div class="node">
-          <strong>Model Executor</strong>
-          <span>Wraps concrete model calls; every attempt enters request admission.</span>
-        </div>
-        <div class="node">
-          <strong>Request Admission</strong>
-          <span>AIMD/provider-model-domain request leases, pressure snapshots, and event stream.</span>
-        </div>
-      </div>
-
-      <div class="two-col">
-        <div>
-          <h3>Primary Code Areas</h3>
-          <ul>
-            <li><code>data_designer.config.scheduling</code>: generator-facing scheduling metadata and validation.</li>
-            <li><code>TaskSchedulingResolver</code>: bridge from static metadata into scheduler groups/resources.</li>
-            <li><code>FairTaskQueue</code>: non-mutating select plus explicit commit, with queue views for policy.</li>
-            <li><code>TaskAdmissionController</code>: scheduler task leases and resource accounting.</li>
-            <li><code>ModelRequestExecutor</code>: durable per-attempt model-call boundary.</li>
-            <li><code>AdaptiveRequestAdmissionController</code>: provider/model/domain AIMD and request leases.</li>
-            <li><code>observability.py</code> and <code>capacity.py</code>: scheduler/request event DTOs, snapshots, and plan evidence.</li>
-          </ul>
-        </div>
-        <div>
-          <h3>Cleanup Direction</h3>
-          <ul>
-            <li>Legacy scheduler hint naming is removed in favor of scheduling metadata and task scheduling.</li>
-            <li>Transport-local throttle wrappers are replaced by request admission and model request execution.</li>
-            <li>Docs and diagrams were updated across architecture docs, devnotes, and Fern assets.</li>
-            <li>Run config keeps request admission internal for V1; benchmark evidence informs future public knobs.</li>
-          </ul>
-        </div>
-      </div>
-    </section>
-
-    <section>
-      <h2>Verification and QA Gates</h2>
-      <p>
-        The implementation has been checked with unit, package, static, and live-provider QA passes.
-        The live benchmark agents also exercised alternative workflows, config shapes, and endpoint/model
-        combinations.
-      </p>
-      <div class="table-wrap">
-        <table>
-          <thead>
-            <tr>
-              <th>Gate</th>
-              <th>Status</th>
-              <th>Evidence</th>
-            </tr>
-          </thead>
-          <tbody>
-            <tr>
-              <td>Ruff lint</td>
-              <td><span class="status ok">Passed</span></td>
-              <td><code>.venv/bin/ruff check packages scripts tests_e2e</code></td>
-            </tr>
-            <tr>
-              <td>Format check</td>
-              <td><span class="status ok">Passed</span></td>
-              <td><code>.venv/bin/ruff format --check packages scripts tests_e2e</code></td>
-            </tr>
-            <tr>
-              <td>Whitespace diff check</td>
-              <td><span class="status ok">Passed</span></td>
-              <td><code>git diff --check</code></td>
-            </tr>
-            <tr>
-              <td>Config package tests</td>
-              <td><span class="status ok">570 passed</span></td>
-              <td>Includes scheduling metadata validation and config regression coverage.</td>
-            </tr>
-            <tr>
-              <td>Engine package tests</td>
-              <td><span class="status ok">1,995 passed</span></td>
-              <td>Includes scheduler, fair queue, task admission, request admission, executor, and capacity tests.</td>
-            </tr>
-            <tr>
-              <td>Interface package tests</td>
-              <td><span class="status ok">899 passed</span></td>
-              <td>One skipped test in the interface suite.</td>
-            </tr>
-            <tr>
-              <td>Benchmark schema checks</td>
-              <td><span class="status ok">Passed</span></td>
-              <td>Deterministic benchmark artifact and derived metric checks passed.</td>
-            </tr>
-            <tr>
-              <td>Legacy naming gates</td>
-              <td><span class="status ok">Passed</span></td>
-              <td>Stale throttle/semaphore/scheduling-hint term checks were clean except expected removals and migration references.</td>
-            </tr>
-          </tbody>
-        </table>
-      </div>
-      <div class="callout warn">
-        <strong>Provider compatibility findings:</strong> GPT-5.5 rejected <code>temperature=0</code>, so live traffic
-        omits temperature. GPT-5.5 also showed output-limit brittleness at <code>max_tokens=24</code>; short
-        GPT-5.5 prompts should use a safer ceiling such as 64+ tokens.
-      </div>
-    </section>
-
-    <section>
-      <h2>Live Benchmark Matrix</h2>
-      <h3>GPT-5 Nano 1024+ Scale Runs</h3>
-      <div class="table-wrap">
-        <table>
-          <thead>
-            <tr>
-              <th>Lane</th>
-              <th>Scenario</th>
-              <th>Shape</th>
-              <th>Buffer</th>
-              <th>Requests</th>
-              <th>Wall</th>
-              <th>Cap</th>
-              <th>Signal</th>
-            </tr>
-          </thead>
-          <tbody>
-            <tr>
-              <td>Cap scale</td>
-              <td><code>cap8_rows512_cols2_buffer32</code></td>
-              <td>512 x 2 Nano columns</td>
-              <td class="num">32</td>
-              <td class="num">1024/1024</td>
-              <td class="num">160.30s</td>
-              <td>8/8</td>
-              <td>Start CV 0.252; max waiters 30.</td>
-            </tr>
-            <tr>
-              <td>Cap scale</td>
-              <td><code>cap8_rows512_cols2_buffer512</code></td>
-              <td>512 x 2 Nano columns</td>
-              <td class="num">512</td>
-              <td class="num">1024/1024</td>
-              <td class="num">167.08s</td>
-              <td>8/8</td>
-              <td>Start CV 0.246; max waiters 31.</td>
-            </tr>
-            <tr>
-              <td>Cap sweep</td>
-              <td><code>final1024_cap8_buffer32</code></td>
-              <td>512 x 2 Nano columns</td>
-              <td class="num">32</td>
-              <td class="num">1024/1024</td>
-              <td class="num">155.37s</td>
-              <td>8/8</td>
-              <td>Steady traffic; cap enforced.</td>
-            </tr>
-            <tr>
-              <td>Cap sweep</td>
-              <td><code>final1024_cap8_buffer128</code></td>
-              <td>512 x 2 Nano columns</td>
-              <td class="num">128</td>
-              <td class="num">1024/1024</td>
-              <td class="num">153.96s</td>
-              <td>8/8</td>
-              <td>Steady traffic; cap enforced.</td>
-            </tr>
-            <tr>
-              <td>Cap sweep</td>
-              <td><code>final1024_cap8_buffer512</code></td>
-              <td>512 x 2 Nano columns</td>
-              <td class="num">512</td>
-              <td class="num">1024/1024</td>
-              <td class="num">150.27s</td>
-              <td>8/8</td>
-              <td>Lowest start CV in that lane.</td>
-            </tr>
-            <tr>
-              <td>AIMD scale</td>
-              <td><code>final-512x2-buffer32</code></td>
-              <td>512 x 2 Nano columns</td>
-              <td class="num">32</td>
-              <td class="num">1024/1024</td>
-              <td class="num">104.823s</td>
-              <td>16/16</td>
-              <td>Limit ramped 1 -> 16 in 50.332s; p95 wait 19.371s.</td>
-            </tr>
-            <tr>
-              <td>AIMD scale</td>
-              <td><code>final-512x2-buffer512</code></td>
-              <td>512 x 2 Nano columns</td>
-              <td class="num">512</td>
-              <td class="num">1024/1024</td>
-              <td class="num">114.522s</td>
-              <td>16/16</td>
-              <td>Limit ramped 1 -> 16 in 57.062s; p95 wait 22.949s.</td>
-            </tr>
-            <tr>
-              <td>Fan scale</td>
-              <td><code>fanout_buffer32</code></td>
-              <td>256 x 4 Nano branches</td>
-              <td class="num">32</td>
-              <td class="num">1024/1024</td>
-              <td class="num">132.24s</td>
-              <td>8/8</td>
-              <td>Balanced branch traffic; p95 wait 8.97s.</td>
-            </tr>
-            <tr>
-              <td>Fan scale</td>
-              <td><code>fanout_buffer256</code></td>
-              <td>256 x 4 Nano branches</td>
-              <td class="num">256</td>
-              <td class="num">1024/1024</td>
-              <td class="num">125.94s</td>
-              <td>8/8</td>
-              <td>Balanced branch traffic; p95 wait 8.09s.</td>
-            </tr>
-            <tr>
-              <td>Fan scale</td>
-              <td><code>fanin_buffer256</code></td>
-              <td>256 rows, 3 upstream + 1 synthesis</td>
-              <td class="num">256</td>
-              <td class="num">1024/1024</td>
-              <td class="num">141.64s</td>
-              <td>8/8</td>
-              <td>Row-level fan-in; downstream delay p95 1.281s.</td>
-            </tr>
-          </tbody>
-        </table>
-      </div>
-
-      <h3>Mixed GPT-5.5 Upstream -> GPT-5 Nano Downstream</h3>
-      <div class="table-wrap">
-        <table>
-          <thead>
-            <tr>
-              <th>Lane</th>
-              <th>Scenario</th>
-              <th>Shape</th>
-              <th>Buffer</th>
-              <th>Requests</th>
-              <th>Wall</th>
-              <th>Idle p95/max</th>
-              <th>Interpretation</th>
-            </tr>
-          </thead>
-          <tbody>
-            <tr>
-              <td>Dedicated mixed pipeline</td>
-              <td><code>mixed_rows512_buf32_heavy4_nano8</code></td>
-              <td>512 GPT-5.5 + 512 Nano</td>
-              <td class="num">32</td>
-              <td class="num">1024/1024</td>
-              <td class="num">368.46s</td>
-              <td class="num">0.00484s / 0.05287s</td>
-              <td>Steady interleaving; GPT-5.5 waiters 92; Nano did not starve.</td>
-            </tr>
-            <tr>
-              <td>Dedicated mixed pipeline</td>
-              <td><code>mixed_rows512_buf256_heavy4_nano8</code></td>
-              <td>512 GPT-5.5 + 512 Nano</td>
-              <td class="num">256</td>
-              <td class="num">1024/1024</td>
-              <td class="num">366.44s</td>
-              <td class="num">0.07076s / 0.13278s</td>
-              <td>Steady interleaving; GPT-5.5 waiters 252 with larger buffer.</td>
-            </tr>
-            <tr>
-              <td>Alternate bottleneck lane</td>
-              <td><code>mixed_rows512_buf32_1024gens</code></td>
-              <td>512 GPT-5.5 + 512 Nano</td>
-              <td class="num">32</td>
-              <td class="num">1024/1024</td>
-              <td class="num">259.315s</td>
-              <td class="num">0.002748s / 0.028367s</td>
-              <td>Wave-like per-second starts, but very low downstream idle.</td>
-            </tr>
-            <tr>
-              <td>Alternate bottleneck lane</td>
-              <td><code>mixed_rows512_buf256_1024gens</code></td>
-              <td>512 GPT-5.5 + 512 Nano</td>
-              <td class="num">256</td>
-              <td class="num">1024/1024</td>
-              <td class="num">264.457s</td>
-              <td class="num">0.111947s / 0.356167s</td>
-              <td>Wave-like traffic; larger buffer amplified GPT-5.5 wait p95 to 129.107s.</td>
-            </tr>
-          </tbody>
-        </table>
-      </div>
-
-      <h3>Initial GPT-5.5 Live QA</h3>
-      <div class="table-wrap">
-        <table>
-          <thead>
-            <tr>
-              <th>Scenario</th>
-              <th>Cap</th>
-              <th>Requests</th>
-              <th>Wall</th>
-              <th>Max in-flight</th>
-              <th>Result</th>
-            </tr>
-          </thead>
-          <tbody>
-            <tr>
-              <td><code>serialish_cap1_2rows_2cols</code></td>
-              <td class="num">1</td>
-              <td class="num">4</td>
-              <td class="num">9.210s</td>
-              <td class="num">1</td>
-              <td><span class="status ok">Cap held</span></td>
-            </tr>
-            <tr>
-              <td><code>stress_cap2_3rows_2cols</code></td>
-              <td class="num">2</td>
-              <td class="num">6</td>
-              <td class="num">4.566s</td>
-              <td class="num">2</td>
-              <td><span class="status ok">Cap held</span></td>
-            </tr>
-            <tr>
-              <td><code>fanout_cap3_2rows_3cols</code></td>
-              <td class="num">3</td>
-              <td class="num">6</td>
-              <td class="num">3.523s</td>
-              <td class="num">3</td>
-              <td><span class="status ok">Cap held</span></td>
-            </tr>
-          </tbody>
-        </table>
-      </div>
-    </section>
-
-    <section>
-      <h2>What The Benchmarks Say</h2>
-      <div class="two-col">
-        <div>
-          <h3>Confirmed</h3>
-          <ul>
-            <li><span class="status ok">Caps</span> Request in-flight counts did not exceed configured caps in completed scale lanes.</li>
-            <li><span class="status ok">AIMD</span> Nano AIMD ramps were monotonic to cap with no decreases or rate limits.</li>
-            <li><span class="status ok">Fan-in</span> Downstream work starts per row, not after a global upstream wave.</li>
-            <li><span class="status ok">Mixed model flow</span> Slow GPT-5.5 upstream and fast Nano downstream overlap for most of the run.</li>
-            <li><span class="status ok">Lease cleanup</span> Successful runs ended with no active request leases or waiters in reported pressure snapshots.</li>
-          </ul>
-        </div>
-        <div>
-          <h3>Watch Items</h3>
-          <ul>
-            <li><span class="status watch">Buffers</span> Larger buffers can increase upstream waiters and wait p95 even when wall time is similar.</li>
-            <li><span class="status watch">Telemetry wiring</span> The benchmark agents injected sinks in-process; product/operator wiring should make this easier without monkeypatching.</li>
-            <li><span class="status watch">Provider params</span> Model-specific parameter compatibility needs explicit docs: GPT-5.5 rejects <code>temperature=0</code>.</li>
-            <li><span class="status watch">Output limits</span> GPT-5.5 short prompts can fail at <code>max_tokens=24</code>; use safer defaults in examples.</li>
-          </ul>
-        </div>
-      </div>
-    </section>
-
-    <section>
-      <h2>Artifact Index</h2>
-      <p>
-        The report is intentionally a summary. Use these artifacts for timelines, request events,
-        scheduler events, task traces, flow buckets, and per-scenario JSON summaries.
-      </p>
-      <ul class="artifact-list">
-        <li><a href="../artifacts/645-live-bench-nano/agent-cap-scale/README.md">Nano cap scale README</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-cap-scale/combined_summary.json">Nano cap scale combined JSON</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-cap-sweep/README_final1024.md">Nano cap sweep final 1024 README</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-cap-sweep/combined_summary_final1024.json">Nano cap sweep final JSON</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-aimd-scale/README.md">Nano AIMD scale README</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-aimd-scale/combined_summary.json">Nano AIMD scale combined JSON</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-fan-scale/README.md">Nano fan scale README</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-fan-scale/combined_summary.json">Nano fan scale combined JSON</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-mixed-pipeline/README.md">Dedicated mixed pipeline README</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-mixed-pipeline/combined_summary.json">Dedicated mixed pipeline JSON</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-bottleneck/README_mixed.md">Alternate mixed bottleneck README</a></li>
-        <li><a href="../artifacts/645-live-bench-nano/agent-bottleneck/combined_mixed_summary.json">Alternate mixed bottleneck JSON</a></li>
-        <li><a href="../artifacts/645-live-bench/agent-maxparallel/maxparallel_live_benchmark.md">Initial GPT-5.5 max-parallel README</a></li>
-        <li><a href="../artifacts/645-live-bench/agent-fan/README.md">Initial GPT-5.5 fan README</a></li>
-        <li><a href="../plans/645/benchmark-plan.md">Benchmark plan</a></li>
-        <li><a href="../plans/645/observability.md">Observability plan</a></li>
-      </ul>
-    </section>
-
-    <section>
-      <h2>Recommended Follow-Ups</h2>
-      <ul>
-        <li>Promote the benchmark sink injection pattern into an operator-facing debug/benchmark hook so future runs do not need monkeypatches.</li>
-        <li>Add a concise buffer-size guidance note: smaller buffers reduce upstream wait amplification; larger buffers can improve throughput in pure Nano fan-out but may deepen slow-model queues.</li>
-        <li>Add a mixed-model benchmark to the reusable harness: <code>slow upstream -> fast downstream</code> with idle-time and overlap metrics as first-class outputs.</li>
-        <li>Capture the provider compatibility notes in model docs or example configs: omit temperature for GPT-5.5 and avoid very low GPT-5.5 token ceilings.</li>
-        <li>Keep the 1024+ Nano scenarios as regression recipes for cap enforcement, AIMD ramp, fan-in readiness, and buffer-induced waves.</li>
-      </ul>
-    </section>
-  </main>
-
-  <footer>
-    Report generated from local benchmark artifacts and the issue 645 plan. It does not include prompts,
-    completions, row values, secrets, or raw provider payloads.
-  </footer>
-</body>
-</html>
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-</head>
-<body>
-<main>
-<section class="hero">
-<p class="eyebrow">DataDesigner async scheduler</p>
-<h1>Idle-Time Analysis</h1>
-<p class="lede">This report measures where model-capacity idle time appears, which knobs change it, and which remaining idle looks avoidable by scheduler or admission-control changes.</p>
-<div class="summary-grid"><div class="summary-card"><span>Benchmark cases</span><strong>21</strong></div><div class="summary-card"><span>Largest synthetic generation count</span><strong>8,192</strong></div><div class="summary-card"><span>Median llm_wait utilization</span><strong>70.5%</strong></div><div class="summary-card"><span>Worst total llm_wait idle</span><strong>91.9%</strong></div><div class="summary-card"><span>Worst starved llm_wait idle</span><strong>40.0%</strong></div><div class="summary-card"><span>Best llm_wait utilization</span><strong>89.4%</strong></div><div class="summary-card"><span>Validation</span><strong>pass</strong></div></div>
-</section>
-<section>
-<h2>What Counts As Idle</h2>
-<p><strong>General idle</strong> is unused configured capacity. It includes expected idle when dependencies are not ready. <strong>Starved idle</strong> is the actionable subset: a resource has idle slots while the scheduler already has queued work that requests that resource. For this refactor, the most important resource is <code>llm_wait</code>, because it maps to model-serving capacity that should stay busy when runnable model work exists.</p>
-</section>
-<section>
-<h2>Findings</h2>
-<ul class="finding-list"><li><strong>Scale helps once enough runnable work exists.</strong> The row-scale sweep moved from 75.2% to 82.0% llm_wait utilization (+6.8 pp). Larger runs amortize startup and drain phases.</li><li><strong>Runnable frontier width is the main controllable idle source.</strong> In the row-group sweep, utilization moved from 16.7% to 89.4% (+72.7 pp), while starved llm_wait idle improved by 8.0% between the narrowest case and the best observed case.</li><li><strong>Total idle and starved idle diagnose different causes.</strong> <code>narrow-frontier-high-cap</code> had 91.9% total llm_wait idle but only 21.1% starved idle. The remaining idle is dependency-horizon idle: the scheduler often had no ready model work because the admitted frontier was too small.</li><li><strong>Capacity increases expose underfeeding.</strong> In the capacity sweep, modeled llm_wait cap 2 reached 85.1% utilization, while cap 8 reached 33.4% with the same small row frontier.</li><li><strong>Custom model scheduling metadata changes traffic shape.</strong> With synthetic <code>custom_model</code> weight forced to 1, llm_wait utilization was 81.9%; with the harness defaulting the weight to modeled model capacity it was 68.6%. This run did not show weight=1 underfeeding because each synthetic column is its own group, but it does show that group identity and weight are first-order benchmark inputs.</li><li><strong>High capacity magnifies traffic-shape effects.</strong> The stress case with one large row group and high cap reached 8.1% utilization; the wide-frontier shape reached 70.5%. This is the vLLM-server-idle failure mode the scheduler needs to expose and track over time.</li><li><strong>Worst observed actionable idle:</strong> rows-256 had 40.0% starved llm_wait idle and 5.57 s max ready-to-dispatch gap.</li></ul>
-</section>
-<section>
-<h2>Figures</h2>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Scale: more rows smooth the model-resource flow"><text class="chart-title" x="64" y="24">Scale: more rows smooth the model-resource flow</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">synthetic model generations</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,107.5 116.8,127.7 222.4,147.6 433.6,118.5 856.0,89.6" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="107.5" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7520</title></circle><circle cx="116.8" cy="127.7" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.6753</title></circle><circle cx="222.4" cy="147.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.6000</title></circle><circle cx="433.6" cy="118.5" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7101</title></circle><circle cx="856.0" cy="89.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8196</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,241.8 116.8,220.5 222.4,200.5 433.6,229.5 856.0,258.4" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="241.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2431</title></circle><circle cx="116.8" cy="220.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3241</title></circle><circle cx="222.4" cy="200.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3997</title></circle><circle cx="433.6" cy="229.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2898</title></circle><circle cx="856.0" cy="258.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1804</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">512</text><text class="tick" x="116.8" y="332" text-anchor="middle">1.0k</text><text class="tick" x="222.4" y="332" text-anchor="middle">2.0k</text><text class="tick" x="433.6" y="332" text-anchor="middle">4.1k</text><text class="tick" x="856.0" y="332" text-anchor="middle">8.2k</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Frontier width: row-group concurrency changes starvation"><text class="chart-title" x="64" y="24">Frontier width: row-group concurrency changes starvation</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">row-group concurrency</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,256.6 177.1,217.4 403.4,129.1 856.0,42.0" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="256.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.1673</title></circle><circle cx="177.1" cy="217.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.3001</title></circle><circle cx="403.4" cy="129.1" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5992</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8941</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,252.1 177.1,233.2 403.4,238.4 856.0,275.6" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="252.1" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1825</title></circle><circle cx="177.1" cy="233.2" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2467</title></circle><circle cx="403.4" cy="238.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2291</title></circle><circle cx="856.0" cy="275.6" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1029</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,305.7 177.1,305.6 403.4,305.5 856.0,304.3" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="305.7" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0012</title></circle><circle cx="177.1" cy="305.6" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0015</title></circle><circle cx="403.4" cy="305.5" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0017</title></circle><circle cx="856.0" cy="304.3" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0056</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">downstream ready p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="177.1" y="332" text-anchor="middle">2</text><text class="tick" x="403.4" y="332" text-anchor="middle">4</text><text class="tick" x="856.0" y="332" text-anchor="middle">8</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Buffer size: row-group shape affects traffic waves"><text class="chart-title" x="64" y="24">Buffer size: row-group shape affects traffic waves</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">buffer size</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,296.1 73.3,296.4 110.6,296.6 259.7,296.9 856.0,297.5" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="296.1" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8200</title></circle><circle cx="73.3" cy="296.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7904</title></circle><circle cx="110.6" cy="296.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7766</title></circle><circle cx="259.7" cy="296.9" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7497</title></circle><circle cx="856.0" cy="297.5" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7024</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,303.8 73.3,303.5 110.6,303.3 259.7,303.0 856.0,302.4" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="303.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1799</title></circle><circle cx="73.3" cy="303.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2095</title></circle><circle cx="110.6" cy="303.3" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2232</title></circle><circle cx="259.7" cy="303.0" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2502</title></circle><circle cx="856.0" cy="302.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2975</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,305.1 73.3,301.8 110.6,286.7 259.7,196.9 856.0,42.0" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="305.1" r="4" fill="#6c5ce7"><title>llm ready gap p95: 0.0742</title></circle><circle cx="73.3" cy="301.8" r="4" fill="#6c5ce7"><title>llm ready gap p95: 0.3464</title></circle><circle cx="110.6" cy="286.7" r="4" fill="#6c5ce7"><title>llm ready gap p95: 1.5943</title></circle><circle cx="259.7" cy="196.9" r="4" fill="#6c5ce7"><title>llm ready gap p95: 8.9931</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#6c5ce7"><title>llm ready gap p95: 21.7638</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">llm ready gap p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="73.3" y="332" text-anchor="middle">4</text><text class="tick" x="110.6" y="332" text-anchor="middle">16</text><text class="tick" x="259.7" y="332" text-anchor="middle">64</text><text class="tick" x="856.0" y="332" text-anchor="middle">256</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Capacity scaling: underfeeding becomes visible as cap rises"><text class="chart-title" x="64" y="24">Capacity scaling: underfeeding becomes visible as cap rises</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">modeled llm_wait capacity</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / kgen/s</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,92.6 328.0,162.4 856.0,222.2" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="92.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8510</title></circle><circle cx="328.0" cy="162.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5724</title></circle><circle cx="856.0" cy="222.2" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.3341</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,269.0 328.0,243.8 856.0,230.4" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="269.0" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1476</title></circle><circle cx="328.0" cy="243.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2481</title></circle><circle cx="856.0" cy="230.4" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3016</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,144.2 328.0,132.1 856.0,42.0" fill="none" stroke="#326273" stroke-width="3"/><circle cx="64.0" cy="144.2" r="4" fill="#326273"><title>throughput / 1000: 0.6451</title></circle><circle cx="328.0" cy="132.1" r="4" fill="#326273"><title>throughput / 1000: 0.6935</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#326273"><title>throughput / 1000: 1.0527</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#326273"/><text x="666" y="62" class="legend-text">throughput / 1000</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">2</text><text class="tick" x="328.0" y="332" text-anchor="middle">4</text><text class="tick" x="856.0" y="332" text-anchor="middle">8</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Custom-model metadata changes per-stage fairness"><text class="chart-title" x="64" y="24">Custom-model metadata changes per-stage fairness</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="230.0" y="116.2" width="29.0" height="161.8" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.6855</title></rect><rect x="262.0" y="203.8" width="29.0" height="74.2" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.3144</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">model cap</text><rect x="626.0" y="84.8" width="29.0" height="193.2" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8187</title></rect><rect x="658.0" y="235.2" width="29.0" height="42.8" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1812</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">weight 1</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Stress shapes: narrow vs wide runnable horizon"><text class="chart-title" x="64" y="24">Stress shapes: narrow vs wide runnable horizon</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="198.0" y="259.0" width="29.0" height="19.0" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.0806</title></rect><rect x="230.0" y="61.0" width="29.0" height="217.0" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.9194</title></rect><rect x="262.0" y="228.3" width="29.0" height="49.7" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2108</title></rect><rect x="294.0" y="190.5" width="29.0" height="87.5" rx="2" fill="#326273"><title>burstiness / 2: 0.3709</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">narrow frontier high cap</text><rect x="594.0" y="111.6" width="29.0" height="166.4" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.7049</title></rect><rect x="626.0" y="208.4" width="29.0" height="69.6" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.2951</title></rect><rect x="658.0" y="208.4" width="29.0" height="69.6" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2951</title></rect><rect x="690.0" y="231.7" width="29.0" height="46.3" rx="2" fill="#326273"><title>burstiness / 2: 0.1964</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">wide frontier high cap</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#8a6f3d"/><text x="666" y="45" class="legend-text">total llm_wait idle</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">starved llm_wait idle</text></g><g class="legend"><rect x="650" y="69" width="10" height="10" fill="#326273"/><text x="666" y="79" class="legend-text">burstiness / 2</text></g></svg></figure>
-</section>
-<section>
-<h2>Benchmark Matrix</h2>
-<div class="table-wrap"><table><thead><tr><th>case</th><th>sweep</th><th>generations</th><th>rows</th><th>buffer</th><th>rg conc</th><th>llm cap</th><th>util</th><th>idle</th><th>starved idle</th><th>ready p95</th><th>overlap</th><th>throughput</th><th>artifact</th></tr></thead><tbody><tr><td>buffer-1</td><td>buffer-size</td><td>4,096</td><td>512</td><td>1</td><td>4</td><td>4</td><td>82.0%</td><td>18.0%</td><td>18.0%</td><td>74.2 ms</td><td>100.0%</td><td>342/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-16</td><td>buffer-size</td><td>4,096</td><td>512</td><td>16</td><td>4</td><td>4</td><td>77.7%</td><td>22.3%</td><td>22.3%</td><td>1.59 s</td><td>99.9%</td><td>385/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-16/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-256</td><td>buffer-size</td><td>4,096</td><td>512</td><td>256</td><td>4</td><td>4</td><td>70.2%</td><td>29.8%</td><td>29.8%</td><td>21.76 s</td><td>99.9%</td><td>163/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-256/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-4</td><td>buffer-size</td><td>4,096</td><td>512</td><td>4</td><td>4</td><td>4</td><td>79.0%</td><td>21.0%</td><td>20.9%</td><td>346.4 ms</td><td>99.9%</td><td>450/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-64</td><td>buffer-size</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>75.0%</td><td>25.0%</td><td>25.0%</td><td>8.99 s</td><td>99.9%</td><td>223/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/buffer-size/buffer-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-custom-model-1</td><td>custom-model-weight</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>81.9%</td><td>18.1%</td><td>18.1%</td><td>11.79 s</td><td>99.9%</td><td>185/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/custom-model-weight/weight-custom-model-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-model-capacity</td><td>custom-model-weight</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>68.6%</td><td>31.4%</td><td>31.4%</td><td>8.65 s</td><td>99.9%</td><td>236/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/custom-model-weight/weight-model-capacity/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-16</td><td>llm-capacity</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>8</td><td>33.4%</td><td>66.6%</td><td>30.2%</td><td>1.0 ms</td><td>99.6%</td><td>1,053/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/llm-capacity/capacity-16/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-4</td><td>llm-capacity</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>2</td><td>85.1%</td><td>14.9%</td><td>14.8%</td><td>4.6 ms</td><td>99.8%</td><td>645/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/llm-capacity/capacity-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-8</td><td>llm-capacity</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>57.2%</td><td>42.8%</td><td>24.8%</td><td>2.5 ms</td><td>99.7%</td><td>693/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/llm-capacity/capacity-8/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-1</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>1</td><td>4</td><td>16.7%</td><td>83.3%</td><td>18.2%</td><td>1.1 ms</td><td>99.9%</td><td>319/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-2</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>2</td><td>4</td><td>30.0%</td><td>70.0%</td><td>24.7%</td><td>1.4 ms</td><td>99.8%</td><td>518/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-2/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-4</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>59.9%</td><td>40.1%</td><td>22.9%</td><td>1.4 ms</td><td>99.6%</td><td>919/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-8</td><td>row-group-concurrency</td><td>1,024</td><td>512</td><td>1</td><td>8</td><td>4</td><td>89.4%</td><td>10.6%</td><td>10.3%</td><td>4.5 ms</td><td>99.6%</td><td>913/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-group-concurrency/row-groups-8/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-1024</td><td>row-scale</td><td>8,192</td><td>1,024</td><td>64</td><td>4</td><td>4</td><td>82.0%</td><td>18.0%</td><td>18.0%</td><td>15.99 s</td><td>100.0%</td><td>144/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-1024/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-128</td><td>row-scale</td><td>1,024</td><td>128</td><td>64</td><td>4</td><td>4</td><td>67.5%</td><td>32.5%</td><td>32.4%</td><td>1.58 s</td><td>99.3%</td><td>567/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-128/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-256</td><td>row-scale</td><td>2,048</td><td>256</td><td>64</td><td>4</td><td>4</td><td>60.0%</td><td>40.0%</td><td>40.0%</td><td>5.16 s</td><td>99.7%</td><td>346/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-256/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-512</td><td>row-scale</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>71.0%</td><td>29.0%</td><td>29.0%</td><td>8.58 s</td><td>99.9%</td><td>234/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-512/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-64</td><td>row-scale</td><td>512</td><td>64</td><td>64</td><td>4</td><td>4</td><td>75.2%</td><td>24.8%</td><td>24.3%</td><td>598.8 ms</td><td>98.8%</td><td>763/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/row-scale/rows-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>narrow-frontier-high-cap</td><td>stress-shape</td><td>2,048</td><td>1,024</td><td>1</td><td>1</td><td>8</td><td>8.1%</td><td>91.9%</td><td>21.1%</td><td>2.8 ms</td><td>99.9%</td><td>169/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/stress-shape/narrow-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr><tr><td>wide-frontier-high-cap</td><td>stress-shape</td><td>8,192</td><td>1,024</td><td>64</td><td>16</td><td>8</td><td>70.5%</td><td>29.5%</td><td>29.5%</td><td>70.81 s</td><td>99.8%</td><td>98/s</td><td><a href="../artifacts/async-scheduling-idle-analysis/stress-shape/wide-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr></tbody></table></div>
-<p class="artifact-note">Raw artifacts are under <a href="../artifacts/async-scheduling-idle-analysis">artifacts/async-scheduling-idle-analysis</a>.</p>
-</section>
-<section>
-<h2>Potential Changes</h2>
-<ol class="recommendations"><li><strong>Add an adaptive row-group admission controller.</strong> A subclass or policy layer can watch bottleneck-resource starved idle and admit another row group when model capacity is idle while queued or pending model work remains, bounded by buffer and memory limits. When total idle is high but starved idle is low, the controller should treat that as a frontier problem and prefetch more row groups rather than tuning request AIMD.</li><li><strong>Keep resource-aware idle in the standard benchmark output.</strong> The new <code>llm_wait</code> utilization, starved idle, burstiness, and ready-gap metrics identify whether a vLLM-like resource is genuinely idle or merely waiting for dependencies.</li><li><strong>Document custom-model group identity and weight.</strong> <code>SchedulingMetadata.custom_model(...)</code> controls fairness and per-group admission. Plugins that share one external model should use a shared model identity and a weight that reflects real serving capacity; plugins that represent distinct resources should keep distinct identities.</li><li><strong>Use the report as a regression suite.</strong> The worst case here is <code>rows-256</code>; future scheduler or admission changes should improve that case without increasing ready gaps or violating final zero-lease checks.</li></ol>
-</section>
-</main>
-</body>
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-<body>
-<main>
-<section class="hero">
-<p class="eyebrow">DataDesigner async scheduler</p>
-<h1>Idle-Time Analysis</h1>
-<p class="lede">This report measures where model-capacity idle time appears, which knobs change it, and which remaining idle looks avoidable by scheduler or admission-control changes.</p>
-<div class="summary-grid"><div class="summary-card"><span>Benchmark cases</span><strong>22</strong></div><div class="summary-card"><span>Largest synthetic generation count</span><strong>8,192</strong></div><div class="summary-card"><span>Median llm_wait utilization</span><strong>79.2%</strong></div><div class="summary-card"><span>Worst total llm_wait idle</span><strong>91.1%</strong></div><div class="summary-card"><span>Worst starved llm_wait idle</span><strong>33.7%</strong></div><div class="summary-card"><span>Best llm_wait utilization</span><strong>93.0%</strong></div><div class="summary-card"><span>Validation</span><strong>pass</strong></div></div>
-</section>
-<section>
-<h2>What Counts As Idle</h2>
-<p><strong>General idle</strong> is unused configured capacity. It includes expected idle when dependencies are not ready. <strong>Starved idle</strong> is the actionable subset: a resource has idle slots while the scheduler already has queued work that requests that resource. <strong>Frontier/dependency-horizon idle</strong> is total idle minus starved idle: capacity is unused because dependency work has not yet exposed a ready scheduler task. <strong>Scheduler queue age</strong> measures ready-enqueued to lease-acquired time; <strong>downstream ready gap</strong> measures dependency-complete to downstream-dispatch time. For this refactor, the most important resource is <code>llm_wait</code>, because it maps to model-serving capacity that should stay busy when runnable model work exists.</p>
-</section>
-<section>
-<h2>Implementation Improvements</h2>
-<div class="summary-grid"><div class="summary-card"><span>Classification now explicit</span><strong>5 frontier, 16 queued</strong></div><div class="summary-card"><span>Quick-suite proof gates</span><strong>22 cases, zero validation failures</strong></div><div class="summary-card"><span>Largest refreshed case</span><strong>8,192 generations</strong></div><div class="summary-card"><span>Narrow frontier idle identified</span><strong>75.1%</strong></div><div class="summary-card"><span>Row-groups=1 frontier idle</span><strong>67.8%</strong></div><div class="summary-card"><span>Wide queued starvation age</span><strong>86.97 s</strong></div></div><div class="improvement-grid"><article class="improvement-card"><div><h3>Idle partition metrics</h3><span class="status">Implemented</span></div><p>The report now splits total idle into queued-work starvation and frontier/dependency-horizon idle. This prevents the narrow high-capacity case from being mistaken for a request-admission problem.</p><p class="evidence"><strong>Evidence:</strong> <code>narrow-frontier-high-cap</code>: 75.1% frontier idle, 16.0% starved idle.</p><a href="../scripts/benchmarks/benchmark_async_scheduling.py">source</a></article><article class="improvement-card"><div><h3>Queue age vs downstream delay</h3><span class="status">Implemented</span></div><p>Ready-to-lease queue age is now shown separately from dependency-complete-to-dispatch delay. This distinguishes scheduler backlog from slow dependency propagation.</p><p class="evidence"><strong>Evidence:</strong> <code>rows-256</code>: 5.71 s scheduler queue p95, 13.7 ms downstream p95.</p><a href="../scripts/benchmarks/generate_async_scheduling_idle_report.py">source</a></article><article class="improvement-card"><div><h3>Batched frontier enqueue</h3><span class="status">Implemented</span></div><p>A frontier delta with many ready tasks now enters the fair queue in one enqueue operation, while pre-batch parking, dropped rows, and per-task observability are preserved.</p><p class="evidence"><strong>Evidence:</strong> Focused scheduler tests prove one queue operation for a 5-task frontier and one operation for a pre-batch flush after a dropped row.</p><a href="../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py">source</a></article><article class="improvement-card"><div><h3>Resource-overlap peer pressure</h3><span class="status">Implemented</span></div><p>Task admission now applies group-cap peer pressure only for queued peers that can use the same typed resource and are hard-resource eligible. Local/submission-only peers no longer create false pressure on idle model capacity.</p><p class="evidence"><strong>Evidence:</strong> Unit tests cover non-overlapping peers, overlapping <code>llm_wait</code> peers, and peers blocked by another hard resource.</p><a href="../packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py">source</a></article><article class="improvement-card"><div><h3>Adaptive row-group admission</h3><span class="status">Implemented + benchmarked</span></div><p>The scheduler can now start with one admitted row group, raise the row-group target when model capacity is idle and queued model demand is low, and remain bounded by the configured hard cap.</p><p class="evidence"><strong>Evidence:</strong> <code>adaptive-row-groups-fixed-low</code> to <code>adaptive-row-groups-adaptive</code>: utilization +57.2 pp, frontier idle reduction 66.6%, observed target 8.</p><a href="../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py">source</a></article><article class="improvement-card"><div><h3>Request-pressure advisory</h3><span class="status">Implemented + benchmarked</span></div><p>Task selection can now use request pressure snapshots to prefer an open same-frontier peer before spawning work that would immediately wait on request capacity. It does not yield and reacquire leases inside running generators.</p><p class="evidence"><strong>Evidence:</strong> Leased request wait moved from 49.8 ms to 3.2 ms; first dispatch moved from <code>a_pressured</code> to <code>z_open</code>.</p><a href="../packages/data-designer-engine/src/data_designer/engine/models/request_admission">source</a></article></div>
-</section>
-<section>
-<h2>Findings</h2>
-<ul class="finding-list"><li><strong>Scale helps once enough runnable work exists.</strong> The row-scale sweep moved from 79.6% to 66.2% llm_wait utilization (-13.4 pp). Larger runs amortize startup and drain phases.</li><li><strong>Runnable frontier width is the main controllable idle source.</strong> In the row-group sweep, utilization moved from 17.8% to 58.8% (+41.0 pp), while starved llm_wait idle improved by 0.0% between the narrowest case and the best observed case.</li><li><strong>Total idle and starved idle diagnose different causes.</strong> <code>narrow-frontier-high-cap</code> had 91.1% total llm_wait idle but only 16.0% starved idle. The remaining 75.1% was frontier/dependency-horizon idle: the scheduler often had no ready model work because the admitted frontier was too small.</li><li><strong>Capacity increases expose underfeeding.</strong> In the capacity sweep, modeled llm_wait cap 2 reached 86.8% utilization, while cap 4 reached 54.5% with the same small row frontier.</li><li><strong>Custom model scheduling metadata changes traffic shape.</strong> With synthetic <code>custom_model</code> weight forced to 1, llm_wait utilization was 75.8%; with the harness defaulting the weight to modeled model capacity it was 83.9%. This run did not show weight=1 underfeeding because each synthetic column is its own group, but it does show that group identity and weight are first-order benchmark inputs.</li><li><strong>High capacity magnifies traffic-shape effects.</strong> The stress case with one large row group and high cap reached 8.9% utilization; the wide-frontier shape reached 79.4%. This is the vLLM-server-idle failure mode the scheduler needs to expose and track over time.</li><li><strong>Worst observed actionable idle:</strong> rows-256 had 33.7% starved llm_wait idle and 6.11 s max scheduler queue age.</li></ul>
-</section>
-<section>
-<h2>Idle Classification Board</h2>
-<p>This board is the main analysis surface for optimization work. Frontier/dependency-horizon cases need row-group or scheduling-shard changes; queued-work starvation cases need queue/admission/resource selection changes; request-pressure cases need request-backed evidence before scheduler policy changes.</p><div class="table-wrap"><table><thead><tr><th>case</th><th>dominant idle class</th><th>util</th><th>starved idle</th><th>frontier idle</th><th>queue age p95</th><th>downstream p95</th><th>next lever</th></tr></thead><tbody><tr><td>stress-shape/narrow-frontier-high-cap</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>8.9%</td><td>16.0%</td><td>75.1%</td><td>1.7 ms</td><td>1.9 ms</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>adaptations/adaptive-row-groups-fixed-low</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>10.5%</td><td>8.2%</td><td>81.3%</td><td>630 us</td><td>718 us</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>row-group-concurrency/row-groups-1</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>17.8%</td><td>14.5%</td><td>67.8%</td><td>747 us</td><td>867 us</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>row-group-concurrency/row-groups-2</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>31.4%</td><td>23.0%</td><td>45.6%</td><td>1.1 ms</td><td>1.7 ms</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>adaptations/adaptive-row-groups-fixed-high</td><td><span class="idle-class idle-frontier-dependency-horizon">frontier/dependency-horizon</span></td><td>62.4%</td><td>17.3%</td><td>20.4%</td><td>2.1 ms</td><td>2.7 ms</td><td>adaptive row-group admission with memory and queue guardrails</td></tr><tr><td>llm-capacity/capacity-8</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>54.5%</td><td>24.6%</td><td>20.9%</td><td>2.0 ms</td><td>3.0 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>row-group-concurrency/row-groups-4</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>58.8%</td><td>22.8%</td><td>18.4%</td><td>1.4 ms</td><td>2.0 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>row-scale/rows-256</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>66.2%</td><td>33.7%</td><td>0.1%</td><td>5.71 s</td><td>13.7 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>adaptations/adaptive-row-groups-adaptive</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>67.7%</td><td>17.6%</td><td>14.7%</td><td>1.7 ms</td><td>3.0 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>custom-model-weight/weight-custom-model-1</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>75.8%</td><td>24.2%</td><td>0.0%</td><td>10.77 s</td><td>16.9 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>buffer-size/buffer-64</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.0%</td><td>20.9%</td><td>0.0%</td><td>10.66 s</td><td>12.9 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>stress-shape/wide-frontier-high-cap</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.4%</td><td>20.6%</td><td>0.0%</td><td>86.97 s</td><td>113.8 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>row-scale/rows-128</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.5%</td><td>20.3%</td><td>0.3%</td><td>1.60 s</td><td>9.9 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>row-scale/rows-64</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>79.6%</td><td>19.4%</td><td>1.1%</td><td>541.6 ms</td><td>7.6 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>buffer-size/buffer-16</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>80.5%</td><td>19.4%</td><td>0.0%</td><td>3.68 s</td><td>52.4 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>adaptations/adaptive-request-pressure-control</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>82.4%</td><td>15.7%</td><td>1.7%</td><td>3.5 ms</td><td>0 us</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>buffer-size/buffer-1</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>82.6%</td><td>17.4%</td><td>0.0%</td><td>47.4 ms</td><td>44.7 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>custom-model-weight/weight-model-capacity</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>83.9%</td><td>16.1%</td><td>0.0%</td><td>11.60 s</td><td>13.8 ms</td><td>resource-aware task admission, candidate selection, or smaller scheduling shards</td></tr><tr><td>adaptations/adaptive-request-pressure-combined</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>84.4%</td><td>14.5%</td><td>1.1%</td><td>10.4 ms</td><td>0 us</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>adaptations/request-pressure-advisory</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>85.3%</td><td>13.2%</td><td>1.4%</td><td>124.8 ms</td><td>0 us</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>llm-capacity/capacity-4</td><td><span class="idle-class idle-queued-work-starvation">queued-work starvation</span></td><td>86.8%</td><td>13.0%</td><td>0.2%</td><td>5.5 ms</td><td>4.6 ms</td><td>task admission/fairness policy and eligible-starved-idle metrics</td></tr><tr><td>adaptations/request-pressure-control</td><td><span class="idle-class idle-healthy-control">healthy/control</span></td><td>93.0%</td><td>5.6%</td><td>1.4%</td><td>143.7 ms</td><td>0 us</td><td>keep as control case and watch for regressions</td></tr></tbody></table></div>
-</section>
-<section>
-<h2>Adaptation Benchmarks</h2>
-<div class="summary-grid"><div class="summary-card"><span>Adaptive utilization delta</span><strong>+57.2 pp</strong></div><div class="summary-card"><span>Adaptive frontier-idle reduction</span><strong>66.6%</strong></div><div class="summary-card"><span>Adaptive vs fixed-high</span><strong>+5.3 pp</strong></div><div class="summary-card"><span>Adaptive observed target</span><strong>8</strong></div><div class="summary-card"><span>Request leased-wait reduction</span><strong>46.6 ms</strong></div><div class="summary-card"><span>Advisory first dispatch</span><strong>z_open</strong></div><div class="summary-card"><span>Request queue-age p95 reduction</span><strong>18.9 ms</strong></div><div class="summary-card"><span>Combined leased-wait delta</span><strong>-26.8 ms</strong></div><div class="summary-card"><span>Combined request-idle delta</span><strong>+3.7 pp</strong></div></div><p>The row-group adaptation is judged against a fixed-low frontier because that is the failure shape where capacity is idle before enough dependency work exists. It should reduce frontier idle or raise utilization while staying below the hard row-group cap. The fixed-high control isolates how close the adaptive policy gets to simply admitting the full hard-cap frontier up front. The fixed-high ceiling reached 62.4% utilization with 20.4% frontier idle.</p><p>The request-pressure adaptation is judged by whether it avoids dispatching the pressured model first and reduces request-wait time while a scheduler task lease is already held. The combined case keeps adaptive row-group admission enabled while request pressure is present, so it measures whether the scheduler can widen the frontier and still avoid sending the first available leases into a saturated model.</p><div class="table-wrap"><table><thead><tr><th>adaptation</th><th>case</th><th>util</th><th>frontier idle</th><th>observed target</th><th>leased request wait</th><th>first model dispatch</th></tr></thead><tbody><tr><td>adaptive fixed-low control</td><td>adaptive-row-groups-fixed-low</td><td>10.5%</td><td>81.3%</td><td>1</td><td>-</td><td>-</td></tr><tr><td>adaptive enabled</td><td>adaptive-row-groups-adaptive</td><td>67.7%</td><td>14.7%</td><td>8</td><td>-</td><td>-</td></tr><tr><td>adaptive fixed-high control</td><td>adaptive-row-groups-fixed-high</td><td>62.4%</td><td>20.4%</td><td>8</td><td>-</td><td>-</td></tr><tr><td>request control</td><td>request-pressure-control</td><td>93.0%</td><td>1.4%</td><td>-</td><td>49.8 ms</td><td>a_pressured</td></tr><tr><td>request advisory</td><td>request-pressure-advisory</td><td>85.3%</td><td>1.4%</td><td>-</td><td>3.2 ms</td><td>z_open</td></tr><tr><td>combined control</td><td>adaptive-request-pressure-control</td><td>82.4%</td><td>1.7%</td><td>5</td><td>488.3 ms</td><td>a_pressured</td></tr><tr><td>combined enabled</td><td>adaptive-request-pressure-combined</td><td>84.4%</td><td>1.1%</td><td>5</td><td>515.1 ms</td><td>z_open</td></tr></tbody></table></div>
-</section>
-<section>
-<h2>Figures</h2>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Scale: more rows smooth the model-resource flow"><text class="chart-title" x="64" y="24">Scale: more rows smooth the model-resource flow</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">synthetic model generations</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,96.0 328.0,96.2 856.0,131.3" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="96.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7955</title></circle><circle cx="328.0" cy="96.2" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7946</title></circle><circle cx="856.0" cy="131.3" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.6617</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,254.8 328.0,252.5 856.0,216.9" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="254.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1940</title></circle><circle cx="328.0" cy="252.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2026</title></circle><circle cx="856.0" cy="216.9" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.3374</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">512</text><text class="tick" x="328.0" y="332" text-anchor="middle">1.0k</text><text class="tick" x="856.0" y="332" text-anchor="middle">2.0k</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Frontier width: row-group concurrency changes starvation"><text class="chart-title" x="64" y="24">Frontier width: row-group concurrency changes starvation</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">row-group concurrency</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,226.2 328.0,164.9 856.0,42.0" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="226.2" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.1776</title></circle><circle cx="328.0" cy="164.9" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.3142</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5878</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,241.0 328.0,202.8 856.0,203.6" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="241.0" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1447</title></circle><circle cx="328.0" cy="202.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2299</title></circle><circle cx="856.0" cy="203.6" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2280</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,305.6 328.0,305.2 856.0,305.1" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="305.6" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0009</title></circle><circle cx="328.0" cy="305.2" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0017</title></circle><circle cx="856.0" cy="305.1" r="4" fill="#6c5ce7"><title>downstream ready p95: 0.0020</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">downstream ready p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="328.0" y="332" text-anchor="middle">2</text><text class="tick" x="856.0" y="332" text-anchor="middle">4</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Buffer size: row-group shape affects traffic waves"><text class="chart-title" x="64" y="24">Buffer size: row-group shape affects traffic waves</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">buffer size</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / seconds</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,285.6 252.6,286.1 856.0,286.4" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="285.6" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8259</title></circle><circle cx="252.6" cy="286.1" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8054</title></circle><circle cx="856.0" cy="286.4" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.7904</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,301.7 252.6,301.2 856.0,300.8" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="301.7" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1738</title></circle><circle cx="252.6" cy="301.2" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1944</title></circle><circle cx="856.0" cy="300.8" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2094</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,304.8 252.6,214.8 856.0,42.0" fill="none" stroke="#6c5ce7" stroke-width="3"/><circle cx="64.0" cy="304.8" r="4" fill="#6c5ce7"><title>scheduler queue age p95: 0.0474</title></circle><circle cx="252.6" cy="214.8" r="4" fill="#6c5ce7"><title>scheduler queue age p95: 3.6832</title></circle><circle cx="856.0" cy="42.0" r="4" fill="#6c5ce7"><title>scheduler queue age p95: 10.6643</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#6c5ce7"/><text x="666" y="62" class="legend-text">scheduler queue age p95</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">1</text><text class="tick" x="252.6" y="332" text-anchor="middle">16</text><text class="tick" x="856.0" y="332" text-anchor="middle">64</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Capacity scaling: underfeeding becomes visible as cap rises"><text class="chart-title" x="64" y="24">Capacity scaling: underfeeding becomes visible as cap rises</text><line x1="64" y1="306" x2="856" y2="306" class="axis"/><line x1="64" y1="42" x2="64" y2="306" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">modeled llm_wait capacity</text><text class="axis-label" transform="translate(16 174.0) rotate(-90)" text-anchor="middle">ratio / kgen/s</text><text class="tick" x="54" y="306" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="174.0" x2="856" y2="174.0" class="grid"/><polyline points="64.0,42.0 856.0,140.3" fill="none" stroke="#1f7a8c" stroke-width="3"/><circle cx="64.0" cy="42.0" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.8679</title></circle><circle cx="856.0" cy="140.3" r="4" fill="#1f7a8c"><title>llm_wait utilization: 0.5448</title></circle><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><polyline points="64.0,266.5 856.0,231.1" fill="none" stroke="#b23a48" stroke-width="3"/><circle cx="64.0" cy="266.5" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.1297</title></circle><circle cx="856.0" cy="231.1" r="4" fill="#b23a48"><title>starved llm_wait idle: 0.2462</title></circle><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g><polyline points="64.0,124.7 856.0,72.4" fill="none" stroke="#326273" stroke-width="3"/><circle cx="64.0" cy="124.7" r="4" fill="#326273"><title>throughput / 1000: 0.5960</title></circle><circle cx="856.0" cy="72.4" r="4" fill="#326273"><title>throughput / 1000: 0.7681</title></circle><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#326273"/><text x="666" y="62" class="legend-text">throughput / 1000</text></g><text class="tick" x="64.0" y="332" text-anchor="middle">2</text><text class="tick" x="856.0" y="332" text-anchor="middle">4</text></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Custom-model metadata changes per-stage fairness"><text class="chart-title" x="64" y="24">Custom-model metadata changes per-stage fairness</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="230.0" y="80.1" width="29.0" height="197.9" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8385</title></rect><rect x="262.0" y="239.9" width="29.0" height="38.1" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1613</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">model cap</text><rect x="626.0" y="99.1" width="29.0" height="178.9" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.7579</title></rect><rect x="658.0" y="220.9" width="29.0" height="57.1" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2419</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">weight 1</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#b23a48"/><text x="666" y="45" class="legend-text">starved llm_wait idle</text></g></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Stress shapes: narrow vs wide runnable horizon"><text class="chart-title" x="64" y="24">Stress shapes: narrow vs wide runnable horizon</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="182.0" y="257.0" width="29.0" height="21.0" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.0890</title></rect><rect x="214.0" y="63.0" width="29.0" height="215.0" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.9110</title></rect><rect x="246.0" y="240.3" width="29.0" height="37.7" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1598</title></rect><rect x="278.0" y="100.7" width="29.0" height="177.3" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.7511</title></rect><rect x="310.0" y="203.0" width="29.0" height="75.0" rx="2" fill="#326273"><title>burstiness / 2: 0.3177</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">narrow frontier high cap</text><rect x="578.0" y="90.5" width="29.0" height="187.5" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.7943</title></rect><rect x="610.0" y="229.5" width="29.0" height="48.5" rx="2" fill="#8a6f3d"><title>total llm_wait idle: 0.2057</title></rect><rect x="642.0" y="229.5" width="29.0" height="48.5" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.2056</title></rect><rect x="674.0" y="278.0" width="29.0" height="0.0" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.0001</title></rect><rect x="706.0" y="252.3" width="29.0" height="25.7" rx="2" fill="#326273"><title>burstiness / 2: 0.1091</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">wide frontier high cap</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#8a6f3d"/><text x="666" y="45" class="legend-text">total llm_wait idle</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">starved llm_wait idle</text></g><g class="legend"><rect x="650" y="69" width="10" height="10" fill="#6c5ce7"/><text x="666" y="79" class="legend-text">frontier/dependency idle</text></g><g class="legend"><rect x="650" y="86" width="10" height="10" fill="#326273"/><text x="666" y="96" class="legend-text">burstiness / 2</text></g></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Adaptation: adaptive row groups target frontier idle"><text class="chart-title" x="64" y="24">Adaptation: adaptive row groups target frontier idle</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="148.0" y="118.3" width="29.0" height="159.7" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.6768</title></rect><rect x="180.0" y="243.2" width="29.0" height="34.8" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.1473</title></rect><rect x="212.0" y="236.5" width="29.0" height="41.5" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1759</title></rect><text class="tick" x="196.0" y="312" text-anchor="middle">adaptive</text><rect x="412.0" y="130.8" width="29.0" height="147.2" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.6236</title></rect><rect x="444.0" y="229.9" width="29.0" height="48.1" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.2038</title></rect><rect x="476.0" y="237.3" width="29.0" height="40.7" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.1725</title></rect><text class="tick" x="460.0" y="312" text-anchor="middle">fixed-high</text><rect x="676.0" y="253.3" width="29.0" height="24.7" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.1047</title></rect><rect x="708.0" y="86.0" width="29.0" height="192.0" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.8134</title></rect><rect x="740.0" y="258.7" width="29.0" height="19.3" rx="2" fill="#b23a48"><title>starved llm_wait idle: 0.0820</title></rect><text class="tick" x="724.0" y="312" text-anchor="middle">fixed-low</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#6c5ce7"/><text x="666" y="45" class="legend-text">frontier/dependency idle</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">starved llm_wait idle</text></g></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Adaptation: request-pressure advisory avoids leased request wait"><text class="chart-title" x="64" y="24">Adaptation: request-pressure advisory avoids leased request wait</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="214.0" y="272.8" width="29.0" height="5.2" rx="2" fill="#b23a48"><title>request wait while leased: 0.0032</title></rect><rect x="246.0" y="73.0" width="29.0" height="205.0" rx="2" fill="#6c5ce7"><title>scheduler queue age p95: 0.1248</title></rect><rect x="278.0" y="256.0" width="29.0" height="22.0" rx="2" fill="#1f7a8c"><title>wall seconds / 10: 0.0134</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">advisory</text><rect x="610.0" y="196.3" width="29.0" height="81.7" rx="2" fill="#b23a48"><title>request wait while leased: 0.0498</title></rect><rect x="642.0" y="42.0" width="29.0" height="236.0" rx="2" fill="#6c5ce7"><title>scheduler queue age p95: 0.1437</title></rect><rect x="674.0" y="253.1" width="29.0" height="24.9" rx="2" fill="#1f7a8c"><title>wall seconds / 10: 0.0152</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">control</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#b23a48"/><text x="666" y="28" class="legend-text">request wait while leased</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#6c5ce7"/><text x="666" y="45" class="legend-text">scheduler queue age p95</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#1f7a8c"/><text x="666" y="62" class="legend-text">wall seconds / 10</text></g></svg></figure>
-<figure class="chart"><svg viewBox="0 0 880 360" role="img" aria-label="Adaptation: combined adaptive frontier and request pressure"><text class="chart-title" x="64" y="24">Adaptation: combined adaptive frontier and request pressure</text><line x1="64" y1="278" x2="856" y2="278" class="axis"/><line x1="64" y1="42" x2="64" y2="278" class="axis"/><text class="axis-label" x="460.0" y="352" text-anchor="middle">case</text><text class="axis-label" transform="translate(16 160.0) rotate(-90)" text-anchor="middle">ratio</text><text class="tick" x="54" y="278" text-anchor="end">0</text><text class="tick" x="54" y="46" text-anchor="end">max</text><line x1="64" y1="160.0" x2="856" y2="160.0" class="grid"/><rect x="198.0" y="78.9" width="29.0" height="199.1" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8438</title></rect><rect x="230.0" y="67.4" width="29.0" height="210.6" rx="2" fill="#326273"><title>request utilization: 0.8924</title></rect><rect x="262.0" y="273.0" width="29.0" height="5.0" rx="2" fill="#b23a48"><title>request starved idle: 0.0211</title></rect><rect x="294.0" y="275.4" width="29.0" height="2.6" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.0110</title></rect><text class="tick" x="262.0" y="312" text-anchor="middle">combined</text><rect x="594.0" y="83.4" width="29.0" height="194.6" rx="2" fill="#1f7a8c"><title>llm_wait utilization: 0.8245</title></rect><rect x="626.0" y="75.9" width="29.0" height="202.1" rx="2" fill="#326273"><title>request utilization: 0.8563</title></rect><rect x="658.0" y="272.9" width="29.0" height="5.1" rx="2" fill="#b23a48"><title>request starved idle: 0.0217</title></rect><rect x="690.0" y="273.9" width="29.0" height="4.1" rx="2" fill="#6c5ce7"><title>frontier/dependency idle: 0.0174</title></rect><text class="tick" x="658.0" y="312" text-anchor="middle">control</text><g class="legend"><rect x="650" y="18" width="10" height="10" fill="#1f7a8c"/><text x="666" y="28" class="legend-text">llm_wait utilization</text></g><g class="legend"><rect x="650" y="35" width="10" height="10" fill="#326273"/><text x="666" y="45" class="legend-text">request utilization</text></g><g class="legend"><rect x="650" y="52" width="10" height="10" fill="#b23a48"/><text x="666" y="62" class="legend-text">request starved idle</text></g><g class="legend"><rect x="650" y="69" width="10" height="10" fill="#6c5ce7"/><text x="666" y="79" class="legend-text">frontier/dependency idle</text></g></svg></figure>
-</section>
-<section>
-<h2>Benchmark Matrix</h2>
-<div class="table-wrap"><table><thead><tr><th>case</th><th>sweep</th><th>scenario</th><th>generations</th><th>rows</th><th>buffer</th><th>rg conc</th><th>llm cap</th><th>util</th><th>idle</th><th>starved idle</th><th>frontier idle</th><th>queue age p95</th><th>downstream gap p95</th><th>leased request wait</th><th>request util</th><th>request starved idle</th><th>advisory skips</th><th>first model</th><th>overlap</th><th>throughput</th><th>artifact</th></tr></thead><tbody><tr><td>adaptive-request-pressure-combined</td><td>adaptations</td><td>adaptive-request-pressure</td><td>256</td><td>128</td><td>1</td><td>8</td><td>2</td><td>84.4%</td><td>15.6%</td><td>14.5%</td><td>1.1%</td><td>10.4 ms</td><td>0 us</td><td>515.1 ms</td><td>89.2%</td><td>2.1%</td><td>193</td><td>z_open</td><td>0.0%</td><td>689/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-request-pressure-combined/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-request-pressure-control</td><td>adaptations</td><td>adaptive-request-pressure</td><td>256</td><td>128</td><td>1</td><td>8</td><td>2</td><td>82.4%</td><td>17.4%</td><td>15.7%</td><td>1.7%</td><td>3.5 ms</td><td>0 us</td><td>488.3 ms</td><td>85.6%</td><td>2.2%</td><td>0</td><td>a_pressured</td><td>0.0%</td><td>723/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-request-pressure-control/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-row-groups-adaptive</td><td>adaptations</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>8</td><td>8</td><td>67.7%</td><td>32.3%</td><td>17.6%</td><td>14.7%</td><td>1.7 ms</td><td>3.0 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.3%</td><td>1,108/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-row-groups-adaptive/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-row-groups-fixed-high</td><td>adaptations</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>8</td><td>8</td><td>62.4%</td><td>37.6%</td><td>17.3%</td><td>20.4%</td><td>2.1 ms</td><td>2.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.2%</td><td>1,062/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-row-groups-fixed-high/async_scheduling_benchmark.json">json</a></td></tr><tr><td>adaptive-row-groups-fixed-low</td><td>adaptations</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>1</td><td>8</td><td>10.5%</td><td>89.5%</td><td>8.2%</td><td>81.3%</td><td>630 us</td><td>718 us</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.8%</td><td>229/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/adaptive-row-groups-fixed-low/async_scheduling_benchmark.json">json</a></td></tr><tr><td>request-pressure-advisory</td><td>adaptations</td><td>request-pressure-advisory</td><td>64</td><td>32</td><td>32</td><td>1</td><td>1</td><td>85.3%</td><td>14.7%</td><td>13.2%</td><td>1.4%</td><td>124.8 ms</td><td>0 us</td><td>3.2 ms</td><td>49.0%</td><td>1.3%</td><td>29</td><td>z_open</td><td>0.0%</td><td>478/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/request-pressure-advisory/async_scheduling_benchmark.json">json</a></td></tr><tr><td>request-pressure-control</td><td>adaptations</td><td>request-pressure-advisory</td><td>64</td><td>32</td><td>32</td><td>1</td><td>1</td><td>93.0%</td><td>7.0%</td><td>5.6%</td><td>1.4%</td><td>143.7 ms</td><td>0 us</td><td>49.8 ms</td><td>41.6%</td><td>0.7%</td><td>0</td><td>a_pressured</td><td>0.0%</td><td>422/s</td><td><a href="../artifacts/async-scheduling-idle-regression/adaptations/request-pressure-control/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-1</td><td>buffer-size</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>1</td><td>4</td><td>4</td><td>82.6%</td><td>17.4%</td><td>17.4%</td><td>0.0%</td><td>47.4 ms</td><td>44.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>100.0%</td><td>379/s</td><td><a href="../artifacts/async-scheduling-idle-regression/buffer-size/buffer-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-16</td><td>buffer-size</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>16</td><td>4</td><td>4</td><td>80.5%</td><td>19.5%</td><td>19.4%</td><td>0.0%</td><td>3.68 s</td><td>52.4 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>100.0%</td><td>204/s</td><td><a href="../artifacts/async-scheduling-idle-regression/buffer-size/buffer-16/async_scheduling_benchmark.json">json</a></td></tr><tr><td>buffer-64</td><td>buffer-size</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>79.0%</td><td>21.0%</td><td>20.9%</td><td>0.0%</td><td>10.66 s</td><td>12.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>195/s</td><td><a href="../artifacts/async-scheduling-idle-regression/buffer-size/buffer-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-custom-model-1</td><td>custom-model-weight</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>75.8%</td><td>24.2%</td><td>24.2%</td><td>0.0%</td><td>10.77 s</td><td>16.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>192/s</td><td><a href="../artifacts/async-scheduling-idle-regression/custom-model-weight/weight-custom-model-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>weight-model-capacity</td><td>custom-model-weight</td><td>real-pipeline-overlap</td><td>4,096</td><td>512</td><td>64</td><td>4</td><td>4</td><td>83.9%</td><td>16.1%</td><td>16.1%</td><td>0.0%</td><td>11.60 s</td><td>13.8 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>185/s</td><td><a href="../artifacts/async-scheduling-idle-regression/custom-model-weight/weight-model-capacity/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-4</td><td>llm-capacity</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>2</td><td>86.8%</td><td>13.2%</td><td>13.0%</td><td>0.2%</td><td>5.5 ms</td><td>4.6 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>596/s</td><td><a href="../artifacts/async-scheduling-idle-regression/llm-capacity/capacity-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>capacity-8</td><td>llm-capacity</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>54.5%</td><td>45.5%</td><td>24.6%</td><td>20.9%</td><td>2.0 ms</td><td>3.0 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>768/s</td><td><a href="../artifacts/async-scheduling-idle-regression/llm-capacity/capacity-8/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-1</td><td>row-group-concurrency</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>1</td><td>4</td><td>17.8%</td><td>82.2%</td><td>14.5%</td><td>67.8%</td><td>747 us</td><td>867 us</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>356/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-group-concurrency/row-groups-1/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-2</td><td>row-group-concurrency</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>2</td><td>4</td><td>31.4%</td><td>68.6%</td><td>23.0%</td><td>45.6%</td><td>1.1 ms</td><td>1.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.8%</td><td>525/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-group-concurrency/row-groups-2/async_scheduling_benchmark.json">json</a></td></tr><tr><td>row-groups-4</td><td>row-group-concurrency</td><td>real-pipeline-overlap</td><td>1,024</td><td>512</td><td>1</td><td>4</td><td>4</td><td>58.8%</td><td>41.2%</td><td>22.8%</td><td>18.4%</td><td>1.4 ms</td><td>2.0 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>867/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-group-concurrency/row-groups-4/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-128</td><td>row-scale</td><td>real-pipeline-overlap</td><td>1,024</td><td>128</td><td>64</td><td>4</td><td>4</td><td>79.5%</td><td>20.5%</td><td>20.3%</td><td>0.3%</td><td>1.60 s</td><td>9.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.3%</td><td>578/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-scale/rows-128/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-256</td><td>row-scale</td><td>real-pipeline-overlap</td><td>2,048</td><td>256</td><td>64</td><td>4</td><td>4</td><td>66.2%</td><td>33.8%</td><td>33.7%</td><td>0.1%</td><td>5.71 s</td><td>13.7 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.7%</td><td>332/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-scale/rows-256/async_scheduling_benchmark.json">json</a></td></tr><tr><td>rows-64</td><td>row-scale</td><td>real-pipeline-overlap</td><td>512</td><td>64</td><td>64</td><td>4</td><td>4</td><td>79.6%</td><td>20.4%</td><td>19.4%</td><td>1.1%</td><td>541.6 ms</td><td>7.6 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>98.6%</td><td>859/s</td><td><a href="../artifacts/async-scheduling-idle-regression/row-scale/rows-64/async_scheduling_benchmark.json">json</a></td></tr><tr><td>narrow-frontier-high-cap</td><td>stress-shape</td><td>real-pipeline-overlap</td><td>2,048</td><td>1,024</td><td>1</td><td>1</td><td>8</td><td>8.9%</td><td>91.1%</td><td>16.0%</td><td>75.1%</td><td>1.7 ms</td><td>1.9 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>193/s</td><td><a href="../artifacts/async-scheduling-idle-regression/stress-shape/narrow-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr><tr><td>wide-frontier-high-cap</td><td>stress-shape</td><td>real-pipeline-overlap</td><td>8,192</td><td>1,024</td><td>64</td><td>16</td><td>8</td><td>79.4%</td><td>20.6%</td><td>20.6%</td><td>0.0%</td><td>86.97 s</td><td>113.8 ms</td><td>0 us</td><td>0.0%</td><td>0.0%</td><td>0</td><td>-</td><td>99.9%</td><td>88/s</td><td><a href="../artifacts/async-scheduling-idle-regression/stress-shape/wide-frontier-high-cap/async_scheduling_benchmark.json">json</a></td></tr></tbody></table></div>
-<p class="artifact-note">Raw artifacts are under <a href="../artifacts/async-scheduling-idle-regression">artifacts/async-scheduling-idle-regression</a>.</p>
-</section>
-<section>
-<h2>Potential Changes</h2>
-<ol class="recommendations"><li><strong>Add an adaptive row-group admission controller.</strong> A subclass or policy layer can watch bottleneck-resource starved idle and admit another row group when model capacity is idle while queued or pending model work remains, bounded by buffer and memory limits. When total idle is high but starved idle is low, the controller should treat that as a frontier problem and prefetch more row groups rather than tuning request AIMD.</li><li><strong>Keep resource-aware idle in the standard benchmark output.</strong> The new <code>llm_wait</code> utilization, starved idle, frontier/dependency-horizon idle, scheduler queue-age, downstream ready-gap, and burstiness metrics identify whether a vLLM-like resource is genuinely idle or merely waiting for dependencies.</li><li><strong>Document custom-model group identity and weight.</strong> <code>SchedulingMetadata.custom_model(...)</code> controls fairness and per-group admission. Plugins that share one external model should use a shared model identity and a weight that reflects real serving capacity; plugins that represent distinct resources should keep distinct identities.</li><li><strong>Use the report as a regression suite.</strong> The worst case here is <code>rows-256</code>; future scheduler or admission changes should improve that case without increasing ready gaps or violating final zero-lease checks.</li></ol>
-</section>
-</main>
-</body>
-</html>

From d5edf05972dc10aedac6bec77e506cdc39c96aaa Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Tue, 19 May 2026 20:39:24 -0400
Subject: [PATCH 21/33] chore: remove local benchmark scripts

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 .gitignore                                    |    4 +
 .../engine/test_async_scheduling_benchmark.py |  671 ------
 plans/645/module-ownership.md                 |    2 +-
 .../benchmarks/benchmark_async_scheduling.py  | 1800 -----------------
 .../export_async_scheduling_perfetto.py       |  431 ----
 .../generate_async_scheduling_idle_report.py  | 1775 ----------------
 .../run_async_scheduling_idle_regression.py   |  659 ------
 7 files changed, 5 insertions(+), 5337 deletions(-)
 delete mode 100644 packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
 delete mode 100644 scripts/benchmarks/benchmark_async_scheduling.py
 delete mode 100644 scripts/benchmarks/export_async_scheduling_perfetto.py
 delete mode 100644 scripts/benchmarks/generate_async_scheduling_idle_report.py
 delete mode 100644 scripts/benchmarks/run_async_scheduling_idle_regression.py

diff --git a/.gitignore b/.gitignore
index c65a30138..e4ac9d9bf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -88,6 +88,10 @@ venv.bak/
 # Generated benchmark/report output
 /artifacts/
 /reports/
+/scripts/benchmarks/benchmark_async_scheduling.py
+/scripts/benchmarks/export_async_scheduling_perfetto.py
+/scripts/benchmarks/generate_async_scheduling_idle_report.py
+/scripts/benchmarks/run_async_scheduling_idle_regression.py
 
 docs/notebooks/
 docs/notebook_source/*.ipynb
diff --git a/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py b/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
deleted file mode 100644
index 868e7d589..000000000
--- a/packages/data-designer-engine/tests/engine/test_async_scheduling_benchmark.py
+++ /dev/null
@@ -1,671 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-import importlib.util
-import sys
-from pathlib import Path
-from types import ModuleType, SimpleNamespace
-from typing import Any
-
-import pytest
-
-
-def _load_benchmark_module() -> ModuleType:
-    benchmark_path = Path(__file__).resolve().parents[4] / "scripts" / "benchmarks" / "benchmark_async_scheduling.py"
-    spec = importlib.util.spec_from_file_location("benchmark_async_scheduling", benchmark_path)
-    assert spec is not None
-    assert spec.loader is not None
-    module = importlib.util.module_from_spec(spec)
-    sys.modules[spec.name] = module
-    spec.loader.exec_module(module)
-    return module
-
-
-def _load_idle_regression_module() -> ModuleType:
-    benchmark_dir = Path(__file__).resolve().parents[4] / "scripts" / "benchmarks"
-    module_name = "run_async_scheduling_idle_regression"
-    spec = importlib.util.spec_from_file_location(module_name, benchmark_dir / f"{module_name}.py")
-    assert spec is not None
-    assert spec.loader is not None
-    sys.path.insert(0, str(benchmark_dir))
-    try:
-        module = importlib.util.module_from_spec(spec)
-        sys.modules[spec.name] = module
-        spec.loader.exec_module(module)
-    finally:
-        sys.path.remove(str(benchmark_dir))
-    return module
-
-
-def _load_idle_report_module() -> ModuleType:
-    _load_idle_regression_module()
-    return sys.modules["generate_async_scheduling_idle_report"]
-
-
-def _capacity_plan() -> SimpleNamespace:
-    return SimpleNamespace(
-        observed_maxima=SimpleNamespace(
-            row_groups_in_flight=1,
-            queued_tasks_by_group={},
-        )
-    )
-
-
-def _valid_artifact(iteration: dict[str, Any]) -> dict[str, Any]:
-    iteration.setdefault(
-        "utilization_metrics",
-        {
-            "scheduler_resources": {
-                "submission": {
-                    "capacity_seconds": 1.0,
-                    "busy_capacity_seconds": 0.0,
-                    "idle_capacity_seconds": 1.0,
-                    "starved_idle_seconds": 0.0,
-                    "utilization_ratio": 0.0,
-                }
-            },
-            "request_resources": {},
-        },
-    )
-    return {
-        "inputs": SimpleNamespace(task_admission_capacity=2),
-        "derived_metrics": {
-            "final_zero_task_leases": True,
-            "final_zero_request_leases": True,
-            "final_zero_request_waiters": True,
-            "p95_async_request_wakeup_seconds": 0.0,
-        },
-        "iterations": [iteration],
-    }
-
-
-def test_pipeline_metrics_fails_when_downstream_traces_are_missing() -> None:
-    benchmark = _load_benchmark_module()
-    traces = [
-        {
-            "column": "heavy_0",
-            "row_group": 0,
-            "row_index": 0,
-            "dispatched_at": 1.0,
-            "completed_at": 2.0,
-        }
-    ]
-
-    metrics = benchmark._pipeline_metrics(
-        traces,
-        [],
-        upstream_cols=["heavy_0"],
-        downstream_cols=["fast_0"],
-        submission_capacity=1,
-        llm_wait_capacity=1,
-        row_group_concurrency=1,
-        capacity_plan=_capacity_plan(),
-        expected_task_count=1,
-    )
-
-    assert metrics["upstream_task_count"] == 1
-    assert metrics["downstream_task_count"] == 0
-    assert metrics["downstream_ready_gap_count"] == 0
-    assert not metrics["validation"]["expected_downstream_task_count"]
-    assert not metrics["validation"]["expected_downstream_ready_gap_count"]
-    assert not metrics["validation_passed"]
-
-
-def test_benchmark_validation_rejects_early_queue_exit() -> None:
-    benchmark = _load_benchmark_module()
-    artifact = _valid_artifact(
-        {
-            "accepted_task_count": 2,
-            "selected_task_count": 1,
-            "per_layer_observed_maxima": {
-                "task_leases_by_resource": {},
-                "request_in_flight_by_resource": {},
-            },
-        }
-    )
-
-    with pytest.raises(RuntimeError, match="queue drained early"):
-        benchmark._validate_artifact(artifact)
-
-
-def test_benchmark_validation_rejects_cap_violations() -> None:
-    benchmark = _load_benchmark_module()
-    artifact = _valid_artifact(
-        {
-            "accepted_task_count": 2,
-            "selected_task_count": 2,
-            "per_layer_observed_maxima": {
-                "task_leases_by_resource": {"submission": 3},
-                "request_in_flight_by_resource": {"mock": 2},
-            },
-        }
-    )
-
-    with pytest.raises(RuntimeError, match="exceeded submission task cap"):
-        benchmark._validate_artifact(artifact)
-
-
-def test_scheduler_utilization_metrics_record_idle_and_starved_capacity() -> None:
-    benchmark = _load_benchmark_module()
-    events = [
-        _scheduler_event(
-            "ready_enqueued",
-            timestamp=0.0,
-            sequence=1,
-            task_id="task-a",
-            leased_resources={},
-            resource_request={"submission": 1},
-        ),
-        _scheduler_event(
-            "task_lease_acquired",
-            timestamp=1.0,
-            sequence=2,
-            task_id="task-a",
-            leased_resources={"submission": 1},
-            resource_request={"submission": 1},
-        ),
-        _scheduler_event(
-            "task_lease_released",
-            timestamp=3.0,
-            sequence=3,
-            task_id="task-a",
-            leased_resources={},
-            resource_request={"submission": 1},
-        ),
-    ]
-
-    metrics = benchmark._scheduler_utilization_metrics(events)["submission"]
-
-    assert metrics["capacity"] == 2
-    assert metrics["busy_capacity_seconds"] == pytest.approx(2.0)
-    assert metrics["idle_capacity_seconds"] == pytest.approx(4.0)
-    assert metrics["starved_idle_seconds"] == pytest.approx(2.0)
-    assert metrics["dependency_horizon_idle_seconds"] == pytest.approx(2.0)
-    assert metrics["frontier_dependency_horizon_idle_ratio"] == pytest.approx(1 / 3)
-    assert metrics["utilization_ratio"] == pytest.approx(1 / 3)
-    assert metrics["scheduler_queue_age_max_seconds"] == pytest.approx(1.0)
-    assert metrics["ready_to_dispatch_gap_max_seconds"] == pytest.approx(1.0)
-    assert metrics["ready_to_dispatch_sample_count"] == 1
-
-
-def test_request_utilization_metrics_record_waiter_starved_idle() -> None:
-    benchmark = _load_benchmark_module()
-    events = [
-        _request_event("request_wait_started", timestamp=0.0, sequence=1, in_flight=0, waiters=1),
-        _request_event("request_wait_completed", timestamp=1.0, sequence=2, in_flight=1, waiters=0),
-        _request_event("request_lease_released", timestamp=3.0, sequence=3, in_flight=0, waiters=0),
-    ]
-
-    metrics = benchmark._request_utilization_metrics(events)["mock-resource"]
-
-    assert metrics["capacity"] == 2
-    assert metrics["busy_capacity_seconds"] == pytest.approx(2.0)
-    assert metrics["idle_capacity_seconds"] == pytest.approx(4.0)
-    assert metrics["starved_idle_seconds"] == pytest.approx(2.0)
-    assert metrics["dependency_horizon_idle_seconds"] == pytest.approx(2.0)
-    assert metrics["scheduler_queue_age_max_seconds"] == pytest.approx(1.0)
-    assert metrics["ready_to_dispatch_gap_max_seconds"] == pytest.approx(1.0)
-
-
-def test_benchmark_validation_requires_scheduler_utilization_metrics() -> None:
-    benchmark = _load_benchmark_module()
-    artifact = _valid_artifact(
-        {
-            "accepted_task_count": 1,
-            "selected_task_count": 1,
-            "per_layer_observed_maxima": {
-                "task_leases_by_resource": {},
-                "request_in_flight_by_resource": {},
-            },
-            "utilization_metrics": {"scheduler_resources": {}, "request_resources": {}},
-        }
-    )
-
-    with pytest.raises(RuntimeError, match="did not record scheduler utilization metrics"):
-        benchmark._validate_artifact(artifact)
-
-
-def test_idle_regression_guardrails_pass_for_expected_summary() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert all(check.passed for check in checks if check.severity == "error")
-
-
-def test_idle_regression_detects_validation_failure() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    summary["cases"]["row-scale/rows-64"]["validation_passed"] = False
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(not check.passed and check.name == "row-scale/rows-64 validation" for check in checks)
-
-
-def test_idle_regression_detects_baseline_utilization_drop() -> None:
-    regression = _load_idle_regression_module()
-    baseline = _idle_regression_summary(utilization=0.80)
-    current = _idle_regression_summary(utilization=0.70)
-
-    checks = regression.evaluate_idle_regression_summary(current, baseline_summary=baseline)
-
-    assert any(
-        not check.passed and check.category == "baseline" and check.name.endswith("utilization regression")
-        for check in checks
-    )
-
-
-def test_idle_regression_detects_bad_idle_partition() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    summary["cases"]["row-scale/rows-64"]["llm_wait_frontier_dependency_horizon_idle_ratio"] = 0.99
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(not check.passed and check.name == "row-scale/rows-64 idle partition" for check in checks)
-
-
-def test_idle_regression_skip_run_does_not_generate_missing_artifacts(tmp_path: Path) -> None:
-    report = _load_idle_report_module()
-    case = report.IdleBenchmarkCase(
-        name="missing",
-        sweep="reuse",
-        record_count=1,
-        buffer_size=1,
-        row_group_concurrency=1,
-        task_admission_capacity=1,
-        fanout_width=1,
-        upstream_latency_seconds=0.0,
-        downstream_latency_seconds=0.0,
-    )
-
-    with pytest.raises(FileNotFoundError, match="Cannot reuse benchmark artifact"):
-        report._run_or_load_case(case, tmp_path, skip_run=True)
-
-
-def test_idle_benchmark_defaults_write_to_scratch() -> None:
-    report = _load_idle_report_module()
-    regression = _load_idle_regression_module()
-
-    assert report.DEFAULT_ARTIFACT_DIR == Path(".scratch/async-scheduling-idle-analysis/artifacts")
-    assert report.DEFAULT_REPORT_PATH == Path(
-        ".scratch/async-scheduling-idle-analysis/async-scheduling-idle-analysis.html"
-    )
-    assert regression.DEFAULT_ARTIFACT_DIR == Path(".scratch/async-scheduling-idle-regression/artifacts")
-    assert regression.DEFAULT_REPORT_PATH == Path(
-        ".scratch/async-scheduling-idle-regression/async-scheduling-idle-regression.html"
-    )
-
-
-def test_idle_report_links_artifacts_relative_to_report() -> None:
-    report = _load_idle_report_module()
-
-    assert (
-        report._relative_href(
-            Path(".scratch/async-scheduling-idle-analysis/async-scheduling-idle-analysis.html"),
-            Path(".scratch/async-scheduling-idle-analysis/artifacts/case.json"),
-        )
-        == "artifacts/case.json"
-    )
-    assert report._relative_href(Path("reports/idle.html"), Path("artifacts/case.json")) == "../artifacts/case.json"
-
-
-def test_idle_regression_requires_adaptation_controls() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    del summary["cases"]["adaptations/adaptive-row-groups-fixed-high"]
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(
-        not check.passed and check.name == "required case adaptations/adaptive-row-groups-fixed-high"
-        for check in checks
-    )
-
-
-def test_idle_regression_detects_bad_adaptive_row_group_response() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    adaptive = summary["cases"]["adaptations/adaptive-row-groups-adaptive"]
-    adaptive["llm_wait_utilization_ratio"] = 0.45
-    adaptive["llm_wait_idle_ratio"] = 0.55
-    adaptive["llm_wait_starved_idle_ratio"] = 0.10
-    adaptive["llm_wait_frontier_dependency_horizon_idle_ratio"] = 0.45
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(not check.passed and check.name == "adaptive row-group utilization response" for check in checks)
-    assert any(not check.passed and check.name == "adaptive row-group fixed-high isolation" for check in checks)
-
-
-def test_idle_regression_detects_request_pressure_control_not_pressured() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    control = summary["cases"]["adaptations/request-pressure-control"]
-    control["first_model_dispatch_column"] = "z_open"
-    control["request_wait_seconds_while_task_leased_mean"] = 0.0
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(not check.passed and check.name == "request-pressure control dispatch choice" for check in checks)
-    assert any(not check.passed and check.name == "request-pressure control leased wait present" for check in checks)
-
-
-def test_idle_regression_detects_request_pressure_advisory_regression() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    summary["cases"]["adaptations/request-pressure-advisory"]["request_wait_seconds_while_task_leased_mean"] = 0.05
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(not check.passed and check.name == "request-pressure advisory leased-wait response" for check in checks)
-
-
-def test_idle_regression_detects_combined_adaptive_request_pressure_regression() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    combined = summary["cases"]["adaptations/adaptive-request-pressure-combined"]
-    combined["row_group_admission_observed_max_target"] = 1
-    combined["request_pressure_advisory_skip_count"] = 0
-    combined["request_idle_ratio"] = 0.70
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(not check.passed and check.name == "combined adaptive/request target grew" for check in checks)
-    assert any(
-        not check.passed and check.name == "combined adaptive/request advisory skipped pressured work"
-        for check in checks
-    )
-    assert any(not check.passed and check.name == "combined adaptive/request request-idle response" for check in checks)
-
-
-def test_idle_regression_detects_request_cleanup_failure() -> None:
-    regression = _load_idle_regression_module()
-    summary = _idle_regression_summary()
-    summary["cases"]["adaptations/request-pressure-advisory"]["final_zero_request_waiters"] = False
-
-    checks = regression.evaluate_idle_regression_summary(summary)
-
-    assert any(
-        not check.passed and check.name == "adaptations/request-pressure-advisory final_zero_request_waiters"
-        for check in checks
-    )
-
-
-def test_benchmark_validation_uses_scenario_task_resource_limits() -> None:
-    benchmark = _load_benchmark_module()
-    artifact = _valid_artifact(
-        {
-            "accepted_task_count": 2,
-            "selected_task_count": 2,
-            "capacity_plan": SimpleNamespace(
-                configured=SimpleNamespace(
-                    task_resource_limits=SimpleNamespace(
-                        value={"submission": 2, "llm_wait": 2, "local": 2},
-                    )
-                )
-            ),
-            "final_request_snapshot": {
-                "zero_active_request_leases": True,
-                "zero_request_waiters": True,
-                "domains": {
-                    "pressured": SimpleNamespace(effective_max=1, current_limit=1),
-                },
-            },
-            "per_layer_observed_maxima": {
-                "task_leases_by_resource": {"submission": 2, "llm_wait": 2},
-                "request_in_flight_by_resource": {"pressured": 1},
-            },
-        }
-    )
-
-    benchmark._validate_artifact(artifact)
-
-
-def _scheduler_event(
-    event_kind: str,
-    *,
-    timestamp: float,
-    sequence: int,
-    task_id: str,
-    leased_resources: dict[str, int],
-    resource_request: dict[str, int],
-) -> SimpleNamespace:
-    return SimpleNamespace(
-        event_kind=event_kind,
-        captured_at_monotonic=timestamp,
-        sequence=sequence,
-        task_id=task_id,
-        diagnostics={"resource_request": resource_request},
-        snapshot=SimpleNamespace(
-            resource_limits={"submission": 2},
-            leased_resources=leased_resources,
-        ),
-    )
-
-
-def _idle_regression_summary(*, utilization: float = 0.80) -> dict[str, Any]:
-    cases = {
-        "row-scale/rows-64": _idle_regression_case(utilization=utilization, record_count=64, generation_count=512),
-        "row-scale/rows-256": _idle_regression_case(
-            utilization=utilization,
-            record_count=256,
-            generation_count=2048,
-        ),
-        "row-group-concurrency/row-groups-1": _idle_regression_case(
-            utilization=0.50,
-            idle=0.50,
-            starved=0.20,
-            row_group_concurrency=1,
-            generation_count=1024,
-        ),
-        "row-group-concurrency/row-groups-4": _idle_regression_case(
-            utilization=utilization,
-            idle=1.0 - utilization,
-            starved=0.10,
-            row_group_concurrency=4,
-            generation_count=1024,
-        ),
-        "buffer-size/buffer-1": _idle_regression_case(
-            utilization=utilization,
-            idle=1.0 - utilization,
-            starved=0.10,
-            buffer_size=1,
-            generation_count=4096,
-        ),
-        "stress-shape/narrow-frontier-high-cap": _idle_regression_case(
-            utilization=0.30,
-            idle=0.70,
-            starved=0.20,
-            record_count=1024,
-            generation_count=2048,
-        ),
-        "stress-shape/wide-frontier-high-cap": _idle_regression_case(
-            utilization=utilization,
-            idle=1.0 - utilization,
-            starved=0.10,
-            record_count=1024,
-            generation_count=8192,
-        ),
-        "custom-model-weight/weight-model-capacity": _idle_regression_case(
-            utilization=utilization,
-            idle=1.0 - utilization,
-            starved=0.10,
-            generation_count=4096,
-        ),
-        "adaptations/adaptive-row-groups-fixed-low": _idle_regression_case(
-            utilization=0.40,
-            idle=0.60,
-            starved=0.10,
-            record_count=512,
-            row_group_concurrency=1,
-            generation_count=1024,
-        ),
-        "adaptations/adaptive-row-groups-adaptive": _idle_regression_case(
-            utilization=0.75,
-            idle=0.25,
-            starved=0.08,
-            record_count=512,
-            row_group_concurrency=8,
-            generation_count=1024,
-            row_group_admission_observed_max_target=4,
-        ),
-        "adaptations/adaptive-row-groups-fixed-high": _idle_regression_case(
-            utilization=0.78,
-            idle=0.22,
-            starved=0.08,
-            record_count=512,
-            row_group_concurrency=8,
-            generation_count=1024,
-        ),
-        "adaptations/request-pressure-control": _idle_regression_case(
-            utilization=0.70,
-            idle=0.30,
-            starved=0.10,
-            generation_count=64,
-            request_utilization=0.50,
-            request_idle=0.50,
-            request_starved=0.30,
-            request_wait_seconds_while_task_leased_mean=0.05,
-            first_model_dispatch_column="a_pressured",
-        ),
-        "adaptations/request-pressure-advisory": _idle_regression_case(
-            utilization=0.70,
-            idle=0.30,
-            starved=0.10,
-            generation_count=64,
-            request_utilization=0.60,
-            request_idle=0.40,
-            request_starved=0.20,
-            request_wait_seconds_while_task_leased_mean=0.0,
-            request_pressure_advisory_enabled=True,
-            request_pressure_advisory_skip_count=4,
-            first_model_dispatch_column="z_open",
-        ),
-        "adaptations/adaptive-request-pressure-control": _idle_regression_case(
-            utilization=0.65,
-            idle=0.35,
-            starved=0.12,
-            record_count=512,
-            row_group_concurrency=8,
-            generation_count=1024,
-            request_utilization=0.50,
-            request_idle=0.50,
-            request_starved=0.30,
-            request_wait_seconds_while_task_leased_mean=0.20,
-            first_model_dispatch_column="a_pressured",
-            row_group_admission_observed_max_target=4,
-        ),
-        "adaptations/adaptive-request-pressure-combined": _idle_regression_case(
-            utilization=0.70,
-            idle=0.30,
-            starved=0.10,
-            record_count=512,
-            row_group_concurrency=8,
-            generation_count=1024,
-            request_utilization=0.65,
-            request_idle=0.35,
-            request_starved=0.20,
-            request_wait_seconds_while_task_leased_mean=0.05,
-            request_pressure_advisory_enabled=True,
-            request_pressure_advisory_skip_count=8,
-            first_model_dispatch_column="z_open",
-            row_group_admission_observed_max_target=4,
-        ),
-    }
-    for index in range(3):
-        cases[f"filler/filler-{index}"] = _idle_regression_case(
-            utilization=utilization,
-            idle=1.0 - utilization,
-            starved=0.10,
-        )
-    return {
-        "suite_id": "async-scheduling-idle-regression",
-        "suite_version": "1.1",
-        "mode": "quick",
-        "largest_generation_count": 8192,
-        "cases": cases,
-    }
-
-
-def _idle_regression_case(
-    *,
-    utilization: float,
-    idle: float | None = None,
-    starved: float = 0.10,
-    record_count: int = 512,
-    generation_count: int = 1024,
-    row_group_concurrency: int = 4,
-    buffer_size: int = 64,
-    request_utilization: float = 0.0,
-    request_idle: float = 0.0,
-    request_starved: float = 0.0,
-    request_wait_seconds_while_task_leased_mean: float = 0.0,
-    request_pressure_advisory_enabled: bool = False,
-    request_pressure_advisory_skip_count: int = 0,
-    first_model_dispatch_column: str | None = None,
-    row_group_admission_observed_max_target: int | None = None,
-) -> dict[str, Any]:
-    resolved_idle = 1.0 - utilization if idle is None else idle
-    return {
-        "case": {
-            "name": "case",
-            "sweep": "sweep",
-            "record_count": record_count,
-            "buffer_size": buffer_size,
-            "row_group_concurrency": row_group_concurrency,
-            "task_admission_capacity": 8,
-            "fanout_width": 1,
-            "upstream_latency_seconds": 0.003,
-            "downstream_latency_seconds": 0.0003,
-            "scenario": "real-pipeline-overlap",
-            "request_latency_seconds": 0.0,
-            "model_stage_weight": 0,
-            "adaptive_row_group_admission": False,
-            "request_pressure_advisory": False,
-            "iterations": 1,
-            "warmups": 0,
-        },
-        "generation_count": generation_count,
-        "llm_wait_utilization_ratio": utilization,
-        "llm_wait_idle_ratio": resolved_idle,
-        "llm_wait_starved_idle_ratio": starved,
-        "llm_wait_frontier_dependency_horizon_idle_ratio": max(0.0, resolved_idle - starved),
-        "llm_wait_scheduler_queue_age_p95_seconds": 0.001,
-        "request_utilization_ratio": request_utilization,
-        "request_idle_ratio": request_idle,
-        "request_starved_idle_ratio": request_starved,
-        "request_frontier_dependency_horizon_idle_ratio": max(0.0, request_idle - request_starved),
-        "request_wait_seconds_while_task_leased_mean": request_wait_seconds_while_task_leased_mean,
-        "request_wait_seconds_while_task_leased_max": request_wait_seconds_while_task_leased_mean,
-        "request_pressure_advisory_enabled": request_pressure_advisory_enabled,
-        "request_pressure_advisory_skip_count": request_pressure_advisory_skip_count,
-        "first_model_dispatch_column": first_model_dispatch_column,
-        "row_group_admission_observed_max_target": row_group_admission_observed_max_target,
-        "throughput_generations_per_second": 100.0,
-        "validation_passed": True,
-        "final_zero_task_leases": True,
-        "final_zero_request_leases": True,
-        "final_zero_request_waiters": True,
-    }
-
-
-def _request_event(
-    event_kind: str, *, timestamp: float, sequence: int, in_flight: int, waiters: int
-) -> SimpleNamespace:
-    return SimpleNamespace(
-        event_kind=event_kind,
-        captured_at_monotonic=timestamp,
-        sequence=sequence,
-        request_attempt_id="request-a",
-        pressure_snapshot=SimpleNamespace(
-            resource="mock-resource",
-            effective_max=2,
-            in_flight_count=in_flight,
-            waiters=waiters,
-        ),
-    )
diff --git a/plans/645/module-ownership.md b/plans/645/module-ownership.md
index affa88d0a..125caac01 100644
--- a/plans/645/module-ownership.md
+++ b/plans/645/module-ownership.md
@@ -213,4 +213,4 @@ Tests mirror target module ownership:
 | capacity diagnostics | `packages/data-designer-engine/tests/engine/test_capacity.py` |
 | runtime observability | `packages/data-designer-engine/tests/engine/test_observability.py` |
 
-Test fakes live under tests near their consumers. Benchmark fakes and reusable scenarios live under `scripts/benchmarks/async_scheduling/`, with `scripts/benchmarks/benchmark_async_scheduling.py` as the runnable entrypoint. Production `data_designer.engine.testing` helpers are not part of the target architecture.
+Test fakes live under tests near their consumers. Benchmark fakes and reusable scenarios used for branch validation are scratch artifacts, not committed target architecture. Production `data_designer.engine.testing` helpers are not part of the target architecture.
diff --git a/scripts/benchmarks/benchmark_async_scheduling.py b/scripts/benchmarks/benchmark_async_scheduling.py
deleted file mode 100644
index 25f265395..000000000
--- a/scripts/benchmarks/benchmark_async_scheduling.py
+++ /dev/null
@@ -1,1800 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-"""Deterministic async scheduling benchmark smoke harness."""
-
-from __future__ import annotations
-
-import argparse
-import asyncio
-import csv
-import hashlib
-import json
-import platform
-import statistics
-import subprocess
-import sys
-import time
-import uuid
-from collections.abc import Mapping
-from dataclasses import dataclass, fields, is_dataclass
-from enum import Enum
-from pathlib import Path
-from typing import Any
-
-import data_designer.lazy_heavy_imports as lazy
-from data_designer.config.column_configs import (
-    ExpressionColumnConfig,
-    GenerationStrategy,
-    LLMTextColumnConfig,
-    SamplerColumnConfig,
-)
-from data_designer.config.sampler_params import SamplerType
-from data_designer.config.scheduling import SchedulingMetadata
-from data_designer.engine.capacity import (
-    AsyncCapacityConfigured,
-    AsyncCapacityObservedMaxima,
-    AsyncCapacityPlan,
-    AsyncCapacityRuntimeSnapshot,
-    CapacityValue,
-    RequestAdmissionConfigSnapshot,
-    RowGroupAdmission,
-)
-from data_designer.engine.column_generators.generators.base import ColumnGenerator, FromScratchColumnGenerator
-from data_designer.engine.dataset_builders.async_scheduler import AsyncTaskScheduler
-from data_designer.engine.dataset_builders.scheduling.completion import CompletionTracker
-from data_designer.engine.dataset_builders.scheduling.queue import FairTaskQueue
-from data_designer.engine.dataset_builders.scheduling.resources import (
-    SchedulableTask,
-    SchedulerResourceRequest,
-    TaskGroupKey,
-    TaskGroupSpec,
-)
-from data_designer.engine.dataset_builders.scheduling.task_admission import TaskAdmissionConfig, TaskAdmissionController
-from data_designer.engine.dataset_builders.scheduling.task_model import Task
-from data_designer.engine.dataset_builders.utils.execution_graph import ExecutionGraph
-from data_designer.engine.dataset_builders.utils.row_group_buffer import RowGroupBufferManager
-from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
-from data_designer.engine.models.request_admission.controller import (
-    AdaptiveRequestAdmissionController,
-    RequestAdmissionLease,
-)
-from data_designer.engine.models.request_admission.outcomes import RequestReleaseOutcome
-from data_designer.engine.models.request_admission.resources import (
-    RequestAdmissionItem,
-    RequestDomain,
-    RequestEventContext,
-    RequestGroupSpec,
-    RequestResourceKey,
-)
-from data_designer.engine.models.resources import ProviderModelKey, ProviderModelStaticCap
-from data_designer.engine.observability import (
-    InMemoryAdmissionEventSink,
-    SchedulerAdmissionEvent,
-    runtime_correlation_provider,
-)
-
-ARTIFACT_SCHEMA_VERSION = "async-scheduling-benchmark-v1"
-HARNESS_VERSION = "1.1"
-ASYNC_WAKEUP_GATE_SECONDS = 0.025
-
-
-@dataclass(frozen=True)
-class BenchmarkInputs:
-    baseline_ref: str
-    candidate_ref: str
-    scenario: str
-    record_count: int
-    buffer_size: int
-    row_group_concurrency: int
-    task_admission_capacity: int
-    request_latency_seconds: float
-    upstream_latency_seconds: float
-    downstream_latency_seconds: float
-    fanout_width: int
-    model_stage_weight: int
-    adaptive_row_group_admission: bool
-    request_pressure_advisory: bool
-    warmups: int
-    iterations: int
-    seed: int
-    scenario_version: str
-    harness_version: str
-
-
-class _BenchmarkStorage:
-    dataset_name = "async-scheduling-benchmark"
-
-    def __init__(self) -> None:
-        self.write_count = 0
-
-    def get_file_paths(self) -> dict[str, str]:
-        return {}
-
-    def write_batch_to_parquet_file(self, **kwargs: object) -> str:
-        self.write_count += 1
-        return f"/tmp/async-scheduling-benchmark/partial-{self.write_count}.parquet"
-
-    def move_partial_result_to_final_file_path(self, batch_number: int) -> str:
-        return f"/tmp/async-scheduling-benchmark/final-{batch_number}.parquet"
-
-
-class _BenchmarkSeedGenerator(FromScratchColumnGenerator[ExpressionColumnConfig]):
-    @staticmethod
-    def get_generation_strategy() -> GenerationStrategy:
-        return GenerationStrategy.FULL_COLUMN
-
-    def generate(self, data: lazy.pd.DataFrame) -> lazy.pd.DataFrame:
-        return data
-
-    def generate_from_scratch(self, num_records: int) -> lazy.pd.DataFrame:
-        return lazy.pd.DataFrame({self.config.name: list(range(num_records))})
-
-
-class _BenchmarkTimedCellGenerator(ColumnGenerator[ExpressionColumnConfig]):
-    def __init__(
-        self,
-        *args: object,
-        delay_seconds: float,
-        model_stage: bool = False,
-        model_stage_weight: int = 1,
-        **kwargs: object,
-    ) -> None:
-        super().__init__(*args, **kwargs)
-        self._delay_seconds = delay_seconds
-        self._model_stage = model_stage
-        self._model_stage_weight = model_stage_weight
-
-    @staticmethod
-    def get_generation_strategy() -> GenerationStrategy:
-        return GenerationStrategy.CELL_BY_CELL
-
-    def get_scheduling_metadata(self) -> SchedulingMetadata:
-        if self._model_stage:
-            return SchedulingMetadata.custom_model(
-                "benchmark-provider",
-                self.config.name,
-                "chat",
-                weight=self._model_stage_weight,
-            )
-        return super().get_scheduling_metadata()
-
-    def generate(self, data: dict) -> dict:
-        data[self.config.name] = f"{self.config.name}_{data.get('seed', '?')}"
-        return data
-
-    async def agenerate(self, data: dict) -> dict:
-        if self._delay_seconds:
-            await asyncio.sleep(self._delay_seconds)
-        return self.generate(data)
-
-
-class _BenchmarkRequestPressureCellGenerator(ColumnGenerator[ExpressionColumnConfig]):
-    def __init__(
-        self,
-        *args: object,
-        request_admission: AdaptiveRequestAdmissionController,
-        provider_name: str,
-        model_id: str,
-        delay_seconds: float,
-        **kwargs: object,
-    ) -> None:
-        super().__init__(*args, **kwargs)
-        self._request_admission = request_admission
-        self._resource = RequestResourceKey(provider_name, model_id, RequestDomain.CHAT)
-        self._delay_seconds = delay_seconds
-
-    @staticmethod
-    def get_generation_strategy() -> GenerationStrategy:
-        return GenerationStrategy.CELL_BY_CELL
-
-    def get_scheduling_metadata(self) -> SchedulingMetadata:
-        return SchedulingMetadata.model(
-            self._resource.provider_name,
-            self._resource.model_id,
-            self._resource.domain.value,
-            weight=1,
-        )
-
-    def generate(self, data: dict) -> dict:
-        data[self.config.name] = f"{self.config.name}_{data.get('seed', '?')}"
-        return data
-
-    async def agenerate(self, data: dict) -> dict:
-        item = RequestAdmissionItem(
-            self._resource,
-            RequestGroupSpec(self._resource),
-            queue_wait_timeout_seconds=30.0,
-            event_context=RequestEventContext(
-                captured_correlation=runtime_correlation_provider.current(),
-                request_attempt_id=f"benchmark-request-{uuid.uuid4().hex}",
-            ),
-        )
-        lease = await self._request_admission.acquire_async(item)
-        try:
-            if self._delay_seconds:
-                await asyncio.sleep(self._delay_seconds)
-            return self.generate(data)
-        finally:
-            self._request_admission.release(lease, RequestReleaseOutcome(kind="success"))
-
-
-def main() -> None:
-    args = _parse_args()
-    output_dir = Path(args.output_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    inputs = BenchmarkInputs(
-        baseline_ref=args.baseline_ref,
-        candidate_ref=args.candidate_ref,
-        scenario=args.scenario,
-        record_count=args.record_count,
-        buffer_size=args.buffer_size,
-        row_group_concurrency=args.row_group_concurrency,
-        task_admission_capacity=args.task_admission_capacity,
-        request_latency_seconds=args.request_latency_seconds,
-        upstream_latency_seconds=args.upstream_latency_seconds,
-        downstream_latency_seconds=args.downstream_latency_seconds,
-        fanout_width=args.fanout_width,
-        model_stage_weight=args.model_stage_weight or max(1, args.task_admission_capacity // 2),
-        adaptive_row_group_admission=args.adaptive_row_group_admission,
-        request_pressure_advisory=args.request_pressure_advisory,
-        warmups=args.warmups,
-        iterations=args.iterations,
-        seed=args.seed,
-        scenario_version=args.scenario_version,
-        harness_version=args.harness_version,
-    )
-
-    for _ in range(args.warmups):
-        _run_iteration(inputs, measured=False)
-
-    iterations = [_run_iteration(inputs, measured=True) for _ in range(args.iterations)]
-    artifact = _artifact(inputs, iterations)
-    _validate_artifact(artifact)
-    json_path = output_dir / "async_scheduling_benchmark.json"
-    csv_path = output_dir / "async_scheduling_benchmark.csv"
-    md_path = output_dir / "async_scheduling_benchmark.md"
-    json_path.write_text(json.dumps(_to_jsonable(artifact), indent=2, sort_keys=True) + "\n", encoding="utf-8")
-    _write_csv(csv_path, artifact)
-    _write_markdown(md_path, artifact)
-    print(f"Wrote {json_path}")
-    print(f"Wrote {csv_path}")
-    print(f"Wrote {md_path}")
-
-
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--baseline-ref", default="origin/main")
-    parser.add_argument("--candidate-ref", default="HEAD")
-    parser.add_argument("--scenario", default="queue-admission-smoke")
-    parser.add_argument("--record-count", type=int, default=128)
-    parser.add_argument("--buffer-size", type=int, default=64)
-    parser.add_argument("--row-group-concurrency", type=int, default=2)
-    parser.add_argument("--task-admission-capacity", type=int, default=8)
-    parser.add_argument("--request-latency-seconds", type=float, default=0.0)
-    parser.add_argument("--upstream-latency-seconds", type=float, default=0.01)
-    parser.add_argument("--downstream-latency-seconds", type=float, default=0.0)
-    parser.add_argument("--fanout-width", type=int, default=3)
-    parser.add_argument(
-        "--model-stage-weight",
-        type=int,
-        default=0,
-        help="Synthetic custom-model scheduling weight. Defaults to the modeled llm_wait capacity.",
-    )
-    parser.add_argument("--adaptive-row-group-admission", action="store_true")
-    parser.add_argument("--request-pressure-advisory", action="store_true")
-    parser.add_argument("--warmups", type=int, default=1)
-    parser.add_argument("--iterations", type=int, default=5)
-    parser.add_argument("--output-dir", required=True)
-    parser.add_argument("--seed", type=int, default=645)
-    parser.add_argument("--scenario-version", default="1")
-    parser.add_argument("--harness-version", default=HARNESS_VERSION)
-    return parser.parse_args()
-
-
-def _run_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
-    if inputs.scenario == "adaptive-request-pressure":
-        return _run_adaptive_request_pressure_iteration(inputs, measured=measured)
-    if inputs.scenario == "request-pressure-advisory":
-        return _run_request_pressure_iteration(inputs, measured=measured)
-    if inputs.scenario == "real-pipeline-overlap":
-        return _run_real_pipeline_iteration(inputs, measured=measured)
-    return _run_queue_admission_iteration(inputs, measured=measured)
-
-
-def _run_queue_admission_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
-    sink = InMemoryAdmissionEventSink()
-    task_config = TaskAdmissionConfig(
-        submission_capacity=inputs.task_admission_capacity,
-        resource_limits={
-            "llm_wait": max(1, inputs.task_admission_capacity // 2),
-            "local": inputs.task_admission_capacity,
-        },
-    )
-    task_controller = TaskAdmissionController(task_config)
-    queue = FairTaskQueue()
-    request_config = RequestAdmissionConfig(increase_after_successes=1)
-    request_controller = AdaptiveRequestAdmissionController(request_config, event_sink=sink)
-    request_controller.register(
-        provider_name="mock-provider",
-        model_id="mock-model",
-        alias="mock-alias",
-        max_parallel_requests=max(1, inputs.task_admission_capacity // 2),
-    )
-
-    groups = (
-        TaskGroupSpec(
-            TaskGroupKey("model", ("model", "mock-provider", "mock-model", "chat", "root")),
-            weight=2.0,
-            admitted_limit=4,
-        ),
-        TaskGroupSpec(TaskGroupKey("local", ("local", "default", "downstream")), weight=1.0),
-    )
-    tasks = _schedulable_tasks(inputs.record_count, groups)
-    accepted = queue.enqueue(tasks)
-    selected: list[str] = []
-    request_resource = RequestResourceKey("mock-provider", "mock-model", RequestDomain.CHAT)
-    request_item = RequestAdmissionItem(request_resource, RequestGroupSpec(request_resource))
-    started = time.monotonic()
-    sequence = 0
-    accepted_by_id = {task.task_id: task for task in tasks if task.task_id in accepted}
-    for task_id in accepted:
-        sequence += 1
-        task = accepted_by_id[task_id]
-        sink.emit_scheduler_event(
-            SchedulerAdmissionEvent.capture(
-                "ready_enqueued",
-                sequence=sequence,
-                task_id=task.task_id,
-                snapshot=task_controller.view(),
-                diagnostics={"resource_request": dict(task.resource_request.amounts)},
-            )
-        )
-
-    while queue.has_queued_tasks:
-        selection = queue.select_next(lambda item, view: task_controller.is_eligible(item, view))
-        if selection is None:
-            break
-        sequence += 1
-        sink.emit_scheduler_event(
-            SchedulerAdmissionEvent.capture(
-                "selected",
-                sequence=sequence,
-                task_id=selection.item.task_id,
-                snapshot=task_controller.view(),
-                diagnostics={"resource_request": dict(selection.item.resource_request.amounts)},
-            )
-        )
-        decision = task_controller.try_acquire(selection.item, selection.queue_view)
-        if not hasattr(decision, "lease_id"):
-            break
-        lease = decision
-        committed = queue.commit(selection)
-        if committed is None:
-            task_controller.release(lease)
-            continue
-        sequence += 1
-        sink.emit_scheduler_event(
-            SchedulerAdmissionEvent.capture(
-                "task_lease_acquired",
-                sequence=sequence,
-                task_id=committed.task_id,
-                task_lease_id=lease.lease_id,
-                snapshot=task_controller.view(),
-                diagnostics={"resource_request": dict(committed.resource_request.amounts)},
-            )
-        )
-        request_lease = request_controller.acquire_sync(request_item)
-        if inputs.request_latency_seconds:
-            time.sleep(inputs.request_latency_seconds)
-        request_controller.release(request_lease, RequestReleaseOutcome(kind="success"))
-        task_controller.release(lease)
-        selected.append(committed.task_id)
-
-    wall_time = time.monotonic() - started
-    async_request_wakeup_seconds = asyncio.run(_measure_async_request_wakeup_seconds())
-    task_snapshot = task_controller.view()
-    request_snapshots = request_controller.pressure.snapshots()
-    global_snapshots = request_controller.pressure.global_snapshots()
-    output_hash = hashlib.sha256("\n".join(selected).encode()).hexdigest()
-    max_task_leases = _max_task_leases_by_resource(sink.scheduler_events)
-    max_request_in_flight = _max_request_in_flight_by_resource(sink.request_events)
-    utilization_metrics = {
-        "scheduler_resources": _scheduler_utilization_metrics(sink.scheduler_events),
-        "request_resources": _request_utilization_metrics(sink.request_events),
-    }
-    timeline = [{"stream": "scheduler", **_event_payload(event)} for event in sink.scheduler_events] + [
-        {"stream": "request", **_event_payload(event)} for event in sink.request_events
-    ]
-    timeline.sort(key=lambda event: (event["captured_at_monotonic"], event["sequence"]))
-    return {
-        "measured": measured,
-        "wall_time_seconds": wall_time,
-        "async_request_wakeup_seconds": async_request_wakeup_seconds,
-        "timeline": timeline,
-        "utilization_metrics": utilization_metrics,
-        "final_task_snapshot": task_snapshot,
-        "final_request_snapshot": {
-            "domains": request_snapshots,
-            "provider_models": global_snapshots,
-            "zero_active_request_leases": all(
-                snapshot.active_lease_count == 0 for snapshot in request_snapshots.values()
-            ),
-            "zero_request_waiters": all(snapshot.waiters == 0 for snapshot in request_snapshots.values()),
-        },
-        "output_hashes": {"selected_task_ids": output_hash},
-        "per_layer_observed_maxima": {
-            "selected_tasks": len(selected),
-            "accepted_tasks": len(accepted),
-            "task_leases_by_resource": max_task_leases,
-            "request_in_flight_by_resource": max_request_in_flight,
-            "active_task_leases_at_end": sum(task_snapshot.leased_resources.values()),
-            "active_request_leases_at_end": sum(snapshot.active_lease_count for snapshot in request_snapshots.values()),
-        },
-        "accepted_task_count": len(accepted),
-        "selected_task_count": len(selected),
-    }
-
-
-def _run_real_pipeline_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
-    return asyncio.run(_run_real_pipeline_iteration_async(inputs, measured=measured))
-
-
-async def _run_real_pipeline_iteration_async(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
-    sink = InMemoryAdmissionEventSink()
-    fanout_width = max(1, inputs.fanout_width)
-    upstream_cols = [f"heavy_{index}" for index in range(fanout_width)]
-    downstream_cols = [f"fast_{index}" for index in range(fanout_width)]
-    configs = [
-        SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
-        *[LLMTextColumnConfig(name=col, prompt="{{ seed }}", model_alias="benchmark") for col in upstream_cols],
-        *[
-            LLMTextColumnConfig(name=downstream, prompt=f"{{{{ {upstream} }}}}", model_alias="benchmark")
-            for upstream, downstream in zip(upstream_cols, downstream_cols)
-        ],
-    ]
-    strategies: dict[str, GenerationStrategy] = {"seed": GenerationStrategy.FULL_COLUMN}
-    strategies.update({col: GenerationStrategy.CELL_BY_CELL for col in (*upstream_cols, *downstream_cols)})
-    provider = object()
-    generators: dict[str, ColumnGenerator] = {
-        "seed": _BenchmarkSeedGenerator(
-            config=ExpressionColumnConfig(name="seed", expr="{{ x }}"), resource_provider=provider
-        ),
-        **{
-            col: _BenchmarkTimedCellGenerator(
-                config=ExpressionColumnConfig(name=col, expr="{{ x }}"),
-                resource_provider=provider,
-                delay_seconds=inputs.upstream_latency_seconds,
-                model_stage=True,
-                model_stage_weight=inputs.model_stage_weight,
-            )
-            for col in upstream_cols
-        },
-        **{
-            col: _BenchmarkTimedCellGenerator(
-                config=ExpressionColumnConfig(name=col, expr="{{ x }}"),
-                resource_provider=provider,
-                delay_seconds=inputs.downstream_latency_seconds,
-                model_stage=True,
-                model_stage_weight=inputs.model_stage_weight,
-            )
-            for col in downstream_cols
-        },
-    }
-    row_groups = _row_groups(inputs.record_count, inputs.buffer_size)
-    graph = ExecutionGraph.create(configs, strategies)
-    tracker = CompletionTracker.with_graph(graph, row_groups)
-    storage = _BenchmarkStorage()
-    buffer_manager = RowGroupBufferManager(storage)
-    scheduler = AsyncTaskScheduler(
-        generators=generators,
-        graph=graph,
-        tracker=tracker,
-        row_groups=row_groups,
-        buffer_manager=buffer_manager,
-        max_concurrent_row_groups=inputs.row_group_concurrency,
-        max_submitted_tasks=inputs.task_admission_capacity,
-        max_model_task_admission=max(1, inputs.task_admission_capacity // 2),
-        scheduler_event_sink=sink,
-        trace=True,
-        num_records=inputs.record_count,
-        buffer_size=inputs.buffer_size,
-        adaptive_row_group_admission=inputs.adaptive_row_group_admission,
-    )
-
-    started = time.monotonic()
-    await scheduler.run()
-    wall_time = time.monotonic() - started
-    async_request_wakeup_seconds = await _measure_async_request_wakeup_seconds()
-    traces = [_trace_payload(trace) for trace in scheduler.traces]
-    pipeline_metrics = _pipeline_metrics(
-        traces,
-        sink.scheduler_events,
-        upstream_cols=upstream_cols,
-        downstream_cols=downstream_cols,
-        submission_capacity=inputs.task_admission_capacity,
-        llm_wait_capacity=max(1, inputs.task_admission_capacity // 2),
-        row_group_concurrency=inputs.row_group_concurrency,
-        capacity_plan=scheduler.capacity_plan(),
-        expected_task_count=inputs.record_count * fanout_width,
-    )
-    utilization_metrics = {
-        "scheduler_resources": _scheduler_utilization_metrics(sink.scheduler_events),
-        "request_resources": {},
-    }
-    timeline = [{"stream": "scheduler", **_event_payload(event)} for event in sink.scheduler_events]
-    timeline.sort(key=lambda event: (event["captured_at_monotonic"], event["sequence"]))
-    task_snapshot = scheduler.task_admission_snapshot()
-    return {
-        "measured": measured,
-        "wall_time_seconds": wall_time,
-        "async_request_wakeup_seconds": async_request_wakeup_seconds,
-        "timeline": timeline,
-        "traces": traces,
-        "pipeline_metrics": pipeline_metrics,
-        "utilization_metrics": utilization_metrics,
-        "capacity_plan": scheduler.capacity_plan(),
-        "final_task_snapshot": task_snapshot,
-        "final_request_snapshot": {
-            "domains": {},
-            "provider_models": {},
-            "zero_active_request_leases": True,
-            "zero_request_waiters": True,
-        },
-        "output_hashes": {
-            "completed_task_trace": hashlib.sha256(
-                "\n".join(f"{trace['column']}:{trace['row_group']}:{trace['row_index']}" for trace in traces).encode()
-            ).hexdigest()
-        },
-        "per_layer_observed_maxima": {
-            "selected_tasks": len(traces),
-            "active_task_leases_at_end": sum(task_snapshot.leased_resources.values()),
-            "active_request_leases_at_end": 0,
-            **pipeline_metrics["observed_maxima"],
-        },
-        "accepted_task_count": len(traces),
-        "selected_task_count": len(traces),
-    }
-
-
-def _run_request_pressure_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
-    return asyncio.run(
-        _run_request_pressure_iteration_async(
-            inputs,
-            measured=measured,
-            row_groups=[(0, inputs.record_count)],
-            max_concurrent_row_groups=1,
-            adaptive_row_group_admission=False,
-        )
-    )
-
-
-def _run_adaptive_request_pressure_iteration(inputs: BenchmarkInputs, *, measured: bool) -> dict[str, Any]:
-    return asyncio.run(
-        _run_request_pressure_iteration_async(
-            inputs,
-            measured=measured,
-            row_groups=_row_groups(inputs.record_count, inputs.buffer_size),
-            max_concurrent_row_groups=inputs.row_group_concurrency,
-            adaptive_row_group_admission=inputs.adaptive_row_group_admission,
-        )
-    )
-
-
-async def _run_request_pressure_iteration_async(
-    inputs: BenchmarkInputs,
-    *,
-    measured: bool,
-    row_groups: list[tuple[int, int]],
-    max_concurrent_row_groups: int,
-    adaptive_row_group_admission: bool,
-) -> dict[str, Any]:
-    sink = InMemoryAdmissionEventSink()
-    request_controller = AdaptiveRequestAdmissionController(
-        RequestAdmissionConfig(default_queue_wait_timeout_seconds=30.0),
-        event_sink=sink,
-    )
-    request_controller.register(
-        provider_name="aa-pressured-provider",
-        model_id="pressured-model",
-        alias="pressured",
-        max_parallel_requests=1,
-    )
-    request_controller.register(
-        provider_name="zz-open-provider",
-        model_id="open-model",
-        alias="open",
-        max_parallel_requests=1,
-    )
-    pressured_resource = RequestResourceKey("aa-pressured-provider", "pressured-model", RequestDomain.CHAT)
-    holder_item = RequestAdmissionItem(pressured_resource, RequestGroupSpec(pressured_resource))
-    holder_lease = request_controller.try_acquire(holder_item)
-    if not isinstance(holder_lease, RequestAdmissionLease):
-        raise RuntimeError(f"Expected pressure holder request lease, got {holder_lease.reason}")
-
-    async def release_holder() -> None:
-        await asyncio.sleep(max(inputs.request_latency_seconds, inputs.upstream_latency_seconds))
-        request_controller.release(holder_lease, RequestReleaseOutcome(kind="success"))
-
-    release_task = asyncio.create_task(release_holder())
-    configs = [
-        SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
-        LLMTextColumnConfig(name="a_pressured", prompt="{{ seed }}", model_alias="pressured"),
-        LLMTextColumnConfig(name="z_open", prompt="{{ seed }}", model_alias="open"),
-    ]
-    strategies = {
-        "seed": GenerationStrategy.FULL_COLUMN,
-        "a_pressured": GenerationStrategy.CELL_BY_CELL,
-        "z_open": GenerationStrategy.CELL_BY_CELL,
-    }
-    provider = object()
-    generators: dict[str, ColumnGenerator] = {
-        "seed": _BenchmarkSeedGenerator(
-            config=ExpressionColumnConfig(name="seed", expr="{{ x }}"), resource_provider=provider
-        ),
-        "a_pressured": _BenchmarkRequestPressureCellGenerator(
-            config=ExpressionColumnConfig(name="a_pressured", expr="{{ x }}"),
-            resource_provider=provider,
-            request_admission=request_controller,
-            provider_name="aa-pressured-provider",
-            model_id="pressured-model",
-            delay_seconds=inputs.downstream_latency_seconds,
-        ),
-        "z_open": _BenchmarkRequestPressureCellGenerator(
-            config=ExpressionColumnConfig(name="z_open", expr="{{ x }}"),
-            resource_provider=provider,
-            request_admission=request_controller,
-            provider_name="zz-open-provider",
-            model_id="open-model",
-            delay_seconds=inputs.downstream_latency_seconds,
-        ),
-    }
-    graph = ExecutionGraph.create(configs, strategies)
-    tracker = CompletionTracker.with_graph(graph, row_groups)
-    storage = _BenchmarkStorage()
-    buffer_manager = RowGroupBufferManager(storage)
-    scheduler = AsyncTaskScheduler(
-        generators=generators,
-        graph=graph,
-        tracker=tracker,
-        row_groups=row_groups,
-        buffer_manager=buffer_manager,
-        max_concurrent_row_groups=max_concurrent_row_groups,
-        max_submitted_tasks=max(1, inputs.task_admission_capacity),
-        max_model_task_admission=max(1, inputs.task_admission_capacity),
-        scheduler_event_sink=sink,
-        trace=True,
-        num_records=inputs.record_count,
-        buffer_size=inputs.buffer_size,
-        adaptive_row_group_admission=adaptive_row_group_admission,
-        request_pressure_provider=request_controller.pressure,
-        request_pressure_advisory=inputs.request_pressure_advisory,
-    )
-
-    started = time.monotonic()
-    try:
-        await scheduler.run()
-    finally:
-        await release_task
-    wall_time = time.monotonic() - started
-    async_request_wakeup_seconds = await _measure_async_request_wakeup_seconds()
-    traces = [_trace_payload(trace) for trace in scheduler.traces]
-    utilization_metrics = {
-        "scheduler_resources": _scheduler_utilization_metrics(sink.scheduler_events),
-        "request_resources": _request_utilization_metrics(sink.request_events),
-    }
-    task_snapshot = scheduler.task_admission_snapshot()
-    request_snapshots = request_controller.pressure.snapshots()
-    global_snapshots = request_controller.pressure.global_snapshots()
-    request_wait_while_leased = _request_wait_while_task_leased_seconds(sink.scheduler_events, sink.request_events)
-    timeline = [{"stream": "scheduler", **_event_payload(event)} for event in sink.scheduler_events] + [
-        {"stream": "request", **_event_payload(event)} for event in sink.request_events
-    ]
-    timeline.sort(key=lambda event: (event["captured_at_monotonic"], event["sequence"]))
-    return {
-        "measured": measured,
-        "wall_time_seconds": wall_time,
-        "async_request_wakeup_seconds": async_request_wakeup_seconds,
-        "timeline": timeline,
-        "traces": traces,
-        "request_pressure_metrics": {
-            "request_wait_seconds_while_task_leased": request_wait_while_leased,
-            "request_pressure_advisory_enabled": inputs.request_pressure_advisory,
-            "request_pressure_advisory_skip_count": sum(
-                1 for event in sink.scheduler_events if event.event_kind == "request_pressure_advisory_skipped"
-            ),
-            "first_model_dispatch_column": _first_model_dispatch_column(traces),
-        },
-        "utilization_metrics": utilization_metrics,
-        "capacity_plan": scheduler.capacity_plan(),
-        "final_task_snapshot": task_snapshot,
-        "final_request_snapshot": {
-            "domains": request_snapshots,
-            "provider_models": global_snapshots,
-            "zero_active_request_leases": all(
-                snapshot.active_lease_count == 0 for snapshot in request_snapshots.values()
-            ),
-            "zero_request_waiters": all(snapshot.waiters == 0 for snapshot in request_snapshots.values()),
-        },
-        "output_hashes": {
-            "completed_task_trace": hashlib.sha256(
-                "\n".join(f"{trace['column']}:{trace['row_group']}:{trace['row_index']}" for trace in traces).encode()
-            ).hexdigest()
-        },
-        "per_layer_observed_maxima": {
-            "selected_tasks": len(traces),
-            "accepted_tasks": len(traces),
-            "task_leases_by_resource": _max_task_leases_by_resource(sink.scheduler_events),
-            "request_in_flight_by_resource": _max_request_in_flight_by_resource(sink.request_events),
-            "active_task_leases_at_end": sum(task_snapshot.leased_resources.values()),
-            "active_request_leases_at_end": sum(snapshot.active_lease_count for snapshot in request_snapshots.values()),
-        },
-        "accepted_task_count": len(traces),
-        "selected_task_count": len(traces),
-    }
-
-
-def _schedulable_tasks(record_count: int, groups: tuple[TaskGroupSpec, TaskGroupSpec]) -> tuple[SchedulableTask, ...]:
-    tasks: list[SchedulableTask] = []
-    for index in range(record_count):
-        group = groups[index % len(groups)]
-        resource_request = (
-            SchedulerResourceRequest({"submission": 1, "llm_wait": 1})
-            if group.key.kind == "model"
-            else SchedulerResourceRequest({"submission": 1, "local": 1})
-        )
-        task = Task(
-            column=f"col_{index % 4}",
-            row_group=index // 16,
-            row_index=index % 16,
-            task_type="cell",
-        )
-        digest = hashlib.sha1(f"{task.column}:{task.row_group}:{task.row_index}:{task.task_type}".encode()).hexdigest()[
-            :16
-        ]
-        tasks.append(SchedulableTask(f"task-{digest}", task, group, resource_request))
-    return tuple(tasks)
-
-
-def _row_groups(record_count: int, buffer_size: int) -> list[tuple[int, int]]:
-    row_groups: list[tuple[int, int]] = []
-    remaining = record_count
-    row_group = 0
-    while remaining > 0:
-        size = min(buffer_size, remaining)
-        row_groups.append((row_group, size))
-        row_group += 1
-        remaining -= size
-    return row_groups
-
-
-def _trace_payload(trace: Any) -> dict[str, Any]:
-    return {
-        "column": trace.column,
-        "row_group": trace.row_group,
-        "row_index": trace.row_index,
-        "task_type": trace.task_type,
-        "dispatched_at": trace.dispatched_at,
-        "completed_at": trace.completed_at,
-        "status": trace.status,
-        "error": trace.error,
-    }
-
-
-def _first_model_dispatch_column(traces: list[dict[str, Any]]) -> str | None:
-    model_traces = [trace for trace in traces if trace["column"] in {"a_pressured", "z_open"}]
-    if not model_traces:
-        return None
-    return min(model_traces, key=lambda trace: trace["dispatched_at"])["column"]
-
-
-def _request_wait_while_task_leased_seconds(
-    scheduler_events: list[SchedulerAdmissionEvent],
-    request_events: list[Any],
-) -> float:
-    lease_starts_by_lease: dict[str, float] = {}
-    lease_intervals: dict[str, tuple[float, float]] = {}
-    for event in _ordered_events(scheduler_events):
-        lease_id = getattr(event, "task_lease_id", None)
-        if lease_id is None:
-            continue
-        if event.event_kind == "task_lease_acquired":
-            lease_starts_by_lease[lease_id] = float(event.captured_at_monotonic)
-        elif event.event_kind == "task_lease_released":
-            task_execution_id = getattr(event, "task_execution_id", None)
-            started = lease_starts_by_lease.pop(lease_id, None)
-            if started is not None and task_execution_id is not None:
-                lease_intervals[task_execution_id] = (started, float(event.captured_at_monotonic))
-
-    wait_starts: dict[tuple[str, str | None], float] = {}
-    total = 0.0
-    for event in _ordered_events(request_events):
-        correlation = getattr(event, "captured_correlation", None)
-        task_execution_id = getattr(correlation, "task_execution_id", None)
-        if task_execution_id is None:
-            continue
-        attempt_key = (task_execution_id, getattr(event, "request_attempt_id", None))
-        if event.event_kind == "request_wait_started":
-            wait_starts[attempt_key] = float(event.captured_at_monotonic)
-        elif event.event_kind in {
-            "request_wait_completed",
-            "request_wait_timeout",
-            "request_wait_cancelled",
-            "request_acquire_denied",
-        }:
-            wait_started = wait_starts.pop(attempt_key, None)
-            lease_interval = lease_intervals.get(task_execution_id)
-            if wait_started is None or lease_interval is None:
-                continue
-            wait_ended = float(event.captured_at_monotonic)
-            lease_started, lease_ended = lease_interval
-            total += max(0.0, min(wait_ended, lease_ended) - max(wait_started, lease_started))
-    return total
-
-
-def _pipeline_metrics(
-    traces: list[dict[str, Any]],
-    scheduler_events: list[SchedulerAdmissionEvent],
-    *,
-    upstream_cols: list[str],
-    downstream_cols: list[str],
-    submission_capacity: int,
-    llm_wait_capacity: int,
-    row_group_concurrency: int,
-    capacity_plan: AsyncCapacityPlan,
-    expected_task_count: int,
-) -> dict[str, Any]:
-    upstream = [trace for trace in traces if trace["column"] in upstream_cols]
-    downstream = [trace for trace in traces if trace["column"] in downstream_cols]
-    first_upstream_dispatch = min((trace["dispatched_at"] for trace in upstream), default=None)
-    last_upstream_dispatch = max((trace["dispatched_at"] for trace in upstream), default=None)
-    last_upstream_complete = max((trace["completed_at"] for trace in upstream), default=None)
-    first_downstream_dispatch = min((trace["dispatched_at"] for trace in downstream), default=None)
-    ready_gaps = _downstream_ready_gaps(upstream, downstream, upstream_cols, downstream_cols)
-    max_task_leases = _max_task_leases_by_resource(scheduler_events)
-    observed_row_groups = capacity_plan.observed_maxima.row_groups_in_flight
-    observed_queued = dict(capacity_plan.observed_maxima.queued_tasks_by_group)
-    has_overlap_points = (
-        first_downstream_dispatch is not None
-        and last_upstream_dispatch is not None
-        and last_upstream_complete is not None
-        and first_upstream_dispatch is not None
-    )
-    overlap_seconds = max(0.0, last_upstream_complete - first_downstream_dispatch) if has_overlap_points else 0.0
-    upstream_duration = max(0.0, last_upstream_complete - first_upstream_dispatch) if has_overlap_points else 0.0
-    validation = {
-        "expected_upstream_task_count": len(upstream) == expected_task_count,
-        "expected_downstream_task_count": len(downstream) == expected_task_count,
-        "expected_downstream_ready_gap_count": len(ready_gaps) == expected_task_count,
-        "submission_cap_respected": max_task_leases.get("submission", 0) <= submission_capacity,
-        "llm_wait_cap_respected": max_task_leases.get("llm_wait", 0) <= llm_wait_capacity,
-        "row_group_cap_respected": observed_row_groups <= row_group_concurrency,
-        "downstream_interleaved_before_all_upstream_dispatched": has_overlap_points
-        and first_downstream_dispatch < last_upstream_dispatch,
-        "downstream_interleaved_before_all_upstream_completed": has_overlap_points
-        and first_downstream_dispatch < last_upstream_complete,
-    }
-    return {
-        "upstream_columns": tuple(upstream_cols),
-        "downstream_columns": tuple(downstream_cols),
-        "expected_task_count": expected_task_count,
-        "upstream_task_count": len(upstream),
-        "downstream_task_count": len(downstream),
-        "downstream_ready_gap_count": len(ready_gaps),
-        "first_downstream_dispatch_delay_seconds": (
-            max(0.0, first_downstream_dispatch - first_upstream_dispatch) if has_overlap_points else 0.0
-        ),
-        "downstream_ready_gap_mean_seconds": statistics.fmean(ready_gaps) if ready_gaps else 0.0,
-        "downstream_ready_gap_p95_seconds": _percentile(ready_gaps, 0.95),
-        "downstream_ready_gap_max_seconds": max(ready_gaps) if ready_gaps else 0.0,
-        "upstream_downstream_overlap_seconds": overlap_seconds,
-        "upstream_downstream_overlap_ratio": _safe_ratio(overlap_seconds, upstream_duration),
-        "observed_maxima": {
-            "row_groups_in_flight": observed_row_groups,
-            "task_leases_by_resource": max_task_leases,
-            "queued_tasks_by_group": observed_queued,
-        },
-        "validation": validation,
-        "validation_passed": all(validation.values()),
-    }
-
-
-def _downstream_ready_gaps(
-    upstream: list[dict[str, Any]],
-    downstream: list[dict[str, Any]],
-    upstream_cols: list[str],
-    downstream_cols: list[str],
-) -> list[float]:
-    upstream_by_key = {(trace["column"], trace["row_group"], trace["row_index"]): trace for trace in upstream}
-    gaps: list[float] = []
-    for upstream_col, downstream_col in zip(upstream_cols, downstream_cols):
-        for downstream_trace in downstream:
-            if downstream_trace["column"] != downstream_col:
-                continue
-            upstream_trace = upstream_by_key.get(
-                (upstream_col, downstream_trace["row_group"], downstream_trace["row_index"])
-            )
-            if upstream_trace is None:
-                continue
-            gaps.append(max(0.0, downstream_trace["dispatched_at"] - upstream_trace["completed_at"]))
-    return gaps
-
-
-def _max_task_leases_by_resource(events: list[SchedulerAdmissionEvent]) -> dict[str, int]:
-    maxima: dict[str, int] = {}
-    for event in events:
-        snapshot = event.snapshot
-        leased = getattr(snapshot, "leased_resources", {})
-        for resource, count in leased.items():
-            maxima[str(resource)] = max(maxima.get(str(resource), 0), int(count))
-    return maxima
-
-
-def _max_request_in_flight_by_resource(events: list[Any]) -> dict[str, int]:
-    maxima: dict[str, int] = {}
-    for event in events:
-        snapshot = getattr(event, "pressure_snapshot", None)
-        if snapshot is None:
-            continue
-        resource = getattr(snapshot, "resource", None) or getattr(event, "request_resource_key", None)
-        if resource is None:
-            continue
-        count = int(getattr(snapshot, "in_flight_count", 0))
-        maxima[str(resource)] = max(maxima.get(str(resource), 0), count)
-    return maxima
-
-
-def _scheduler_utilization_metrics(events: list[Any]) -> dict[str, dict[str, float | int]]:
-    ordered = _ordered_events(events)
-    if len(ordered) < 2:
-        return {}
-
-    resource_limits: dict[str, int] = {}
-    active_by_resource: dict[str, int] = {}
-    queued_by_task: dict[str, dict[str, int]] = {}
-    queued_at: dict[str, float] = {}
-    queue_ages: dict[str, list[float]] = {}
-    totals = _empty_utilization_totals()
-
-    for index, event in enumerate(ordered):
-        now = float(event.captured_at_monotonic)
-        snapshot = getattr(event, "snapshot", None)
-        if snapshot is not None:
-            for resource, limit in _int_mapping(getattr(snapshot, "resource_limits", {})).items():
-                resource_limits[resource] = max(resource_limits.get(resource, 0), limit)
-            active_by_resource = _int_mapping(getattr(snapshot, "leased_resources", {}))
-
-        task_id = getattr(event, "task_id", None)
-        resource_request = _event_resource_request(event)
-        if event.event_kind == "ready_enqueued" and task_id is not None:
-            queued_by_task[task_id] = resource_request
-            queued_at[task_id] = now
-        elif event.event_kind == "task_lease_acquired" and task_id is not None:
-            task_resources = queued_by_task.pop(task_id, resource_request)
-            started = queued_at.pop(task_id, None)
-            if started is not None:
-                age = max(0.0, now - started)
-                for resource, amount in task_resources.items():
-                    if amount > 0:
-                        queue_ages.setdefault(resource, []).append(age)
-
-        if index == len(ordered) - 1:
-            continue
-        next_time = float(ordered[index + 1].captured_at_monotonic)
-        interval_seconds = max(0.0, next_time - now)
-        queued_demand = _queued_resource_demand(queued_by_task)
-        _record_utilization_interval(
-            totals,
-            resource_limits=resource_limits,
-            active_by_resource=active_by_resource,
-            queued_or_waiting_by_resource=queued_demand,
-            interval_seconds=interval_seconds,
-        )
-
-    return _finalize_utilization_metrics(totals, queue_ages)
-
-
-def _request_utilization_metrics(events: list[Any]) -> dict[str, dict[str, float | int]]:
-    ordered = [event for event in _ordered_events(events) if getattr(event, "pressure_snapshot", None) is not None]
-    if len(ordered) < 2:
-        return {}
-
-    capacities: dict[str, int] = {}
-    active_by_resource: dict[str, int] = {}
-    waiters_by_resource: dict[str, int] = {}
-    wait_started: dict[str, float] = {}
-    wait_ages: dict[str, list[float]] = {}
-    totals = _empty_utilization_totals()
-
-    for index, event in enumerate(ordered):
-        now = float(event.captured_at_monotonic)
-        snapshot = event.pressure_snapshot
-        resource = str(snapshot.resource)
-        capacity = max(1, int(getattr(snapshot, "effective_max", 1)))
-        capacities[resource] = max(capacities.get(resource, 0), capacity)
-        active_by_resource[resource] = int(getattr(snapshot, "in_flight_count", 0))
-        waiters_by_resource[resource] = int(getattr(snapshot, "waiters", 0))
-
-        attempt_id = getattr(event, "request_attempt_id", None)
-        if attempt_id is not None and event.event_kind == "request_wait_started":
-            wait_started[attempt_id] = now
-        elif attempt_id is not None and event.event_kind in {
-            "request_wait_completed",
-            "request_wait_timeout",
-            "request_wait_cancelled",
-            "request_acquire_denied",
-        }:
-            started = wait_started.pop(attempt_id, None)
-            if started is not None:
-                wait_ages.setdefault(resource, []).append(max(0.0, now - started))
-
-        if index == len(ordered) - 1:
-            continue
-        next_time = float(ordered[index + 1].captured_at_monotonic)
-        interval_seconds = max(0.0, next_time - now)
-        _record_utilization_interval(
-            totals,
-            resource_limits=capacities,
-            active_by_resource=active_by_resource,
-            queued_or_waiting_by_resource=waiters_by_resource,
-            interval_seconds=interval_seconds,
-        )
-
-    return _finalize_utilization_metrics(totals, wait_ages)
-
-
-def _ordered_events(events: list[Any]) -> list[Any]:
-    return sorted(events, key=lambda event: (event.captured_at_monotonic, event.sequence))
-
-
-def _empty_utilization_totals() -> dict[str, dict[str, Any]]:
-    return {}
-
-
-def _record_utilization_interval(
-    totals: dict[str, dict[str, Any]],
-    *,
-    resource_limits: dict[str, int],
-    active_by_resource: dict[str, int],
-    queued_or_waiting_by_resource: dict[str, int],
-    interval_seconds: float,
-) -> None:
-    if interval_seconds <= 0.0:
-        return
-    resources = {*resource_limits, *active_by_resource, *queued_or_waiting_by_resource}
-    for resource in resources:
-        active = max(0, active_by_resource.get(resource, 0))
-        capacity = max(1, resource_limits.get(resource, max(active, queued_or_waiting_by_resource.get(resource, 0))))
-        idle_slots = max(0, capacity - active)
-        queued_or_waiting = queued_or_waiting_by_resource.get(resource, 0)
-        resource_totals = totals.setdefault(
-            resource,
-            {
-                "capacity": capacity,
-                "active_window_seconds": 0.0,
-                "capacity_seconds": 0.0,
-                "busy_capacity_seconds": 0.0,
-                "idle_capacity_seconds": 0.0,
-                "starved_idle_seconds": 0.0,
-                "max_in_flight": 0,
-                "max_idle_slots": 0,
-                "samples": [],
-            },
-        )
-        resource_totals["capacity"] = max(int(resource_totals["capacity"]), capacity)
-        resource_totals["active_window_seconds"] += interval_seconds
-        resource_totals["capacity_seconds"] += capacity * interval_seconds
-        resource_totals["busy_capacity_seconds"] += active * interval_seconds
-        resource_totals["idle_capacity_seconds"] += idle_slots * interval_seconds
-        if queued_or_waiting > 0 and idle_slots > 0:
-            resource_totals["starved_idle_seconds"] += idle_slots * interval_seconds
-        resource_totals["max_in_flight"] = max(int(resource_totals["max_in_flight"]), active)
-        resource_totals["max_idle_slots"] = max(int(resource_totals["max_idle_slots"]), idle_slots)
-        resource_totals["samples"].append((interval_seconds, active))
-
-
-def _finalize_utilization_metrics(
-    totals: dict[str, dict[str, Any]],
-    queue_ages: dict[str, list[float]],
-) -> dict[str, dict[str, float | int]]:
-    metrics: dict[str, dict[str, float | int]] = {}
-    for resource, resource_totals in totals.items():
-        capacity_seconds = float(resource_totals["capacity_seconds"])
-        active_window_seconds = float(resource_totals["active_window_seconds"])
-        busy_capacity_seconds = float(resource_totals["busy_capacity_seconds"])
-        idle_capacity_seconds = float(resource_totals["idle_capacity_seconds"])
-        starved_idle_seconds = float(resource_totals["starved_idle_seconds"])
-        dependency_horizon_idle_seconds = max(0.0, idle_capacity_seconds - starved_idle_seconds)
-        ages = queue_ages.get(resource, [])
-        if busy_capacity_seconds == 0.0 and starved_idle_seconds == 0.0 and not ages:
-            continue
-        active_count_mean = _safe_ratio(busy_capacity_seconds, active_window_seconds)
-        active_count_stdev = _weighted_stdev(resource_totals["samples"])
-        queue_age_mean_seconds = statistics.fmean(ages) if ages else 0.0
-        queue_age_p50_seconds = _percentile(ages, 0.50)
-        queue_age_p95_seconds = _percentile(ages, 0.95)
-        queue_age_max_seconds = max(ages) if ages else 0.0
-        metrics[resource] = {
-            "capacity": int(resource_totals["capacity"]),
-            "active_window_seconds": active_window_seconds,
-            "capacity_seconds": capacity_seconds,
-            "busy_capacity_seconds": busy_capacity_seconds,
-            "idle_capacity_seconds": idle_capacity_seconds,
-            "starved_idle_seconds": starved_idle_seconds,
-            "dependency_horizon_idle_seconds": dependency_horizon_idle_seconds,
-            "frontier_dependency_horizon_idle_seconds": dependency_horizon_idle_seconds,
-            "utilization_ratio": _safe_ratio(busy_capacity_seconds, capacity_seconds),
-            "idle_ratio": _safe_ratio(idle_capacity_seconds, capacity_seconds),
-            "starved_idle_ratio": _safe_ratio(starved_idle_seconds, capacity_seconds),
-            "dependency_horizon_idle_ratio": _safe_ratio(dependency_horizon_idle_seconds, capacity_seconds),
-            "frontier_dependency_horizon_idle_ratio": _safe_ratio(
-                dependency_horizon_idle_seconds,
-                capacity_seconds,
-            ),
-            "active_count_mean": active_count_mean,
-            "active_count_stdev": active_count_stdev,
-            "burstiness_coefficient": _safe_ratio(active_count_stdev, active_count_mean),
-            "max_in_flight": int(resource_totals["max_in_flight"]),
-            "max_idle_slots": int(resource_totals["max_idle_slots"]),
-            "scheduler_queue_age_mean_seconds": queue_age_mean_seconds,
-            "scheduler_queue_age_p50_seconds": queue_age_p50_seconds,
-            "scheduler_queue_age_p95_seconds": queue_age_p95_seconds,
-            "scheduler_queue_age_max_seconds": queue_age_max_seconds,
-            "scheduler_queue_age_sample_count": len(ages),
-            "ready_to_dispatch_gap_mean_seconds": queue_age_mean_seconds,
-            "ready_to_dispatch_gap_p50_seconds": queue_age_p50_seconds,
-            "ready_to_dispatch_gap_p95_seconds": queue_age_p95_seconds,
-            "ready_to_dispatch_gap_max_seconds": queue_age_max_seconds,
-            "ready_to_dispatch_sample_count": len(ages),
-        }
-    return metrics
-
-
-def _event_resource_request(event: Any) -> dict[str, int]:
-    diagnostics = getattr(event, "diagnostics", {}) or {}
-    return _int_mapping(diagnostics.get("resource_request", {}))
-
-
-def _queued_resource_demand(queued_by_task: Mapping[str, Mapping[str, int]]) -> dict[str, int]:
-    demand: dict[str, int] = {}
-    for resources in queued_by_task.values():
-        for resource, amount in resources.items():
-            demand[resource] = demand.get(resource, 0) + amount
-    return demand
-
-
-def _int_mapping(value: Any) -> dict[str, int]:
-    if not isinstance(value, Mapping):
-        return {}
-    return {str(resource): int(amount) for resource, amount in value.items()}
-
-
-def _weighted_stdev(samples: list[tuple[float, int]]) -> float:
-    total_weight = sum(weight for weight, _value in samples)
-    if total_weight <= 0.0:
-        return 0.0
-    mean = sum(weight * value for weight, value in samples) / total_weight
-    variance = sum(weight * ((value - mean) ** 2) for weight, value in samples) / total_weight
-    return variance**0.5
-
-
-def _safe_ratio(numerator: float, denominator: float) -> float:
-    if denominator <= 0:
-        return 0.0
-    return numerator / denominator
-
-
-def _pipeline_derived_metrics(metrics: list[dict[str, Any]]) -> dict[str, Any]:
-    if not metrics:
-        return {}
-    ready_gap_p95s = [metric["downstream_ready_gap_p95_seconds"] for metric in metrics]
-    ready_gap_maxes = [metric["downstream_ready_gap_max_seconds"] for metric in metrics]
-    overlap_ratios = [metric["upstream_downstream_overlap_ratio"] for metric in metrics]
-    return {
-        "pipeline_validation_passed": all(metric["validation_passed"] for metric in metrics),
-        "pipeline_mean_overlap_ratio": statistics.fmean(overlap_ratios) if overlap_ratios else 0.0,
-        "pipeline_max_downstream_ready_gap_seconds": max(ready_gap_maxes) if ready_gap_maxes else 0.0,
-        "pipeline_p95_downstream_ready_gap_seconds": _percentile(ready_gap_p95s, 0.95),
-        "pipeline_downstream_interleaved_before_all_upstream_completed": all(
-            metric["validation"]["downstream_interleaved_before_all_upstream_completed"] for metric in metrics
-        ),
-    }
-
-
-def _request_pressure_derived_metrics(iterations: list[dict[str, Any]]) -> dict[str, Any]:
-    metrics = [iteration.get("request_pressure_metrics", {}) for iteration in iterations]
-    if not any(metrics):
-        return {}
-    wait_seconds = [float(metric.get("request_wait_seconds_while_task_leased", 0.0)) for metric in metrics]
-    first_dispatches = [metric.get("first_model_dispatch_column") for metric in metrics]
-    skip_counts = [int(metric.get("request_pressure_advisory_skip_count", 0) or 0) for metric in metrics]
-    return {
-        "request_pressure_advisory_enabled": any(
-            bool(metric.get("request_pressure_advisory_enabled", False)) for metric in metrics
-        ),
-        "request_pressure_advisory_skip_count": sum(skip_counts),
-        "request_wait_seconds_while_task_leased_mean": statistics.fmean(wait_seconds),
-        "request_wait_seconds_while_task_leased_max": max(wait_seconds),
-        "first_model_dispatch_column": first_dispatches[0] if first_dispatches else None,
-    }
-
-
-def _aggregate_utilization_metrics(iterations: list[dict[str, Any]]) -> dict[str, Any]:
-    scheduler = _aggregate_resource_utilization(iterations, "scheduler_resources")
-    request = _aggregate_resource_utilization(iterations, "request_resources")
-    all_scheduler = list(scheduler.values())
-    all_request = list(request.values())
-    return {
-        "scheduler_resource_utilization": scheduler,
-        "request_resource_utilization": request,
-        "scheduler_min_utilization_ratio": min(
-            (metric["mean_utilization_ratio"] for metric in all_scheduler),
-            default=0.0,
-        ),
-        "scheduler_max_starved_idle_seconds": max(
-            (metric["max_starved_idle_seconds"] for metric in all_scheduler),
-            default=0.0,
-        ),
-        "scheduler_max_ready_to_dispatch_gap_seconds": max(
-            (metric["max_ready_to_dispatch_gap_seconds"] for metric in all_scheduler),
-            default=0.0,
-        ),
-        "scheduler_max_scheduler_queue_age_seconds": max(
-            (metric["max_scheduler_queue_age_seconds"] for metric in all_scheduler),
-            default=0.0,
-        ),
-        "scheduler_max_burstiness_coefficient": max(
-            (metric["max_burstiness_coefficient"] for metric in all_scheduler),
-            default=0.0,
-        ),
-        "request_min_utilization_ratio": min(
-            (metric["mean_utilization_ratio"] for metric in all_request),
-            default=0.0,
-        ),
-        "request_max_starved_idle_seconds": max(
-            (metric["max_starved_idle_seconds"] for metric in all_request),
-            default=0.0,
-        ),
-        "request_max_ready_to_dispatch_gap_seconds": max(
-            (metric["max_ready_to_dispatch_gap_seconds"] for metric in all_request),
-            default=0.0,
-        ),
-        "request_max_burstiness_coefficient": max(
-            (metric["max_burstiness_coefficient"] for metric in all_request),
-            default=0.0,
-        ),
-    }
-
-
-def _aggregate_resource_utilization(
-    iterations: list[dict[str, Any]],
-    resource_kind: str,
-) -> dict[str, dict[str, float | int]]:
-    by_resource: dict[str, list[dict[str, float | int]]] = {}
-    for iteration in iterations:
-        for resource, metrics in iteration.get("utilization_metrics", {}).get(resource_kind, {}).items():
-            by_resource.setdefault(resource, []).append(metrics)
-
-    aggregated: dict[str, dict[str, float | int]] = {}
-    for resource, metrics in by_resource.items():
-        utilization_ratios = [float(metric["utilization_ratio"]) for metric in metrics]
-        idle_seconds = [float(metric["idle_capacity_seconds"]) for metric in metrics]
-        starved_idle_seconds = [float(metric["starved_idle_seconds"]) for metric in metrics]
-        dependency_horizon_idle_seconds = [_dependency_horizon_idle_seconds(metric) for metric in metrics]
-        dependency_horizon_idle_ratios = [_dependency_horizon_idle_ratio(metric) for metric in metrics]
-        burstiness = [float(metric["burstiness_coefficient"]) for metric in metrics]
-        scheduler_queue_ages = [
-            float(metric.get("scheduler_queue_age_max_seconds", metric["ready_to_dispatch_gap_max_seconds"]))
-            for metric in metrics
-        ]
-        aggregated[resource] = {
-            "iterations_observed": len(metrics),
-            "mean_utilization_ratio": statistics.fmean(utilization_ratios),
-            "min_utilization_ratio": min(utilization_ratios),
-            "max_utilization_ratio": max(utilization_ratios),
-            "mean_idle_capacity_seconds": statistics.fmean(idle_seconds),
-            "mean_starved_idle_seconds": statistics.fmean(starved_idle_seconds),
-            "mean_dependency_horizon_idle_seconds": statistics.fmean(dependency_horizon_idle_seconds),
-            "max_dependency_horizon_idle_seconds": max(dependency_horizon_idle_seconds),
-            "mean_dependency_horizon_idle_ratio": statistics.fmean(dependency_horizon_idle_ratios),
-            "max_dependency_horizon_idle_ratio": max(dependency_horizon_idle_ratios),
-            "mean_frontier_dependency_horizon_idle_seconds": statistics.fmean(dependency_horizon_idle_seconds),
-            "max_frontier_dependency_horizon_idle_seconds": max(dependency_horizon_idle_seconds),
-            "mean_frontier_dependency_horizon_idle_ratio": statistics.fmean(dependency_horizon_idle_ratios),
-            "max_frontier_dependency_horizon_idle_ratio": max(dependency_horizon_idle_ratios),
-            "max_starved_idle_seconds": max(starved_idle_seconds),
-            "max_burstiness_coefficient": max(burstiness),
-            "max_scheduler_queue_age_seconds": max(scheduler_queue_ages),
-            "max_ready_to_dispatch_gap_seconds": max(scheduler_queue_ages),
-        }
-    return aggregated
-
-
-def _dependency_horizon_idle_seconds(metric: Mapping[str, float | int]) -> float:
-    if "dependency_horizon_idle_seconds" in metric:
-        return float(metric["dependency_horizon_idle_seconds"])
-    return max(0.0, float(metric["idle_capacity_seconds"]) - float(metric["starved_idle_seconds"]))
-
-
-def _dependency_horizon_idle_ratio(metric: Mapping[str, float | int]) -> float:
-    if "dependency_horizon_idle_ratio" in metric:
-        return float(metric["dependency_horizon_idle_ratio"])
-    return _safe_ratio(_dependency_horizon_idle_seconds(metric), float(metric["capacity_seconds"]))
-
-
-async def _measure_async_request_wakeup_seconds() -> float:
-    resource = RequestResourceKey("mock-provider", "mock-model", RequestDomain.CHAT)
-    controller = AdaptiveRequestAdmissionController(
-        RequestAdmissionConfig(default_queue_wait_timeout_seconds=30.0, increase_after_successes=1)
-    )
-    controller.register(
-        provider_name="mock-provider",
-        model_id="mock-model",
-        alias="mock-alias",
-        max_parallel_requests=1,
-    )
-    item = RequestAdmissionItem(resource, RequestGroupSpec(resource), queue_wait_timeout_seconds=30.0)
-    first_lease = controller.try_acquire(item)
-    if not isinstance(first_lease, RequestAdmissionLease):
-        raise RuntimeError(f"Expected initial request lease, got {first_lease.reason}")
-
-    queued_task = asyncio.create_task(controller.acquire_async(item))
-    for _ in range(100):
-        snapshot = controller.pressure.snapshot(resource)
-        if snapshot is not None and snapshot.waiters == 1:
-            break
-        await asyncio.sleep(0)
-    else:
-        raise RuntimeError("Async request waiter did not enqueue.")
-
-    started = time.perf_counter()
-    controller.release(first_lease, RequestReleaseOutcome(kind="success"))
-    queued_lease = await asyncio.wait_for(queued_task, timeout=5.0)
-    elapsed = time.perf_counter() - started
-    controller.release(queued_lease, RequestReleaseOutcome(kind="success"))
-    return elapsed
-
-
-def _artifact(inputs: BenchmarkInputs, iterations: list[dict[str, Any]]) -> dict[str, Any]:
-    resource = RequestResourceKey("mock-provider", "mock-model", RequestDomain.CHAT)
-    provider_model = ProviderModelKey("mock-provider", "mock-model")
-    task_capacity = inputs.task_admission_capacity
-    capacity_plan = AsyncCapacityPlan(
-        configured=AsyncCapacityConfigured(
-            buffer_size=CapacityValue(value=inputs.buffer_size, source="run_config"),
-            row_group_admission=RowGroupAdmission(
-                row_group_concurrency=CapacityValue(value=inputs.row_group_concurrency, source="benchmark_override"),
-                observed_in_flight=0,
-            ),
-            submission_capacity=CapacityValue(value=task_capacity, source="benchmark_override"),
-            task_resource_limits=CapacityValue(
-                value={"submission": task_capacity, "llm_wait": max(1, task_capacity // 2), "local": task_capacity},
-                source="benchmark_override",
-            ),
-            request_resources=CapacityValue(value=(resource,), source="benchmark_override"),
-            provider_model_static_caps=CapacityValue(
-                value={
-                    provider_model: ProviderModelStaticCap(
-                        cap=max(1, task_capacity // 2),
-                        aliases=("mock-alias",),
-                        raw_caps={"mock-alias": max(1, task_capacity // 2)},
-                    )
-                },
-                source="model_metadata",
-            ),
-            request_domain_initial_limits=CapacityValue(
-                value={resource: max(1, task_capacity // 2)}, source="benchmark_override"
-            ),
-            request_admission_config=CapacityValue(
-                value=RequestAdmissionConfigSnapshot.from_config(RequestAdmissionConfig()),
-                source="engine_internal_config",
-            ),
-            transport_pool_limits=CapacityValue(value={provider_model: task_capacity}, source="adapter_config"),
-        ),
-        runtime_snapshot=AsyncCapacityRuntimeSnapshot(
-            request_domain_current_limits={resource: max(1, task_capacity // 2)},
-            request_domain_effective_max={resource: max(1, task_capacity // 2)},
-            request_domain_blocked_until={resource: None},
-            provider_model_aggregate_in_flight={provider_model: 0},
-        ),
-        observed_maxima=AsyncCapacityObservedMaxima(
-            row_groups_in_flight=inputs.row_group_concurrency,
-            request_waiters_by_resource={resource: 0},
-            request_in_flight_by_resource={resource: max(1, task_capacity // 2)},
-            provider_model_aggregate_in_flight={provider_model: max(1, task_capacity // 2)},
-            request_domain_current_limits={resource: max(1, task_capacity // 2)},
-            transport_pool_utilization={provider_model: 0},
-        ),
-    )
-    wall_times = [iteration["wall_time_seconds"] for iteration in iterations]
-    async_wakeups = [iteration["async_request_wakeup_seconds"] for iteration in iterations]
-    pipeline_metrics = [iteration["pipeline_metrics"] for iteration in iterations if "pipeline_metrics" in iteration]
-    utilization_metrics = _aggregate_utilization_metrics(iterations)
-    request_pressure_metrics = _request_pressure_derived_metrics(iterations)
-    return {
-        "scenario_id": inputs.scenario,
-        "artifact_schema_version": ARTIFACT_SCHEMA_VERSION,
-        "scenario_version": inputs.scenario_version,
-        "harness_version": inputs.harness_version,
-        "baseline_sha": _git_rev_parse(inputs.baseline_ref),
-        "candidate_sha": _git_rev_parse(inputs.candidate_ref),
-        "worktree_dirty": _worktree_dirty(),
-        "worktree_status_short": _worktree_status_short(),
-        "worktree_diff_sha256": _worktree_diff_hash(),
-        "command_line": sys.argv,
-        "machine": {
-            "platform": platform.platform(),
-            "python": platform.python_version(),
-            "processor": platform.processor(),
-        },
-        "inputs": inputs,
-        "provider_script": {"provider": "mock-provider", "model": "mock-model", "domains": ["chat"]},
-        "clock_script": {"time_source": "time.monotonic", "deterministic_replay": False},
-        "capacity_plan": capacity_plan,
-        "iterations": iterations,
-        "derived_metrics": {
-            "mean_wall_time_seconds": statistics.fmean(wall_times) if wall_times else 0.0,
-            "p50_wall_time_seconds": statistics.median(wall_times) if wall_times else 0.0,
-            "p95_wall_time_seconds": _percentile(wall_times, 0.95),
-            "min_wall_time_seconds": min(wall_times) if wall_times else 0.0,
-            "max_wall_time_seconds": max(wall_times) if wall_times else 0.0,
-            "stdev_wall_time_seconds": statistics.stdev(wall_times) if len(wall_times) > 1 else 0.0,
-            "mean_async_request_wakeup_seconds": statistics.fmean(async_wakeups) if async_wakeups else 0.0,
-            "p95_async_request_wakeup_seconds": _percentile(async_wakeups, 0.95),
-            "max_async_request_wakeup_seconds": max(async_wakeups) if async_wakeups else 0.0,
-            **_pipeline_derived_metrics(pipeline_metrics),
-            **utilization_metrics,
-            **request_pressure_metrics,
-            "max_hidden_scheduler_resource_waiters": 0,
-            "final_zero_task_leases": all(
-                sum(iteration["final_task_snapshot"].leased_resources.values()) == 0 for iteration in iterations
-            ),
-            "final_zero_request_leases": all(
-                iteration["final_request_snapshot"]["zero_active_request_leases"] for iteration in iterations
-            ),
-            "final_zero_request_waiters": all(
-                iteration["final_request_snapshot"]["zero_request_waiters"] for iteration in iterations
-            ),
-        },
-    }
-
-
-def _event_payload(event: Any) -> dict[str, Any]:
-    return {
-        "event_kind": event.event_kind,
-        "captured_at_monotonic": event.captured_at_monotonic,
-        "sequence": event.sequence,
-        "captured_correlation": event.captured_correlation,
-        "task_id": getattr(event, "task_id", None),
-        "task_execution_id": getattr(event, "task_execution_id", None),
-        "task_lease_id": getattr(event, "task_lease_id", None),
-        "request_attempt_id": getattr(event, "request_attempt_id", None),
-        "request_lease_id": getattr(event, "request_lease_id", None),
-        "scheduler_resource_key": getattr(event, "scheduler_resource_key", None),
-        "request_resource_key": getattr(event, "request_resource_key", None),
-        "reason_or_outcome": getattr(event, "reason_or_outcome", None) or getattr(event, "reason_or_result", None),
-        "snapshot": getattr(event, "snapshot", None),
-        "pressure_snapshot": getattr(event, "pressure_snapshot", None),
-        "diagnostics": getattr(event, "diagnostics", {}),
-    }
-
-
-def _iteration_min_utilization_ratio(iteration: Mapping[str, Any], resource_kind: str) -> float:
-    resources = iteration.get("utilization_metrics", {}).get(resource_kind, {})
-    return min((float(metric["utilization_ratio"]) for metric in resources.values()), default=0.0)
-
-
-def _iteration_starved_idle_seconds(iteration: Mapping[str, Any], resource_kind: str) -> float:
-    resources = iteration.get("utilization_metrics", {}).get(resource_kind, {})
-    return sum(float(metric["starved_idle_seconds"]) for metric in resources.values())
-
-
-def _write_csv(path: Path, artifact: Mapping[str, Any]) -> None:
-    with path.open("w", newline="", encoding="utf-8") as handle:
-        writer = csv.DictWriter(
-            handle,
-            fieldnames=[
-                "iteration",
-                "wall_time_seconds",
-                "async_request_wakeup_seconds",
-                "accepted_task_count",
-                "scheduler_min_utilization_ratio",
-                "scheduler_starved_idle_seconds",
-                "output_hash",
-            ],
-        )
-        writer.writeheader()
-        for index, iteration in enumerate(artifact["iterations"]):
-            writer.writerow(
-                {
-                    "iteration": index,
-                    "wall_time_seconds": iteration["wall_time_seconds"],
-                    "async_request_wakeup_seconds": iteration["async_request_wakeup_seconds"],
-                    "accepted_task_count": iteration["accepted_task_count"],
-                    "scheduler_min_utilization_ratio": _iteration_min_utilization_ratio(
-                        iteration,
-                        "scheduler_resources",
-                    ),
-                    "scheduler_starved_idle_seconds": _iteration_starved_idle_seconds(
-                        iteration,
-                        "scheduler_resources",
-                    ),
-                    "output_hash": next(iter(iteration["output_hashes"].values())),
-                }
-            )
-
-
-def _write_markdown(path: Path, artifact: Mapping[str, Any]) -> None:
-    metrics = artifact["derived_metrics"]
-    lines = [
-        "# Async Scheduling Benchmark Smoke",
-        "",
-        f"- scenario: `{artifact['scenario_id']}`",
-        f"- baseline_sha: `{artifact['baseline_sha']}`",
-        f"- candidate_sha: `{artifact['candidate_sha']}`",
-        f"- iterations: `{len(artifact['iterations'])}`",
-        f"- mean_wall_time_seconds: `{metrics['mean_wall_time_seconds']:.6f}`",
-        f"- p95_wall_time_seconds: `{metrics['p95_wall_time_seconds']:.6f}`",
-        f"- mean_async_request_wakeup_seconds: `{metrics['mean_async_request_wakeup_seconds']:.6f}`",
-        f"- p95_async_request_wakeup_seconds: `{metrics['p95_async_request_wakeup_seconds']:.6f}`",
-        f"- max_async_request_wakeup_seconds: `{metrics['max_async_request_wakeup_seconds']:.6f}`",
-        f"- scheduler_min_utilization_ratio: `{metrics['scheduler_min_utilization_ratio']:.6f}`",
-        f"- scheduler_max_starved_idle_seconds: `{metrics['scheduler_max_starved_idle_seconds']:.6f}`",
-        f"- scheduler_max_scheduler_queue_age_seconds: `{metrics['scheduler_max_scheduler_queue_age_seconds']:.6f}`",
-        "- scheduler_max_ready_to_dispatch_gap_seconds: "
-        f"`{metrics['scheduler_max_ready_to_dispatch_gap_seconds']:.6f}`",
-        f"- scheduler_max_burstiness_coefficient: `{metrics['scheduler_max_burstiness_coefficient']:.6f}`",
-        f"- max_hidden_scheduler_resource_waiters: `{metrics['max_hidden_scheduler_resource_waiters']}`",
-        f"- final_zero_task_leases: `{metrics['final_zero_task_leases']}`",
-        f"- final_zero_request_leases: `{metrics['final_zero_request_leases']}`",
-        f"- final_zero_request_waiters: `{metrics['final_zero_request_waiters']}`",
-    ]
-    if "pipeline_validation_passed" in metrics:
-        lines.extend(
-            [
-                f"- pipeline_validation_passed: `{metrics['pipeline_validation_passed']}`",
-                f"- pipeline_mean_overlap_ratio: `{metrics['pipeline_mean_overlap_ratio']:.6f}`",
-                f"- pipeline_p95_downstream_ready_gap_seconds: `{metrics['pipeline_p95_downstream_ready_gap_seconds']:.6f}`",
-                f"- pipeline_max_downstream_ready_gap_seconds: `{metrics['pipeline_max_downstream_ready_gap_seconds']:.6f}`",
-                "- pipeline_downstream_interleaved_before_all_upstream_completed: "
-                f"`{metrics['pipeline_downstream_interleaved_before_all_upstream_completed']}`",
-            ]
-        )
-    lines.extend(
-        _resource_utilization_markdown("Scheduler Resource Utilization", metrics["scheduler_resource_utilization"])
-    )
-    if metrics["request_resource_utilization"]:
-        lines.extend(
-            _resource_utilization_markdown("Request Resource Utilization", metrics["request_resource_utilization"])
-        )
-    lines.append("")
-    path.write_text("\n".join(lines), encoding="utf-8")
-
-
-def _resource_utilization_markdown(title: str, resources: Mapping[str, Mapping[str, Any]]) -> list[str]:
-    if not resources:
-        return []
-    lines = ["", f"## {title}", ""]
-    for resource, metrics in sorted(resources.items()):
-        lines.append(
-            f"- `{resource}`: util=`{float(metrics['mean_utilization_ratio']):.6f}`, "
-            f"idle_s=`{float(metrics['mean_idle_capacity_seconds']):.6f}`, "
-            f"starved_idle_s=`{float(metrics['mean_starved_idle_seconds']):.6f}`, "
-            f"dependency_horizon_idle_s=`{float(metrics['mean_dependency_horizon_idle_seconds']):.6f}`, "
-            f"max_scheduler_queue_age_s=`{float(metrics['max_scheduler_queue_age_seconds']):.6f}`, "
-            f"burstiness=`{float(metrics['max_burstiness_coefficient']):.6f}`"
-        )
-    return lines
-
-
-def _validate_artifact(artifact: Mapping[str, Any]) -> None:
-    metrics = artifact["derived_metrics"]
-    inputs = artifact["inputs"]
-    task_capacity = _input_value(inputs, "task_admission_capacity")
-    failures: list[str] = []
-    if not metrics["final_zero_task_leases"]:
-        failures.append("task leases leaked at end of benchmark")
-    if not metrics["final_zero_request_leases"]:
-        failures.append("request leases leaked at end of benchmark")
-    if not metrics["final_zero_request_waiters"]:
-        failures.append("request waiters leaked at end of benchmark")
-    if metrics["p95_async_request_wakeup_seconds"] > ASYNC_WAKEUP_GATE_SECONDS:
-        failures.append(
-            "async request wakeup p95 exceeded "
-            f"{ASYNC_WAKEUP_GATE_SECONDS:.3f}s: {metrics['p95_async_request_wakeup_seconds']:.6f}s"
-        )
-    if "pipeline_validation_passed" in metrics and not metrics["pipeline_validation_passed"]:
-        failures.append("real-pipeline-overlap validation failed")
-    for index, iteration in enumerate(artifact["iterations"]):
-        selected_count = iteration.get(
-            "selected_task_count",
-            iteration.get("per_layer_observed_maxima", {}).get("selected_tasks", 0),
-        )
-        accepted_count = iteration["accepted_task_count"]
-        if selected_count != accepted_count:
-            failures.append(
-                f"iteration {index} selected {selected_count} tasks but accepted {accepted_count}; queue drained early"
-            )
-        observed = iteration.get("per_layer_observed_maxima", {})
-        task_leases = observed.get("task_leases_by_resource", {})
-        for resource, limit in _task_resource_limits(artifact, iteration, task_capacity).items():
-            observed_count = int(task_leases.get(resource, 0))
-            if observed_count > limit:
-                failures.append(f"iteration {index} exceeded {resource} task cap {limit}: observed {observed_count}")
-        request_limits = _request_resource_limits(iteration, default=max(1, task_capacity // 2))
-        for resource, observed_count in observed.get("request_in_flight_by_resource", {}).items():
-            request_capacity = request_limits.get(str(resource), max(1, task_capacity // 2))
-            if int(observed_count) > request_capacity:
-                failures.append(
-                    f"iteration {index} exceeded request cap {request_capacity} for {resource}: observed {observed_count}"
-                )
-        utilization = iteration.get("utilization_metrics", {})
-        scheduler_utilization = utilization.get("scheduler_resources", {})
-        if not scheduler_utilization:
-            failures.append(f"iteration {index} did not record scheduler utilization metrics")
-        _validate_resource_utilization_metrics(
-            scheduler_utilization,
-            failures=failures,
-            iteration=index,
-            resource_kind="scheduler",
-        )
-        _validate_resource_utilization_metrics(
-            utilization.get("request_resources", {}),
-            failures=failures,
-            iteration=index,
-            resource_kind="request",
-        )
-    if failures:
-        joined = "; ".join(failures)
-        raise RuntimeError(f"Async scheduling benchmark validation failed: {joined}")
-
-
-def _task_resource_limits(
-    artifact: Mapping[str, Any],
-    iteration: Mapping[str, Any],
-    task_capacity: int,
-) -> dict[str, int]:
-    default = {
-        "submission": task_capacity,
-        "llm_wait": max(1, task_capacity // 2),
-        "local": task_capacity,
-    }
-    plan = iteration.get("capacity_plan") or artifact.get("capacity_plan")
-    configured = _field(plan, "configured")
-    capacity_value = _field(configured, "task_resource_limits")
-    value = _field(capacity_value, "value")
-    if not isinstance(value, Mapping):
-        return default
-    return {str(resource): int(limit) for resource, limit in value.items()}
-
-
-def _request_resource_limits(iteration: Mapping[str, Any], *, default: int) -> dict[str, int]:
-    final_request_snapshot = iteration.get("final_request_snapshot", {})
-    domains = _field(final_request_snapshot, "domains")
-    if not isinstance(domains, Mapping):
-        return {}
-    limits: dict[str, int] = {}
-    for resource, snapshot in domains.items():
-        effective_max = _field(snapshot, "effective_max")
-        current_limit = _field(snapshot, "current_limit")
-        limit = effective_max if effective_max is not None else current_limit
-        limits[str(resource)] = int(limit if limit is not None else default)
-    return limits
-
-
-def _field(value: Any, name: str) -> Any:
-    if isinstance(value, Mapping):
-        return value.get(name)
-    return getattr(value, name, None)
-
-
-def _validate_resource_utilization_metrics(
-    resource_metrics: Mapping[str, Mapping[str, Any]],
-    *,
-    failures: list[str],
-    iteration: int,
-    resource_kind: str,
-) -> None:
-    for resource, metrics in resource_metrics.items():
-        capacity_seconds = float(metrics.get("capacity_seconds", 0.0))
-        busy_capacity_seconds = float(metrics.get("busy_capacity_seconds", 0.0))
-        idle_capacity_seconds = float(metrics.get("idle_capacity_seconds", 0.0))
-        starved_idle_seconds = float(metrics.get("starved_idle_seconds", 0.0))
-        dependency_horizon_idle_seconds = float(
-            metrics.get(
-                "dependency_horizon_idle_seconds",
-                max(0.0, idle_capacity_seconds - starved_idle_seconds),
-            )
-        )
-        utilization_ratio = float(metrics.get("utilization_ratio", -1.0))
-        dependency_horizon_idle_ratio = float(
-            metrics.get(
-                "dependency_horizon_idle_ratio",
-                _safe_ratio(dependency_horizon_idle_seconds, capacity_seconds),
-            )
-        )
-        if (
-            min(
-                capacity_seconds,
-                busy_capacity_seconds,
-                idle_capacity_seconds,
-                starved_idle_seconds,
-                dependency_horizon_idle_seconds,
-            )
-            < 0.0
-        ):
-            failures.append(f"iteration {iteration} has negative {resource_kind} utilization metric for {resource}")
-        if busy_capacity_seconds > capacity_seconds + 1e-9:
-            failures.append(f"iteration {iteration} has busy {resource_kind} capacity above total for {resource}")
-        if starved_idle_seconds > idle_capacity_seconds + 1e-9:
-            failures.append(f"iteration {iteration} has starved {resource_kind} idle above total idle for {resource}")
-        if abs((starved_idle_seconds + dependency_horizon_idle_seconds) - idle_capacity_seconds) > 1e-9:
-            failures.append(f"iteration {iteration} has invalid {resource_kind} idle partition for {resource}")
-        if not 0.0 <= utilization_ratio <= 1.0:
-            failures.append(f"iteration {iteration} has invalid {resource_kind} utilization ratio for {resource}")
-        if not 0.0 <= dependency_horizon_idle_ratio <= 1.0:
-            failures.append(
-                f"iteration {iteration} has invalid {resource_kind} dependency-horizon idle ratio for {resource}"
-            )
-
-
-def _input_value(inputs: Any, name: str) -> Any:
-    if isinstance(inputs, Mapping):
-        return inputs[name]
-    return getattr(inputs, name)
-
-
-def _git_rev_parse(ref: str) -> str:
-    try:
-        return subprocess.check_output(["git", "rev-parse", ref], text=True).strip()
-    except Exception:
-        return f"unresolved:{ref}"
-
-
-def _worktree_dirty() -> bool:
-    try:
-        result = subprocess.run(["git", "status", "--short"], check=False, capture_output=True, text=True)
-    except Exception:
-        return True
-    return bool(result.stdout.strip())
-
-
-def _worktree_status_short() -> str:
-    try:
-        result = subprocess.run(["git", "status", "--short"], check=False, capture_output=True, text=True)
-    except Exception as exc:
-        return f"unavailable:{exc}"
-    return result.stdout
-
-
-def _worktree_diff_hash() -> str:
-    try:
-        diff = subprocess.check_output(["git", "diff", "--binary", "HEAD"], text=False)
-    except Exception as exc:
-        return f"unavailable:{exc}"
-    return hashlib.sha256(diff).hexdigest()
-
-
-def _percentile(values: list[float], pct: float) -> float:
-    if not values:
-        return 0.0
-    ordered = sorted(values)
-    index = min(len(ordered) - 1, max(0, int(round((len(ordered) - 1) * pct))))
-    return ordered[index]
-
-
-def _to_jsonable(value: Any) -> Any:
-    if is_dataclass(value):
-        return {field.name: _to_jsonable(getattr(value, field.name)) for field in fields(value)}
-    if isinstance(value, Enum):
-        return value.value
-    if isinstance(value, Mapping):
-        return {str(_to_jsonable(key)): _to_jsonable(item) for key, item in value.items()}
-    if isinstance(value, tuple | list | set):
-        return [_to_jsonable(item) for item in value]
-    return value
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/benchmarks/export_async_scheduling_perfetto.py b/scripts/benchmarks/export_async_scheduling_perfetto.py
deleted file mode 100644
index ea847f58e..000000000
--- a/scripts/benchmarks/export_async_scheduling_perfetto.py
+++ /dev/null
@@ -1,431 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-"""Export async scheduling benchmark sink timelines to Perfetto JSON.
-
-The output is Chrome trace-event JSON, which Perfetto can open directly.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-from collections.abc import Mapping
-from pathlib import Path
-from typing import Any
-
-TRACE_SCHEMA = "async-scheduling-perfetto-trace-v1"
-
-PID = 1
-LANES = {
-    "scheduler_events": 1,
-    "row_groups": 2,
-    "task_execution": 3,
-    "task_leases": 4,
-    "request_waits": 5,
-    "request_leases": 6,
-    "model_requests": 7,
-    "counters": 8,
-}
-
-TERMINAL_REQUEST_WAIT_EVENTS = {
-    "request_wait_completed",
-    "request_wait_timeout",
-    "request_wait_cancelled",
-    "request_acquire_denied",
-}
-
-
-def main() -> None:
-    args = _parse_args()
-    artifact_path = Path(args.artifact)
-    artifact = json.loads(artifact_path.read_text(encoding="utf-8"))
-    output_path = Path(args.output) if args.output else artifact_path.with_suffix(".perfetto.json")
-    trace = benchmark_artifact_to_perfetto(artifact, iteration_index=args.iteration)
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    output_path.write_text(json.dumps(trace, indent=2, sort_keys=True) + "\n", encoding="utf-8")
-    print(f"Wrote {output_path}")
-
-
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("artifact", help="Path to async_scheduling_benchmark.json")
-    parser.add_argument("--output", help="Output .perfetto.json path")
-    parser.add_argument("--iteration", type=int, default=0, help="Benchmark iteration index to export.")
-    return parser.parse_args()
-
-
-def benchmark_artifact_to_perfetto(
-    artifact: Mapping[str, Any],
-    *,
-    iteration_index: int = 0,
-) -> dict[str, Any]:
-    iterations = artifact.get("iterations", [])
-    if not isinstance(iterations, list) or not iterations:
-        raise ValueError("Benchmark artifact has no iterations.")
-    try:
-        iteration = iterations[iteration_index]
-    except IndexError:
-        raise ValueError(f"Iteration {iteration_index} is not present.") from None
-    timeline = sorted(
-        iteration.get("timeline", []),
-        key=lambda event: (
-            float(event.get("captured_at_monotonic", 0.0) or 0.0),
-            int(event.get("sequence", 0) or 0),
-        ),
-    )
-    if not timeline:
-        raise ValueError("Selected iteration has no timeline events.")
-
-    base = min(float(event.get("captured_at_monotonic", 0.0) or 0.0) for event in timeline)
-    trace_events: list[dict[str, Any]] = []
-    trace_events.extend(_metadata_events())
-    trace_events.extend(_counter_events(timeline, base))
-    trace_events.extend(_interval_events(timeline, base))
-    trace_events.extend(_instant_events(timeline, base))
-
-    return {
-        "traceEvents": trace_events,
-        "displayTimeUnit": "ms",
-        "metadata": {
-            "schema": TRACE_SCHEMA,
-            "scenario_id": artifact.get("scenario_id"),
-            "scenario_version": artifact.get("scenario_version"),
-            "harness_version": artifact.get("harness_version"),
-            "iteration_index": iteration_index,
-            "mean_wall_time_seconds": artifact.get("derived_metrics", {}).get("mean_wall_time_seconds"),
-            "candidate_sha": artifact.get("candidate_sha"),
-            "worktree_dirty": artifact.get("worktree_dirty"),
-            "worktree_diff_sha256": artifact.get("worktree_diff_sha256"),
-        },
-    }
-
-
-def _metadata_events() -> list[dict[str, Any]]:
-    return [
-        {
-            "name": "thread_name",
-            "ph": "M",
-            "pid": PID,
-            "tid": tid,
-            "args": {"name": name},
-        }
-        for name, tid in LANES.items()
-    ]
-
-
-def _counter_events(timeline: list[Mapping[str, Any]], base: float) -> list[dict[str, Any]]:
-    events: list[dict[str, Any]] = []
-    for event in timeline:
-        if event.get("event_kind") != "scheduler_health_snapshot":
-            continue
-        diagnostics = _mapping(event.get("diagnostics"))
-        ts = _timestamp_us(event, base)
-        counters = {
-            "active_row_groups": diagnostics.get("active_row_groups"),
-            "target_row_groups": diagnostics.get("target_row_groups"),
-            "active_admitted_rows": diagnostics.get("active_admitted_rows"),
-            "queued_total": diagnostics.get("queued_total"),
-            "in_flight_tasks": diagnostics.get("in_flight_tasks"),
-            "active_workers": diagnostics.get("active_workers"),
-            "deferred_tasks": diagnostics.get("deferred_tasks"),
-            "request_pressure_advisory_skips": diagnostics.get("request_pressure_advisory_skips"),
-        }
-        for name, value in counters.items():
-            if isinstance(value, int | float):
-                events.append(_counter_event(name, ts, value))
-        for resource, value in _mapping(diagnostics.get("leased_resources")).items():
-            if isinstance(value, int | float):
-                events.append(_counter_event(f"leased:{resource}", ts, value))
-        for resource, value in _mapping(diagnostics.get("resources_available")).items():
-            if isinstance(value, int | float):
-                events.append(_counter_event(f"available:{resource}", ts, value))
-        for resource, value in _mapping(diagnostics.get("queued_demand_by_resource")).items():
-            if isinstance(value, int | float):
-                events.append(_counter_event(f"queued_demand:{resource}", ts, value))
-        request_pressure = _mapping(diagnostics.get("request_pressure"))
-        for resource, snapshot in _mapping(request_pressure.get("resources")).items():
-            snapshot_map = _mapping(snapshot)
-            for field in ("in_flight_count", "waiters", "current_limit", "effective_max"):
-                value = snapshot_map.get(field)
-                if isinstance(value, int | float):
-                    events.append(_counter_event(f"request:{resource}:{field}", ts, value))
-    return events
-
-
-def _counter_event(name: str, ts: int, value: int | float) -> dict[str, Any]:
-    return {
-        "name": name,
-        "cat": "scheduler_counters",
-        "ph": "C",
-        "pid": PID,
-        "tid": LANES["counters"],
-        "ts": ts,
-        "args": {"value": value},
-    }
-
-
-def _interval_events(timeline: list[Mapping[str, Any]], base: float) -> list[dict[str, Any]]:
-    starts: dict[tuple[str, str], Mapping[str, Any]] = {}
-    intervals: list[dict[str, Any]] = []
-    for event in timeline:
-        kind = str(event.get("event_kind"))
-        if kind == "row_group_admitted":
-            row_group = _diagnostic(event, "row_group")
-            if row_group is not None:
-                starts[("row_group", str(row_group))] = event
-        elif kind == "row_group_checkpointed":
-            row_group = _diagnostic(event, "row_group")
-            if row_group is not None:
-                intervals.extend(
-                    _finish_interval(
-                        starts,
-                        ("row_group", str(row_group)),
-                        event,
-                        base,
-                        lane="row_groups",
-                        cat="row_group",
-                        name=f"row group {row_group}",
-                    )
-                )
-        elif kind == "worker_spawned":
-            task_execution_id = _event_key(event, "task_execution_id")
-            if task_execution_id is not None:
-                starts[("task_execution", task_execution_id)] = event
-        elif kind == "task_completed":
-            task_execution_id = _event_key(event, "task_execution_id")
-            if task_execution_id is not None:
-                intervals.extend(
-                    _finish_interval(
-                        starts,
-                        ("task_execution", task_execution_id),
-                        event,
-                        base,
-                        lane="task_execution",
-                        cat="scheduler_task",
-                        name=f"task {_task_label(event)}",
-                    )
-                )
-        elif kind == "task_lease_acquired":
-            lease_id = _event_key(event, "task_lease_id")
-            if lease_id is not None:
-                starts[("task_lease", lease_id)] = event
-        elif kind == "task_lease_released":
-            lease_id = _event_key(event, "task_lease_id")
-            if lease_id is not None:
-                intervals.extend(
-                    _finish_interval(
-                        starts,
-                        ("task_lease", lease_id),
-                        event,
-                        base,
-                        lane="task_leases",
-                        cat="scheduler_lease",
-                        name=f"task lease {_task_label(event)}",
-                    )
-                )
-        elif kind == "request_wait_started":
-            request_key = _request_attempt_key(event)
-            if request_key is not None:
-                starts[("request_wait", request_key)] = event
-        elif kind in TERMINAL_REQUEST_WAIT_EVENTS:
-            request_key = _request_attempt_key(event)
-            if request_key is not None:
-                intervals.extend(
-                    _finish_interval(
-                        starts,
-                        ("request_wait", request_key),
-                        event,
-                        base,
-                        lane="request_waits",
-                        cat="request_wait",
-                        name=f"request wait {_request_resource_label(event)}",
-                    )
-                )
-        elif kind == "request_lease_acquired":
-            lease_id = _event_key(event, "request_lease_id")
-            if lease_id is not None:
-                starts[("request_lease", lease_id)] = event
-        elif kind == "request_lease_released":
-            lease_id = _event_key(event, "request_lease_id")
-            if lease_id is not None:
-                intervals.extend(
-                    _finish_interval(
-                        starts,
-                        ("request_lease", lease_id),
-                        event,
-                        base,
-                        lane="request_leases",
-                        cat="request_lease",
-                        name=f"request lease {_request_resource_label(event)}",
-                    )
-                )
-        elif kind == "model_request_started":
-            request_key = _request_attempt_key(event) or _event_key(event, "request_lease_id")
-            if request_key is not None:
-                starts[("model_request", request_key)] = event
-        elif kind == "model_request_completed":
-            request_key = _request_attempt_key(event) or _event_key(event, "request_lease_id")
-            if request_key is not None:
-                intervals.extend(
-                    _finish_interval(
-                        starts,
-                        ("model_request", request_key),
-                        event,
-                        base,
-                        lane="model_requests",
-                        cat="model_request",
-                        name=f"model request {_request_resource_label(event)}",
-                    )
-                )
-    return intervals
-
-
-def _finish_interval(
-    starts: dict[tuple[str, str], Mapping[str, Any]],
-    key: tuple[str, str],
-    end_event: Mapping[str, Any],
-    base: float,
-    *,
-    lane: str,
-    cat: str,
-    name: str,
-) -> list[dict[str, Any]]:
-    start_event = starts.pop(key, None)
-    if start_event is None:
-        return []
-    started = _timestamp_us(start_event, base)
-    ended = _timestamp_us(end_event, base)
-    return [
-        {
-            "name": name,
-            "cat": cat,
-            "ph": "X",
-            "pid": PID,
-            "tid": LANES[lane],
-            "ts": started,
-            "dur": max(0, ended - started),
-            "args": _event_args(end_event) | {"start_event": _event_args(start_event)},
-        }
-    ]
-
-
-def _instant_events(timeline: list[Mapping[str, Any]], base: float) -> list[dict[str, Any]]:
-    events = []
-    for event in timeline:
-        stream = str(event.get("stream", "scheduler"))
-        kind = str(event.get("event_kind"))
-        lane = "scheduler_events"
-        events.append(
-            {
-                "name": kind,
-                "cat": f"{stream}_event",
-                "ph": "i",
-                "s": "t",
-                "pid": PID,
-                "tid": LANES[lane],
-                "ts": _timestamp_us(event, base),
-                "args": _event_args(event),
-            }
-        )
-    return events
-
-
-def _event_args(event: Mapping[str, Any]) -> dict[str, Any]:
-    return {
-        "stream": event.get("stream"),
-        "sequence": event.get("sequence"),
-        "task_id": event.get("task_id"),
-        "task_execution_id": event.get("task_execution_id"),
-        "task_lease_id": event.get("task_lease_id"),
-        "request_attempt_id": event.get("request_attempt_id"),
-        "request_lease_id": event.get("request_lease_id"),
-        "request_resource": _request_resource_label(event),
-        "reason_or_outcome": event.get("reason_or_outcome"),
-        "correlation": _jsonable(event.get("captured_correlation"), max_depth=2),
-        "diagnostics": _jsonable(event.get("diagnostics"), max_depth=3),
-    }
-
-
-def _timestamp_us(event: Mapping[str, Any], base: float) -> int:
-    captured_at = float(event.get("captured_at_monotonic", 0.0) or 0.0)
-    return int(round((captured_at - base) * 1_000_000.0))
-
-
-def _event_key(event: Mapping[str, Any], key: str) -> str | None:
-    value = event.get(key)
-    return None if value is None else str(value)
-
-
-def _request_attempt_key(event: Mapping[str, Any]) -> str | None:
-    attempt_id = _event_key(event, "request_attempt_id")
-    if attempt_id is not None:
-        return attempt_id
-    lease_id = _event_key(event, "request_lease_id")
-    if lease_id is not None:
-        return lease_id
-    correlation = _mapping(event.get("captured_correlation"))
-    task_execution_id = correlation.get("task_execution_id")
-    request_resource = _request_resource_label(event)
-    if task_execution_id is None and request_resource is None:
-        return None
-    return f"{task_execution_id}:{request_resource}"
-
-
-def _task_label(event: Mapping[str, Any]) -> str:
-    correlation = _mapping(event.get("captured_correlation"))
-    column = correlation.get("task_column") or "unknown"
-    row_group = correlation.get("row_group")
-    task_type = correlation.get("task_type") or "task"
-    return f"{column} rg={row_group} {task_type}"
-
-
-def _request_resource_label(event: Mapping[str, Any]) -> str | None:
-    resource = event.get("request_resource_key")
-    if resource is None:
-        resource = _diagnostic(event, "request_resource")
-    if isinstance(resource, Mapping):
-        provider = resource.get("provider_name")
-        model = resource.get("model_id")
-        domain = resource.get("domain")
-        if provider is not None and model is not None and domain is not None:
-            return f"{provider}/{model}/{domain}"
-    return None if resource is None else str(resource)
-
-
-def _diagnostic(event: Mapping[str, Any], key: str) -> Any:
-    diagnostics = event.get("diagnostics", {})
-    if not isinstance(diagnostics, Mapping):
-        return None
-    return diagnostics.get(key)
-
-
-def _mapping(value: Any) -> Mapping[str, Any]:
-    return value if isinstance(value, Mapping) else {}
-
-
-def _jsonable(value: Any, *, max_depth: int, depth: int = 0) -> Any:
-    if value is None or isinstance(value, str | int | float | bool):
-        return value
-    if depth >= max_depth:
-        return str(value)
-    if isinstance(value, Mapping):
-        return {
-            str(_jsonable(key, max_depth=max_depth, depth=depth + 1)): _jsonable(
-                item,
-                max_depth=max_depth,
-                depth=depth + 1,
-            )
-            for key, item in list(value.items())[:50]
-        }
-    if isinstance(value, list | tuple | set):
-        items = list(value)
-        result = [_jsonable(item, max_depth=max_depth, depth=depth + 1) for item in items[:50]]
-        if len(items) > 50:
-            result.append(f"... {len(items) - 50} more")
-        return result
-    return str(value)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/benchmarks/generate_async_scheduling_idle_report.py b/scripts/benchmarks/generate_async_scheduling_idle_report.py
deleted file mode 100644
index ad17e1b87..000000000
--- a/scripts/benchmarks/generate_async_scheduling_idle_report.py
+++ /dev/null
@@ -1,1775 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-"""Generate an HTML idle-time analysis report for async scheduling benchmarks."""
-
-from __future__ import annotations
-
-import argparse
-import html
-import json
-import math
-import os
-import subprocess
-import sys
-from collections.abc import Iterable, Mapping, Sequence
-from dataclasses import asdict, dataclass, replace
-from pathlib import Path
-from typing import Any
-
-DEFAULT_OUTPUT_ROOT = Path(".scratch/async-scheduling-idle-analysis")
-DEFAULT_ARTIFACT_DIR = DEFAULT_OUTPUT_ROOT / "artifacts"
-DEFAULT_REPORT_PATH = DEFAULT_OUTPUT_ROOT / "async-scheduling-idle-analysis.html"
-BENCHMARK_SCRIPT = Path("scripts/benchmarks/benchmark_async_scheduling.py")
-IDLE_SUITE_ID = "async-scheduling-idle-regression"
-IDLE_SUITE_VERSION = "1.1"
-IDLE_SUMMARY_SCHEMA_VERSION = "async-scheduling-idle-summary-v1"
-
-
-@dataclass(frozen=True)
-class IdleBenchmarkCase:
-    name: str
-    sweep: str
-    record_count: int
-    buffer_size: int
-    row_group_concurrency: int
-    task_admission_capacity: int
-    fanout_width: int
-    upstream_latency_seconds: float
-    downstream_latency_seconds: float
-    scenario: str = "real-pipeline-overlap"
-    request_latency_seconds: float = 0.0
-    model_stage_weight: int = 0
-    adaptive_row_group_admission: bool = False
-    request_pressure_advisory: bool = False
-    iterations: int = 1
-    warmups: int = 0
-
-    @property
-    def generation_count(self) -> int:
-        return self.record_count * self.fanout_width * 2
-
-
-@dataclass(frozen=True)
-class IdleBenchmarkResult:
-    case: IdleBenchmarkCase
-    artifact_path: Path
-    mean_wall_time_seconds: float
-    p95_wall_time_seconds: float
-    llm_utilization_ratio: float
-    llm_idle_ratio: float
-    llm_starved_idle_ratio: float
-    llm_frontier_dependency_horizon_idle_ratio: float
-    llm_starved_idle_seconds: float
-    llm_frontier_dependency_horizon_idle_seconds: float
-    llm_scheduler_queue_age_p95_seconds: float
-    llm_scheduler_queue_age_max_seconds: float
-    llm_ready_gap_p95_seconds: float
-    llm_ready_gap_max_seconds: float
-    llm_burstiness_coefficient: float
-    submission_utilization_ratio: float
-    submission_starved_idle_ratio: float
-    submission_frontier_dependency_horizon_idle_ratio: float
-    pipeline_overlap_ratio: float
-    downstream_ready_gap_p95_seconds: float
-    downstream_ready_gap_max_seconds: float
-    throughput_generations_per_second: float
-    request_wait_seconds_while_task_leased_mean: float
-    request_wait_seconds_while_task_leased_max: float
-    request_utilization_ratio: float
-    request_idle_ratio: float
-    request_starved_idle_ratio: float
-    request_frontier_dependency_horizon_idle_ratio: float
-    request_burstiness_coefficient: float
-    request_pressure_advisory_skip_count: int
-    first_model_dispatch_column: str | None
-    request_pressure_advisory_enabled: bool
-    row_group_admission_mode: str
-    row_group_admission_target: int | None
-    row_group_admission_observed_max_target: int | None
-    row_group_admission_max_admitted_rows: int | None
-    validation_passed: bool
-    final_zero_task_leases: bool
-    final_zero_request_leases: bool
-    final_zero_request_waiters: bool
-
-
-def main() -> None:
-    args = _parse_args()
-    artifact_dir = Path(args.artifact_dir)
-    report_path = Path(args.report_path)
-    artifact_dir.mkdir(parents=True, exist_ok=True)
-    report_path.parent.mkdir(parents=True, exist_ok=True)
-
-    results = run_idle_benchmark_suite(artifact_dir, quick=args.quick, skip_run=args.skip_run)
-    render_idle_report(results, report_path, artifact_dir)
-    if args.summary_path:
-        write_idle_results_summary(Path(args.summary_path), results, quick=args.quick)
-
-    print(f"Wrote {report_path}")
-    print(f"Wrote benchmark artifacts under {artifact_dir}")
-    if args.summary_path:
-        print(f"Wrote {args.summary_path}")
-
-
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--artifact-dir", default=str(DEFAULT_ARTIFACT_DIR))
-    parser.add_argument("--report-path", default=str(DEFAULT_REPORT_PATH))
-    parser.add_argument("--summary-path")
-    parser.add_argument("--skip-run", action="store_true", help="Reuse existing benchmark JSON files.")
-    parser.add_argument(
-        "--quick",
-        action="store_true",
-        help="Run a shorter smoke suite while preserving every sweep dimension.",
-    )
-    return parser.parse_args()
-
-
-def build_idle_benchmark_cases(*, quick: bool) -> list[IdleBenchmarkCase]:
-    return _build_cases(quick)
-
-
-def run_idle_benchmark_suite(
-    artifact_dir: Path,
-    *,
-    quick: bool,
-    skip_run: bool,
-) -> list[IdleBenchmarkResult]:
-    cases = build_idle_benchmark_cases(quick=quick)
-    return [_run_or_load_case(case, artifact_dir, skip_run=skip_run) for case in cases]
-
-
-def render_idle_report(
-    results: Sequence[IdleBenchmarkResult],
-    report_path: Path,
-    artifact_dir: Path,
-) -> None:
-    report_path.parent.mkdir(parents=True, exist_ok=True)
-    report_path.write_text(_render_report(results, report_path, artifact_dir), encoding="utf-8")
-
-
-def write_idle_results_summary(
-    summary_path: Path,
-    results: Sequence[IdleBenchmarkResult],
-    *,
-    quick: bool,
-) -> None:
-    summary_path.parent.mkdir(parents=True, exist_ok=True)
-    summary = idle_results_summary(results, quick=quick)
-    summary_path.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8")
-
-
-def idle_results_summary(results: Sequence[IdleBenchmarkResult], *, quick: bool) -> dict[str, Any]:
-    cases = {_case_key(result): _result_summary(result) for result in results}
-    return {
-        "summary_schema_version": IDLE_SUMMARY_SCHEMA_VERSION,
-        "suite_id": IDLE_SUITE_ID,
-        "suite_version": IDLE_SUITE_VERSION,
-        "mode": "quick" if quick else "full",
-        "case_count": len(results),
-        "largest_generation_count": max((result.case.generation_count for result in results), default=0),
-        "cases": cases,
-    }
-
-
-def _result_summary(result: IdleBenchmarkResult) -> dict[str, Any]:
-    return {
-        "case": asdict(result.case),
-        "artifact_path": str(result.artifact_path),
-        "generation_count": result.case.generation_count,
-        "mean_wall_time_seconds": result.mean_wall_time_seconds,
-        "p95_wall_time_seconds": result.p95_wall_time_seconds,
-        "llm_wait_utilization_ratio": result.llm_utilization_ratio,
-        "llm_wait_idle_ratio": result.llm_idle_ratio,
-        "llm_wait_starved_idle_ratio": result.llm_starved_idle_ratio,
-        "llm_wait_frontier_dependency_horizon_idle_ratio": result.llm_frontier_dependency_horizon_idle_ratio,
-        "llm_wait_dependency_horizon_idle_ratio": result.llm_frontier_dependency_horizon_idle_ratio,
-        "llm_wait_starved_idle_seconds": result.llm_starved_idle_seconds,
-        "llm_wait_frontier_dependency_horizon_idle_seconds": result.llm_frontier_dependency_horizon_idle_seconds,
-        "llm_wait_dependency_horizon_idle_seconds": result.llm_frontier_dependency_horizon_idle_seconds,
-        "llm_wait_scheduler_queue_age_p95_seconds": result.llm_scheduler_queue_age_p95_seconds,
-        "llm_wait_scheduler_queue_age_max_seconds": result.llm_scheduler_queue_age_max_seconds,
-        "llm_wait_ready_gap_p95_seconds": result.llm_ready_gap_p95_seconds,
-        "llm_wait_ready_gap_max_seconds": result.llm_ready_gap_max_seconds,
-        "llm_wait_burstiness_coefficient": result.llm_burstiness_coefficient,
-        "submission_utilization_ratio": result.submission_utilization_ratio,
-        "submission_starved_idle_ratio": result.submission_starved_idle_ratio,
-        "submission_frontier_dependency_horizon_idle_ratio": (result.submission_frontier_dependency_horizon_idle_ratio),
-        "pipeline_overlap_ratio": result.pipeline_overlap_ratio,
-        "downstream_ready_gap_p95_seconds": result.downstream_ready_gap_p95_seconds,
-        "downstream_ready_gap_max_seconds": result.downstream_ready_gap_max_seconds,
-        "throughput_generations_per_second": result.throughput_generations_per_second,
-        "request_wait_seconds_while_task_leased_mean": result.request_wait_seconds_while_task_leased_mean,
-        "request_wait_seconds_while_task_leased_max": result.request_wait_seconds_while_task_leased_max,
-        "request_utilization_ratio": result.request_utilization_ratio,
-        "request_idle_ratio": result.request_idle_ratio,
-        "request_starved_idle_ratio": result.request_starved_idle_ratio,
-        "request_frontier_dependency_horizon_idle_ratio": result.request_frontier_dependency_horizon_idle_ratio,
-        "request_burstiness_coefficient": result.request_burstiness_coefficient,
-        "request_pressure_advisory_skip_count": result.request_pressure_advisory_skip_count,
-        "first_model_dispatch_column": result.first_model_dispatch_column,
-        "request_pressure_advisory_enabled": result.request_pressure_advisory_enabled,
-        "row_group_admission_mode": result.row_group_admission_mode,
-        "row_group_admission_target": result.row_group_admission_target,
-        "row_group_admission_observed_max_target": result.row_group_admission_observed_max_target,
-        "row_group_admission_max_admitted_rows": result.row_group_admission_max_admitted_rows,
-        "validation_passed": result.validation_passed,
-        "final_zero_task_leases": result.final_zero_task_leases,
-        "final_zero_request_leases": result.final_zero_request_leases,
-        "final_zero_request_waiters": result.final_zero_request_waiters,
-        "dominant_idle_class": _dominant_idle_class(result),
-        "recommended_next_lever": _recommended_next_lever(result),
-    }
-
-
-def _case_key(result: IdleBenchmarkResult) -> str:
-    return f"{result.case.sweep}/{result.case.name}"
-
-
-def _build_cases(quick: bool) -> list[IdleBenchmarkCase]:
-    base = IdleBenchmarkCase(
-        name="baseline-frontier",
-        sweep="baseline",
-        record_count=512,
-        buffer_size=64,
-        row_group_concurrency=4,
-        task_admission_capacity=8,
-        fanout_width=4,
-        upstream_latency_seconds=0.003,
-        downstream_latency_seconds=0.0003,
-    )
-    rows = [64, 128, 256] if quick else [64, 128, 256, 512, 1024]
-    row_group_concurrency = [1, 2, 4] if quick else [1, 2, 4, 8]
-    buffers = [1, 16, 64] if quick else [1, 4, 16, 64, 256]
-    capacities = [4, 8] if quick else [4, 8, 16]
-    adaptation_rows = 512 if quick else 1024
-    combined_adaptation_rows = 128 if quick else 512
-
-    cases = [
-        replace(base, name=f"rows-{record_count}", sweep="row-scale", record_count=record_count)
-        for record_count in rows
-    ]
-    cases.extend(
-        replace(
-            base,
-            name=f"row-groups-{concurrency}",
-            sweep="row-group-concurrency",
-            record_count=512,
-            buffer_size=1,
-            row_group_concurrency=concurrency,
-            fanout_width=1,
-        )
-        for concurrency in row_group_concurrency
-    )
-    cases.extend(
-        replace(base, name=f"buffer-{buffer_size}", sweep="buffer-size", record_count=512, buffer_size=buffer_size)
-        for buffer_size in buffers
-    )
-    cases.extend(
-        replace(
-            base,
-            name=f"capacity-{capacity}",
-            sweep="llm-capacity",
-            record_count=512,
-            buffer_size=1,
-            task_admission_capacity=capacity,
-            row_group_concurrency=4,
-            fanout_width=1,
-        )
-        for capacity in capacities
-    )
-    cases.extend(
-        (
-            replace(
-                base,
-                name="weight-custom-model-1",
-                sweep="custom-model-weight",
-                record_count=512,
-                model_stage_weight=1,
-            ),
-            replace(
-                base,
-                name="weight-model-capacity",
-                sweep="custom-model-weight",
-                record_count=512,
-                model_stage_weight=0,
-            ),
-        )
-    )
-    cases.extend(
-        (
-            replace(
-                base,
-                name="narrow-frontier-high-cap",
-                sweep="stress-shape",
-                record_count=1024,
-                buffer_size=1,
-                row_group_concurrency=1,
-                task_admission_capacity=16,
-                fanout_width=1,
-                upstream_latency_seconds=0.006,
-                downstream_latency_seconds=0.0003,
-                iterations=3,
-            ),
-            replace(
-                base,
-                name="wide-frontier-high-cap",
-                sweep="stress-shape",
-                record_count=1024,
-                buffer_size=64,
-                row_group_concurrency=16,
-                task_admission_capacity=16,
-                fanout_width=4,
-                upstream_latency_seconds=0.006,
-                downstream_latency_seconds=0.0003,
-                iterations=3,
-            ),
-        )
-    )
-    cases.extend(
-        (
-            replace(
-                base,
-                name="adaptive-row-groups-fixed-low",
-                sweep="adaptations",
-                record_count=adaptation_rows,
-                buffer_size=1,
-                row_group_concurrency=1,
-                task_admission_capacity=16,
-                fanout_width=1,
-                upstream_latency_seconds=0.006,
-                downstream_latency_seconds=0.0003,
-            ),
-            replace(
-                base,
-                name="adaptive-row-groups-adaptive",
-                sweep="adaptations",
-                record_count=adaptation_rows,
-                buffer_size=1,
-                row_group_concurrency=8,
-                task_admission_capacity=16,
-                fanout_width=1,
-                upstream_latency_seconds=0.006,
-                downstream_latency_seconds=0.0003,
-                adaptive_row_group_admission=True,
-            ),
-            replace(
-                base,
-                name="adaptive-row-groups-fixed-high",
-                sweep="adaptations",
-                record_count=adaptation_rows,
-                buffer_size=1,
-                row_group_concurrency=8,
-                task_admission_capacity=16,
-                fanout_width=1,
-                upstream_latency_seconds=0.006,
-                downstream_latency_seconds=0.0003,
-            ),
-            replace(
-                base,
-                name="request-pressure-control",
-                sweep="adaptations",
-                scenario="request-pressure-advisory",
-                record_count=32,
-                buffer_size=32,
-                row_group_concurrency=1,
-                task_admission_capacity=1,
-                fanout_width=1,
-                request_latency_seconds=0.05,
-                upstream_latency_seconds=0.0,
-                downstream_latency_seconds=0.001,
-            ),
-            replace(
-                base,
-                name="request-pressure-advisory",
-                sweep="adaptations",
-                scenario="request-pressure-advisory",
-                record_count=32,
-                buffer_size=32,
-                row_group_concurrency=1,
-                task_admission_capacity=1,
-                fanout_width=1,
-                request_latency_seconds=0.05,
-                upstream_latency_seconds=0.0,
-                downstream_latency_seconds=0.001,
-                request_pressure_advisory=True,
-            ),
-            replace(
-                base,
-                name="adaptive-request-pressure-control",
-                sweep="adaptations",
-                scenario="adaptive-request-pressure",
-                record_count=combined_adaptation_rows,
-                buffer_size=1,
-                row_group_concurrency=8,
-                task_admission_capacity=4,
-                fanout_width=1,
-                request_latency_seconds=0.05,
-                upstream_latency_seconds=0.0,
-                downstream_latency_seconds=0.001,
-                adaptive_row_group_admission=True,
-                iterations=3,
-            ),
-            replace(
-                base,
-                name="adaptive-request-pressure-combined",
-                sweep="adaptations",
-                scenario="adaptive-request-pressure",
-                record_count=combined_adaptation_rows,
-                buffer_size=1,
-                row_group_concurrency=8,
-                task_admission_capacity=4,
-                fanout_width=1,
-                request_latency_seconds=0.05,
-                upstream_latency_seconds=0.0,
-                downstream_latency_seconds=0.001,
-                adaptive_row_group_admission=True,
-                request_pressure_advisory=True,
-                iterations=3,
-            ),
-        )
-    )
-    return cases
-
-
-def _run_or_load_case(case: IdleBenchmarkCase, artifact_dir: Path, *, skip_run: bool) -> IdleBenchmarkResult:
-    output_dir = artifact_dir / case.sweep / case.name
-    json_path = output_dir / "async_scheduling_benchmark.json"
-    if skip_run and not json_path.exists():
-        raise FileNotFoundError(
-            f"Cannot reuse benchmark artifact for {case.sweep}/{case.name}: {json_path} does not exist."
-        )
-    if not skip_run:
-        output_dir.mkdir(parents=True, exist_ok=True)
-        command = [
-            sys.executable,
-            str(BENCHMARK_SCRIPT),
-            "--scenario",
-            case.scenario,
-            "--record-count",
-            str(case.record_count),
-            "--buffer-size",
-            str(case.buffer_size),
-            "--row-group-concurrency",
-            str(case.row_group_concurrency),
-            "--task-admission-capacity",
-            str(case.task_admission_capacity),
-            "--fanout-width",
-            str(case.fanout_width),
-            "--upstream-latency-seconds",
-            _format_float(case.upstream_latency_seconds),
-            "--downstream-latency-seconds",
-            _format_float(case.downstream_latency_seconds),
-            "--request-latency-seconds",
-            _format_float(case.request_latency_seconds),
-            "--model-stage-weight",
-            str(case.model_stage_weight),
-            "--warmups",
-            str(case.warmups),
-            "--iterations",
-            str(case.iterations),
-            "--output-dir",
-            str(output_dir),
-        ]
-        if case.adaptive_row_group_admission:
-            command.append("--adaptive-row-group-admission")
-        if case.request_pressure_advisory:
-            command.append("--request-pressure-advisory")
-        subprocess.run(command, check=True)
-    artifact = json.loads(json_path.read_text(encoding="utf-8"))
-    return _extract_result(case, json_path, artifact)
-
-
-def _extract_result(case: IdleBenchmarkCase, artifact_path: Path, artifact: Mapping[str, Any]) -> IdleBenchmarkResult:
-    metrics = artifact["derived_metrics"]
-    scheduler_resources = metrics.get("scheduler_resource_utilization", {})
-    request_resources = metrics.get("request_resource_utilization", {})
-    llm = scheduler_resources.get("llm_wait", {})
-    submission = scheduler_resources.get("submission", {})
-    mean_wall = _float(metrics, "mean_wall_time_seconds")
-    row_group_admission = _row_group_admission_snapshot(artifact)
-    return IdleBenchmarkResult(
-        case=case,
-        artifact_path=artifact_path,
-        mean_wall_time_seconds=mean_wall,
-        p95_wall_time_seconds=_float(metrics, "p95_wall_time_seconds"),
-        llm_utilization_ratio=_float(llm, "mean_utilization_ratio"),
-        llm_idle_ratio=_safe_ratio(
-            _float(llm, "mean_idle_capacity_seconds"),
-            _mean_capacity_seconds(artifact, "llm_wait"),
-        ),
-        llm_starved_idle_ratio=_safe_ratio(
-            _float(llm, "mean_starved_idle_seconds"),
-            _mean_capacity_seconds(artifact, "llm_wait"),
-        ),
-        llm_frontier_dependency_horizon_idle_ratio=_idle_ratio(
-            llm,
-            artifact,
-            "llm_wait",
-            "mean_frontier_dependency_horizon_idle_ratio",
-        ),
-        llm_starved_idle_seconds=_float(llm, "mean_starved_idle_seconds"),
-        llm_frontier_dependency_horizon_idle_seconds=_float(
-            llm,
-            "mean_frontier_dependency_horizon_idle_seconds",
-            fallback_key="mean_dependency_horizon_idle_seconds",
-        ),
-        llm_scheduler_queue_age_p95_seconds=max(
-            _max_iteration_metric(
-                artifact,
-                "llm_wait",
-                "scheduler_queue_age_p95_seconds",
-            ),
-            _max_iteration_metric(
-                artifact,
-                "llm_wait",
-                "ready_to_dispatch_gap_p95_seconds",
-            ),
-        ),
-        llm_scheduler_queue_age_max_seconds=_float(
-            llm,
-            "max_scheduler_queue_age_seconds",
-            fallback_key="max_ready_to_dispatch_gap_seconds",
-        ),
-        llm_ready_gap_p95_seconds=_max_iteration_metric(
-            artifact,
-            "llm_wait",
-            "ready_to_dispatch_gap_p95_seconds",
-        ),
-        llm_ready_gap_max_seconds=_float(llm, "max_ready_to_dispatch_gap_seconds"),
-        llm_burstiness_coefficient=_float(llm, "max_burstiness_coefficient"),
-        submission_utilization_ratio=_float(submission, "mean_utilization_ratio"),
-        submission_starved_idle_ratio=_safe_ratio(
-            _float(submission, "mean_starved_idle_seconds"),
-            _mean_capacity_seconds(artifact, "submission"),
-        ),
-        submission_frontier_dependency_horizon_idle_ratio=_idle_ratio(
-            submission,
-            artifact,
-            "submission",
-            "mean_frontier_dependency_horizon_idle_ratio",
-        ),
-        pipeline_overlap_ratio=_float(metrics, "pipeline_mean_overlap_ratio"),
-        downstream_ready_gap_p95_seconds=_float(metrics, "pipeline_p95_downstream_ready_gap_seconds"),
-        downstream_ready_gap_max_seconds=_float(metrics, "pipeline_max_downstream_ready_gap_seconds"),
-        throughput_generations_per_second=_safe_ratio(case.generation_count, mean_wall),
-        request_wait_seconds_while_task_leased_mean=_float(metrics, "request_wait_seconds_while_task_leased_mean"),
-        request_wait_seconds_while_task_leased_max=_float(metrics, "request_wait_seconds_while_task_leased_max"),
-        request_utilization_ratio=_mean_resource_metric(request_resources, "mean_utilization_ratio"),
-        request_idle_ratio=_safe_ratio(
-            _sum_resource_metric(request_resources, "mean_idle_capacity_seconds"),
-            _sum_request_capacity_seconds(artifact),
-        ),
-        request_starved_idle_ratio=_safe_ratio(
-            _sum_resource_metric(request_resources, "mean_starved_idle_seconds"),
-            _sum_request_capacity_seconds(artifact),
-        ),
-        request_frontier_dependency_horizon_idle_ratio=_safe_ratio(
-            _sum_resource_metric(
-                request_resources,
-                "mean_frontier_dependency_horizon_idle_seconds",
-                fallback_key="mean_dependency_horizon_idle_seconds",
-            ),
-            _sum_request_capacity_seconds(artifact),
-        ),
-        request_burstiness_coefficient=_max_resource_metric(request_resources, "max_burstiness_coefficient"),
-        request_pressure_advisory_skip_count=int(metrics.get("request_pressure_advisory_skip_count", 0) or 0),
-        first_model_dispatch_column=_optional_str(metrics.get("first_model_dispatch_column")),
-        request_pressure_advisory_enabled=bool(metrics.get("request_pressure_advisory_enabled", False)),
-        row_group_admission_mode=str(row_group_admission.get("mode", "fixed")),
-        row_group_admission_target=_optional_int(row_group_admission.get("target_in_flight")),
-        row_group_admission_observed_max_target=_optional_int(row_group_admission.get("observed_max_target")),
-        row_group_admission_max_admitted_rows=_optional_int(row_group_admission.get("max_admitted_rows")),
-        validation_passed=bool(metrics.get("pipeline_validation_passed", True)),
-        final_zero_task_leases=bool(metrics.get("final_zero_task_leases", False)),
-        final_zero_request_leases=bool(metrics.get("final_zero_request_leases", False)),
-        final_zero_request_waiters=bool(metrics.get("final_zero_request_waiters", False)),
-    )
-
-
-def _render_report(
-    results: Sequence[IdleBenchmarkResult],
-    report_path: Path,
-    artifact_dir: Path,
-) -> str:
-    by_sweep = _group_results(results)
-    all_valid = all(
-        result.validation_passed
-        and result.final_zero_task_leases
-        and result.final_zero_request_leases
-        and result.final_zero_request_waiters
-        for result in results
-    )
-    max_generations = max((result.case.generation_count for result in results), default=0)
-    worst_idle = max(results, key=lambda result: result.llm_starved_idle_ratio)
-    worst_total_idle = max(results, key=lambda result: result.llm_idle_ratio)
-    best_util = max(results, key=lambda result: result.llm_utilization_ratio)
-    median_util = _median(result.llm_utilization_ratio for result in results)
-    artifact_link = _relative_href(report_path, artifact_dir)
-
-    return "\n".join(
-        [
-            "<!doctype html>",
-            '<html lang="en">',
-            "<head>",
-            '<meta charset="utf-8">',
-            '<meta name="viewport" content="width=device-width, initial-scale=1">',
-            "<title>Async Scheduling Idle-Time Analysis</title>",
-            _style_block(),
-            "</head>",
-            "<body>",
-            "<main>",
-            '<section class="hero">',
-            '<p class="eyebrow">DataDesigner async scheduler</p>',
-            "<h1>Idle-Time Analysis</h1>",
-            '<p class="lede">This report measures where model-capacity idle time appears, '
-            "which knobs change it, and which remaining idle looks avoidable by scheduler or "
-            "admission-control changes.</p>",
-            _summary_grid(
-                (
-                    ("Benchmark cases", f"{len(results)}"),
-                    ("Largest synthetic generation count", f"{max_generations:,}"),
-                    ("Median llm_wait utilization", _format_percent(median_util)),
-                    ("Worst total llm_wait idle", _format_percent(worst_total_idle.llm_idle_ratio)),
-                    ("Worst starved llm_wait idle", _format_percent(worst_idle.llm_starved_idle_ratio)),
-                    ("Best llm_wait utilization", _format_percent(best_util.llm_utilization_ratio)),
-                    ("Validation", "pass" if all_valid else "check failures"),
-                )
-            ),
-            "</section>",
-            "<section>",
-            "<h2>What Counts As Idle</h2>",
-            "<p><strong>General idle</strong> is unused configured capacity. It includes expected idle when "
-            "dependencies are not ready. <strong>Starved idle</strong> is the actionable subset: a resource "
-            "has idle slots while the scheduler already has queued work that requests that resource. "
-            "<strong>Frontier/dependency-horizon idle</strong> is total idle minus starved idle: capacity "
-            "is unused because dependency work has not yet exposed a ready scheduler task. "
-            "<strong>Scheduler queue age</strong> measures ready-enqueued to lease-acquired time; "
-            "<strong>downstream ready gap</strong> measures dependency-complete to downstream-dispatch time. "
-            "For this refactor, the most important resource is <code>llm_wait</code>, because it maps to "
-            "model-serving capacity that should stay busy when runnable model work exists.</p>",
-            "</section>",
-            "<section>",
-            "<h2>Implementation Improvements</h2>",
-            _implementation_improvements_html(results),
-            "</section>",
-            "<section>",
-            "<h2>Findings</h2>",
-            _findings_html(results, by_sweep),
-            "</section>",
-            "<section>",
-            "<h2>Idle Classification Board</h2>",
-            _idle_classification_table(results),
-            "</section>",
-            "<section>",
-            "<h2>Adaptation Benchmarks</h2>",
-            _adaptation_benchmarks_html(results),
-            "</section>",
-            "<section>",
-            "<h2>Figures</h2>",
-            _line_chart(
-                "Scale: more rows smooth the model-resource flow",
-                _sort(by_sweep["row-scale"], "generation_count"),
-                x_getter=lambda result: result.case.generation_count,
-                y_series=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
-                ),
-                y_max=1.0,
-                x_label="synthetic model generations",
-                y_label="ratio",
-            ),
-            _line_chart(
-                "Frontier width: row-group concurrency changes starvation",
-                _sort(by_sweep["row-group-concurrency"], "row_group_concurrency"),
-                x_getter=lambda result: result.case.row_group_concurrency,
-                y_series=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
-                    ("downstream ready p95", lambda result: result.downstream_ready_gap_p95_seconds, "#6c5ce7"),
-                ),
-                x_label="row-group concurrency",
-                y_label="ratio / seconds",
-            ),
-            _line_chart(
-                "Buffer size: row-group shape affects traffic waves",
-                _sort(by_sweep["buffer-size"], "buffer_size"),
-                x_getter=lambda result: result.case.buffer_size,
-                y_series=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
-                    ("scheduler queue age p95", lambda result: result.llm_scheduler_queue_age_p95_seconds, "#6c5ce7"),
-                ),
-                x_label="buffer size",
-                y_label="ratio / seconds",
-            ),
-            _line_chart(
-                "Capacity scaling: underfeeding becomes visible as cap rises",
-                _sort(by_sweep["llm-capacity"], "task_admission_capacity"),
-                x_getter=lambda result: result.case.task_admission_capacity // 2,
-                y_series=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
-                    ("throughput / 1000", lambda result: result.throughput_generations_per_second / 1000.0, "#326273"),
-                ),
-                x_label="modeled llm_wait capacity",
-                y_label="ratio / kgen/s",
-            ),
-            _bar_chart(
-                "Custom-model metadata changes per-stage fairness",
-                _sort(by_sweep["custom-model-weight"], "model_stage_weight"),
-                label_getter=_weight_label,
-                bars=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
-                ),
-                y_max=1.0,
-            ),
-            _bar_chart(
-                "Stress shapes: narrow vs wide runnable horizon",
-                _sort(by_sweep["stress-shape"], "name"),
-                label_getter=lambda result: result.case.name.replace("-", " "),
-                bars=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    ("total llm_wait idle", lambda result: result.llm_idle_ratio, "#8a6f3d"),
-                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
-                    (
-                        "frontier/dependency idle",
-                        lambda result: result.llm_frontier_dependency_horizon_idle_ratio,
-                        "#6c5ce7",
-                    ),
-                    ("burstiness / 2", lambda result: result.llm_burstiness_coefficient / 2.0, "#326273"),
-                ),
-                y_max=1.0,
-            ),
-            _bar_chart(
-                "Adaptation: adaptive row groups target frontier idle",
-                [
-                    result
-                    for result in _sort(by_sweep.get("adaptations", []), "name")
-                    if result.case.name.startswith("adaptive-row-groups")
-                ],
-                label_getter=lambda result: result.case.name.replace("adaptive-row-groups-", ""),
-                bars=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    (
-                        "frontier/dependency idle",
-                        lambda result: result.llm_frontier_dependency_horizon_idle_ratio,
-                        "#6c5ce7",
-                    ),
-                    ("starved llm_wait idle", lambda result: result.llm_starved_idle_ratio, "#b23a48"),
-                ),
-                y_max=1.0,
-            ),
-            _bar_chart(
-                "Adaptation: request-pressure advisory avoids leased request wait",
-                [
-                    result
-                    for result in _sort(by_sweep.get("adaptations", []), "name")
-                    if result.case.name.startswith("request-pressure")
-                ],
-                label_getter=lambda result: result.case.name.replace("request-pressure-", ""),
-                bars=(
-                    (
-                        "request wait while leased",
-                        lambda result: result.request_wait_seconds_while_task_leased_mean,
-                        "#b23a48",
-                    ),
-                    ("scheduler queue age p95", lambda result: result.llm_scheduler_queue_age_p95_seconds, "#6c5ce7"),
-                    ("wall seconds / 10", lambda result: result.mean_wall_time_seconds / 10.0, "#1f7a8c"),
-                ),
-            ),
-            _bar_chart(
-                "Adaptation: combined adaptive frontier and request pressure",
-                [
-                    result
-                    for result in _sort(by_sweep.get("adaptations", []), "name")
-                    if result.case.name.startswith("adaptive-request-pressure")
-                ],
-                label_getter=lambda result: result.case.name.replace("adaptive-request-pressure-", ""),
-                bars=(
-                    ("llm_wait utilization", lambda result: result.llm_utilization_ratio, "#1f7a8c"),
-                    ("request utilization", lambda result: result.request_utilization_ratio, "#326273"),
-                    ("request starved idle", lambda result: result.request_starved_idle_ratio, "#b23a48"),
-                    (
-                        "frontier/dependency idle",
-                        lambda result: result.llm_frontier_dependency_horizon_idle_ratio,
-                        "#6c5ce7",
-                    ),
-                ),
-                y_max=1.0,
-            ),
-            "</section>",
-            "<section>",
-            "<h2>Benchmark Matrix</h2>",
-            _results_table(results, report_path),
-            f'<p class="artifact-note">Raw artifacts are under <a href="{html.escape(artifact_link)}">'
-            f"{html.escape(str(artifact_dir))}</a>.</p>",
-            "</section>",
-            "<section>",
-            "<h2>Potential Changes</h2>",
-            _recommendations_html(results),
-            "</section>",
-            "</main>",
-            "</body>",
-            "</html>",
-            "",
-        ]
-    )
-
-
-def _findings_html(
-    results: Sequence[IdleBenchmarkResult],
-    by_sweep: Mapping[str, list[IdleBenchmarkResult]],
-) -> str:
-    row_scale = _sort(by_sweep["row-scale"], "generation_count")
-    row_gain = row_scale[-1].llm_utilization_ratio - row_scale[0].llm_utilization_ratio
-    rg_results = _sort(by_sweep["row-group-concurrency"], "row_group_concurrency")
-    rg_util_gain = rg_results[-1].llm_utilization_ratio - rg_results[0].llm_utilization_ratio
-    rg_idle_delta = rg_results[0].llm_starved_idle_ratio - min(result.llm_starved_idle_ratio for result in rg_results)
-    capacity_results = _sort(by_sweep["llm-capacity"], "task_admission_capacity")
-    weight_results = by_sweep["custom-model-weight"]
-    weight_one = next(result for result in weight_results if result.case.model_stage_weight == 1)
-    weight_auto = next(result for result in weight_results if result.case.model_stage_weight == 0)
-    stress = {result.case.name: result for result in by_sweep["stress-shape"]}
-    narrow = stress["narrow-frontier-high-cap"]
-    wide = stress["wide-frontier-high-cap"]
-    worst = max(results, key=lambda result: result.llm_starved_idle_ratio)
-
-    return (
-        '<ul class="finding-list">'
-        f"<li><strong>Scale helps once enough runnable work exists.</strong> The row-scale sweep moved "
-        f"from {_format_percent(row_scale[0].llm_utilization_ratio)} to "
-        f"{_format_percent(row_scale[-1].llm_utilization_ratio)} llm_wait utilization "
-        f"({_format_signed_percent(row_gain)}). Larger runs amortize startup and drain phases.</li>"
-        f"<li><strong>Runnable frontier width is the main controllable idle source.</strong> In the "
-        f"row-group sweep, utilization moved from {_format_percent(rg_results[0].llm_utilization_ratio)} "
-        f"to {_format_percent(rg_results[-1].llm_utilization_ratio)} "
-        f"({_format_signed_percent(rg_util_gain)}), while starved llm_wait idle improved by "
-        f"{_format_percent(max(0.0, rg_idle_delta))} between the narrowest case and the best observed case.</li>"
-        f"<li><strong>Total idle and starved idle diagnose different causes.</strong> "
-        f"<code>{html.escape(narrow.case.name)}</code> had {_format_percent(narrow.llm_idle_ratio)} total "
-        f"llm_wait idle but only {_format_percent(narrow.llm_starved_idle_ratio)} starved idle. The remaining "
-        f"{_format_percent(narrow.llm_frontier_dependency_horizon_idle_ratio)} was frontier/dependency-horizon "
-        "idle: the scheduler often had no ready model work because the admitted frontier was too small.</li>"
-        f"<li><strong>Capacity increases expose underfeeding.</strong> In the capacity sweep, modeled "
-        f"llm_wait cap {max(1, capacity_results[0].case.task_admission_capacity // 2)} reached "
-        f"{_format_percent(capacity_results[0].llm_utilization_ratio)} utilization, while cap "
-        f"{max(1, capacity_results[-1].case.task_admission_capacity // 2)} reached "
-        f"{_format_percent(capacity_results[-1].llm_utilization_ratio)} with the same small row frontier.</li>"
-        f"<li><strong>Custom model scheduling metadata changes traffic shape.</strong> With synthetic "
-        f"<code>custom_model</code> weight forced to 1, llm_wait utilization was "
-        f"{_format_percent(weight_one.llm_utilization_ratio)}; with the harness defaulting the weight "
-        f"to modeled model capacity it was {_format_percent(weight_auto.llm_utilization_ratio)}. "
-        "This run did not show weight=1 underfeeding because each synthetic column is its own group, "
-        "but it does show that group identity and weight are first-order benchmark inputs.</li>"
-        f"<li><strong>High capacity magnifies traffic-shape effects.</strong> The stress case with one "
-        f"large row group and high cap reached {_format_percent(narrow.llm_utilization_ratio)} utilization; "
-        f"the wide-frontier shape reached {_format_percent(wide.llm_utilization_ratio)}. "
-        "This is the vLLM-server-idle failure mode the scheduler needs to expose and track over time.</li>"
-        f"<li><strong>Worst observed actionable idle:</strong> {html.escape(worst.case.name)} had "
-        f"{_format_percent(worst.llm_starved_idle_ratio)} starved llm_wait idle and "
-        f"{_format_seconds(worst.llm_scheduler_queue_age_max_seconds)} max scheduler queue age.</li>"
-        "</ul>"
-    )
-
-
-def _find_result(
-    results: Sequence[IdleBenchmarkResult],
-    sweep: str,
-    name: str,
-) -> IdleBenchmarkResult | None:
-    return next((result for result in results if result.case.sweep == sweep and result.case.name == name), None)
-
-
-def _adaptive_row_group_card_evidence(
-    control: IdleBenchmarkResult | None,
-    adaptive: IdleBenchmarkResult | None,
-    row_groups_one: IdleBenchmarkResult,
-    narrow: IdleBenchmarkResult,
-) -> str:
-    if control is None or adaptive is None:
-        return (
-            f"<code>{html.escape(row_groups_one.case.name)}</code> and "
-            f"<code>{html.escape(narrow.case.name)}</code> remain the target proof cases; adaptation "
-            "benchmark artifacts were not available in this run."
-        )
-    return (
-        f"<code>{html.escape(control.case.name)}</code> to <code>{html.escape(adaptive.case.name)}</code>: "
-        f"utilization {_format_signed_percent(adaptive.llm_utilization_ratio - control.llm_utilization_ratio)}, "
-        f"frontier idle reduction "
-        f"{_format_percent(max(0.0, control.llm_frontier_dependency_horizon_idle_ratio - adaptive.llm_frontier_dependency_horizon_idle_ratio))}, "
-        f"observed target {_format_optional_int(adaptive.row_group_admission_observed_max_target)}."
-    )
-
-
-def _request_pressure_card_evidence(
-    control: IdleBenchmarkResult | None,
-    advisory: IdleBenchmarkResult | None,
-) -> str:
-    if control is None or advisory is None:
-        return "Needs adaptation benchmark artifacts from a non-skip idle-regression run."
-    return (
-        f"Leased request wait moved from {_format_seconds(control.request_wait_seconds_while_task_leased_mean)} "
-        f"to {_format_seconds(advisory.request_wait_seconds_while_task_leased_mean)}; first dispatch moved "
-        f"from <code>{html.escape(control.first_model_dispatch_column or 'unknown')}</code> to "
-        f"<code>{html.escape(advisory.first_model_dispatch_column or 'unknown')}</code>."
-    )
-
-
-def _implementation_improvements_html(results: Sequence[IdleBenchmarkResult]) -> str:
-    narrow = next(result for result in results if result.case.name == "narrow-frontier-high-cap")
-    row_groups_one = next(result for result in results if result.case.name == "row-groups-1")
-    rows_256 = next(result for result in results if result.case.name == "rows-256")
-    wide = next(result for result in results if result.case.name == "wide-frontier-high-cap")
-    adaptive_control = _find_result(results, "adaptations", "adaptive-row-groups-fixed-low")
-    adaptive = _find_result(results, "adaptations", "adaptive-row-groups-adaptive")
-    pressure_control = _find_result(results, "adaptations", "request-pressure-control")
-    pressure_advisory = _find_result(results, "adaptations", "request-pressure-advisory")
-    classified = _class_counts(results)
-    return (
-        _summary_grid(
-            (
-                (
-                    "Classification now explicit",
-                    f"{classified.get('frontier/dependency-horizon', 0)} frontier, "
-                    f"{classified.get('queued-work starvation', 0)} queued",
-                ),
-                ("Quick-suite proof gates", f"{len(results)} cases, zero validation failures"),
-                ("Largest refreshed case", f"{max(result.case.generation_count for result in results):,} generations"),
-                ("Narrow frontier idle identified", _format_percent(narrow.llm_frontier_dependency_horizon_idle_ratio)),
-                (
-                    "Row-groups=1 frontier idle",
-                    _format_percent(row_groups_one.llm_frontier_dependency_horizon_idle_ratio),
-                ),
-                ("Wide queued starvation age", _format_seconds(wide.llm_scheduler_queue_age_p95_seconds)),
-            )
-        )
-        + '<div class="improvement-grid">'
-        + _improvement_card(
-            "Idle partition metrics",
-            "Implemented",
-            "The report now splits total idle into queued-work starvation and frontier/dependency-horizon idle. "
-            "This prevents the narrow high-capacity case from being mistaken for a request-admission problem.",
-            (
-                f"<code>{html.escape(narrow.case.name)}</code>: "
-                f"{_format_percent(narrow.llm_frontier_dependency_horizon_idle_ratio)} frontier idle, "
-                f"{_format_percent(narrow.llm_starved_idle_ratio)} starved idle."
-            ),
-            "../scripts/benchmarks/benchmark_async_scheduling.py",
-        )
-        + _improvement_card(
-            "Queue age vs downstream delay",
-            "Implemented",
-            "Ready-to-lease queue age is now shown separately from dependency-complete-to-dispatch delay. "
-            "This distinguishes scheduler backlog from slow dependency propagation.",
-            (
-                f"<code>{html.escape(rows_256.case.name)}</code>: "
-                f"{_format_seconds(rows_256.llm_scheduler_queue_age_p95_seconds)} scheduler queue p95, "
-                f"{_format_seconds(rows_256.downstream_ready_gap_p95_seconds)} downstream p95."
-            ),
-            "../scripts/benchmarks/generate_async_scheduling_idle_report.py",
-        )
-        + _improvement_card(
-            "Batched frontier enqueue",
-            "Implemented",
-            "A frontier delta with many ready tasks now enters the fair queue in one enqueue operation, while "
-            "pre-batch parking, dropped rows, and per-task observability are preserved.",
-            "Focused scheduler tests prove one queue operation for a 5-task frontier and one operation for a "
-            "pre-batch flush after a dropped row.",
-            "../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py",
-        )
-        + _improvement_card(
-            "Resource-overlap peer pressure",
-            "Implemented",
-            "Task admission now applies group-cap peer pressure only for queued peers that can use the same "
-            "typed resource and are hard-resource eligible. Local/submission-only peers no longer create "
-            "false pressure on idle model capacity.",
-            "Unit tests cover non-overlapping peers, overlapping <code>llm_wait</code> peers, and peers blocked "
-            "by another hard resource.",
-            "../packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py",
-        )
-        + _improvement_card(
-            "Adaptive row-group admission",
-            "Implemented + benchmarked",
-            "The scheduler can now start with one admitted row group, raise the row-group target when "
-            "model capacity is idle and queued model demand is low, and remain bounded by the configured "
-            "hard cap.",
-            _adaptive_row_group_card_evidence(adaptive_control, adaptive, row_groups_one, narrow),
-            "../packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py",
-        )
-        + _improvement_card(
-            "Request-pressure advisory",
-            "Implemented + benchmarked",
-            "Task selection can now use request pressure snapshots to prefer an open same-frontier peer before "
-            "spawning work that would immediately wait on request capacity. It does not yield and reacquire "
-            "leases inside running generators.",
-            _request_pressure_card_evidence(pressure_control, pressure_advisory),
-            "../packages/data-designer-engine/src/data_designer/engine/models/request_admission",
-        )
-        + "</div>"
-    )
-
-
-def _improvement_card(
-    title: str,
-    status: str,
-    body: str,
-    evidence: str,
-    href: str,
-) -> str:
-    return (
-        '<article class="improvement-card">'
-        f'<div><h3>{html.escape(title)}</h3><span class="status">{html.escape(status)}</span></div>'
-        f"<p>{body}</p>"
-        f'<p class="evidence"><strong>Evidence:</strong> {evidence}</p>'
-        f'<a href="{html.escape(href)}">source</a>'
-        "</article>"
-    )
-
-
-def _idle_classification_table(results: Sequence[IdleBenchmarkResult]) -> str:
-    headers = [
-        "case",
-        "dominant idle class",
-        "util",
-        "starved idle",
-        "frontier idle",
-        "queue age p95",
-        "downstream p95",
-        "next lever",
-    ]
-    rows = []
-    for result in sorted(
-        results,
-        key=lambda item: (
-            _idle_class_priority(_dominant_idle_class(item)),
-            -(item.llm_starved_idle_ratio + item.llm_frontier_dependency_horizon_idle_ratio),
-            item.case.sweep,
-            item.case.name,
-        ),
-    ):
-        idle_class = _dominant_idle_class(result)
-        rows.append(
-            [
-                html.escape(f"{result.case.sweep}/{result.case.name}"),
-                f'<span class="idle-class idle-{html.escape(_class_slug(idle_class))}">{html.escape(idle_class)}</span>',
-                _format_percent(result.llm_utilization_ratio),
-                _format_percent(result.llm_starved_idle_ratio),
-                _format_percent(result.llm_frontier_dependency_horizon_idle_ratio),
-                _format_seconds(result.llm_scheduler_queue_age_p95_seconds),
-                _format_seconds(result.downstream_ready_gap_p95_seconds),
-                html.escape(_recommended_next_lever(result)),
-            ]
-        )
-    return (
-        "<p>This board is the main analysis surface for optimization work. Frontier/dependency-horizon cases "
-        "need row-group or scheduling-shard changes; queued-work starvation cases need queue/admission/resource "
-        "selection changes; request-pressure cases need request-backed evidence before scheduler policy changes.</p>"
-        + _html_table(headers, rows)
-    )
-
-
-def _adaptation_benchmarks_html(results: Sequence[IdleBenchmarkResult]) -> str:
-    adaptive_control = _find_result(results, "adaptations", "adaptive-row-groups-fixed-low")
-    adaptive = _find_result(results, "adaptations", "adaptive-row-groups-adaptive")
-    adaptive_high = _find_result(results, "adaptations", "adaptive-row-groups-fixed-high")
-    pressure_control = _find_result(results, "adaptations", "request-pressure-control")
-    pressure_advisory = _find_result(results, "adaptations", "request-pressure-advisory")
-    combined_control = _find_result(results, "adaptations", "adaptive-request-pressure-control")
-    combined = _find_result(results, "adaptations", "adaptive-request-pressure-combined")
-    if adaptive_control is None or adaptive is None or pressure_control is None or pressure_advisory is None:
-        return (
-            "<p>The adaptation benchmark cases are not present in this artifact set. Re-run the idle "
-            "regression suite without <code>--skip-run</code> to collect them.</p>"
-        )
-
-    adaptive_util_delta = adaptive.llm_utilization_ratio - adaptive_control.llm_utilization_ratio
-    adaptive_frontier_delta = (
-        adaptive_control.llm_frontier_dependency_horizon_idle_ratio
-        - adaptive.llm_frontier_dependency_horizon_idle_ratio
-    )
-    adaptive_high_delta = adaptive.llm_utilization_ratio - adaptive_high.llm_utilization_ratio if adaptive_high else 0.0
-    request_wait_delta = (
-        pressure_control.request_wait_seconds_while_task_leased_mean
-        - pressure_advisory.request_wait_seconds_while_task_leased_mean
-    )
-    queue_age_delta = (
-        pressure_control.llm_scheduler_queue_age_p95_seconds - pressure_advisory.llm_scheduler_queue_age_p95_seconds
-    )
-    combined_request_wait_delta = 0.0
-    combined_request_idle_delta = 0.0
-    if combined_control is not None and combined is not None:
-        combined_request_wait_delta = (
-            combined_control.request_wait_seconds_while_task_leased_mean
-            - combined.request_wait_seconds_while_task_leased_mean
-        )
-        combined_request_idle_delta = combined_control.request_idle_ratio - combined.request_idle_ratio
-    high_context = ""
-    if adaptive_high is not None:
-        high_context = (
-            f" The fixed-high ceiling reached {_format_percent(adaptive_high.llm_utilization_ratio)} utilization "
-            f"with {_format_percent(adaptive_high.llm_frontier_dependency_horizon_idle_ratio)} frontier idle."
-        )
-
-    summary = _summary_grid(
-        (
-            ("Adaptive utilization delta", _format_signed_percent(adaptive_util_delta)),
-            ("Adaptive frontier-idle reduction", _format_percent(max(0.0, adaptive_frontier_delta))),
-            ("Adaptive vs fixed-high", _format_signed_percent(adaptive_high_delta)),
-            (
-                "Adaptive observed target",
-                _format_optional_int(adaptive.row_group_admission_observed_max_target),
-            ),
-            ("Request leased-wait reduction", _format_seconds(max(0.0, request_wait_delta))),
-            (
-                "Advisory first dispatch",
-                pressure_advisory.first_model_dispatch_column or "unknown",
-            ),
-            ("Request queue-age p95 reduction", _format_seconds(max(0.0, queue_age_delta))),
-            ("Combined leased-wait delta", _format_signed_seconds(combined_request_wait_delta)),
-            ("Combined request-idle delta", _format_signed_percent(combined_request_idle_delta)),
-        )
-    )
-    rows = [
-        [
-            "adaptive fixed-low control",
-            html.escape(adaptive_control.case.name),
-            _format_percent(adaptive_control.llm_utilization_ratio),
-            _format_percent(adaptive_control.llm_frontier_dependency_horizon_idle_ratio),
-            _format_optional_int(adaptive_control.row_group_admission_observed_max_target),
-            "-",
-            "-",
-        ],
-        [
-            "adaptive enabled",
-            html.escape(adaptive.case.name),
-            _format_percent(adaptive.llm_utilization_ratio),
-            _format_percent(adaptive.llm_frontier_dependency_horizon_idle_ratio),
-            _format_optional_int(adaptive.row_group_admission_observed_max_target),
-            "-",
-            "-",
-        ],
-        [
-            "request control",
-            html.escape(pressure_control.case.name),
-            _format_percent(pressure_control.llm_utilization_ratio),
-            _format_percent(pressure_control.llm_frontier_dependency_horizon_idle_ratio),
-            "-",
-            _format_seconds(pressure_control.request_wait_seconds_while_task_leased_mean),
-            html.escape(pressure_control.first_model_dispatch_column or "unknown"),
-        ],
-        [
-            "request advisory",
-            html.escape(pressure_advisory.case.name),
-            _format_percent(pressure_advisory.llm_utilization_ratio),
-            _format_percent(pressure_advisory.llm_frontier_dependency_horizon_idle_ratio),
-            "-",
-            _format_seconds(pressure_advisory.request_wait_seconds_while_task_leased_mean),
-            html.escape(pressure_advisory.first_model_dispatch_column or "unknown"),
-        ],
-    ]
-    if combined_control is not None:
-        rows.append(
-            [
-                "combined control",
-                html.escape(combined_control.case.name),
-                _format_percent(combined_control.llm_utilization_ratio),
-                _format_percent(combined_control.llm_frontier_dependency_horizon_idle_ratio),
-                _format_optional_int(combined_control.row_group_admission_observed_max_target),
-                _format_seconds(combined_control.request_wait_seconds_while_task_leased_mean),
-                html.escape(combined_control.first_model_dispatch_column or "unknown"),
-            ]
-        )
-    if combined is not None:
-        rows.append(
-            [
-                "combined enabled",
-                html.escape(combined.case.name),
-                _format_percent(combined.llm_utilization_ratio),
-                _format_percent(combined.llm_frontier_dependency_horizon_idle_ratio),
-                _format_optional_int(combined.row_group_admission_observed_max_target),
-                _format_seconds(combined.request_wait_seconds_while_task_leased_mean),
-                html.escape(combined.first_model_dispatch_column or "unknown"),
-            ]
-        )
-    if adaptive_high is not None:
-        rows.insert(
-            2,
-            [
-                "adaptive fixed-high control",
-                html.escape(adaptive_high.case.name),
-                _format_percent(adaptive_high.llm_utilization_ratio),
-                _format_percent(adaptive_high.llm_frontier_dependency_horizon_idle_ratio),
-                _format_optional_int(adaptive_high.row_group_admission_observed_max_target),
-                "-",
-                "-",
-            ],
-        )
-    return (
-        summary + "<p>The row-group adaptation is judged against a fixed-low frontier because that is the failure "
-        "shape where capacity is idle before enough dependency work exists. It should reduce frontier idle "
-        "or raise utilization while staying below the hard row-group cap. The fixed-high control isolates "
-        "how close the adaptive policy gets to simply admitting the full hard-cap frontier up front."
-        f"{high_context}</p>"
-        "<p>The request-pressure adaptation is judged by whether it avoids dispatching the pressured model "
-        "first and reduces request-wait time while a scheduler task lease is already held. The combined "
-        "case keeps adaptive row-group admission enabled while request pressure is present, so it measures "
-        "whether the scheduler can widen the frontier and still avoid sending the first available leases "
-        "into a saturated model.</p>"
-        + _html_table(
-            [
-                "adaptation",
-                "case",
-                "util",
-                "frontier idle",
-                "observed target",
-                "leased request wait",
-                "first model dispatch",
-            ],
-            rows,
-        )
-    )
-
-
-def _recommendations_html(results: Sequence[IdleBenchmarkResult]) -> str:
-    worst = max(results, key=lambda result: result.llm_starved_idle_ratio)
-    return (
-        '<ol class="recommendations">'
-        "<li><strong>Add an adaptive row-group admission controller.</strong> A subclass or policy layer "
-        "can watch bottleneck-resource starved idle and admit another row group when model capacity is "
-        "idle while queued or pending model work remains, bounded by buffer and memory limits. When total "
-        "idle is high but starved idle is low, the controller should treat that as a frontier problem and "
-        "prefetch more row groups rather than tuning request AIMD.</li>"
-        "<li><strong>Keep resource-aware idle in the standard benchmark output.</strong> The new "
-        "<code>llm_wait</code> utilization, starved idle, frontier/dependency-horizon idle, "
-        "scheduler queue-age, downstream ready-gap, and burstiness metrics identify "
-        "whether a vLLM-like resource is genuinely idle or merely waiting for dependencies.</li>"
-        "<li><strong>Document custom-model group identity and weight.</strong> "
-        "<code>SchedulingMetadata.custom_model(...)</code> controls fairness and per-group admission. "
-        "Plugins that share one external model should use a shared model identity and a weight that reflects "
-        "real serving capacity; plugins that represent distinct resources should keep distinct identities.</li>"
-        "<li><strong>Use the report as a regression suite.</strong> The worst case here is "
-        f"<code>{html.escape(worst.case.name)}</code>; future scheduler or admission changes should improve "
-        "that case without increasing ready gaps or violating final zero-lease checks.</li>"
-        "</ol>"
-    )
-
-
-def _results_table(results: Sequence[IdleBenchmarkResult], report_path: Path) -> str:
-    headers = [
-        "case",
-        "sweep",
-        "scenario",
-        "generations",
-        "rows",
-        "buffer",
-        "rg conc",
-        "llm cap",
-        "util",
-        "idle",
-        "starved idle",
-        "frontier idle",
-        "queue age p95",
-        "downstream gap p95",
-        "leased request wait",
-        "request util",
-        "request starved idle",
-        "advisory skips",
-        "first model",
-        "overlap",
-        "throughput",
-        "artifact",
-    ]
-    rows = []
-    for result in sorted(results, key=lambda item: (item.case.sweep, item.case.name)):
-        rows.append(
-            [
-                html.escape(result.case.name),
-                html.escape(result.case.sweep),
-                html.escape(result.case.scenario),
-                f"{result.case.generation_count:,}",
-                f"{result.case.record_count:,}",
-                f"{result.case.buffer_size:,}",
-                f"{result.case.row_group_concurrency}",
-                f"{max(1, result.case.task_admission_capacity // 2)}",
-                _format_percent(result.llm_utilization_ratio),
-                _format_percent(result.llm_idle_ratio),
-                _format_percent(result.llm_starved_idle_ratio),
-                _format_percent(result.llm_frontier_dependency_horizon_idle_ratio),
-                _format_seconds(result.llm_scheduler_queue_age_p95_seconds),
-                _format_seconds(result.downstream_ready_gap_p95_seconds),
-                _format_seconds(result.request_wait_seconds_while_task_leased_mean),
-                _format_percent(result.request_utilization_ratio),
-                _format_percent(result.request_starved_idle_ratio),
-                f"{result.request_pressure_advisory_skip_count:,}",
-                html.escape(result.first_model_dispatch_column or "-"),
-                _format_percent(result.pipeline_overlap_ratio),
-                f"{result.throughput_generations_per_second:,.0f}/s",
-                f'<a href="{html.escape(_relative_href(report_path, result.artifact_path))}">json</a>',
-            ]
-        )
-    return _html_table(headers, rows)
-
-
-def _line_chart(
-    title: str,
-    results: Sequence[IdleBenchmarkResult],
-    *,
-    x_getter: Any,
-    y_series: Sequence[tuple[str, Any, str]],
-    x_label: str,
-    y_label: str,
-    y_max: float | None = None,
-) -> str:
-    width = 880
-    height = 360
-    left = 64
-    right = 24
-    top = 42
-    bottom = 54
-    plot_w = width - left - right
-    plot_h = height - top - bottom
-    xs = [float(x_getter(result)) for result in results]
-    y_values = [float(getter(result)) for _label, getter, _color in y_series for result in results]
-    min_x = min(xs, default=0.0)
-    max_x = max(xs, default=1.0)
-    if math.isclose(min_x, max_x):
-        max_x = min_x + 1.0
-    max_y = y_max if y_max is not None else max(y_values, default=1.0)
-    max_y = max(max_y, 0.001)
-
-    elements = [_chart_frame(width, height, left, top, plot_w, plot_h, title, x_label, y_label)]
-    for series_index, (label, getter, color) in enumerate(y_series):
-        points = []
-        for result in results:
-            x_value = float(x_getter(result))
-            y_value = min(float(getter(result)), max_y)
-            x = left + ((x_value - min_x) / (max_x - min_x)) * plot_w
-            y = top + plot_h - (y_value / max_y) * plot_h
-            points.append((x, y, y_value))
-        point_attr = " ".join(f"{x:.1f},{y:.1f}" for x, y, _value in points)
-        elements.append(f'<polyline points="{point_attr}" fill="none" stroke="{color}" stroke-width="3"/>')
-        for x, y, value in points:
-            elements.append(
-                f'<circle cx="{x:.1f}" cy="{y:.1f}" r="4" fill="{color}"><title>{label}: {value:.4f}</title></circle>'
-            )
-        elements.append(_legend_item(label, color, series_index))
-
-    for result in results:
-        x_value = float(x_getter(result))
-        x = left + ((x_value - min_x) / (max_x - min_x)) * plot_w
-        elements.append(
-            f'<text class="tick" x="{x:.1f}" y="{height - 28}" text-anchor="middle">{_compact_number(x_value)}</text>'
-        )
-    return f'<figure class="chart">{"".join(elements)}</svg></figure>'
-
-
-def _bar_chart(
-    title: str,
-    results: Sequence[IdleBenchmarkResult],
-    *,
-    label_getter: Any,
-    bars: Sequence[tuple[str, Any, str]],
-    y_max: float | None = None,
-) -> str:
-    width = 880
-    height = 360
-    left = 64
-    top = 42
-    bottom = 82
-    plot_w = width - left - 24
-    plot_h = height - top - bottom
-    y_values = [float(getter(result)) for _label, getter, _color in bars for result in results]
-    max_y = max(y_max or max(y_values, default=1.0), 0.001)
-    group_w = plot_w / max(1, len(results))
-    bar_w = min(32.0, (group_w - 16.0) / max(1, len(bars)))
-
-    elements = [_chart_frame(width, height, left, top, plot_w, plot_h, title, "case", "ratio")]
-    for result_index, result in enumerate(results):
-        group_x = left + result_index * group_w + group_w / 2
-        for bar_index, (label, getter, color) in enumerate(bars):
-            value = min(float(getter(result)), max_y)
-            x = group_x - (bar_w * len(bars)) / 2 + bar_index * bar_w
-            bar_h = (value / max_y) * plot_h
-            y = top + plot_h - bar_h
-            elements.append(
-                f'<rect x="{x:.1f}" y="{y:.1f}" width="{bar_w - 3:.1f}" height="{bar_h:.1f}" '
-                f'rx="2" fill="{color}"><title>{label}: {value:.4f}</title></rect>'
-            )
-        elements.append(
-            f'<text class="tick" x="{group_x:.1f}" y="{height - 48}" text-anchor="middle">'
-            f"{html.escape(label_getter(result))}</text>"
-        )
-    for series_index, (label, _getter, color) in enumerate(bars):
-        elements.append(_legend_item(label, color, series_index))
-    return f'<figure class="chart">{"".join(elements)}</svg></figure>'
-
-
-def _chart_frame(
-    width: int,
-    height: int,
-    left: int,
-    top: int,
-    plot_w: int,
-    plot_h: int,
-    title: str,
-    x_label: str,
-    y_label: str,
-) -> str:
-    bottom = top + plot_h
-    right = left + plot_w
-    return (
-        f'<svg viewBox="0 0 {width} {height}" role="img" aria-label="{html.escape(title)}">'
-        f'<text class="chart-title" x="{left}" y="24">{html.escape(title)}</text>'
-        f'<line x1="{left}" y1="{bottom}" x2="{right}" y2="{bottom}" class="axis"/>'
-        f'<line x1="{left}" y1="{top}" x2="{left}" y2="{bottom}" class="axis"/>'
-        f'<text class="axis-label" x="{left + plot_w / 2:.1f}" y="{height - 8}" text-anchor="middle">{html.escape(x_label)}</text>'
-        f'<text class="axis-label" transform="translate(16 {top + plot_h / 2:.1f}) rotate(-90)" text-anchor="middle">{html.escape(y_label)}</text>'
-        f'<text class="tick" x="{left - 10}" y="{bottom}" text-anchor="end">0</text>'
-        f'<text class="tick" x="{left - 10}" y="{top + 4}" text-anchor="end">max</text>'
-        f'<line x1="{left}" y1="{top + plot_h / 2:.1f}" x2="{right}" y2="{top + plot_h / 2:.1f}" class="grid"/>'
-    )
-
-
-def _legend_item(label: str, color: str, index: int) -> str:
-    x = 650
-    y = 28 + (index % 5) * 17
-    return (
-        f'<g class="legend"><rect x="{x}" y="{y - 10}" width="10" height="10" fill="{color}"/>'
-        f'<text x="{x + 16}" y="{y}" class="legend-text">{html.escape(label)}</text></g>'
-    )
-
-
-def _summary_grid(items: Iterable[tuple[str, str]]) -> str:
-    cards = []
-    for label, value in items:
-        cards.append(
-            f'<div class="summary-card"><span>{html.escape(label)}</span><strong>{html.escape(value)}</strong></div>'
-        )
-    return f'<div class="summary-grid">{"".join(cards)}</div>'
-
-
-def _html_table(headers: Sequence[str], rows: Sequence[Sequence[str]]) -> str:
-    header_html = "".join(f"<th>{html.escape(header)}</th>" for header in headers)
-    row_html = []
-    for row in rows:
-        row_html.append("<tr>" + "".join(f"<td>{cell}</td>" for cell in row) + "</tr>")
-    return f'<div class="table-wrap"><table><thead><tr>{header_html}</tr></thead><tbody>{"".join(row_html)}</tbody></table></div>'
-
-
-def _style_block() -> str:
-    return """
-<style>
-:root {
-  color-scheme: light;
-  --ink: #17212b;
-  --muted: #526070;
-  --line: #d8dee6;
-  --panel: #f7f9fb;
-  --accent: #1f7a8c;
-  --warn: #b23a48;
-}
-* { box-sizing: border-box; }
-body {
-  margin: 0;
-  font-family: Inter, ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
-  color: var(--ink);
-  background: #ffffff;
-}
-main { max-width: 1120px; margin: 0 auto; padding: 44px 28px 72px; }
-.hero { border-bottom: 1px solid var(--line); padding-bottom: 28px; }
-.eyebrow { color: var(--accent); font-weight: 700; text-transform: uppercase; letter-spacing: .08em; font-size: 12px; }
-h1 { font-size: 44px; line-height: 1.05; margin: 8px 0 14px; letter-spacing: 0; }
-h2 { font-size: 24px; margin: 34px 0 12px; letter-spacing: 0; }
-p, li { color: var(--muted); line-height: 1.6; }
-code { font-family: "SFMono-Regular", Consolas, monospace; font-size: .94em; background: #eef2f6; padding: 1px 5px; border-radius: 4px; }
-.lede { max-width: 820px; font-size: 18px; }
-.summary-grid { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 12px; margin-top: 26px; }
-.summary-card { background: var(--panel); border: 1px solid var(--line); border-radius: 8px; padding: 16px; }
-.summary-card span { display: block; color: var(--muted); font-size: 13px; margin-bottom: 8px; }
-.summary-card strong { display: block; font-size: 24px; }
-.finding-list, .recommendations { padding-left: 22px; }
-.finding-list li, .recommendations li { margin: 10px 0; }
-.improvement-grid { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 14px; margin-top: 18px; }
-.improvement-card { border: 1px solid var(--line); border-radius: 8px; padding: 16px; background: #fff; }
-.improvement-card div { display: flex; align-items: baseline; justify-content: space-between; gap: 12px; margin-bottom: 8px; }
-.improvement-card h3 { margin: 0; font-size: 17px; letter-spacing: 0; }
-.improvement-card p { margin: 8px 0; }
-.improvement-card a { font-size: 13px; font-weight: 700; }
-.status { border: 1px solid var(--line); border-radius: 999px; padding: 3px 8px; color: var(--accent); font-size: 12px; font-weight: 700; white-space: nowrap; }
-.evidence { font-size: 13px; }
-.idle-class { display: inline-block; border-radius: 999px; padding: 3px 9px; font-weight: 700; font-size: 12px; }
-.idle-frontier-dependency-horizon { color: #46348a; background: #eeeafd; }
-.idle-queued-work-starvation { color: #8a2430; background: #fdebed; }
-.idle-healthy-control { color: #1d5f35; background: #e8f5ec; }
-.chart { border: 1px solid var(--line); border-radius: 8px; background: #fff; margin: 18px 0; padding: 10px; overflow-x: auto; }
-svg { width: 100%; min-width: 760px; height: auto; }
-.axis { stroke: #7c8794; stroke-width: 1; }
-.grid { stroke: #e5e9ef; stroke-width: 1; }
-.chart-title { font-size: 17px; font-weight: 700; fill: var(--ink); }
-.axis-label, .tick, .legend-text { fill: var(--muted); font-size: 12px; }
-.legend-text { font-weight: 600; }
-.table-wrap { overflow-x: auto; border: 1px solid var(--line); border-radius: 8px; }
-table { border-collapse: collapse; min-width: 1080px; width: 100%; font-size: 13px; }
-th, td { padding: 10px 12px; border-bottom: 1px solid var(--line); text-align: left; white-space: nowrap; }
-th { background: var(--panel); color: var(--ink); font-size: 12px; text-transform: uppercase; letter-spacing: .04em; }
-td { color: var(--muted); }
-.artifact-note { font-size: 13px; }
-a { color: var(--accent); }
-@media (max-width: 760px) {
-  main { padding: 28px 18px 52px; }
-  h1 { font-size: 34px; }
-  .summary-grid { grid-template-columns: 1fr; }
-  .improvement-grid { grid-template-columns: 1fr; }
-}
-</style>
-"""
-
-
-def _group_results(results: Sequence[IdleBenchmarkResult]) -> dict[str, list[IdleBenchmarkResult]]:
-    grouped: dict[str, list[IdleBenchmarkResult]] = {}
-    for result in results:
-        grouped.setdefault(result.case.sweep, []).append(result)
-    return grouped
-
-
-def _sort(results: Sequence[IdleBenchmarkResult], attr: str) -> list[IdleBenchmarkResult]:
-    return sorted(results, key=lambda result: getattr(result.case, attr))
-
-
-def _float(values: Mapping[str, Any], key: str, *, fallback_key: str | None = None) -> float:
-    value = values.get(key)
-    if value is None and fallback_key is not None:
-        value = values.get(fallback_key)
-    return float(value or 0.0)
-
-
-def _idle_ratio(
-    values: Mapping[str, Any],
-    artifact: Mapping[str, Any],
-    resource: str,
-    key: str,
-) -> float:
-    del key
-    capacity_seconds = _mean_capacity_seconds(artifact, resource)
-    idle_seconds = _float(values, "mean_idle_capacity_seconds")
-    starved_seconds = _float(values, "mean_starved_idle_seconds")
-    return _safe_ratio(max(0.0, idle_seconds - starved_seconds), capacity_seconds)
-
-
-def _mean_capacity_seconds(artifact: Mapping[str, Any], resource: str) -> float:
-    values = []
-    for iteration in artifact.get("iterations", []):
-        metrics = iteration.get("utilization_metrics", {}).get("scheduler_resources", {}).get(resource, {})
-        values.append(float(metrics.get("capacity_seconds", 0.0) or 0.0))
-    if not values:
-        return 0.0
-    return sum(values) / len(values)
-
-
-def _sum_request_capacity_seconds(artifact: Mapping[str, Any]) -> float:
-    values = []
-    for iteration in artifact.get("iterations", []):
-        resources = iteration.get("utilization_metrics", {}).get("request_resources", {})
-        if not isinstance(resources, Mapping):
-            continue
-        values.append(sum(float(metrics.get("capacity_seconds", 0.0) or 0.0) for metrics in resources.values()))
-    if not values:
-        return 0.0
-    return sum(values) / len(values)
-
-
-def _mean_resource_metric(resources: Mapping[str, Mapping[str, Any]], key: str) -> float:
-    values = [float(metrics.get(key, 0.0) or 0.0) for metrics in resources.values()]
-    if not values:
-        return 0.0
-    return sum(values) / len(values)
-
-
-def _sum_resource_metric(
-    resources: Mapping[str, Mapping[str, Any]],
-    key: str,
-    *,
-    fallback_key: str | None = None,
-) -> float:
-    total = 0.0
-    for metrics in resources.values():
-        value = metrics.get(key)
-        if value is None and fallback_key is not None:
-            value = metrics.get(fallback_key)
-        total += float(value or 0.0)
-    return total
-
-
-def _max_resource_metric(resources: Mapping[str, Mapping[str, Any]], key: str) -> float:
-    return max((float(metrics.get(key, 0.0) or 0.0) for metrics in resources.values()), default=0.0)
-
-
-def _max_iteration_metric(artifact: Mapping[str, Any], resource: str, metric: str) -> float:
-    values = []
-    for iteration in artifact.get("iterations", []):
-        resource_metrics = iteration.get("utilization_metrics", {}).get("scheduler_resources", {}).get(resource, {})
-        values.append(float(resource_metrics.get(metric, 0.0) or 0.0))
-    return max(values, default=0.0)
-
-
-def _row_group_admission_snapshot(artifact: Mapping[str, Any]) -> Mapping[str, Any]:
-    iteration_snapshots = [
-        _nested_mapping(
-            iteration,
-            ("capacity_plan", "configured", "row_group_admission"),
-        )
-        for iteration in artifact.get("iterations", [])
-    ]
-    for snapshot in reversed(iteration_snapshots):
-        if snapshot:
-            return snapshot
-    return _nested_mapping(artifact, ("capacity_plan", "configured", "row_group_admission"))
-
-
-def _nested_mapping(values: Mapping[str, Any], keys: Sequence[str]) -> Mapping[str, Any]:
-    current: Any = values
-    for key in keys:
-        if not isinstance(current, Mapping):
-            return {}
-        current = current.get(key)
-    return current if isinstance(current, Mapping) else {}
-
-
-def _safe_ratio(numerator: float, denominator: float) -> float:
-    if denominator <= 0.0:
-        return 0.0
-    return numerator / denominator
-
-
-def _safe_subtract(left: float, right: float) -> float:
-    return max(0.0, left - right)
-
-
-def _median(values: Iterable[float]) -> float:
-    sorted_values = sorted(values)
-    if not sorted_values:
-        return 0.0
-    midpoint = len(sorted_values) // 2
-    if len(sorted_values) % 2:
-        return sorted_values[midpoint]
-    return (sorted_values[midpoint - 1] + sorted_values[midpoint]) / 2.0
-
-
-def _format_float(value: float) -> str:
-    return f"{value:.8f}".rstrip("0").rstrip(".")
-
-
-def _format_percent(value: float) -> str:
-    return f"{value * 100.0:.1f}%"
-
-
-def _format_signed_percent(value: float) -> str:
-    sign = "+" if value >= 0.0 else ""
-    return f"{sign}{value * 100.0:.1f} pp"
-
-
-def _format_signed_seconds(value: float) -> str:
-    sign = "+" if value >= 0.0 else "-"
-    return f"{sign}{_format_seconds(abs(value))}"
-
-
-def _format_seconds(value: float) -> str:
-    if value < 0.001:
-        return f"{value * 1_000_000.0:.0f} us"
-    if value < 1.0:
-        return f"{value * 1000.0:.1f} ms"
-    return f"{value:.2f} s"
-
-
-def _compact_number(value: float) -> str:
-    if value >= 1000:
-        return f"{value / 1000.0:.1f}k"
-    if value.is_integer():
-        return str(int(value))
-    return f"{value:.2f}"
-
-
-def _format_optional_int(value: int | None) -> str:
-    return "n/a" if value is None else f"{value:,}"
-
-
-def _optional_int(value: Any) -> int | None:
-    if value is None:
-        return None
-    try:
-        return int(value)
-    except (TypeError, ValueError):
-        return None
-
-
-def _optional_str(value: Any) -> str | None:
-    if value is None:
-        return None
-    return str(value)
-
-
-def _weight_label(result: IdleBenchmarkResult) -> str:
-    if result.case.model_stage_weight == 0:
-        return "model cap"
-    return f"weight {result.case.model_stage_weight}"
-
-
-def _dominant_idle_class(result: IdleBenchmarkResult) -> str:
-    if (
-        result.llm_frontier_dependency_horizon_idle_ratio >= 0.10
-        and result.llm_frontier_dependency_horizon_idle_ratio > result.llm_starved_idle_ratio
-    ):
-        return "frontier/dependency-horizon"
-    if result.llm_starved_idle_ratio >= 0.10:
-        return "queued-work starvation"
-    if result.llm_scheduler_queue_age_p95_seconds > max(1.0, result.downstream_ready_gap_p95_seconds * 10.0):
-        return "queued-work starvation"
-    return "healthy/control"
-
-
-def _recommended_next_lever(result: IdleBenchmarkResult) -> str:
-    idle_class = _dominant_idle_class(result)
-    if idle_class == "frontier/dependency-horizon":
-        return "adaptive row-group admission with memory and queue guardrails"
-    if idle_class == "queued-work starvation" and result.llm_scheduler_queue_age_p95_seconds > 1.0:
-        return "resource-aware task admission, candidate selection, or smaller scheduling shards"
-    if idle_class == "queued-work starvation":
-        return "task admission/fairness policy and eligible-starved-idle metrics"
-    return "keep as control case and watch for regressions"
-
-
-def _class_counts(results: Sequence[IdleBenchmarkResult]) -> dict[str, int]:
-    counts: dict[str, int] = {}
-    for result in results:
-        idle_class = _dominant_idle_class(result)
-        counts[idle_class] = counts.get(idle_class, 0) + 1
-    return counts
-
-
-def _idle_class_priority(idle_class: str) -> int:
-    priorities = {
-        "frontier/dependency-horizon": 0,
-        "queued-work starvation": 1,
-        "healthy/control": 2,
-    }
-    return priorities.get(idle_class, 99)
-
-
-def _class_slug(idle_class: str) -> str:
-    return idle_class.replace("/", "-").replace(" ", "-")
-
-
-def _relative_href(report_path: Path, target_path: Path) -> str:
-    try:
-        if target_path.is_absolute():
-            return target_path.as_uri()
-        return Path(os.path.relpath(target_path, start=report_path.parent)).as_posix()
-    except ValueError:
-        return str(target_path)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/benchmarks/run_async_scheduling_idle_regression.py b/scripts/benchmarks/run_async_scheduling_idle_regression.py
deleted file mode 100644
index f182f08f9..000000000
--- a/scripts/benchmarks/run_async_scheduling_idle_regression.py
+++ /dev/null
@@ -1,659 +0,0 @@
-# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-# SPDX-License-Identifier: Apache-2.0
-
-"""Run the canonical async scheduling idle-time regression suite."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import sys
-from collections.abc import Mapping, Sequence
-from dataclasses import asdict, dataclass
-from pathlib import Path
-from typing import Any
-
-from generate_async_scheduling_idle_report import (
-    IDLE_SUITE_ID,
-    IDLE_SUITE_VERSION,
-    idle_results_summary,
-    render_idle_report,
-    run_idle_benchmark_suite,
-    write_idle_results_summary,
-)
-
-DEFAULT_OUTPUT_ROOT = Path(".scratch/async-scheduling-idle-regression")
-DEFAULT_ARTIFACT_DIR = DEFAULT_OUTPUT_ROOT / "artifacts"
-DEFAULT_REPORT_PATH = DEFAULT_OUTPUT_ROOT / "async-scheduling-idle-regression.html"
-DEFAULT_SUMMARY_PATH = DEFAULT_ARTIFACT_DIR / "idle_regression_summary.json"
-DEFAULT_CHECKS_PATH = DEFAULT_ARTIFACT_DIR / "idle_regression_checks.json"
-CHECKS_SCHEMA_VERSION = "async-scheduling-idle-checks-v1"
-
-
-@dataclass(frozen=True)
-class RegressionCheck:
-    name: str
-    category: str
-    severity: str
-    passed: bool
-    observed: float | int | str | bool
-    expected: str
-    detail: str = ""
-
-
-def main() -> None:
-    args = _parse_args()
-    artifact_dir = Path(args.artifact_dir)
-    report_path = Path(args.report_path)
-    summary_path = Path(args.summary_path)
-    checks_path = Path(args.checks_path)
-
-    results = run_idle_benchmark_suite(artifact_dir, quick=args.quick, skip_run=args.skip_run)
-    render_idle_report(results, report_path, artifact_dir)
-    write_idle_results_summary(summary_path, results, quick=args.quick)
-
-    summary = idle_results_summary(results, quick=args.quick)
-    baseline_summary = _load_json(Path(args.baseline_summary)) if args.baseline_summary else None
-    checks = evaluate_idle_regression_summary(
-        summary,
-        baseline_summary=baseline_summary,
-        utilization_tolerance=args.utilization_tolerance,
-        idle_tolerance=args.idle_tolerance,
-    )
-    checks_payload = _checks_payload(checks)
-    checks_path.parent.mkdir(parents=True, exist_ok=True)
-    checks_path.write_text(json.dumps(checks_payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
-
-    _print_check_summary(checks, report_path, summary_path, checks_path)
-    if _has_error(checks) and not args.allow_failures:
-        sys.exit(1)
-
-
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--artifact-dir", default=str(DEFAULT_ARTIFACT_DIR))
-    parser.add_argument("--report-path", default=str(DEFAULT_REPORT_PATH))
-    parser.add_argument("--summary-path", default=str(DEFAULT_SUMMARY_PATH))
-    parser.add_argument("--checks-path", default=str(DEFAULT_CHECKS_PATH))
-    parser.add_argument("--baseline-summary")
-    parser.add_argument("--skip-run", action="store_true", help="Reuse existing benchmark JSON files.")
-    parser.add_argument("--quick", action="store_true", help="Run the shorter suite while preserving coverage shape.")
-    parser.add_argument(
-        "--utilization-tolerance",
-        type=float,
-        default=0.05,
-        help="Allowed absolute llm_wait utilization drop when comparing to --baseline-summary.",
-    )
-    parser.add_argument(
-        "--idle-tolerance",
-        type=float,
-        default=0.05,
-        help="Allowed absolute llm_wait idle increase when comparing to --baseline-summary.",
-    )
-    parser.add_argument("--allow-failures", action="store_true", help="Write artifacts but exit zero on failed checks.")
-    return parser.parse_args()
-
-
-def evaluate_idle_regression_summary(
-    summary: Mapping[str, Any],
-    *,
-    baseline_summary: Mapping[str, Any] | None = None,
-    utilization_tolerance: float = 0.05,
-    idle_tolerance: float = 0.05,
-) -> list[RegressionCheck]:
-    cases = _cases(summary)
-    mode = str(summary.get("mode", "full"))
-    checks: list[RegressionCheck] = [
-        _check(
-            "suite id",
-            category="shape",
-            passed=summary.get("suite_id") == IDLE_SUITE_ID,
-            observed=str(summary.get("suite_id")),
-            expected=IDLE_SUITE_ID,
-        ),
-        _check(
-            "suite version",
-            category="shape",
-            passed=summary.get("suite_version") == IDLE_SUITE_VERSION,
-            observed=str(summary.get("suite_version")),
-            expected=IDLE_SUITE_VERSION,
-        ),
-        _check(
-            "case count",
-            category="shape",
-            passed=len(cases) >= _minimum_case_count(mode),
-            observed=len(cases),
-            expected=f">= {_minimum_case_count(mode)}",
-        ),
-        _check(
-            "large generation case present",
-            category="shape",
-            passed=int(summary.get("largest_generation_count", 0)) >= 8192,
-            observed=int(summary.get("largest_generation_count", 0)),
-            expected=">= 8192 generations",
-        ),
-    ]
-
-    checks.extend(_required_case_checks(cases, mode=mode))
-    checks.extend(_per_case_metric_checks(cases))
-    checks.extend(_suite_behavior_checks(cases, mode=mode))
-    if baseline_summary is not None:
-        checks.extend(
-            _baseline_comparison_checks(
-                cases,
-                _cases(baseline_summary),
-                utilization_tolerance=utilization_tolerance,
-                idle_tolerance=idle_tolerance,
-            )
-        )
-    return checks
-
-
-def _required_case_checks(cases: Mapping[str, Mapping[str, Any]], *, mode: str) -> list[RegressionCheck]:
-    required = {
-        "adaptations/adaptive-row-groups-adaptive",
-        "adaptations/adaptive-row-groups-fixed-high",
-        "adaptations/adaptive-row-groups-fixed-low",
-        "adaptations/adaptive-request-pressure-combined",
-        "adaptations/adaptive-request-pressure-control",
-        "adaptations/request-pressure-advisory",
-        "adaptations/request-pressure-control",
-        "row-scale/rows-64",
-        "row-group-concurrency/row-groups-1",
-        "buffer-size/buffer-1",
-        "stress-shape/narrow-frontier-high-cap",
-        "stress-shape/wide-frontier-high-cap",
-        "custom-model-weight/weight-model-capacity",
-    }
-    if mode != "quick":
-        required.update(
-            {
-                "row-scale/rows-1024",
-                "row-group-concurrency/row-groups-8",
-                "buffer-size/buffer-256",
-                "llm-capacity/capacity-16",
-            }
-        )
-    else:
-        required.add("row-scale/rows-256")
-    return [
-        _check(
-            f"required case {case_key}",
-            category="shape",
-            passed=case_key in cases,
-            observed=case_key in cases,
-            expected="present",
-        )
-        for case_key in sorted(required)
-    ]
-
-
-def _per_case_metric_checks(cases: Mapping[str, Mapping[str, Any]]) -> list[RegressionCheck]:
-    checks: list[RegressionCheck] = []
-    for case_key, case in sorted(cases.items()):
-        checks.append(
-            _check(
-                f"{case_key} validation",
-                category="correctness",
-                passed=bool(case.get("validation_passed")),
-                observed=bool(case.get("validation_passed")),
-                expected="true",
-            )
-        )
-        for cleanup_metric in (
-            "final_zero_task_leases",
-            "final_zero_request_leases",
-            "final_zero_request_waiters",
-        ):
-            checks.append(
-                _check(
-                    f"{case_key} {cleanup_metric}",
-                    category="correctness",
-                    passed=bool(case.get(cleanup_metric)),
-                    observed=bool(case.get(cleanup_metric)),
-                    expected="true",
-                )
-            )
-        for metric in (
-            "llm_wait_utilization_ratio",
-            "llm_wait_idle_ratio",
-            "llm_wait_starved_idle_ratio",
-            "llm_wait_frontier_dependency_horizon_idle_ratio",
-            "request_utilization_ratio",
-            "request_idle_ratio",
-            "request_starved_idle_ratio",
-            "request_frontier_dependency_horizon_idle_ratio",
-        ):
-            value = _metric(case, metric)
-            checks.append(
-                _check(
-                    f"{case_key} {metric}",
-                    category="metrics",
-                    passed=math.isfinite(value) and 0.0 <= value <= 1.0,
-                    observed=value,
-                    expected="finite ratio in [0, 1]",
-                )
-            )
-        queue_age_p95 = _metric(case, "llm_wait_scheduler_queue_age_p95_seconds")
-        checks.append(
-            _check(
-                f"{case_key} llm_wait_scheduler_queue_age_p95_seconds",
-                category="metrics",
-                passed=math.isfinite(queue_age_p95) and queue_age_p95 >= 0.0,
-                observed=queue_age_p95,
-                expected="finite seconds >= 0",
-            )
-        )
-        checks.append(
-            _check(
-                f"{case_key} idle dominates starved idle",
-                category="metrics",
-                passed=_metric(case, "llm_wait_idle_ratio") + 1e-9 >= _metric(case, "llm_wait_starved_idle_ratio"),
-                observed=_metric(case, "llm_wait_idle_ratio") - _metric(case, "llm_wait_starved_idle_ratio"),
-                expected="idle ratio >= starved idle ratio",
-            )
-        )
-        checks.append(
-            _check(
-                f"{case_key} idle partition",
-                category="metrics",
-                passed=abs(
-                    _metric(case, "llm_wait_idle_ratio")
-                    - _metric(case, "llm_wait_starved_idle_ratio")
-                    - _metric(case, "llm_wait_frontier_dependency_horizon_idle_ratio")
-                )
-                <= 1e-6,
-                observed=(
-                    _metric(case, "llm_wait_starved_idle_ratio")
-                    + _metric(case, "llm_wait_frontier_dependency_horizon_idle_ratio")
-                    - _metric(case, "llm_wait_idle_ratio")
-                ),
-                expected="starved + frontier/dependency-horizon idle == total idle",
-            )
-        )
-    return checks
-
-
-def _suite_behavior_checks(cases: Mapping[str, Mapping[str, Any]], *, mode: str) -> list[RegressionCheck]:
-    checks: list[RegressionCheck] = []
-    row_group_cases = [case for key, case in cases.items() if key.startswith("row-group-concurrency/")]
-    if row_group_cases:
-        low = min(row_group_cases, key=lambda case: int(case["case"]["row_group_concurrency"]))
-        high = max(row_group_cases, key=lambda case: int(case["case"]["row_group_concurrency"]))
-        low_util = _metric(low, "llm_wait_utilization_ratio")
-        high_util = _metric(high, "llm_wait_utilization_ratio")
-        checks.append(
-            _check(
-                "row-group frontier response",
-                category="optimization",
-                passed=high_util >= low_util + 0.20 or low_util >= 0.60,
-                observed=high_util - low_util,
-                expected="highest row-group concurrency improves utilization by >= 20pp unless low case is already >= 60%",
-            )
-        )
-        checks.append(
-            _check(
-                "wide row-group frontier utilization",
-                category="optimization",
-                passed=high_util >= _wide_row_group_utilization_floor(mode),
-                observed=high_util,
-                expected=f">= {_wide_row_group_utilization_floor(mode):.2f} llm_wait utilization",
-            )
-        )
-
-    if "buffer-size/buffer-1" in cases:
-        checks.append(
-            _check(
-                "small-buffer model utilization",
-                category="optimization",
-                passed=_metric(cases["buffer-size/buffer-1"], "llm_wait_utilization_ratio") >= 0.65,
-                observed=_metric(cases["buffer-size/buffer-1"], "llm_wait_utilization_ratio"),
-                expected=">= 0.65 llm_wait utilization",
-            )
-        )
-    if "llm-capacity/capacity-16" in cases:
-        checks.append(
-            _check(
-                "high-capacity underfeeding floor",
-                category="optimization",
-                passed=_metric(cases["llm-capacity/capacity-16"], "llm_wait_utilization_ratio") >= 0.25,
-                observed=_metric(cases["llm-capacity/capacity-16"], "llm_wait_utilization_ratio"),
-                expected=">= 0.25 llm_wait utilization",
-            )
-        )
-    if "stress-shape/wide-frontier-high-cap" in cases:
-        floor = _wide_high_capacity_stress_utilization_floor(mode)
-        checks.append(
-            _check(
-                "wide high-capacity stress utilization",
-                category="optimization",
-                passed=_metric(cases["stress-shape/wide-frontier-high-cap"], "llm_wait_utilization_ratio") >= floor,
-                observed=_metric(cases["stress-shape/wide-frontier-high-cap"], "llm_wait_utilization_ratio"),
-                expected=f">= {floor:.2f} llm_wait utilization",
-            )
-        )
-    if {
-        "stress-shape/narrow-frontier-high-cap",
-        "stress-shape/wide-frontier-high-cap",
-    }.issubset(cases):
-        narrow_dependency_idle = _metric(
-            cases["stress-shape/narrow-frontier-high-cap"],
-            "llm_wait_frontier_dependency_horizon_idle_ratio",
-        )
-        wide_dependency_idle = _metric(
-            cases["stress-shape/wide-frontier-high-cap"],
-            "llm_wait_frontier_dependency_horizon_idle_ratio",
-        )
-        checks.append(
-            _check(
-                "wide frontier dependency-horizon idle response",
-                category="optimization",
-                passed=wide_dependency_idle <= narrow_dependency_idle + 0.10,
-                observed=wide_dependency_idle - narrow_dependency_idle,
-                expected="wide frontier dependency-horizon idle does not exceed narrow by > 10pp",
-            )
-        )
-    if {
-        "adaptations/adaptive-row-groups-fixed-low",
-        "adaptations/adaptive-row-groups-fixed-high",
-        "adaptations/adaptive-row-groups-adaptive",
-    }.issubset(cases):
-        control = cases["adaptations/adaptive-row-groups-fixed-low"]
-        fixed_high = cases["adaptations/adaptive-row-groups-fixed-high"]
-        adaptive = cases["adaptations/adaptive-row-groups-adaptive"]
-        control_util = _metric(control, "llm_wait_utilization_ratio")
-        fixed_high_util = _metric(fixed_high, "llm_wait_utilization_ratio")
-        adaptive_util = _metric(adaptive, "llm_wait_utilization_ratio")
-        control_frontier = _metric(control, "llm_wait_frontier_dependency_horizon_idle_ratio")
-        fixed_high_frontier = _metric(fixed_high, "llm_wait_frontier_dependency_horizon_idle_ratio")
-        adaptive_frontier = _metric(adaptive, "llm_wait_frontier_dependency_horizon_idle_ratio")
-        checks.append(
-            _check(
-                "adaptive row-group utilization response",
-                category="optimization",
-                passed=adaptive_util >= control_util + 0.10 or control_util >= 0.70,
-                observed=adaptive_util - control_util,
-                expected="adaptive row groups improve low-frontier utilization by >= 10pp unless control is >= 70%",
-            )
-        )
-        checks.append(
-            _check(
-                "adaptive row-group fixed-high isolation",
-                category="optimization",
-                passed=adaptive_util + 0.10 >= fixed_high_util,
-                observed=adaptive_util - fixed_high_util,
-                expected="adaptive row groups stay within 10pp utilization of fixed-high hard-cap control",
-            )
-        )
-        checks.append(
-            _check(
-                "adaptive row-group frontier-idle response",
-                category="optimization",
-                passed=adaptive_frontier <= min(control_frontier, fixed_high_frontier) + 0.05,
-                observed=adaptive_frontier - min(control_frontier, fixed_high_frontier),
-                expected="adaptive row groups do not exceed the better fixed-control frontier idle by > 5pp",
-            )
-        )
-        checks.append(
-            _check(
-                "adaptive row-group target grew",
-                category="optimization",
-                passed=int(adaptive.get("row_group_admission_observed_max_target", 0) or 0) > 1,
-                observed=int(adaptive.get("row_group_admission_observed_max_target", 0) or 0),
-                expected="observed adaptive target > 1",
-            )
-        )
-    if {
-        "adaptations/request-pressure-control",
-        "adaptations/request-pressure-advisory",
-    }.issubset(cases):
-        control = cases["adaptations/request-pressure-control"]
-        advisory = cases["adaptations/request-pressure-advisory"]
-        control_wait = _metric(control, "request_wait_seconds_while_task_leased_mean")
-        advisory_wait = _metric(advisory, "request_wait_seconds_while_task_leased_mean")
-        checks.append(
-            _check(
-                "request-pressure control dispatch choice",
-                category="optimization",
-                passed=control.get("first_model_dispatch_column") == "a_pressured",
-                observed=str(control.get("first_model_dispatch_column")),
-                expected="a_pressured",
-            )
-        )
-        checks.append(
-            _check(
-                "request-pressure control leased wait present",
-                category="optimization",
-                passed=control_wait >= 0.01,
-                observed=control_wait,
-                expected=">= 0.01 seconds",
-            )
-        )
-        checks.append(
-            _check(
-                "request-pressure advisory leased-wait response",
-                category="optimization",
-                passed=advisory_wait <= control_wait - 0.005,
-                observed=advisory_wait - control_wait,
-                expected="advisory leased request wait at least 5ms lower than control",
-            )
-        )
-        checks.append(
-            _check(
-                "request-pressure advisory dispatch choice",
-                category="optimization",
-                passed=advisory.get("first_model_dispatch_column") == "z_open",
-                observed=str(advisory.get("first_model_dispatch_column")),
-                expected="z_open",
-            )
-        )
-    if {
-        "adaptations/adaptive-request-pressure-control",
-        "adaptations/adaptive-request-pressure-combined",
-    }.issubset(cases):
-        control = cases["adaptations/adaptive-request-pressure-control"]
-        combined = cases["adaptations/adaptive-request-pressure-combined"]
-        control_wait = _metric(control, "request_wait_seconds_while_task_leased_mean")
-        combined_wait = _metric(combined, "request_wait_seconds_while_task_leased_mean")
-        control_llm_util = _metric(control, "llm_wait_utilization_ratio")
-        combined_llm_util = _metric(combined, "llm_wait_utilization_ratio")
-        control_request_idle = _metric(control, "request_idle_ratio")
-        combined_request_idle = _metric(combined, "request_idle_ratio")
-        checks.extend(
-            [
-                _check(
-                    "combined adaptive/request target grew",
-                    category="optimization",
-                    passed=int(combined.get("row_group_admission_observed_max_target", 0) or 0) > 1,
-                    observed=int(combined.get("row_group_admission_observed_max_target", 0) or 0),
-                    expected="observed adaptive target > 1",
-                ),
-                _check(
-                    "combined adaptive/request advisory enabled",
-                    category="optimization",
-                    passed=bool(combined.get("request_pressure_advisory_enabled")),
-                    observed=bool(combined.get("request_pressure_advisory_enabled")),
-                    expected="true",
-                ),
-                _check(
-                    "combined adaptive/request advisory skipped pressured work",
-                    category="optimization",
-                    passed=int(combined.get("request_pressure_advisory_skip_count", 0) or 0) > 0,
-                    observed=int(combined.get("request_pressure_advisory_skip_count", 0) or 0),
-                    expected="> 0 skip events",
-                ),
-                _check(
-                    "combined adaptive/request leased wait captured",
-                    category="optimization",
-                    passed=math.isfinite(combined_wait) and combined_wait >= 0.0 and control_wait >= 0.0,
-                    observed=combined_wait - control_wait,
-                    expected="finite leased request wait delta",
-                ),
-                _check(
-                    "combined adaptive/request llm utilization response",
-                    category="optimization",
-                    passed=combined_llm_util + 0.05 >= control_llm_util,
-                    observed=combined_llm_util - control_llm_util,
-                    expected="combined llm utilization stays within 5pp of control or improves",
-                ),
-                _check(
-                    "combined adaptive/request request-idle response",
-                    category="optimization",
-                    passed=combined_request_idle <= control_request_idle + 0.05,
-                    observed=combined_request_idle - control_request_idle,
-                    expected="combined request idle does not exceed control by > 5pp",
-                ),
-            ]
-        )
-    return checks
-
-
-def _baseline_comparison_checks(
-    cases: Mapping[str, Mapping[str, Any]],
-    baseline_cases: Mapping[str, Mapping[str, Any]],
-    *,
-    utilization_tolerance: float,
-    idle_tolerance: float,
-) -> list[RegressionCheck]:
-    checks: list[RegressionCheck] = []
-    for case_key, baseline in sorted(baseline_cases.items()):
-        current = cases.get(case_key)
-        if current is None:
-            checks.append(
-                _check(
-                    f"{case_key} baseline case present",
-                    category="baseline",
-                    passed=False,
-                    observed="missing",
-                    expected="present",
-                )
-            )
-            continue
-        baseline_util = _metric(baseline, "llm_wait_utilization_ratio")
-        current_util = _metric(current, "llm_wait_utilization_ratio")
-        checks.append(
-            _check(
-                f"{case_key} utilization regression",
-                category="baseline",
-                passed=current_util + utilization_tolerance >= baseline_util,
-                observed=current_util - baseline_util,
-                expected=f">= -{utilization_tolerance:.3f}",
-            )
-        )
-        for metric in (
-            "llm_wait_idle_ratio",
-            "llm_wait_starved_idle_ratio",
-            "llm_wait_frontier_dependency_horizon_idle_ratio",
-        ):
-            baseline_idle = _metric(baseline, metric)
-            current_idle = _metric(current, metric)
-            checks.append(
-                _check(
-                    f"{case_key} {metric} regression",
-                    category="baseline",
-                    passed=current_idle <= baseline_idle + idle_tolerance,
-                    observed=current_idle - baseline_idle,
-                    expected=f"<= +{idle_tolerance:.3f}",
-                )
-            )
-        baseline_throughput = _metric(baseline, "throughput_generations_per_second")
-        current_throughput = _metric(current, "throughput_generations_per_second")
-        checks.append(
-            _check(
-                f"{case_key} throughput smoke",
-                category="baseline",
-                severity="warning",
-                passed=current_throughput >= baseline_throughput * 0.75,
-                observed=current_throughput - baseline_throughput,
-                expected=">= 75% of baseline throughput",
-            )
-        )
-    return checks
-
-
-def _wide_row_group_utilization_floor(mode: str) -> float:
-    return 0.55 if mode == "quick" else 0.70
-
-
-def _wide_high_capacity_stress_utilization_floor(mode: str) -> float:
-    return 0.53 if mode == "quick" else 0.55
-
-
-def _checks_payload(checks: Sequence[RegressionCheck]) -> dict[str, Any]:
-    return {
-        "checks_schema_version": CHECKS_SCHEMA_VERSION,
-        "suite_id": IDLE_SUITE_ID,
-        "suite_version": IDLE_SUITE_VERSION,
-        "passed": not _has_error(checks),
-        "error_count": sum(1 for check in checks if check.severity == "error" and not check.passed),
-        "warning_count": sum(1 for check in checks if check.severity == "warning" and not check.passed),
-        "checks": [asdict(check) for check in checks],
-    }
-
-
-def _print_check_summary(
-    checks: Sequence[RegressionCheck],
-    report_path: Path,
-    summary_path: Path,
-    checks_path: Path,
-) -> None:
-    errors = [check for check in checks if check.severity == "error" and not check.passed]
-    warnings = [check for check in checks if check.severity == "warning" and not check.passed]
-    status = "PASS" if not errors else "FAIL"
-    print(f"Idle regression suite: {status} ({len(errors)} errors, {len(warnings)} warnings)")
-    print(f"Wrote {report_path}")
-    print(f"Wrote {summary_path}")
-    print(f"Wrote {checks_path}")
-    for check in errors[:10]:
-        print(f"ERROR {check.name}: observed {check.observed}; expected {check.expected}")
-    for check in warnings[:5]:
-        print(f"WARN {check.name}: observed {check.observed}; expected {check.expected}")
-
-
-def _has_error(checks: Sequence[RegressionCheck]) -> bool:
-    return any(check.severity == "error" and not check.passed for check in checks)
-
-
-def _check(
-    name: str,
-    *,
-    category: str,
-    passed: bool,
-    observed: float | int | str | bool,
-    expected: str,
-    severity: str = "error",
-    detail: str = "",
-) -> RegressionCheck:
-    return RegressionCheck(
-        name=name,
-        category=category,
-        severity=severity,
-        passed=passed,
-        observed=observed,
-        expected=expected,
-        detail=detail,
-    )
-
-
-def _cases(summary: Mapping[str, Any]) -> Mapping[str, Mapping[str, Any]]:
-    cases = summary.get("cases", {})
-    if not isinstance(cases, Mapping):
-        return {}
-    return cases
-
-
-def _minimum_case_count(mode: str) -> int:
-    return 15 if mode == "quick" else 21
-
-
-def _metric(case: Mapping[str, Any], name: str) -> float:
-    return float(case.get(name, 0.0) or 0.0)
-
-
-def _load_json(path: Path) -> Mapping[str, Any]:
-    return json.loads(path.read_text(encoding="utf-8"))
-
-
-if __name__ == "__main__":
-    main()

From 87a2143aca8bda41069f051e515a9d1466c1230f Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Tue, 19 May 2026 20:41:08 -0400
Subject: [PATCH 22/33] chore: restore historical devnote posts

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 .../architecture-layers.svg                   |  12 ++--
 .../async-all-the-way-down/task-lifecycle.png | Bin 47355 -> 30470 bytes
 .../native-model-client-layers.png            | Bin 54814 -> 196456 bytes
 .../owning-the-model-stack/retry-boundary.png | Bin 42573 -> 331171 bytes
 ...request-keying.png => throttle-keying.png} | Bin
 docs/devnotes/posts/async-all-the-way-down.md |  47 +++++++-------
 docs/devnotes/posts/owning-the-model-stack.md |  58 ++++++++++++------
 7 files changed, 70 insertions(+), 47 deletions(-)
 rename docs/devnotes/posts/assets/owning-the-model-stack/{request-keying.png => throttle-keying.png} (100%)

diff --git a/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg b/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg
index dec3601a6..122d3d95d 100644
--- a/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg
+++ b/docs/devnotes/posts/assets/async-all-the-way-down/architecture-layers.svg
@@ -39,21 +39,21 @@
   <rect x="80" y="180" width="640" height="28" rx="8" fill="#76B900" opacity="0.1"/>
   <text x="400" y="200" text-anchor="middle" fill="#8FD619" font-size="13" font-weight="bold">Row-Group Admission &amp; Buffer Management</text>
   <text x="100" y="225" fill="#CCC" font-size="11">Memory-bounded execution with streaming checkpoints</text>
-  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Admission-gated row groups</text>
+  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Semaphore-gated row-group admission</text>
   <text x="100" y="258" fill="#999" font-size="10">&#x2022; Per-group buffer with cell-level writes</text>
   <text x="460" y="243" fill="#999" font-size="10">&#x2022; Checkpoint-to-parquet on completion</text>
-  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Task admission caps in-flight work</text>
+  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Submission semaphore caps in-flight tasks</text>
 
   <!-- Arrow -->
   <polygon points="395,292 400,302 405,292" fill="#76B900" opacity="0.7"/>
 
-  <!-- Layer 3: Request Admission / AIMD -->
+  <!-- Layer 3: ThrottleManager / AIMD -->
   <rect x="80" y="305" width="640" height="95" rx="8" fill="url(#darkGrad)" stroke="#76B900" stroke-width="1" stroke-opacity="0.4"/>
   <rect x="80" y="305" width="640" height="28" rx="8" fill="#76B900" opacity="0.07"/>
-  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">Request Admission (AIMD Adaptive Concurrency)</text>
-  <text x="100" y="350" fill="#CCC" font-size="11">Provider/model/domain rate-limit adaptation</text>
+  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">ThrottleManager (AIMD Adaptive Concurrency)</text>
+  <text x="100" y="350" fill="#CCC" font-size="11">Per-model, per-provider rate-limit adaptation</text>
   <text x="100" y="368" fill="#999" font-size="10">&#x2022; Additive increase on success streaks</text>
   <text x="100" y="383" fill="#999" font-size="10">&#x2022; Multiplicative decrease on 429s</text>
-  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent request resources</text>
+  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent pools per (provider, model)</text>
   <text x="460" y="383" fill="#999" font-size="10">&#x2022; Global cap across model aliases</text>
 </svg>
diff --git a/docs/devnotes/posts/assets/async-all-the-way-down/task-lifecycle.png b/docs/devnotes/posts/assets/async-all-the-way-down/task-lifecycle.png
index 1d0330513266760ecdab07c6ea13368b87a0de93..69262a3b50fd2264985d92d52df7e3196fd19583 100644
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diff --git a/docs/devnotes/posts/assets/owning-the-model-stack/request-keying.png b/docs/devnotes/posts/assets/owning-the-model-stack/throttle-keying.png
similarity index 100%
rename from docs/devnotes/posts/assets/owning-the-model-stack/request-keying.png
rename to docs/devnotes/posts/assets/owning-the-model-stack/throttle-keying.png
diff --git a/docs/devnotes/posts/async-all-the-way-down.md b/docs/devnotes/posts/async-all-the-way-down.md
index ceada599c..14338337b 100644
--- a/docs/devnotes/posts/async-all-the-way-down.md
+++ b/docs/devnotes/posts/async-all-the-way-down.md
@@ -59,7 +59,7 @@ Getting this right required solving three problems at different levels of the st
 
 <div style="text-align: center;" markdown>
 
-![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, and request admission for provider capacity](assets/async-all-the-way-down/architecture-layers.svg){ style="max-width:100%; height:auto" }
+![Three layers: AsyncTaskScheduler for dependency dispatch, row-group admission for memory, ThrottleManager for rate limits](assets/async-all-the-way-down/architecture-layers.svg){ style="max-width:100%; height:auto" }
 
 </div>
 
@@ -67,42 +67,43 @@ Getting this right required solving three problems at different levels of the st
 
 At the top sits the `AsyncTaskScheduler`. It builds an `ExecutionGraph` from your column configs using [Kahn's algorithm](https://en.wikipedia.org/wiki/Topological_sorting#Kahn's_algorithm) for topological ordering, then tracks per-cell completion via a `CompletionTracker`. When a cell completes, the tracker determines which downstream cells are now ready and pushes them onto the dispatch queue.
 
-The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: enqueue ready tasks, ask `FairTaskQueue` to select the next eligible task, acquire a scheduler-resource lease from `TaskAdmissionController`, commit the queue selection, then spawn a worker. When the worker completes, mark the cell done, release the lease, and make any newly-ready downstream tasks visible. The loop runs until every cell in every row group has completed or been dropped.
+The scheduler maintains a *frontier* — the set of tasks whose inputs are all satisfied. Dispatch is a loop: pull ready tasks from the frontier, acquire a [semaphore](https://en.wikipedia.org/wiki/Semaphore_(programming)) slot, spawn a worker. When the worker completes, mark the cell done, which may add new tasks to the frontier. The loop runs until every cell in every row group has completed or been dropped.
 
-There's a subtlety in how the scheduler manages task-stage pressure. Scheduler resources and provider request resources are separate. The scheduler decides which dependency-ready cell may become a running worker; `ModelRequestExecutor` and request admission decide when an individual provider call may execute. Keeping those ledgers separate prevents provider cooldown or request backpressure from masquerading as DAG readiness.
+There's a subtlety in how the scheduler manages its task slots, and getting it right required a delicate dance between two semaphores. A naïve approach would hold a submission slot for the entire lifetime of a task. That's fine for the outbound HTTP call — the slot is released before the request goes out. But the `ThrottleManager` can impose an internal timeout while waiting for a permit during AIMD cooldown, and *that* wait would hold the submission slot hostage. If enough tasks are blocked waiting for throttle permits, the scheduler can't dispatch new work even when the frontier has ready tasks.
 
-The durable scheduler contract is selection plus admission plus commit:
+The fix is a one-way semaphore handoff. The scheduler maintains two pools: a *submission* semaphore that caps how many tasks can be dispatched, and an *LLM-wait* semaphore (sized larger) for tasks that are blocked on a model call. When a task is about to call the model, it acquires an LLM-wait slot and releases its submission slot in the same atomic operation — stepping from one pool to the other mid-flight. The dispatch loop immediately sees a free submission slot and can send another task. When the LLM responds, the LLM-wait slot is released. Non-LLM generators (samplers, Jinja expressions) skip the handoff and hold their submission slot for the full duration, which is fine because they complete quickly.
 
 ```py
-selection = queue.select_next(lambda item, view: admission.is_eligible(item, view))
-decision = admission.try_acquire(selection.item, selection.queue_view)
-committed = queue.commit(selection)
-spawn_worker(committed, decision)
+if is_llm_bound:
+    await self._llm_wait_semaphore.acquire()
+    holds_llm_wait = True
+    self._submission_semaphore.release()
+    holds_submission = False
 ```
 
-This keeps ready-work ordering, scheduler-resource accounting, and request-capacity adaptation explicit. `SchedulingMetadata` describes static generator pressure, `TaskSchedulingResolver` turns it into scheduler inputs, `FairTaskQueue` handles ready ordering, and `TaskAdmissionController` owns leases for scheduler resources.
+This keeps the dispatch loop saturated without unbounded coroutine growth — the submission semaphore controls how fast tasks enter, and the LLM-wait semaphore controls how many are waiting on the network.
 
 ### **Layer 2: Row-group admission**
 
-Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. Row-group admission limits how many groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
+Below the scheduler, the 10,000 rows you requested aren't all in memory at once. They're partitioned into row groups that checkpoint to parquet independently. A semaphore limits how many row groups are in flight simultaneously, preventing memory from growing unboundedly on large runs.
 
 When a row group completes — all columns generated for all its rows — the buffer manager flushes it to disk and releases the memory. Partial results appear on disk during generation. If something fails, you keep everything that already checkpointed. This is also the basis for fault tolerance, discussed later — the unit of loss is a row group, not the entire run.
 
 ### **Layer 3: Adaptive rate limiting**
 
-At the bottom, each provider/model/request-domain resource gets [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) request admission. When the provider returns a 429, `AdaptiveRequestAdmissionController` reduces admitted concurrency for that request resource. On streaks of successful requests, it gradually increases. Because this happens per request resource, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](owning-the-model-stack.md) dev note covers this layer in depth.
+At the bottom, each (provider, model) pair gets an independent concurrency pool with [additive-increase, multiplicative-decrease (AIMD)](https://en.wikipedia.org/wiki/Additive_increase/multiplicative_decrease) rate adaptation. When the provider returns a 429, the pool cuts its concurrency. On streaks of successful requests, it gradually increases. Because this happens per-model, a judge model running on one provider can saturate its endpoint while a generator on another provider is backing off. The [Owning the Model Stack](owning-the-model-stack.md) dev note covers this layer in depth.
 
 ### **How they compose**
 
-The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The request-admission layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
+The layers are independent. The scheduler decides *what* to run next. The row-group layer decides *how much* to keep in memory at once. The throttle layer discovers *how fast* each provider will accept requests. No layer needs to know about the others.
 
 A single task's lifecycle makes the composition concrete:
 
 <div style="text-align: center;">
-  <img src="../assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier to fair queue to task admission to generator to request admission to LLM call to complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
+  <img src="../assets/async-all-the-way-down/task-lifecycle.png" alt="Task lifecycle: Frontier → Dispatch → Generator → Throttle → LLM Call → Complete, with downstream cells looping back to the frontier" style="max-width:85%; height:auto" />
 </div>
 
-A cell enters the frontier when its upstream dependencies are satisfied. The scheduler enqueues it, selects it through `FairTaskQueue`, acquires a `TaskAdmissionLease`, commits the selection, and hands the task to a worker. The worker runs the generator; any model call goes through `ModelRequestExecutor`, which acquires and releases the request-admission lease around provider execution. On completion, leases are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
+A cell enters the frontier when its upstream dependencies are satisfied. The dispatch loop acquires a submission slot and hands it to a worker. The worker runs the generator, which acquires a throttle permit before making the LLM call. On completion, permits are released, the cell is marked done in the `CompletionTracker`, and any downstream cells whose dependencies are now satisfied enter the frontier. The cycle continues until every cell has completed or been dropped.
 
 ---
 
@@ -137,13 +138,13 @@ The pattern is clear: speedup scales with the amount of parallelism available in
 
 The **narrow** workload is a sequential chain with no cross-column parallelism. The async engine still ekes out a small gain from overlapping row-level dispatch, but there's no structural parallelism to exploit. This is expected: async can't speed up a fundamentally serial pipeline.
 
-The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model maps to its own request-admission resources. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
+The **dual-model** workload is the most interesting case. Three generation columns use one model, and three judge columns use another. Each model gets its own ThrottleManager pool. The judge model starts processing rows as soon as the first generator finishes, running at full concurrency while the generator is still producing. In the sync engine, all generation has to finish before any judging starts.
 
 ### **At higher record counts**
 
-The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, adaptive request admission backs off, and the backoff can cascade through downstream columns that were waiting on the rate-limited model's output.
+The benchmarks above use 10 records deliberately — small batches isolate the scheduling benefit from rate-limit effects. At higher record counts, the bottleneck shifts. The async engine dispatches requests more aggressively, which means it discovers the provider's rate limits sooner. When a 429 hits, the AIMD controller backs off, and the backoff can cascade through downstream columns that were waiting on the throttled model's output.
 
-This is where per-model request resources become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same provider capacity. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
+This is where the per-model throttle pools become important. Single-model pipelines are most susceptible to cascading backoff because all columns compete for the same pool. Multi-model pipelines hold up well because each model adapts independently — a 429 on the generator model doesn't slow down the judge. In our larger runs, dual-model and multi-provider workloads consistently showed the largest async gains.
 
 The primary tuning lever is `max_parallel_requests` per model. Set it to a generous upper bound and let AIMD find the real ceiling. See the [Owning the Model Stack](owning-the-model-stack.md) dev note for the full story on adaptive concurrency.
 
@@ -179,7 +180,7 @@ The performance numbers are satisfying, but raw throughput is only part of the p
 
 ### **Progress you can see**
 
-Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and request-admission layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
+Because rows complete out of order and row groups checkpoint independently, results start appearing on disk within seconds. The new progress bars — sticky ANSI bars that redraw in-place at the bottom of the terminal — update on every task completion rather than waiting for a full column to finish. Log messages from the scheduler and throttle layer render above the bars, so you see both the high-level progress and the per-event detail. A 10-minute generation run no longer means staring at nothing until the end.
 
 ```
   column 'topic'    ████████████████████████████████████░░░░  89% | 890/1000 |  148.3 rec/s | eta 1s  | 0 failed
@@ -188,13 +189,13 @@ Because rows complete out of order and row groups checkpoint independently, resu
   column 'analysis' ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░  35% | 350/1000 |   87.5 rec/s | eta 7s  | 1 failed
 ```
 
-When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired scheduler resources, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
+When tracing is enabled (`DATA_DESIGNER_ASYNC_TRACE=1` or `RunConfig(async_trace=True)`), the scheduler also records a `TaskTrace` for every task: when it was dispatched, when it acquired a semaphore slot, when it completed, and its status. These traces are available on the result object after the run, so you can reconstruct the scheduler's timeline and understand where time was spent.
 
 ### **Fault tolerance**
 
 Failures in a long-running pipeline are not exceptional — they're expected. Model endpoints return 429s, connections time out, prompts produce unparseable output. The scheduler classifies errors into two buckets.
 
-Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding row-group admission capacity forever and blocking admission of new row groups.
+Retryable errors (rate limits, timeouts, transient server errors) are deferred rather than dropped. The task stays on the frontier so it can be re-attempted. If a row group stalls — all of its pending tasks are deferred and nothing is in flight — the scheduler detects the deadlock and runs *salvage rounds*: it re-dispatches the deferred tasks inline, up to a configurable maximum number of attempts. Tasks that still fail after salvage are dropped, and the row group is checkpointed with whatever succeeded. This prevents a stalled row group from holding its semaphore slot forever and blocking admission of new row groups.
 
 Non-retryable errors (malformed output, validation failures) drop the row immediately. The `CompletionTracker` knows which downstream tasks depended on that row and removes them from the frontier, so no work is wasted on a row that's already lost.
 
@@ -202,7 +203,7 @@ In both cases, completed row groups are already on disk. The unit of loss is at
 
 ### **Multi-model concurrency**
 
-Multi-model pipelines are where the architecture pays for itself. With independent request resources per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
+Multi-model pipelines are where the architecture pays for itself. With independent throttle pools per model, there's no reason not to use the right model for each job: a reasoning model for generation, a smaller model for judging, an embedding model for deduplication, each running at its own optimal concurrency. The previous engine supported multi-model configs, but running them concurrently is what makes them practical at scale.
 
 ### **Adoption**
 
@@ -214,7 +215,7 @@ Adoption is opt-in. Set `DATA_DESIGNER_ASYNC_ENGINE=1` in your environment. Your
 
 This was a ground-up rebuild of the execution layer, delivered across six PRs over four weeks.
 
-It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The historical predecessor to request admission and task-stage capacity control landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
+It started with the data structures: `ExecutionGraph`, `CompletionTracker`, and task models ([#356](https://github.com/NVIDIA-NeMo/DataDesigner/pull/356)). Next came the generator migration ([#378](https://github.com/NVIDIA-NeMo/DataDesigner/pull/378)), where we added symmetric `generate()`/`agenerate()` bridging so every generator works in both modes without rewriting. The core scheduler and buffer manager followed in [#404](https://github.com/NVIDIA-NeMo/DataDesigner/pull/404), then integration into `DatasetBuilder` with callbacks and trace export ([#429](https://github.com/NVIDIA-NeMo/DataDesigner/pull/429)). The `ThrottledModelClient` and dual-semaphore scheduler landed in [#449](https://github.com/NVIDIA-NeMo/DataDesigner/pull/449), wiring AIMD concurrency control into every outbound model request. A final polish pass ([#456](https://github.com/NVIDIA-NeMo/DataDesigner/pull/456)) added async preview, unified lifecycle callbacks, and sticky ANSI progress bars.
 
 The symmetric bridging was critical for adoption. Every `ColumnGenerator` has both a `generate()` and an `agenerate()` method. Implement one, and the base class synthesizes the other:
 
@@ -277,7 +278,7 @@ Key Resources:
 
 1. [NeMo Data Designer on GitHub](https://github.com/NVIDIA-NeMo/DataDesigner)
 2. [Data Designer Documentation](https://nvidia-nemo.github.io/DataDesigner/)
-3. [Owning the Model Stack: Adaptive Concurrency](owning-the-model-stack.md) — companion dev note on the native client layer and AIMD request admission
+3. [Owning the Model Stack: Adaptive Concurrency](owning-the-model-stack.md) — companion dev note on the native client layer and AIMD throttling
 4. [Async Engine Plan (#346)](https://github.com/NVIDIA-NeMo/DataDesigner/issues/346) — original issue and architecture plan
 
 *Want to learn more about NeMo Data Designer? Check out our [documentation](https://nvidia-nemo.github.io/DataDesigner/) and start building your own synthetic data pipelines today.*
diff --git a/docs/devnotes/posts/owning-the-model-stack.md b/docs/devnotes/posts/owning-the-model-stack.md
index 7c3e51ee3..062c5cb35 100644
--- a/docs/devnotes/posts/owning-the-model-stack.md
+++ b/docs/devnotes/posts/owning-the-model-stack.md
@@ -8,11 +8,11 @@ authors:
 
 Picture this: you're generating a million-record dataset. Thirty two concurrent requests per model, three models in the pipeline, two providers. Everything hums along for the first ten minutes — then one provider starts returning 429s, your retry logic kicks in, and suddenly you're in a feedback loop where retries *cause* more 429s. The run stalls. You restart with lower concurrency, waste throughput for hours, and wonder if there's a better way.
 
-There is. This post is about the native model client layer we built with adaptive request admission (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
+There is. This post is about the native model client layer we built with adaptive throttling (a system that discovers provider capacity at runtime) replacing our dependency on LiteLLM along the way.
 
 <!-- more -->
 
-![From chaotic request flow to calibrated concurrency via adaptive request admission](assets/owning-the-model-stack/native-model-client-hero.png)
+![From chaotic request flow to calibrated concurrency via adaptive throttling](assets/owning-the-model-stack/native-model-client-hero.png)
 
 ## **Why We Made the Move**
 
@@ -38,9 +38,9 @@ From top to bottom:
 
 1. **ModelFacade**: orchestrates correction loops, MCP tool-calling, and usage tracking. This is the public API. Column generators talk to this layer, and it was untouched during the migration. If you've written a Data Designer pipeline, nothing about your code changes.
 
-2. **ModelRequestExecutor**: the request execution layer. It maps every outbound model call to a provider/model/domain request resource, acquires a request-admission lease before provider execution, releases it on every terminal path, and feeds the outcome (success, 429, or error) back to request admission.
+2. **ThrottledModelClient**: the new layer. It's a decorator around `HttpModelClient` — same `ModelClient` protocol, but every outbound call is wrapped with a throttle permit: acquire a concurrency slot before the call, release it after, and feed the outcome (success, 429, or error) back to `ThrottleManager`. This is where adaptive throttling lives.
 
-3. **AdaptiveRequestAdmissionController**: the Additive Increase / Multiplicative Decrease (AIMD) controller used by `ModelRequestExecutor`. A single shared controller is created at pipeline startup and shared across all model clients. It owns the mutable request-admission state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
+3. **ThrottleManager**: the Additive Increase / Multiplicative Decrease (AIMD) controller that `ThrottledModelClient` delegates to. A single instance is created at pipeline startup and shared across all model clients. It owns all the mutable concurrency state — per-domain AIMD counters, global caps, cascade dampening, and cooldown timers.
 
 4. **HttpModelClient**: an abstract base class that defines the interface for all provider adapters. It owns the shared `httpx` transport lifecycle — connection pooling, timeouts, and transport-level retries for transient failures (502, 503, 504). Boring but important.
 
@@ -50,7 +50,7 @@ From top to bottom:
 
 The boundary between `ModelFacade` and the client layer is defined by canonical types. `ChatCompletionRequest`, `ChatCompletionResponse`, `EmbeddingRequest`, `EmbeddingResponse`, `ImageGenerationRequest`, `ImageGenerationResponse`, and `ProviderError`. These are plain dataclasses. No provider SDK objects cross this line. A `ModelClient` protocol defines the contract that all adapters implement, and that's the only interface the rest of the system sees.
 
-## **Adaptive Request Admission: The Centerpiece**
+## **Adaptive Throttling: The Centerpiece**
 
 With this client stack in place, we had the foundation to build something that wasn't possible before. Adaptive concurrency control. Let's start with the problem.
 
@@ -95,23 +95,23 @@ Here's a subtlety that bit us during testing. When the system is running at capa
 
 Real pipelines aren't simple. A single provider+model combination might serve chat completions, embeddings, and image generation, potentially on different rate-limit budgets. And multiple [model aliases](../../concepts/models/model-configs.md) in your pipeline might point to the same underlying provider and model (say, one alias for generation and another for judging, both hitting the same NVIDIA endpoint).
 
-Request admission handles this with two-level keying:
+The throttle manager handles this with two-level keying:
 
 <div style="text-align: center;" markdown>
 
-![Two-level request-resource keying: global cap per provider+model, independent domain states for chat, embedding, image](assets/owning-the-model-stack/request-keying.png){ style="max-width:75%; height:auto" }
+![Two-level throttle keying: global cap per provider+model, independent domain states for chat, embedding, image](assets/owning-the-model-stack/throttle-keying.png){ style="max-width:75%; height:auto" }
 
 </div>
 
 - **Global cap**: keyed by `(provider_name, model_id)`. When multiple model aliases target the same provider and model, the effective max is `min()` of their configured `max_parallel_requests`. This enforces the most conservative limit for shared upstream capacity, because the provider doesn't care what you *call* the model, it sees the same API key.
 
-- **Domain state**: keyed by `(provider_name, model_id, request_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
+- **Domain state**: keyed by `(provider_name, model_id, throttle_domain)`. Each domain (`chat`, `embedding`, `image`, `healthcheck`) maintains its own AIMD state: `current_limit`, `in_flight`, `blocked_until`, `success_streak`, and `rate_limit_ceiling`. Domains float independently but are always capped by the global max.
 
 The practical effect is that a burst of 429s on the chat route doesn't starve embedding requests, and vice versa. Each route adapts to its own capacity independently while respecting the shared upstream limit.
 
 ## **The Retry Boundary**
 
-There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire adaptive request-admission system.
+There's a design choice here that isn't obvious until you think about it, and getting it wrong would break the entire throttling system.
 
 The transport layer (via `httpx` with `RetryTransport`) handles transient server failures like 502, 503, 504, and connection errors. These are hiccups. The server is temporarily broken. Retry with exponential backoff and jitter, and move on.
 
@@ -119,25 +119,37 @@ But **429 is explicitly excluded from transport retries**.
 
 <div style="text-align: center;" markdown>
 
-![Retry boundary: 502/503/504 retried at transport, 429 passed through to ModelRequestExecutor for AIMD feedback](assets/owning-the-model-stack/retry-boundary.png){ style="max-width:75%; height:auto" }
+![Retry boundary: 502/503/504 retried at transport, 429 passed through to ThrottledModelClient for AIMD feedback](assets/owning-the-model-stack/retry-boundary.png){ style="max-width:75%; height:auto" }
 
 </div>
 
-Why? Because if the retry layer swallows 429s, request admission never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ModelRequestExecutor` so it can release the request lease as rate-limited, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the request-admission path before making another HTTP call.
+Why? Because if the retry layer swallows 429s, the throttle manager never learns the provider is overloaded. The whole AIMD feedback loop depends on seeing raw rate-limit signals. A 429 must bubble up to `ThrottledModelClient` so it can call `release_rate_limited()`, cut the concurrency limit, apply the cooldown, and record the ceiling. The next attempt then re-enters the throttle acquire path, waiting for a permit, before making another HTTP call.
 
-The split is clean and worth remembering. Transport retries handle *server problems*. Request admission handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
+The split is clean and worth remembering. Transport retries handle *server problems*. Throttle adaptation handles *capacity problems*. The provider is working fine, you're just sending too many requests. Conflating the two is how you get retry storms.
 
-One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ModelRequestExecutor` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](async-all-the-way-down.md) for the full story on the async engine.
+One caveat: this boundary behaves differently depending on the execution mode. In async mode (currently experimental, enabled with `DATA_DESIGNER_ASYNC_ENGINE=1`), 429s bypass transport retries entirely and flow straight to `ThrottledModelClient` for AIMD feedback — this is the full adaptive loop described above. In sync mode, 429s are retried at the transport layer since there's no salvage queue to re-attempt failed rows. AIMD is still wired up but only fires if all transport retries are exhausted. This is temporary — once the async engine graduates from experimental, it will become the default path and the sync codepath will be retired. See [Async All the Way Down](async-all-the-way-down.md) for the full story on the async engine.
 
 ## **Configuration**
 
-Adaptive request admission is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
+The throttle system is designed to work well out of the box. The defaults are conservative and handle most workloads without tuning. The primary user-facing knob is still `max_parallel_requests` on your model's inference parameters, which sets the hard upper bound for concurrency. AIMD floats below it.
+
+For workloads where you want to fine-tune the adaptation behavior, `ThrottleConfig` is available on `RunConfig`:
 
 ```python
 import data_designer.config as dd
 from data_designer.interface import DataDesigner
 
 data_designer = DataDesigner()
+data_designer.set_run_config(
+    dd.RunConfig(
+        throttle=dd.ThrottleConfig(
+            reduce_factor=0.75,
+            success_window=25,
+            cooldown_seconds=2.0,
+            ceiling_overshoot=0.10,
+        )
+    )
+)
 config_builder = dd.DataDesignerConfigBuilder(
     model_configs=[
         dd.ModelConfig(
@@ -159,11 +171,21 @@ create_result = data_designer.create(
 )
 ```
 
-Most users will never need more than `max_parallel_requests`. The system adapts automatically, and capacity diagnostics are exposed through runtime logs and `AsyncCapacityPlan` rather than public controller tuning knobs.
+| Parameter | Default | What it does |
+|---|---|---|
+| `reduce_factor` | 0.75 | Multiplicative decrease on 429 (0.75 = reduce by 25%) |
+| `additive_increase` | 1 | How much to increase the limit after a success window |
+| `success_window` | 25 | Consecutive successes before additive increase |
+| `cooldown_seconds` | 2.0 | Default cooldown when no `Retry-After` header |
+| `ceiling_overshoot` | 0.10 | How far above the observed ceiling to probe (10%) |
+
+In practice, the parameter most worth adjusting is `success_window`. A smaller window (say, 10) makes the system more aggressive about reclaiming throughput after a pullback, useful when you know the provider's capacity fluctuates quickly. A larger window (say, 50) makes it more conservative, better for providers with strict, stable rate limits where you'd rather not probe at all.
+
+Most users will never need to touch any of these. The system adapts automatically.
 
 ## **What It Looks Like in the Logs**
 
-Request admission logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
+`ThrottleManager` logs every state transition at `INFO` level, so the adaptation story is visible in your terminal as the run progresses.
 
 ```
 # When the system hits a 429 and cuts concurrency:
@@ -186,9 +208,9 @@ Reading these lines in sequence tells you exactly what happened: where the syste
 
 ## **Where This Leaves Us**
 
-This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates request state per route, and separates retry logic from rate-limit adaptation. Adaptive request admission is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](async-all-the-way-down.md) for details.
+This shipped in Data Designer v0.5.4. If you're using Data Designer today, nothing changes in your pipeline code. `ModelFacade` is the same API it's always been. What changes is what happens underneath. The system now discovers provider capacity at runtime, isolates throttle state per route, and separates retry logic from rate-limit adaptation. Adaptive throttling is enabled by default for all providers. You don't opt in or configure anything; it just starts learning. If you want to see this fully in action, turn on async mode — see [Async All the Way Down](async-all-the-way-down.md) for details.
 
-For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without public controller tuning.
+For most workloads, the defaults are all you need. Set `max_parallel_requests` to a generous upper bound and let AIMD find the right level. If you're running against a stack that returns 429s, the system adapts to the available capacity without any tuning. If you want finer control, `ThrottleConfig` is there — but the goal is that you spend your time designing datasets, not tuning concurrency knobs.
 
 Key Resources:
 

From 74eb432055e9306e299529a896498ef2b6979b62 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Tue, 19 May 2026 20:43:54 -0400
Subject: [PATCH 23/33] chore: restore fern image assets

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 .../architecture-layers.svg                   |  12 ++++++------
 .../async-all-the-way-down/task-lifecycle.png | Bin 47355 -> 30470 bytes
 fern/assets/images/architecture-layers.svg    |  12 ++++++------
 .../images/native-model-client-layers.png     | Bin 54814 -> 196456 bytes
 fern/assets/images/retry-boundary.png         | Bin 42573 -> 331171 bytes
 fern/assets/images/task-lifecycle.png         | Bin 47355 -> 30470 bytes
 ...request-keying.png => throttle-keying.png} | Bin
 .../native-model-client-layers.png            | Bin 54814 -> 196456 bytes
 .../owning-the-model-stack/retry-boundary.png | Bin 42573 -> 331171 bytes
 9 files changed, 12 insertions(+), 12 deletions(-)
 rename fern/assets/images/{request-keying.png => throttle-keying.png} (100%)

diff --git a/fern/assets/async-all-the-way-down/architecture-layers.svg b/fern/assets/async-all-the-way-down/architecture-layers.svg
index dec3601a6..122d3d95d 100644
--- a/fern/assets/async-all-the-way-down/architecture-layers.svg
+++ b/fern/assets/async-all-the-way-down/architecture-layers.svg
@@ -39,21 +39,21 @@
   <rect x="80" y="180" width="640" height="28" rx="8" fill="#76B900" opacity="0.1"/>
   <text x="400" y="200" text-anchor="middle" fill="#8FD619" font-size="13" font-weight="bold">Row-Group Admission &amp; Buffer Management</text>
   <text x="100" y="225" fill="#CCC" font-size="11">Memory-bounded execution with streaming checkpoints</text>
-  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Admission-gated row groups</text>
+  <text x="100" y="243" fill="#999" font-size="10">&#x2022; Semaphore-gated row-group admission</text>
   <text x="100" y="258" fill="#999" font-size="10">&#x2022; Per-group buffer with cell-level writes</text>
   <text x="460" y="243" fill="#999" font-size="10">&#x2022; Checkpoint-to-parquet on completion</text>
-  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Task admission caps in-flight work</text>
+  <text x="460" y="258" fill="#999" font-size="10">&#x2022; Submission semaphore caps in-flight tasks</text>
 
   <!-- Arrow -->
   <polygon points="395,292 400,302 405,292" fill="#76B900" opacity="0.7"/>
 
-  <!-- Layer 3: Request Admission / AIMD -->
+  <!-- Layer 3: ThrottleManager / AIMD -->
   <rect x="80" y="305" width="640" height="95" rx="8" fill="url(#darkGrad)" stroke="#76B900" stroke-width="1" stroke-opacity="0.4"/>
   <rect x="80" y="305" width="640" height="28" rx="8" fill="#76B900" opacity="0.07"/>
-  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">Request Admission (AIMD Adaptive Concurrency)</text>
-  <text x="100" y="350" fill="#CCC" font-size="11">Provider/model/domain rate-limit adaptation</text>
+  <text x="400" y="325" text-anchor="middle" fill="#5A8F00" font-size="13" font-weight="bold">ThrottleManager (AIMD Adaptive Concurrency)</text>
+  <text x="100" y="350" fill="#CCC" font-size="11">Per-model, per-provider rate-limit adaptation</text>
   <text x="100" y="368" fill="#999" font-size="10">&#x2022; Additive increase on success streaks</text>
   <text x="100" y="383" fill="#999" font-size="10">&#x2022; Multiplicative decrease on 429s</text>
-  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent request resources</text>
+  <text x="460" y="368" fill="#999" font-size="10">&#x2022; Independent pools per (provider, model)</text>
   <text x="460" y="383" fill="#999" font-size="10">&#x2022; Global cap across model aliases</text>
 </svg>
diff --git a/fern/assets/async-all-the-way-down/task-lifecycle.png b/fern/assets/async-all-the-way-down/task-lifecycle.png
index 1d0330513266760ecdab07c6ea13368b87a0de93..69262a3b50fd2264985d92d52df7e3196fd19583 100644
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From bc83ae3dcb5e49da45c353a62965aaa8dd72fd40 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Tue, 19 May 2026 20:45:09 -0400
Subject: [PATCH 24/33] chore: restore latest devnotes index

Signed-off-by: Eric W. Tramel <eric.tramel@gmail.com>
---
 fern/versions/latest/pages/devnotes/index.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/fern/versions/latest/pages/devnotes/index.mdx b/fern/versions/latest/pages/devnotes/index.mdx
index 504c4b6f7..1d5fe68da 100644
--- a/fern/versions/latest/pages/devnotes/index.mdx
+++ b/fern/versions/latest/pages/devnotes/index.mdx
@@ -44,7 +44,7 @@ Welcome to NeMo Data Designer Dev Notes — in-depth guides, benchmark write-ups
   <BlogCard
     href="/dev-notes/owning-the-model-stack"
     title="Owning the Model Stack"
-    description="Adaptive concurrency, request-resource keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
+    description="Adaptive concurrency, throttle keying, retry boundaries — owning the whole model client to discover provider capacity at runtime."
     date="Mar 25, 2026"
     authors={["nmulepati"]}
     image={<img src="/assets/owning-the-model-stack/native-model-client-hero.png" alt="" loading="lazy" />}

From 9dd92ab9c9673dcd0f974a462f33f1e5e38c5866 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 13:58:14 -0400
Subject: [PATCH 25/33] Add request admission tuning config

---
 .../src/data_designer/config/__init__.py      |   7 +-
 .../src/data_designer/config/run_config.py    |  75 ++++++++++++++++--
 .../tests/config/test_run_config.py           |  60 +++++++++++++-
 .../src/data_designer/engine/capacity.py      |   2 +
 .../data_designer/engine/models/factory.py    |  28 ++++++-
 .../engine/models/request_admission/config.py |   1 +
 .../models/request_admission/controller.py    |  57 +++++++++++--
 .../engine/models/request_admission/limits.py |   2 +
 .../request_admission/test_controller.py      |  63 +++++++++++++++
 .../engine/models/test_model_registry.py      |  30 +++++++
 .../tests/engine/test_capacity.py             |   7 +-
 ...ncSchedulingRequestAdmissionClassModel.png | Bin 120203 -> 121362 bytes
 ...ncSchedulingSupportContractsClassModel.png | Bin 90775 -> 91516 bytes
 plans/645/async-scheduling-epic.puml          |   2 +
 plans/645/capacity-model.md                   |   4 +-
 plans/645/contracts.md                        |   8 +-
 plans/645/migration-and-cleanup.md            |   2 +-
 plans/645/module-ownership.md                 |   2 +-
 plans/645/request-admission.md                |   5 +-
 19 files changed, 329 insertions(+), 26 deletions(-)

diff --git a/packages/data-designer-config/src/data_designer/config/__init__.py b/packages/data-designer-config/src/data_designer/config/__init__.py
index d3af55838..29c300e75 100644
--- a/packages/data-designer-config/src/data_designer/config/__init__.py
+++ b/packages/data-designer-config/src/data_designer/config/__init__.py
@@ -58,7 +58,11 @@
         ProcessorType,
         SchemaTransformProcessorConfig,
     )
-    from data_designer.config.run_config import JinjaRenderingEngine, RunConfig  # noqa: F401
+    from data_designer.config.run_config import (  # noqa: F401
+        JinjaRenderingEngine,
+        RequestAdmissionTuningConfig,
+        RunConfig,
+    )
     from data_designer.config.sampler_constraints import (  # noqa: F401
         ColumnInequalityConstraint,
         ConstraintType,
@@ -177,6 +181,7 @@
     "SchemaTransformProcessorConfig": (_MOD_PROCESSORS, "SchemaTransformProcessorConfig"),
     # run_config
     "JinjaRenderingEngine": (f"{_MOD_BASE}.run_config", "JinjaRenderingEngine"),
+    "RequestAdmissionTuningConfig": (f"{_MOD_BASE}.run_config", "RequestAdmissionTuningConfig"),
     "RunConfig": (f"{_MOD_BASE}.run_config", "RunConfig"),
     # scheduling metadata
     "SchedulingMetadata": (f"{_MOD_BASE}.scheduling", "SchedulingMetadata"),
diff --git a/packages/data-designer-config/src/data_designer/config/run_config.py b/packages/data-designer-config/src/data_designer/config/run_config.py
index a67740aed..2620954cf 100644
--- a/packages/data-designer-config/src/data_designer/config/run_config.py
+++ b/packages/data-designer-config/src/data_designer/config/run_config.py
@@ -3,7 +3,7 @@
 
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, ClassVar
 
 from pydantic import Field, model_validator
 from typing_extensions import Self
@@ -19,6 +19,67 @@ class JinjaRenderingEngine(StrEnum):
     SECURE = "secure"
 
 
+class RequestAdmissionTuningConfig(ConfigBase):
+    """Advanced request-admission AIMD tuning for model API calls.
+
+    Most workloads should tune model capacity with ``max_parallel_requests`` on
+    inference parameters. These fields adjust the adaptive recovery behavior
+    below that cap and are intended for provider/runtime support cases.
+    """
+
+    _LEGACY_FIELD_NAMES: ClassVar[dict[str, str]] = {
+        "reduce_factor": "multiplicative_decrease_factor",
+        "additive_increase": "additive_increase_step",
+        "success_window": "increase_after_successes",
+        "rampup_seconds": "startup_ramp_seconds",
+    }
+
+    multiplicative_decrease_factor: float = Field(
+        default=0.75,
+        gt=0.0,
+        lt=1.0,
+        description="Factor applied to the adaptive concurrency limit after a provider rate-limit signal.",
+    )
+    additive_increase_step: int = Field(
+        default=1,
+        ge=1,
+        description="Slots added to the adaptive concurrency limit after each successful recovery window.",
+    )
+    increase_after_successes: int = Field(
+        default=25,
+        ge=1,
+        description="Successful releases required before additive recovery increases the adaptive limit.",
+    )
+    cooldown_seconds: float = Field(
+        default=2.0,
+        gt=0.0,
+        description="Fallback cooldown after a rate-limit signal when the provider omits Retry-After.",
+    )
+    startup_ramp_seconds: float = Field(
+        default=0.0,
+        ge=0.0,
+        description=(
+            "Startup ramp duration. When greater than zero, each request resource starts at one "
+            "concurrent request and linearly ramps to its configured cap unless a rate-limit aborts the ramp."
+        ),
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def normalize_legacy_field_names(cls, data: Any) -> Any:
+        """Accept old throttle-era names inside the new request-admission DTO."""
+        if not isinstance(data, dict):
+            return data
+        normalized = dict(data)
+        for old_name, new_name in cls._LEGACY_FIELD_NAMES.items():
+            if old_name not in normalized:
+                continue
+            if new_name in normalized:
+                raise ValueError(f"Specify either {old_name!r} or {new_name!r} for request admission tuning, not both.")
+            normalized[new_name] = normalized.pop(old_name)
+        return normalized
+
+
 class RunConfig(ConfigBase):
     """Runtime configuration for dataset generation.
 
@@ -55,10 +116,13 @@ class RunConfig(ConfigBase):
             fewer Data Designer-specific restrictions. ``secure`` uses Data Designer's
             hardened sandbox with additional AST, filter, and output guards.
             Default is ``secure``.
+        request_admission: Advanced AIMD request-admission tuning for provider/model calls.
+            Most users should leave this unset and tune ``max_parallel_requests`` instead.
 
     Notes:
-        Request admission is engine-internal in V1 and is not exposed as a
-        public run-config knob.
+        Request-admission controller internals remain engine-owned. This field
+        exposes only the supported tuning DTO and does not expose controller
+        mutation APIs, leases, queues, or pressure snapshots.
     """
 
     disable_early_shutdown: bool = False
@@ -78,14 +142,15 @@ class RunConfig(ConfigBase):
             "`native` uses Jinja2's built-in sandbox; `secure` uses Data Designer's hardened sandbox."
         ),
     )
+    request_admission: RequestAdmissionTuningConfig | None = None
 
     @model_validator(mode="before")
     @classmethod
     def reject_removed_throttle_config(cls, data: Any) -> Any:
         if isinstance(data, dict) and "throttle" in data:
             raise ValueError(
-                "RunConfig.throttle was removed. Request admission is now managed internally by the async "
-                "scheduling engine; remove the throttle field from your run config."
+                "RunConfig.throttle was removed. Use RunConfig.request_admission with "
+                "RequestAdmissionTuningConfig for supported advanced AIMD tuning."
             )
         return data
 
diff --git a/packages/data-designer-config/tests/config/test_run_config.py b/packages/data-designer-config/tests/config/test_run_config.py
index a90a2d57a..e44b69e63 100644
--- a/packages/data-designer-config/tests/config/test_run_config.py
+++ b/packages/data-designer-config/tests/config/test_run_config.py
@@ -6,7 +6,8 @@
 import pytest
 from pydantic import ValidationError
 
-from data_designer.config.run_config import JinjaRenderingEngine, RunConfig
+import data_designer.config as dd
+from data_designer.config.run_config import JinjaRenderingEngine, RequestAdmissionTuningConfig, RunConfig
 
 
 def test_run_config_defaults_to_secure_jinja_renderer() -> None:
@@ -21,3 +22,60 @@ def test_run_config_accepts_native_renderer() -> None:
 def test_run_config_rejects_removed_throttle_with_targeted_message() -> None:
     with pytest.raises(ValidationError, match="RunConfig.throttle was removed"):
         RunConfig(throttle={"max_concurrent_requests": 1})
+
+
+def test_request_admission_tuning_config_accepts_canonical_fields() -> None:
+    config = RequestAdmissionTuningConfig(
+        multiplicative_decrease_factor=0.5,
+        additive_increase_step=2,
+        increase_after_successes=7,
+        cooldown_seconds=1.5,
+        startup_ramp_seconds=30.0,
+    )
+
+    assert config.multiplicative_decrease_factor == 0.5
+    assert config.additive_increase_step == 2
+    assert config.increase_after_successes == 7
+    assert config.cooldown_seconds == 1.5
+    assert config.startup_ramp_seconds == 30.0
+
+
+def test_request_admission_tuning_config_accepts_throttle_era_field_names() -> None:
+    config = RequestAdmissionTuningConfig(
+        reduce_factor=0.5,
+        additive_increase=2,
+        success_window=7,
+        cooldown_seconds=1.5,
+        rampup_seconds=30.0,
+    )
+
+    assert config.multiplicative_decrease_factor == 0.5
+    assert config.additive_increase_step == 2
+    assert config.increase_after_successes == 7
+    assert config.cooldown_seconds == 1.5
+    assert config.startup_ramp_seconds == 30.0
+
+
+def test_request_admission_tuning_config_rejects_duplicate_legacy_and_canonical_fields() -> None:
+    with pytest.raises(ValidationError, match="Specify either 'reduce_factor' or 'multiplicative_decrease_factor'"):
+        RequestAdmissionTuningConfig(reduce_factor=0.5, multiplicative_decrease_factor=0.75)
+
+
+def test_run_config_accepts_request_admission_tuning() -> None:
+    run_config = RunConfig(request_admission=RequestAdmissionTuningConfig(startup_ramp_seconds=10.0))
+
+    assert run_config.request_admission is not None
+    assert run_config.request_admission.startup_ramp_seconds == 10.0
+
+
+def test_run_config_accepts_request_admission_tuning_dict_with_throttle_era_names() -> None:
+    run_config = RunConfig(request_admission={"reduce_factor": 0.5, "success_window": 7, "rampup_seconds": 10.0})
+
+    assert run_config.request_admission is not None
+    assert run_config.request_admission.multiplicative_decrease_factor == 0.5
+    assert run_config.request_admission.increase_after_successes == 7
+    assert run_config.request_admission.startup_ramp_seconds == 10.0
+
+
+def test_request_admission_tuning_config_is_exported_from_config_package() -> None:
+    assert dd.RequestAdmissionTuningConfig is RequestAdmissionTuningConfig
diff --git a/packages/data-designer-engine/src/data_designer/engine/capacity.py b/packages/data-designer-engine/src/data_designer/engine/capacity.py
index 4dc1abb0b..9ff8281bc 100644
--- a/packages/data-designer-engine/src/data_designer/engine/capacity.py
+++ b/packages/data-designer-engine/src/data_designer/engine/capacity.py
@@ -55,6 +55,7 @@ class RequestAdmissionConfigSnapshot:
     multiplicative_decrease_factor: float
     additive_increase_step: int
     increase_after_successes: int
+    startup_ramp_seconds: float
     default_queue_wait_timeout_seconds: float | None
 
     @classmethod
@@ -68,6 +69,7 @@ def from_config(cls, config: RequestAdmissionConfig) -> RequestAdmissionConfigSn
             multiplicative_decrease_factor=config.multiplicative_decrease_factor,
             additive_increase_step=config.additive_increase_step,
             increase_after_successes=config.increase_after_successes,
+            startup_ramp_seconds=config.startup_ramp_seconds,
             default_queue_wait_timeout_seconds=config.default_queue_wait_timeout_seconds,
         )
 
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/factory.py b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
index 6531d1dbd..b68178af9 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/factory.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
@@ -14,6 +14,7 @@
     from data_designer.config.run_config import RunConfig
     from data_designer.engine.mcp.registry import MCPRegistry
     from data_designer.engine.models.registry import ModelRegistry
+    from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
 
 
 def create_model_registry(
@@ -40,8 +41,8 @@ def create_model_registry(
         client_concurrency_mode: ``"sync"`` (default) or ``"async"``.  Forwarded
             to native HTTP adapters so each client is constrained to a single
             concurrency mode.
-        run_config: Optional runtime configuration. Request admission uses
-            engine-internal defaults in V1; no public run-config knob is exposed.
+        run_config: Optional runtime configuration. Public request-admission
+            tuning is translated to the engine-internal request-admission config.
 
     Returns:
         A configured ModelRegistry instance.
@@ -51,10 +52,13 @@ def create_model_registry(
     from data_designer.engine.models.clients.retry import RetryConfig
     from data_designer.engine.models.facade import ModelFacade
     from data_designer.engine.models.registry import ModelRegistry
+    from data_designer.engine.models.request_admission.config import RequestAdmissionConfig
     from data_designer.engine.models.request_admission.controller import AdaptiveRequestAdmissionController
 
-    _ = run_config or RunConfig()
-    request_admission = AdaptiveRequestAdmissionController()
+    resolved_run_config = run_config or RunConfig()
+    request_admission = AdaptiveRequestAdmissionController(
+        _request_admission_config_from_run_config(resolved_run_config, RequestAdmissionConfig)
+    )
 
     def model_facade_factory(
         model_config: ModelConfig,
@@ -85,3 +89,19 @@ def model_facade_factory(
         request_admission=request_admission,
         retry_config=RetryConfig(),
     )
+
+
+def _request_admission_config_from_run_config(
+    run_config: RunConfig,
+    config_cls: type[RequestAdmissionConfig],
+) -> RequestAdmissionConfig:
+    tuning = run_config.request_admission
+    if tuning is None:
+        return config_cls()
+    return config_cls(
+        cooldown_seconds=tuning.cooldown_seconds,
+        multiplicative_decrease_factor=tuning.multiplicative_decrease_factor,
+        additive_increase_step=tuning.additive_increase_step,
+        increase_after_successes=tuning.increase_after_successes,
+        startup_ramp_seconds=tuning.startup_ramp_seconds,
+    )
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
index 2796698c5..be209029a 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
@@ -17,4 +17,5 @@ class RequestAdmissionConfig:
     multiplicative_decrease_factor: float = 0.75
     additive_increase_step: int = 1
     increase_after_successes: int = 25
+    startup_ramp_seconds: float = 0.0
     default_queue_wait_timeout_seconds: float | None = None
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index 8494d7d69..ab13260a5 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -37,6 +37,7 @@
 
 logger = logging.getLogger(__name__)
 
+DEFAULT_MIN_LIMIT: int = 1
 RequestDenyReason = Literal[
     "no_capacity",
     "cooldown",
@@ -505,6 +506,7 @@ def _denial_for(self, item: RequestAdmissionItem, now: float) -> RequestAdmissio
         if provider_model not in self._global_caps:
             return RequestAdmissionDenied(item=item, reason="hard_policy_denial", diagnostics={"unregistered": True})
         state = self._get_or_create_state(resource)
+        self._apply_startup_ramp_locked(state, resource, now)
         if now < state.blocked_until:
             return RequestAdmissionDenied(
                 item=item,
@@ -571,6 +573,7 @@ def _apply_outcome(
     ) -> None:
         effective_max = self._effective_max_for_resource(resource)
         if outcome.kind == "rate_limited":
+            state.startup_ramp_active = False
             prev_limit = state.current_limit
             should_decrease = admitted_adaptive_limit <= prev_limit
             state.consecutive_rate_limits += 1
@@ -596,6 +599,11 @@ def _apply_outcome(
                         )
                     )
             return
+        if state.startup_ramp_active:
+            self._apply_startup_ramp_locked(state, resource, now)
+            if outcome.kind == "success":
+                state.success_streak = 0
+                return
         if outcome.kind == "success" and now >= state.blocked_until:
             prev_limit = state.current_limit
             state.consecutive_rate_limits = 0
@@ -641,20 +649,59 @@ def _decrement_waiter(self, item: RequestAdmissionItem) -> None:
     def _get_or_create_state(self, resource: RequestResourceKey) -> AdaptiveRequestLimitState:
         state = self._domains.get(resource)
         if state is None:
-            effective_max = self._effective_max_for_resource(resource)
-            initial = self._config.initial_limits.get(resource, effective_max)
-            state = AdaptiveRequestLimitState(current_limit=max(1, min(initial, effective_max)))
+            initial = self._initial_limit_for_resource(resource)
+            ramp_active = self._config.startup_ramp_seconds > 0.0 and initial > DEFAULT_MIN_LIMIT
+            state = AdaptiveRequestLimitState(
+                current_limit=DEFAULT_MIN_LIMIT if ramp_active else initial,
+                startup_ramp_started_at=time.monotonic(),
+                startup_ramp_active=ramp_active,
+            )
             self._domains[resource] = state
         return state
 
+    def _initial_limit_for_resource(self, resource: RequestResourceKey) -> int:
+        effective_max = self._effective_max_for_resource(resource)
+        initial = self._config.initial_limits.get(resource, effective_max)
+        return max(DEFAULT_MIN_LIMIT, min(initial, effective_max))
+
     def _effective_max_for_resource(self, resource: RequestResourceKey) -> int:
         provider_model_cap = self._global_caps.get(resource.provider_model_key)
-        static_cap = provider_model_cap.effective_max if provider_model_cap is not None else 1
+        static_cap = provider_model_cap.effective_max if provider_model_cap is not None else DEFAULT_MIN_LIMIT
         clamp = self._config.max_limit_clamps.get(resource)
-        return max(1, min(static_cap, clamp if clamp is not None else static_cap))
+        return max(DEFAULT_MIN_LIMIT, min(static_cap, clamp if clamp is not None else static_cap))
+
+    def _apply_startup_ramp_locked(
+        self,
+        state: AdaptiveRequestLimitState,
+        resource: RequestResourceKey,
+        now: float,
+    ) -> None:
+        if not state.startup_ramp_active:
+            return
+        target_limit = self._initial_limit_for_resource(resource)
+        if self._config.startup_ramp_seconds <= 0.0 or target_limit <= DEFAULT_MIN_LIMIT:
+            changed = state.current_limit != target_limit or state.startup_ramp_active
+            state.current_limit = min(state.current_limit, target_limit)
+            state.startup_ramp_active = False
+            if changed:
+                self._sequence += 1
+            return
+
+        elapsed = max(0.0, now - state.startup_ramp_started_at)
+        previous_limit = state.current_limit
+        if elapsed >= self._config.startup_ramp_seconds:
+            state.current_limit = target_limit
+            state.startup_ramp_active = False
+        else:
+            fraction = elapsed / self._config.startup_ramp_seconds
+            ramp_slots = math.floor((target_limit - DEFAULT_MIN_LIMIT) * fraction)
+            state.current_limit = min(target_limit, DEFAULT_MIN_LIMIT + ramp_slots)
+        if state.current_limit != previous_limit or not state.startup_ramp_active:
+            self._sequence += 1
 
     def _snapshot_locked(self, resource: RequestResourceKey, now: float) -> RequestPressureSnapshot:
         state = self._get_or_create_state(resource)
+        self._apply_startup_ramp_locked(state, resource, now)
         blocked_until = state.blocked_until if state.blocked_until > now else None
         return RequestPressureSnapshot(
             captured_at=now,
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py
index ad6f779d7..7df1bc278 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/limits.py
@@ -17,3 +17,5 @@ class AdaptiveRequestLimitState:
     consecutive_rate_limits: int = 0
     active_lease_count: int = 0
     last_outcome: str | None = None
+    startup_ramp_started_at: float = 0.0
+    startup_ramp_active: bool = False
diff --git a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index c0c9a5801..c9767f02a 100644
--- a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -213,6 +213,69 @@ def test_request_admission_additive_recovery_after_successes() -> None:
     assert controller.pressure.snapshot(item.resource).current_limit == 2  # type: ignore[union-attr]
 
 
+def test_request_admission_startup_ramp_starts_at_one_and_progresses_to_cap(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    now = 100.0
+    monkeypatch.setattr("data_designer.engine.models.request_admission.controller.time.monotonic", lambda: now)
+    controller = _controller(cap=4, config=RequestAdmissionConfig(startup_ramp_seconds=10.0))
+    item = _item()
+
+    first = controller.try_acquire(item)
+    assert isinstance(first, RequestAdmissionLease)
+    second = controller.try_acquire(item)
+    assert isinstance(second, RequestAdmissionDenied)
+    assert second.reason == "no_capacity"
+    assert controller.pressure.snapshot(item.resource).current_limit == 1  # type: ignore[union-attr]
+    controller.release(first, RequestReleaseOutcome(kind="success"))
+
+    now = 105.0
+    halfway_leases = [controller.try_acquire(item) for _ in range(2)]
+    assert all(isinstance(lease, RequestAdmissionLease) for lease in halfway_leases)
+    denied = controller.try_acquire(item)
+    assert isinstance(denied, RequestAdmissionDenied)
+    assert denied.reason == "no_capacity"
+    assert controller.pressure.snapshot(item.resource).current_limit == 2  # type: ignore[union-attr]
+    for lease in halfway_leases:
+        assert isinstance(lease, RequestAdmissionLease)
+        controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+    now = 110.0
+    full_ramp_leases = [controller.try_acquire(item) for _ in range(4)]
+    assert all(isinstance(lease, RequestAdmissionLease) for lease in full_ramp_leases)
+    assert controller.pressure.snapshot(item.resource).current_limit == 4  # type: ignore[union-attr]
+    for lease in full_ramp_leases:
+        assert isinstance(lease, RequestAdmissionLease)
+        controller.release(lease, RequestReleaseOutcome(kind="success"))
+
+
+def test_request_admission_rate_limit_aborts_startup_ramp(monkeypatch: pytest.MonkeyPatch) -> None:
+    now = 100.0
+    monkeypatch.setattr("data_designer.engine.models.request_admission.controller.time.monotonic", lambda: now)
+    controller = _controller(
+        cap=4,
+        config=RequestAdmissionConfig(
+            cooldown_seconds=0.0,
+            multiplicative_decrease_factor=0.5,
+            startup_ramp_seconds=10.0,
+        ),
+    )
+    item = _item()
+    lease = controller.try_acquire(item)
+    assert isinstance(lease, RequestAdmissionLease)
+
+    controller.release(lease, RequestReleaseOutcome(kind="rate_limited"))
+    now = 110.0
+
+    assert controller.pressure.snapshot(item.resource).current_limit == 1  # type: ignore[union-attr]
+    next_lease = controller.try_acquire(item)
+    assert isinstance(next_lease, RequestAdmissionLease)
+    denied = controller.try_acquire(item)
+    assert isinstance(denied, RequestAdmissionDenied)
+    assert denied.reason == "no_capacity"
+    controller.release(next_lease, RequestReleaseOutcome(kind="success"))
+
+
 def test_request_admission_blocking_timeout_raises_typed_error() -> None:
     controller = _controller(cap=1)
     first = _item()
diff --git a/packages/data-designer-engine/tests/engine/models/test_model_registry.py b/packages/data-designer-engine/tests/engine/models/test_model_registry.py
index 1974f5310..75c067a57 100644
--- a/packages/data-designer-engine/tests/engine/models/test_model_registry.py
+++ b/packages/data-designer-engine/tests/engine/models/test_model_registry.py
@@ -6,6 +6,7 @@
 import pytest
 
 from data_designer.config.models import ChatCompletionInferenceParams, ModelConfig
+from data_designer.config.run_config import RequestAdmissionTuningConfig, RunConfig
 from data_designer.engine.models.errors import ModelAuthenticationError
 from data_designer.engine.models.facade import ModelFacade
 from data_designer.engine.models.factory import create_model_registry
@@ -54,6 +55,35 @@ def test_create_model_registry(
     assert isinstance(model_registry, ModelRegistry)
 
 
+def test_create_model_registry_maps_request_admission_tuning_config(
+    stub_model_configs: list[ModelConfig],
+    stub_secrets_resolver: object,
+    stub_model_provider_registry: object,
+) -> None:
+    model_registry = create_model_registry(
+        model_configs=stub_model_configs,
+        secret_resolver=stub_secrets_resolver,
+        model_provider_registry=stub_model_provider_registry,
+        run_config=RunConfig(
+            request_admission=RequestAdmissionTuningConfig(
+                multiplicative_decrease_factor=0.5,
+                additive_increase_step=2,
+                increase_after_successes=7,
+                cooldown_seconds=1.5,
+                startup_ramp_seconds=30.0,
+            )
+        ),
+    )
+
+    assert model_registry.request_admission is not None
+    request_config = model_registry.request_admission.config
+    assert request_config.multiplicative_decrease_factor == 0.5
+    assert request_config.additive_increase_step == 2
+    assert request_config.increase_after_successes == 7
+    assert request_config.cooldown_seconds == 1.5
+    assert request_config.startup_ramp_seconds == 30.0
+
+
 def test_public_props(stub_model_configs, stub_model_registry):
     assert stub_model_registry.model_configs == {
         model_config.alias: model_config for model_config in stub_model_configs
diff --git a/packages/data-designer-engine/tests/engine/test_capacity.py b/packages/data-designer-engine/tests/engine/test_capacity.py
index 6fd4daa33..856aeba09 100644
--- a/packages/data-designer-engine/tests/engine/test_capacity.py
+++ b/packages/data-designer-engine/tests/engine/test_capacity.py
@@ -19,13 +19,18 @@
 
 def test_request_admission_config_snapshot_records_resources() -> None:
     resource = RequestResourceKey("nvidia", "nemotron", RequestDomain.CHAT)
-    config = RequestAdmissionConfig(initial_limits={resource: 2}, max_limit_clamps={resource: 4})
+    config = RequestAdmissionConfig(
+        initial_limits={resource: 2},
+        max_limit_clamps={resource: 4},
+        startup_ramp_seconds=30.0,
+    )
 
     snapshot = RequestAdmissionConfigSnapshot.from_config(config)
 
     assert snapshot.resources == (resource,)
     assert snapshot.initial_limits[resource] == 2
     assert snapshot.max_limit_clamps[resource] == 4
+    assert snapshot.startup_ramp_seconds == 30.0
 
 
 def test_async_capacity_plan_records_configured_runtime_and_maxima() -> None:
diff --git a/plans/645/AsyncSchedulingRequestAdmissionClassModel.png b/plans/645/AsyncSchedulingRequestAdmissionClassModel.png
index 2f2f002c4fc9436852c026fe8d0fb3474879c491..34bdb2da2b68cfe81a1f14f6c4963fc13171f8cc 100644
GIT binary patch
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diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index dcb12a02e..f35913d0c 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -463,6 +463,7 @@ package "Request config" {
     +multiplicative_decrease_factor
     +additive_increase_step
     +increase_after_successes
+    +startup_ramp_seconds
     +default_queue_wait_timeout
   }
 }
@@ -584,6 +585,7 @@ package "Capacity planning (issue 654)" {
     +multiplicative_decrease_factor
     +additive_increase_step
     +increase_after_successes
+    +startup_ramp_seconds
     +default_queue_wait_timeout
   }
 }
diff --git a/plans/645/capacity-model.md b/plans/645/capacity-model.md
index d39c44a52..f9806d326 100644
--- a/plans/645/capacity-model.md
+++ b/plans/645/capacity-model.md
@@ -54,7 +54,7 @@ Source precedence is per-field, not global:
 | row-group concurrency | existing dataset-builder/runtime setting if present, then documented default |
 | task admission limits | benchmark override for benchmark runs, then engine default |
 | provider/model static cap | canonical model/provider metadata; request-admission config may lower but not raise it |
-| request-domain initial/adaptive settings | benchmark override or engine default, then clamped under provider/model static cap |
+| request-domain initial/adaptive settings | public `RunConfig.request_admission` tuning where supported, benchmark override for non-public harness values, then engine default, all clamped under provider/model static cap |
 | transport pool | adapter/client config, then documented default |
 
 If a value is missing, the capacity plan records the missing source and fallback used. If no safe fallback exists, construction fails with a typed configuration/metadata error before work is scheduled.
@@ -114,4 +114,4 @@ Required per-layer maxima include row groups in flight, queued tasks by group/re
 
 ## Public Knob Rule
 
-Do not add a new public capacity knob until benchmark evidence shows a specific need and the docs explain its layer. Prefer clear defaults, internal configs, and diagnostics first.
+Do not add new public capacity knobs beyond the documented model `max_parallel_requests`, `buffer_size`, and advanced `RunConfig.request_admission` tuning fields until benchmark evidence shows a specific need and the docs explain the layer. Prefer clear defaults, internal configs, and diagnostics first.
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index eb3510646..d753f7ed7 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -21,12 +21,13 @@ The final repository layout is specified in [Module ownership](module-ownership.
 | Contract | V1 status | Owner |
 | --- | --- | --- |
 | `SchedulingMetadata` | public plugin-facing DTO | `data-designer-config` |
+| `RequestAdmissionTuningConfig` | public advanced `RunConfig.request_admission` DTO for supported AIMD tuning only | `data-designer-config` |
 | `TaskAdmissionConfig` | engine-internal config and benchmark injection surface; not a public `RunConfig` knob in V1 | `data-designer-engine` |
 | `RequestAdmissionConfig` | engine-internal config and benchmark injection surface in V1 | `data-designer-engine` |
-| `RunConfig.request_admission` | reserved future public spelling if evidence justifies a user-facing request-admission knob | not introduced by this epic unless a later issue explicitly changes the public API |
+| `RunConfig.request_admission` | public advanced request-admission tuning surface backed by `RequestAdmissionTuningConfig`; does not expose internal controller APIs | `data-designer-config` |
 | `AsyncCapacityPlan` | diagnostic/reporting DTO, emitted to explain a run | `data-designer-engine` |
 
-Public knobs are not added by the task-admission or request-admission implementation slices. Benchmarks may inject capacity values through harness-only configuration so policy evidence can be produced without committing to public API.
+Public request-admission tuning is limited to the supported fields on `RequestAdmissionTuningConfig`: multiplicative decrease factor, additive increase step, successes before increase, fallback cooldown, and startup ramp seconds. Benchmarks may still inject lower-level capacity values through harness-only configuration without committing those values to public API.
 
 ## Metadata Contracts
 
@@ -347,7 +348,7 @@ It has no mutation or admission methods.
 
 Snapshots are immutable and internally consistent for their capture point. Domain snapshots include `captured_at`, monotonic `sequence`, resource, effective max, current limit, in-flight count, active lease count, waiters, blocked-until timing, cooldown remaining, rate-limit ceiling, consecutive rate limits, last outcome summary, and leak diagnostic counters. Global provider/model snapshots include aggregate static cap, aggregate in-flight count across domains, aggregate active lease count, aliases contributing to the cap, and per-domain limit summaries.
 
-`RequestAdmissionConfig` is the durable engine-internal request-admission tuning/config vocabulary for V1. It includes request resources, per-resource `initial_limit`, optional `max_limit_clamp`, configured cooldown, `multiplicative_decrease_factor`, `additive_increase_step`, `increase_after_successes`, and default queue-wait timeout. Legacy request-control config names are not durable names.
+`RequestAdmissionConfig` is the durable engine-internal request-admission tuning/config vocabulary for V1. It includes request resources, per-resource `initial_limit`, optional `max_limit_clamp`, configured cooldown, `multiplicative_decrease_factor`, `additive_increase_step`, `increase_after_successes`, `startup_ramp_seconds`, and default queue-wait timeout. Legacy request-control config names are not durable names; the public `RequestAdmissionTuningConfig` may accept old throttle-era spelling as input aliases for migration, but stores and documents scheduler-era names.
 
 ## Telemetry And Correlation Contracts
 
@@ -478,6 +479,7 @@ RequestAdmissionConfigSnapshot:
   multiplicative_decrease_factor: float
   additive_increase_step: int
   increase_after_successes: int
+  startup_ramp_seconds: float
   default_queue_wait_timeout_seconds: float | None
 
 AsyncCapacityPlan:
diff --git a/plans/645/migration-and-cleanup.md b/plans/645/migration-and-cleanup.md
index 046ad5349..75fc829e3 100644
--- a/plans/645/migration-and-cleanup.md
+++ b/plans/645/migration-and-cleanup.md
@@ -116,7 +116,7 @@ Current maintainer architecture docs should use durable internal names when they
 - `RequestAdmissionConfig`
 - `RuntimeCorrelationProvider`
 
-User/operator docs should expose public run config fields, `AsyncCapacityPlan`, benchmark artifacts, telemetry views, and high-level layer names. They must not present `TaskAdmissionConfig`, `RequestAdmissionConfig`, policies, leases, queues, or controller mutation APIs as public user knobs. Plugin-facing docs should describe metadata only, then link to architecture docs for maintainers/operators.
+User/operator docs should expose public run config fields, including `RequestAdmissionTuningConfig`, `AsyncCapacityPlan`, benchmark artifacts, telemetry views, and high-level layer names. They must not present `TaskAdmissionConfig`, `RequestAdmissionConfig`, policies, leases, queues, pressure snapshots, or controller mutation APIs as public user knobs. Plugin-facing docs should describe metadata only, then link to architecture docs for maintainers/operators.
 
 Current architecture docs, diagrams, generated assets, and plan files must be checked as part of final cleanup. Existing historical dev notes may retain old names only when the text clearly says the name is historical and no longer current API.
 
diff --git a/plans/645/module-ownership.md b/plans/645/module-ownership.md
index affa88d0a..988f80e83 100644
--- a/plans/645/module-ownership.md
+++ b/plans/645/module-ownership.md
@@ -195,7 +195,7 @@ packages/data-designer-engine/src/data_designer/engine/
 | Engine maintainers | scheduler/request admission modules, DTOs, protocols, policies, snapshots, events, capacity diagnostics | config-layer reverse imports, compatibility aliases, duplicate old/new module paths |
 | Tests and benchmarks | local fakes, deterministic model clients, event sinks, benchmark override config | production `engine.testing` helpers, test-module imports from benchmark code, benchmark-module imports from unit tests |
 
-`TaskAdmissionConfig` and `RequestAdmissionConfig` are engine-internal in V1. They may appear inside capacity and benchmark artifacts as explanatory snapshots, but they are not public `RunConfig` knobs unless a later design explicitly promotes them.
+`TaskAdmissionConfig` and `RequestAdmissionConfig` are engine-internal in V1. They may appear inside capacity and benchmark artifacts as explanatory snapshots, but they are not public `RunConfig` knobs. Public request-admission tuning is exposed only through `RequestAdmissionTuningConfig` on `RunConfig.request_admission` and is translated into the engine-internal config at the engine boundary.
 
 ## Tests And Benchmarks
 
diff --git a/plans/645/request-admission.md b/plans/645/request-admission.md
index 366080f36..b4204c3c3 100644
--- a/plans/645/request-admission.md
+++ b/plans/645/request-admission.md
@@ -39,7 +39,7 @@ The durable V1 implementation name is `AdaptiveRequestAdmissionController`.
 
 The durable model-call boundary name is `ModelRequestExecutor`.
 
-The durable internal config vocabulary is `RequestAdmissionConfig`. V1 does not introduce a public `RunConfig.request_admission` knob; that spelling is reserved for a later public surface if benchmark evidence justifies one.
+The durable internal config vocabulary is `RequestAdmissionConfig`. The public `RunConfig.request_admission` surface uses `RequestAdmissionTuningConfig`, a constrained advanced DTO for supported AIMD tuning only. Public config is translated into engine-internal `RequestAdmissionConfig` at the engine boundary; users do not receive controller, queue, lease, pressure snapshot, per-resource initial-limit, max-clamp, or queue-timeout mutation APIs.
 
 Do not keep production aliases, shims, subclasses, adapters, exports, docs paths, or durable tests for the replaced request-control vocabulary. [Migration and cleanup](migration-and-cleanup.md#request-admission-cleanup) lists the exact search terms.
 
@@ -121,7 +121,8 @@ V1 AIMD contract:
 - `effective_max = min(provider_model_static_cap, request_config.max_limit_clamp_for_resource_if_present)`
 - instantaneous aggregate availability is enforced separately by `provider_model_aggregate_in_flight < provider_model_static_cap`
 - `initial_limit` is clamped to `[1, effective_max]`
-- `current_limit` starts at `initial_limit`
+- `current_limit` starts at `initial_limit`, unless `startup_ramp_seconds > 0`, in which case it starts at `1` and ramps linearly to `initial_limit`
+- a provider rate-limit during startup ramp aborts the ramp and switches the resource to normal AIMD recovery
 - provider rate limits apply multiplicative decrease and set `blocked_until_monotonic`
 - success outside cooldown contributes to additive recovery
 - `request_limit_increased`, `request_soft_ceiling_recovered`, and `request_fully_recovered` events are emitted from state transitions, not inferred later by sinks

From c3a39bfe8ae6cca8f55d9d50f6d5586dedbbd1dc Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 14:13:33 -0400
Subject: [PATCH 26/33] Address Greptile admission telemetry feedback

---
 .../dataset_builders/async_scheduler.py       |  4 +-
 .../models/clients/model_request_executor.py  |  9 ++---
 .../dataset_builders/test_async_scheduler.py  | 39 +++++++++++++++++++
 .../clients/test_model_request_executor.py    | 22 +++++++++++
 4 files changed, 67 insertions(+), 7 deletions(-)

diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index df19c5716..55e58dd87 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -916,10 +916,10 @@ def _adaptive_row_group_block_reason(self) -> str | None:
             return "no_llm_wait_resource"
         llm_available = task_view.resources_available.get("llm_wait", 0)
         queued_llm = queue_view.queued_peer_demand_by_resource.get("llm_wait", 0)
-        if llm_available <= queued_llm and queue_view.queued_total > 0:
-            return "queued_llm_demand"
         if llm_available <= 0:
             return "llm_wait_saturated"
+        if llm_available <= queued_llm and queue_view.queued_total > 0:
+            return "queued_llm_demand"
         return None
 
     def _next_unadmitted_row_group_size(self) -> int | None:
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
index 6782c16bc..721afa41a 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/clients/model_request_executor.py
@@ -198,11 +198,10 @@ async def _execute_async_attempt(self, domain: RequestDomain, call: Callable[[],
                 "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "provider_timeout"}
             )
             raise
-        except BaseException:
-            self._request_admission.release(lease, RequestReleaseOutcome(kind="unexpected_exception"))
-            self._emit_model_event(
-                "model_request_completed", item=item, lease=lease, diagnostics={"outcome": "unexpected_exception"}
-            )
+        except BaseException as exc:
+            outcome = "local_cancelled" if isinstance(exc, KeyboardInterrupt) else "unexpected_exception"
+            self._request_admission.release(lease, RequestReleaseOutcome(kind=outcome))
+            self._emit_model_event("model_request_completed", item=item, lease=lease, diagnostics={"outcome": outcome})
             raise
         else:
             self._request_admission.release(lease, RequestReleaseOutcome(kind="success"))
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index ce78e3141..57dd8a0e2 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -2902,6 +2902,45 @@ def test_scheduler_adaptive_row_group_row_guard_blocks_extra_large_groups() -> N
     assert scheduler._row_group_row_guard_allows(9_000)
 
 
+def test_scheduler_adaptive_row_group_block_reason_prefers_llm_saturation() -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="topic", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="model_col", prompt="{{ topic }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "topic": GenerationStrategy.FULL_COLUMN,
+        "model_col": GenerationStrategy.CELL_BY_CELL,
+    }
+    row_groups = [(0, 1), (1, 1)]
+    graph = ExecutionGraph.create(configs, strategies)
+    scheduler = AsyncTaskScheduler(
+        generators={
+            "topic": MockSeedGenerator(config=_expr_config("topic"), resource_provider=provider),
+            "model_col": SlowLLMBoundCellGenerator(
+                config=_expr_config("model_col"),
+                resource_provider=provider,
+                delay=0.0,
+            ),
+        },
+        graph=graph,
+        tracker=CompletionTracker.with_graph(graph, row_groups),
+        row_groups=row_groups,
+        max_concurrent_row_groups=2,
+        adaptive_row_group_admission=True,
+        num_records=2,
+        buffer_size=1,
+    )
+    scheduler._fair_queue = SimpleNamespace(
+        view=lambda: SimpleNamespace(queued_total=1, queued_peer_demand_by_resource={})
+    )
+    scheduler._task_admission = SimpleNamespace(
+        view=lambda: SimpleNamespace(resource_limits={"llm_wait": 1}, resources_available={"llm_wait": 0})
+    )
+
+    assert scheduler._adaptive_row_group_block_reason() == "llm_wait_saturated"
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_raises_when_ready_frontier_blocked_without_in_flight() -> None:
     provider = _mock_provider()
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
index 2c44a0c00..732a2717e 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
@@ -266,6 +266,28 @@ async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionRes
     assert snapshot.last_outcome == "local_cancelled"
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_model_request_executor_classifies_async_keyboard_interrupt_as_cancelled() -> None:
+    class _InterruptingClient(_Client):
+        async def acompletion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
+            raise KeyboardInterrupt
+
+    sink = InMemoryAdmissionEventSink()
+    controller = AdaptiveRequestAdmissionController(event_sink=sink)
+    controller.register(provider_name="nvidia", model_id="nemotron", alias="default", max_parallel_requests=1)
+    executor = ModelRequestExecutor(_InterruptingClient(), controller, "nvidia", "nemotron", event_sink=sink)
+
+    with pytest.raises(KeyboardInterrupt):
+        await executor.acompletion(ChatCompletionRequest(model="nemotron", messages=[]))
+
+    snapshot = controller.pressure.snapshot(next(iter(controller.pressure.snapshots())))
+    assert snapshot is not None
+    assert snapshot.active_lease_count == 0
+    assert snapshot.last_outcome == "local_cancelled"
+    completed = [event for event in sink.request_events if event.event_kind == "model_request_completed"]
+    assert completed[-1].diagnostics["outcome"] == "local_cancelled"
+
+
 def test_model_request_executor_maps_image_chat_domain() -> None:
     executor, controller, _client = _executor()
 

From c97002a9093466a9782a33f9ab7d390713088fc6 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 14:35:14 -0400
Subject: [PATCH 27/33] Address async scheduling review follow-ups

---
 architecture/dataset-builders.md              |  7 +-
 .../src/data_designer/config/__init__.py      |  9 +++
 .../tests/config/test_run_config.py           |  7 ++
 .../dataset_builders/async_scheduler.py       |  6 +-
 .../scheduling/task_policies.py               |  7 +-
 .../src/data_designer/engine/observability.py | 67 ++++++++++++++---
 .../dataset_builders/test_async_scheduler.py  | 72 ++++++++++++++++++-
 .../clients/test_model_request_executor.py    |  4 +-
 .../tests/engine/models/test_facade.py        | 36 +++++++++-
 .../tests/engine/test_observability.py        | 46 +++++++++++-
 plans/645/capacity-model.md                   |  6 +-
 plans/645/issue-map.md                        |  2 +-
 plans/645/observability.md                    | 10 ++-
 plans/645/task-admission.md                   |  4 +-
 14 files changed, 261 insertions(+), 22 deletions(-)

diff --git a/architecture/dataset-builders.md b/architecture/dataset-builders.md
index a1ffbfe2a..fc3981543 100644
--- a/architecture/dataset-builders.md
+++ b/architecture/dataset-builders.md
@@ -123,12 +123,17 @@ DatasetBuilder.build()
       → CompletionTracker.with_graph()
       → AsyncTaskScheduler(task admission, fair queue, salvage_rounds)
   → scheduler.run()
-      → for each row group, fairly admit ready tasks from frontier
+      → admit row groups under the configured row-group cap
+      → fairly admit ready tasks from the frontier through task admission
       → tasks execute generators, update CompletionTracker
       → checkpoints via RowGroupBufferManager
   → collect TaskTraces, emit telemetry
 ```
 
+Row-group admission is fixed by default in the dataset-builder path: the configured row-group concurrency is the hard in-flight cap. The scheduler also has an internal adaptive row-group mode for direct use that only raises a soft target up to that cap; it is additive ramp-up, not AIMD shrink/recovery behavior.
+
+When request admission is available, async scheduling may use request-pressure snapshots as a read-only advisory during fair-queue selection. A request-pressured task can be skipped for an eligible peer without mutating request-admission state; provider/model/domain request limits remain owned by request admission.
+
 ## Design Decisions
 
 - **Dual execution engines behind one API.** The sequential engine is simpler and easier to debug; the async engine adds row-group parallelism for throughput. Users switch via an environment variable without changing their code.
diff --git a/packages/data-designer-config/src/data_designer/config/__init__.py b/packages/data-designer-config/src/data_designer/config/__init__.py
index 29c300e75..a60a21592 100644
--- a/packages/data-designer-config/src/data_designer/config/__init__.py
+++ b/packages/data-designer-config/src/data_designer/config/__init__.py
@@ -233,6 +233,13 @@
     "get_library_version": (f"{_MOD_BASE}.version", "get_library_version"),
 }
 
+_REMOVED_EXPORTS: dict[str, str] = {
+    "ThrottleConfig": (
+        "ThrottleConfig was removed. Use RunConfig.request_admission with "
+        "RequestAdmissionTuningConfig for supported advanced request-admission tuning."
+    ),
+}
+
 __all__ = list(_LAZY_IMPORTS.keys())
 
 
@@ -249,6 +256,8 @@ def __getattr__(name: str) -> object:
         # Cache so subsequent accesses find a real attribute and skip __getattr__.
         globals()[name] = attr
         return attr
+    if name in _REMOVED_EXPORTS:
+        raise ImportError(_REMOVED_EXPORTS[name])
 
     raise AttributeError(f"module 'data_designer.config' has no attribute {name!r}")
 
diff --git a/packages/data-designer-config/tests/config/test_run_config.py b/packages/data-designer-config/tests/config/test_run_config.py
index e44b69e63..70482ffe0 100644
--- a/packages/data-designer-config/tests/config/test_run_config.py
+++ b/packages/data-designer-config/tests/config/test_run_config.py
@@ -79,3 +79,10 @@ def test_run_config_accepts_request_admission_tuning_dict_with_throttle_era_name
 
 def test_request_admission_tuning_config_is_exported_from_config_package() -> None:
     assert dd.RequestAdmissionTuningConfig is RequestAdmissionTuningConfig
+
+
+def test_removed_throttle_config_export_has_migration_message() -> None:
+    with pytest.raises(ImportError, match="ThrottleConfig was removed.*RunConfig.request_admission"):
+        getattr(dd, "ThrottleConfig")
+    with pytest.raises(ImportError, match="ThrottleConfig was removed.*RequestAdmissionTuningConfig"):
+        exec("from data_designer.config import ThrottleConfig", {})
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index 55e58dd87..a2a793f5a 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -806,6 +806,10 @@ def _dispatch_selected_task(self, item: SchedulableTask, lease: TaskAdmissionLea
             self._emit_scheduler_event("worker_spawned", task=task, lease=lease, task_execution_id=task_execution_id)
         except Exception:
             result = self._task_admission.release(lease)
+            self._in_flight.discard(task)
+            self._dispatched.discard(task)
+            if (s := self._rg_states.get(task.row_group)) is not None:
+                s.in_flight_count = max(0, s.in_flight_count - 1)
             self._emit_scheduler_event(
                 "worker_spawn_failed",
                 task=task,
@@ -813,7 +817,6 @@ def _dispatch_selected_task(self, item: SchedulableTask, lease: TaskAdmissionLea
                 task_execution_id=task_execution_id,
                 reason_or_result=result.reason,
             )
-            self._in_flight.discard(task)
             raise
 
     def _schedulable_task(self, task: Task) -> SchedulableTask:
@@ -1090,6 +1093,7 @@ async def _main_dispatch_loop(
                 if self._all_rgs_admitted and not pending_pre_batch:
                     break
                 if pending_pre_batch:
+                    await asyncio.sleep(0)
                     continue
 
             if not self._fair_queue.has_queued_tasks or dispatch_outcome.admission_blocked:
diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
index 72d8b46f5..011e4e703 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/scheduling/task_policies.py
@@ -31,7 +31,12 @@
 
 @dataclass(frozen=True)
 class BoundedBorrowTaskAdmissionPolicyConfig:
-    """Engine-internal bounded-borrow policy configuration."""
+    """Engine-internal bounded-borrow policy configuration.
+
+    Borrow debt is tracked by task group and scheduler resource. Any completed
+    lease in the same group repays debt for the released resources; repayment is
+    not tied to the specific lease that originally borrowed.
+    """
 
     borrow_ceiling_by_group_resource: Mapping[tuple[TaskGroupKey, SchedulerResourceKey], int] = field(
         default_factory=dict
diff --git a/packages/data-designer-engine/src/data_designer/engine/observability.py b/packages/data-designer-engine/src/data_designer/engine/observability.py
index aa04e4a4e..a7a28c41b 100644
--- a/packages/data-designer-engine/src/data_designer/engine/observability.py
+++ b/packages/data-designer-engine/src/data_designer/engine/observability.py
@@ -4,8 +4,11 @@
 from __future__ import annotations
 
 import contextvars
+import math
 import time
-from dataclasses import dataclass, field
+from collections.abc import Mapping
+from dataclasses import dataclass, field, fields, is_dataclass
+from enum import Enum
 from typing import Literal, Protocol
 
 
@@ -20,6 +23,40 @@ class RuntimeCorrelation:
     task_execution_id: str | None
 
 
+JsonValue = str | int | float | bool | None | list["JsonValue"] | dict[str, "JsonValue"]
+
+
+def _json_safe(value: object) -> JsonValue:
+    if value is None or isinstance(value, str | int | bool):
+        return value
+    if isinstance(value, float):
+        return value if math.isfinite(value) else str(value)
+    if isinstance(value, Enum):
+        return _json_safe(value.value)
+    if is_dataclass(value) and not isinstance(value, type):
+        return {field.name: _json_safe(getattr(value, field.name)) for field in fields(value)}
+    if isinstance(value, Mapping):
+        return {_json_safe_key(key): _json_safe(item) for key, item in value.items()}
+    if isinstance(value, list | tuple):
+        return [_json_safe(item) for item in value]
+    if isinstance(value, set | frozenset):
+        return [_json_safe(item) for item in sorted(value, key=repr)]
+    return str(value)
+
+
+def _json_safe_key(value: object) -> str:
+    safe = _json_safe(value)
+    if isinstance(safe, str):
+        return safe
+    return str(safe)
+
+
+def _json_safe_dict(value: Mapping[str, object] | None) -> dict[str, JsonValue]:
+    if value is None:
+        return {}
+    return {_json_safe_key(key): _json_safe(item) for key, item in value.items()}
+
+
 class RuntimeCorrelationProvider:
     """Context-variable backed runtime correlation provider."""
 
@@ -99,14 +136,19 @@ class SchedulerAdmissionEvent:
     event_kind: SchedulerAdmissionEventKind
     captured_at_monotonic: float
     sequence: int
-    captured_correlation: RuntimeCorrelation | None = None
+    captured_correlation: JsonValue = None
     task_id: str | None = None
     task_execution_id: str | None = None
     task_lease_id: str | None = None
     scheduler_resource_key: str | None = None
     reason_or_result: str | None = None
-    snapshot: object | None = None
-    diagnostics: dict[str, object] = field(default_factory=dict)
+    snapshot: JsonValue = None
+    diagnostics: dict[str, JsonValue] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "captured_correlation", _json_safe(self.captured_correlation))
+        object.__setattr__(self, "snapshot", _json_safe(self.snapshot))
+        object.__setattr__(self, "diagnostics", _json_safe_dict(self.diagnostics))
 
     @classmethod
     def capture(
@@ -131,14 +173,21 @@ class RequestAdmissionEvent:
     event_kind: RequestAdmissionEventKind
     captured_at_monotonic: float
     sequence: int
-    captured_correlation: RuntimeCorrelation | None = None
+    captured_correlation: JsonValue = None
     request_attempt_id: str | None = None
     request_lease_id: str | None = None
-    request_resource_key: object | None = None
-    request_group_key: object | None = None
+    request_resource_key: JsonValue = None
+    request_group_key: JsonValue = None
     reason_or_outcome: str | None = None
-    pressure_snapshot: object | None = None
-    diagnostics: dict[str, object] = field(default_factory=dict)
+    pressure_snapshot: JsonValue = None
+    diagnostics: dict[str, JsonValue] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        object.__setattr__(self, "captured_correlation", _json_safe(self.captured_correlation))
+        object.__setattr__(self, "request_resource_key", _json_safe(self.request_resource_key))
+        object.__setattr__(self, "request_group_key", _json_safe(self.request_group_key))
+        object.__setattr__(self, "pressure_snapshot", _json_safe(self.pressure_snapshot))
+        object.__setattr__(self, "diagnostics", _json_safe_dict(self.diagnostics))
 
     @classmethod
     def capture(
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index 57dd8a0e2..52047fd0b 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -1778,6 +1778,76 @@ async def test_drain_frontier_raises_when_ready_but_no_capacity_or_inflight() ->
         await scheduler._drain_frontier(("seed",), False)
 
 
+def test_dispatch_selected_task_rolls_back_scheduler_state_when_worker_spawn_fails(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    provider = _mock_provider()
+    config = ExpressionColumnConfig(name="cell_out", expr="'x'", dtype="str")
+    graph = ExecutionGraph.create([config], {"cell_out": GenerationStrategy.CELL_BY_CELL})
+    scheduler = AsyncTaskScheduler(
+        generators={"cell_out": MockCellGenerator(config=config, resource_provider=provider)},
+        graph=graph,
+        tracker=CompletionTracker.with_graph(graph, [(0, 1)]),
+        row_groups=[(0, 1)],
+        scheduler_event_sink=(sink := InMemoryAdmissionEventSink()),
+    )
+    task = Task(column="cell_out", row_group=0, row_index=0, task_type="cell")
+    item = scheduler._schedulable_task(task)
+    lease = scheduler._task_admission.try_acquire(item, scheduler._fair_queue.view())
+    assert isinstance(lease, TaskAdmissionLease)
+    scheduler._rg_states[0] = SimpleNamespace(size=1, in_flight_count=0)
+
+    def fail_spawn(coro: Any) -> None:
+        coro.close()
+        raise RuntimeError("spawn failed")
+
+    monkeypatch.setattr(scheduler, "_spawn_worker", fail_spawn)
+
+    with pytest.raises(RuntimeError, match="spawn failed"):
+        scheduler._dispatch_selected_task(item, lease)
+
+    assert task not in scheduler._dispatched
+    assert task not in scheduler._in_flight
+    assert scheduler._rg_states[0].in_flight_count == 0
+    assert scheduler.task_admission_snapshot().leased_resources == {}
+    assert scheduler.task_admission_snapshot().running_counts_by_group == {}
+    assert any(event.event_kind == "worker_spawn_failed" for event in sink.scheduler_events)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_main_dispatch_loop_yields_when_pre_batch_is_pending(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    provider = _mock_provider()
+    seed_config = ExpressionColumnConfig(name="seed", expr="'seed'", dtype="str")
+    graph = ExecutionGraph.create([seed_config], {"seed": GenerationStrategy.FULL_COLUMN})
+    scheduler = AsyncTaskScheduler(
+        generators={"seed": MockSeedGenerator(config=seed_config, resource_provider=provider)},
+        graph=graph,
+        tracker=CompletionTracker.with_graph(graph, [(0, 1)]),
+        row_groups=[(0, 1)],
+    )
+    scheduler._all_rgs_admitted = True
+    scheduler._rg_states[0] = SimpleNamespace(size=1, seeds_dispatched=True, pre_batch_done=False)
+    monkeypatch.setattr(scheduler, "_run_seeds_complete_check", lambda seed_cols: None)
+    monkeypatch.setattr(
+        scheduler, "_dispatch_queued_tasks", lambda: SimpleNamespace(dispatched=False, admission_blocked=False)
+    )
+    monkeypatch.setattr(scheduler, "_checkpoint_completed_row_groups", lambda all_columns: None)
+    monkeypatch.setattr(scheduler, "_maybe_update_adaptive_row_group_target", lambda: None)
+    yielded_delays: list[float] = []
+
+    async def record_sleep(delay: float) -> None:
+        yielded_delays.append(delay)
+        scheduler._rg_states[0].pre_batch_done = True
+
+    monkeypatch.setattr(asyncio, "sleep", record_sleep)
+
+    await scheduler._main_dispatch_loop(("seed",), True, ["seed"])
+
+    assert yielded_delays == [0]
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_dispatch_does_not_scan_ready_frontier(monkeypatch: pytest.MonkeyPatch) -> None:
     provider = _mock_provider()
@@ -2796,7 +2866,7 @@ async def test_scheduler_emits_job_health_and_row_group_telemetry() -> None:
     assert started.diagnostics["row_group_count"] == 1
     assert started.diagnostics["graph_depth"] == 2
     column_scheduling = started.diagnostics["column_scheduling"]
-    assert isinstance(column_scheduling, tuple)
+    assert isinstance(column_scheduling, list)
     model_column = next(item for item in column_scheduling if item["column"] == "model_col")
     assert model_column["group_kind"] == "custom_model"
     assert model_column["resource_request"] == {"submission": 1, "llm_wait": 1}
diff --git a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
index 732a2717e..2806ae569 100644
--- a/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
+++ b/packages/data-designer-engine/tests/engine/models/clients/test_model_request_executor.py
@@ -365,7 +365,9 @@ def test_model_request_executor_emits_attempt_events_with_correlation_fields() -
     attempt_events = [event for event in sink.request_events if event.request_attempt_id is not None]
     assert attempt_events
     assert all(event.request_group_key is not None for event in attempt_events)
-    assert all(event.pressure_snapshot.resource == event.request_resource_key for event in attempt_events)
+    for event in attempt_events:
+        assert isinstance(event.pressure_snapshot, dict)
+        assert event.pressure_snapshot["resource"] == event.request_resource_key
 
 
 def test_model_request_executor_logs_sink_failures(caplog: pytest.LogCaptureFixture) -> None:
diff --git a/packages/data-designer-engine/tests/engine/models/test_facade.py b/packages/data-designer-engine/tests/engine/models/test_facade.py
index ee9ace2ce..1369c43b1 100644
--- a/packages/data-designer-engine/tests/engine/models/test_facade.py
+++ b/packages/data-designer-engine/tests/engine/models/test_facade.py
@@ -9,6 +9,7 @@
 import pytest
 
 from data_designer.engine.mcp.errors import MCPConfigurationError, MCPToolError
+from data_designer.engine.models.clients.errors import ProviderError, ProviderErrorKind
 from data_designer.engine.models.clients.types import (
     ChatCompletionResponse,
     EmbeddingResponse,
@@ -16,7 +17,11 @@
     ImagePayload,
     ToolCall,
 )
-from data_designer.engine.models.errors import ImageGenerationError, ModelGenerationValidationFailureError
+from data_designer.engine.models.errors import (
+    ImageGenerationError,
+    ModelGenerationValidationFailureError,
+    ModelTimeoutError,
+)
 from data_designer.engine.models.facade import ModelFacade
 from data_designer.engine.models.parsers.errors import ParserException
 from data_designer.engine.models.utils import ChatMessage
@@ -192,6 +197,35 @@ def test_generate_strips_response_content(
     assert result == expected
 
 
+def test_generate_maps_statusless_provider_timeout_to_model_timeout(stub_model_facade: ModelFacade) -> None:
+    stub_model_facade._client.completion.side_effect = ProviderError(
+        kind=ProviderErrorKind.TIMEOUT,
+        message="request timed out",
+        status_code=None,
+        provider_name="stub",
+        model_name=stub_model_facade.model_name,
+    )
+
+    with pytest.raises(ModelTimeoutError, match="timed out"):
+        stub_model_facade.generate(prompt="test", parser=lambda value: value)
+
+
+@pytest.mark.asyncio
+async def test_agenerate_maps_statusless_provider_timeout_to_model_timeout(stub_model_facade: ModelFacade) -> None:
+    stub_model_facade._client.acompletion = AsyncMock(
+        side_effect=ProviderError(
+            kind=ProviderErrorKind.TIMEOUT,
+            message="request timed out",
+            status_code=None,
+            provider_name="stub",
+            model_name=stub_model_facade.model_name,
+        )
+    )
+
+    with pytest.raises(ModelTimeoutError, match="timed out"):
+        await stub_model_facade.agenerate(prompt="test", parser=lambda value: value)
+
+
 def test_model_alias_property(stub_model_facade: ModelFacade, stub_model_configs: list[Any]) -> None:
     assert stub_model_facade.model_alias == stub_model_configs[0].alias
 
diff --git a/packages/data-designer-engine/tests/engine/test_observability.py b/packages/data-designer-engine/tests/engine/test_observability.py
index 55a2896b1..e7d9ce21b 100644
--- a/packages/data-designer-engine/tests/engine/test_observability.py
+++ b/packages/data-designer-engine/tests/engine/test_observability.py
@@ -3,6 +3,10 @@
 
 from __future__ import annotations
 
+import json
+from dataclasses import asdict, dataclass
+from enum import Enum
+
 from data_designer.engine.observability import (
     CorrelatedRuntimeView,
     InMemoryAdmissionEventSink,
@@ -13,6 +17,16 @@
 )
 
 
+class _DiagnosticMode(Enum):
+    TEST = "test"
+
+
+@dataclass(frozen=True)
+class _DiagnosticPayload:
+    label: str
+    mode: _DiagnosticMode
+
+
 def _correlation() -> RuntimeCorrelation:
     return RuntimeCorrelation(
         run_id="run",
@@ -56,14 +70,42 @@ def test_admission_events_capture_correlation_and_diagnostics() -> None:
         diagnostics={"resource": "chat"},
     )
 
-    assert scheduler_event.captured_correlation == correlation
+    assert scheduler_event.captured_correlation == asdict(correlation)
     assert scheduler_event.task_id == "task-1"
     assert scheduler_event.diagnostics == {"resource": "submission"}
-    assert request_event.captured_correlation == correlation
+    assert request_event.captured_correlation == asdict(correlation)
     assert request_event.request_attempt_id == "request-1"
     assert request_event.diagnostics == {"resource": "chat"}
 
 
+def test_admission_events_are_json_safe_at_construction() -> None:
+    correlation = _correlation()
+    payload = _DiagnosticPayload(label="payload", mode=_DiagnosticMode.TEST)
+
+    scheduler_event = SchedulerAdmissionEvent.capture(
+        "admission_blocked",
+        sequence=1,
+        correlation=correlation,
+        snapshot=payload,
+        diagnostics={"payload": payload, "values": {"b", "a"}, "pair": ("x", _DiagnosticMode.TEST)},
+    )
+    request_event = RequestAdmissionEvent.capture(
+        "request_wait_started",
+        sequence=2,
+        correlation=correlation,
+        request_resource_key=payload,
+        request_group_key=("group", _DiagnosticMode.TEST),
+        pressure_snapshot={"payload": payload},
+        diagnostics={"payload": payload},
+    )
+
+    json.dumps(asdict(scheduler_event), sort_keys=True)
+    json.dumps(asdict(request_event), sort_keys=True)
+    assert scheduler_event.snapshot == {"label": "payload", "mode": "test"}
+    assert scheduler_event.diagnostics["values"] == ["a", "b"]
+    assert request_event.request_resource_key == {"label": "payload", "mode": "test"}
+
+
 def test_in_memory_admission_event_sink_collects_scheduler_and_request_events() -> None:
     sink = InMemoryAdmissionEventSink()
     scheduler_event = SchedulerAdmissionEvent.capture("selected", sequence=1)
diff --git a/plans/645/capacity-model.md b/plans/645/capacity-model.md
index f9806d326..13f6a3e93 100644
--- a/plans/645/capacity-model.md
+++ b/plans/645/capacity-model.md
@@ -79,9 +79,11 @@ HTTP transport pools may be larger than the static provider cap. They are transp
 
 `buffer_size` defines the record-window shape used by the dataset builder. Row groups are the concrete execution partitions produced from that windowing behavior.
 
-Row-group admission remains scheduler-owned but is not changed by the V1 task-admission lease boundary. For this epic, #654 records row-group configured concurrency and observed row groups in flight through the `RowGroupAdmission` section of `AsyncCapacityPlan`; it does not introduce a new row-group scheduling policy unless a later issue explicitly does so.
+Row-group admission remains scheduler-owned and is separate from the V1 task-admission lease boundary. The normal dataset-builder wiring uses fixed row-group admission: `max_concurrent_row_groups` is the hard in-flight cap, and task admission leases then control ready task dispatch inside admitted row groups.
 
-Preview, resume, and checkpoint behavior use the existing dataset-builder partitioning rules. `AsyncCapacityPlan` reports the row-group values that the current engine used rather than redefining those rules.
+The current branch also contains an internal adaptive row-group admission mode for direct scheduler use. That mode is additive-only: it starts from an initial target and can raise the soft in-flight row-group target up to the semaphore hard cap when no local scheduler-pressure reason blocks growth. It does not decrease the target, so docs and telemetry must not describe it as AIMD. It remains off by default unless a later issue explicitly promotes it to a durable scheduler policy.
+
+`AsyncCapacityPlan.configured.row_group_admission` records the mode, configured row-group concurrency, current/observed adaptive target when applicable, observed row groups in flight, optional max-admitted-row guardrail, and blocked reasons. Preview, resume, and checkpoint behavior use the existing dataset-builder partitioning rules. `AsyncCapacityPlan` reports the row-group values that the current engine used rather than redefining those rules.
 
 ## Transitional Values
 
diff --git a/plans/645/issue-map.md b/plans/645/issue-map.md
index f114fac19..14ee92989 100644
--- a/plans/645/issue-map.md
+++ b/plans/645/issue-map.md
@@ -45,7 +45,7 @@ The implementation order remains:
 
 #644 cannot close until task admission uses the final scheduler module homes and the accepted metadata contract. The accepted end state is `SchedulingMetadata` feeding task admission through `TaskSchedulingResolver`; old resolver paths, compatibility adapters, and duplicate module homes are not part of the target architecture.
 
-#660 promotes the stabilized V1 admission/capacity/telemetry docs. #650 and #651 are follow-on policy/design issues; if they change behavior or public/operator guidance, they must update this source-of-truth plan and any promoted docs as part of their own acceptance gates. #651 is design-first unless its issue body explicitly promotes an implementation slice.
+#660 promotes the stabilized V1 admission/capacity/telemetry docs. #650 and #651 are follow-on policy/design issues; if they change behavior or public/operator guidance, they must update this source-of-truth plan and any promoted docs as part of their own acceptance gates. #651 is design-first unless its issue body explicitly promotes an implementation slice. The request-pressure advisory selection path currently in PR #661 is a narrow implementation slice ahead of the broader #651 design; it must remain read-only with respect to request admission until #651 defines a durable provider/resource-aware policy.
 
 ## Evidence Phasing
 
diff --git a/plans/645/observability.md b/plans/645/observability.md
index ffc76ef3a..2b09b5260 100644
--- a/plans/645/observability.md
+++ b/plans/645/observability.md
@@ -14,7 +14,7 @@ Sink failures must never interrupt generation. Event data can be collected under
 
 Event DTOs capture primitive correlation fields at construction time. Sinks receive already-captured event data; they must not read ambient context later to discover which task/request an event belongs to.
 
-All event DTOs include `captured_at_monotonic` and a monotonic per-stream `sequence`. Scheduler events include task id, task execution id when a worker execution exists, task lease id when available, scheduler resource key when applicable, denial/release reason when applicable, optional snapshot, and sanitized diagnostics. Request events include request attempt id when they belong to one concrete attempt, request lease id when available, canonical request resource when resource-specific, request group key when queue/admission-specific, denial/release outcome when applicable, optional pressure snapshot, and sanitized diagnostics.
+All event DTOs include `captured_at_monotonic` and a monotonic per-stream `sequence`. Scheduler events include task id, task execution id when a worker execution exists, task lease id when available, scheduler resource key when applicable, denial/release reason when applicable, optional snapshot, and diagnostics. Request events include request attempt id when they belong to one concrete attempt, request lease id when available, canonical request resource when resource-specific, request group key when queue/admission-specific, denial/release outcome when applicable, optional pressure snapshot, and diagnostics. Event construction normalizes correlation, keys, snapshots, and diagnostics to JSON-compatible values so structured sinks do not need to understand internal dataclass or enum types.
 
 ## Scheduler Admission Events
 
@@ -22,12 +22,20 @@ Scheduler events describe dependency-ready work moving through ready ordering, t
 
 Canonical scheduler event kinds:
 
+- `scheduler_job_started`
+- `scheduler_job_completed`
+- `scheduler_health_snapshot`
 - `dependency_ready`
 - `ready_enqueued`
+- `row_group_admitted`
+- `row_group_admission_blocked`
+- `row_group_admission_target_changed`
+- `row_group_checkpointed`
 - `selected`
 - `queue_empty`
 - `admission_blocked`
 - `group_capped`
+- `request_pressure_advisory_skipped`
 - `task_lease_acquired`
 - `admission_denied`
 - `worker_spawned`
diff --git a/plans/645/task-admission.md b/plans/645/task-admission.md
index 944d84048..1abd3e99b 100644
--- a/plans/645/task-admission.md
+++ b/plans/645/task-admission.md
@@ -102,6 +102,8 @@ V1 includes:
 
 V1 request waits remain inside admitted task execution and the task lease is retained until worker completion. That preserves the lease boundary and makes request waits visible, but it does not by itself solve cross-provider utilization when tasks for a cooled-down provider occupy all scheduler task slots. Issue #651 must address provider/resource-aware task admission or an explicit yield/reacquire design before the epic claims cross-provider scheduling optimization as complete.
 
+The current branch includes a narrow request-pressure advisory inside scheduler selection: when request-admission pressure is visible for one candidate and another eligible peer is not pressured, the scheduler may skip the pressured candidate for that selection pass. This consumes request-pressure snapshots as read-only input and does not mutate request-admission state or duplicate provider/model/domain AIMD. Treat broader provider/resource-aware scheduling as #651 scope.
+
 V1 excludes:
 
 - row-group admission
@@ -172,7 +174,7 @@ Policy constraints:
 - Borrow debt is measured in admitted scheduler-resource units above strict fair share for a group/resource. Strict share is computed from scheduler-known competing groups and their weights; #650 owns the exact rounding rule and benchmark evidence.
 - A group may borrow beyond strict share only up to its configured ceiling while no eligible peer can use the resource.
 - When peer queue pressure exists and a group has borrow debt, that group receives no further admissions for the borrowed resource while an eligible peer has queued work and the required resource is available.
-- Debt repayment happens when peer pressure exists and the indebted group is withheld, or when policy-defined repayment work completes. Repayment changes policy debt counters only, not hard resource availability.
+- Debt repayment happens when peer pressure exists and the indebted group is withheld, or when policy-defined repayment work completes. Runtime debt is tracked by task group and scheduler resource; any completed lease in the same task group repays debt for the resources it releases. Repayment changes policy debt counters only, not hard resource availability.
 - The policy must not traverse the DAG inside `FairTaskQueue`.
 - No public knob is added until benchmark evidence supports it.
 

From da6fa56a1abbe1c5cad841daa635f70a8b9a0c81 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 14:56:29 -0400
Subject: [PATCH 28/33] Address review follow-up migration gaps

---
 .../src/data_designer/config/run_config.py    | 14 ++++++++++
 .../tests/config/test_run_config.py           |  2 ++
 plans/645/architecture.md                     |  2 +-
 plans/645/contracts.md                        | 26 ++++++++++++-------
 4 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/packages/data-designer-config/src/data_designer/config/run_config.py b/packages/data-designer-config/src/data_designer/config/run_config.py
index 2620954cf..306f19b7e 100644
--- a/packages/data-designer-config/src/data_designer/config/run_config.py
+++ b/packages/data-designer-config/src/data_designer/config/run_config.py
@@ -19,6 +19,14 @@ class JinjaRenderingEngine(StrEnum):
     SECURE = "secure"
 
 
+_REMOVED_EXPORTS: dict[str, str] = {
+    "ThrottleConfig": (
+        "ThrottleConfig was removed. Use RunConfig.request_admission with "
+        "RequestAdmissionTuningConfig for supported advanced request-admission tuning."
+    ),
+}
+
+
 class RequestAdmissionTuningConfig(ConfigBase):
     """Advanced request-admission AIMD tuning for model API calls.
 
@@ -160,3 +168,9 @@ def normalize_shutdown_settings(self) -> Self:
         if self.disable_early_shutdown:
             self.shutdown_error_rate = 1.0
         return self
+
+
+def __getattr__(name: str) -> object:
+    if name in _REMOVED_EXPORTS:
+        raise ImportError(_REMOVED_EXPORTS[name])
+    raise AttributeError(f"module 'data_designer.config.run_config' has no attribute {name!r}")
diff --git a/packages/data-designer-config/tests/config/test_run_config.py b/packages/data-designer-config/tests/config/test_run_config.py
index 70482ffe0..6695db648 100644
--- a/packages/data-designer-config/tests/config/test_run_config.py
+++ b/packages/data-designer-config/tests/config/test_run_config.py
@@ -86,3 +86,5 @@ def test_removed_throttle_config_export_has_migration_message() -> None:
         getattr(dd, "ThrottleConfig")
     with pytest.raises(ImportError, match="ThrottleConfig was removed.*RequestAdmissionTuningConfig"):
         exec("from data_designer.config import ThrottleConfig", {})
+    with pytest.raises(ImportError, match="ThrottleConfig was removed.*RequestAdmissionTuningConfig"):
+        exec("from data_designer.config.run_config import ThrottleConfig", {})
diff --git a/plans/645/architecture.md b/plans/645/architecture.md
index da2838002..58f756f07 100644
--- a/plans/645/architecture.md
+++ b/plans/645/architecture.md
@@ -111,7 +111,7 @@ Task admission controls when ready dataset work may become a running worker. Req
 
 The split is required because arbitrary custom Python can make zero, one, or many model calls dynamically. A task's metadata may help group and schedule the task, but it is not a promise of exact request count and must not reserve every future model call up front.
 
-Task admission may later consume request pressure snapshots as read-only policy input. It must not pre-acquire request permits, emulate AIMD, or wrap provider/model/domain request admission.
+Task admission may consume request pressure snapshots as read-only policy input. The current branch includes a narrow request-pressure advisory selection path that can prefer an eligible, unpressured peer over a request-pressured candidate. It must not pre-acquire request permits, emulate AIMD, mutate request admission, or wrap provider/model/domain request admission. Broader provider/resource-aware scheduling remains #651 scope.
 
 In V1, a task waiting inside request admission keeps its scheduler task lease until the task reaches a terminal outcome. This makes request wait visible without adding yield/reacquire complexity to the lease boundary. The cross-provider optimization target, where tasks blocked on one cooled-down provider do not occupy every scheduler slot while another provider has ready work, belongs to #651's provider/resource-aware task policy or an explicit later yield/reacquire design.
 
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index d753f7ed7..e6e14ed9f 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -368,17 +368,25 @@ Snapshots are immutable and internally consistent for their capture point. Domai
 
 `RuntimeCorrelationProvider` owns set/reset/current behavior, likely through context variables. It must not require request admission protocols to import scheduler types.
 
-Scheduler/request events capture primitive correlation values when event DTOs are constructed. Event sinks must not rely on reading mutable ambient context later, because deferred emission could attach the wrong task context.
+Scheduler/request events capture correlation values when event DTOs are constructed and normalize correlation, keys, snapshots, and diagnostics to JSON-compatible values. Event sinks must not rely on reading mutable ambient context later, because deferred emission could attach the wrong task context.
 
 Canonical scheduler `event_kind` values are snake_case and versioned as part of the benchmark artifact schema:
 
 ```text
+scheduler_job_started
+scheduler_job_completed
+scheduler_health_snapshot
 dependency_ready
 ready_enqueued
+row_group_admitted
+row_group_admission_blocked
+row_group_admission_target_changed
+row_group_checkpointed
 selected
 queue_empty
 admission_blocked
 group_capped
+request_pressure_advisory_skipped
 task_lease_acquired
 admission_denied
 worker_spawned
@@ -399,14 +407,14 @@ release_diagnostic
 - `event_kind`
 - `captured_at_monotonic`
 - monotonic `sequence`
-- captured `RuntimeCorrelation | None`
+- captured correlation as JSON-compatible values
 - task id
 - task execution id when a worker execution exists
 - task lease id when available
 - scheduler resource key when applicable
 - decision reason or release result when applicable
-- optional scheduler snapshot
-- sanitized diagnostics
+- optional JSON-compatible scheduler snapshot
+- JSON-compatible diagnostics
 
 Canonical request `event_kind` values are snake_case and versioned as part of the benchmark artifact schema:
 
@@ -437,14 +445,14 @@ request_release_diagnostic
 - `event_kind`
 - `captured_at_monotonic`
 - monotonic `sequence`
-- captured `RuntimeCorrelation | None`
+- captured correlation as JSON-compatible values
 - request attempt id when the event belongs to one concrete model-call attempt
 - request lease id when available
-- canonical `RequestResourceKey` when the event is resource-specific
-- request group key when the event is queue/admission specific
+- canonical request resource as JSON-compatible values when the event is resource-specific
+- request group key as JSON-compatible values when the event is queue/admission specific
 - denial reason or release outcome when applicable
-- optional request pressure snapshot
-- sanitized diagnostics
+- optional JSON-compatible request pressure snapshot
+- JSON-compatible diagnostics
 
 Lease ids, task ids, request attempt ids, and raw model ids are trace/artifact fields only; they are not metric labels. Metric exporters use bounded labels such as `metric_model_label`, model family, or allowlisted model label. The OTel bridge must reject raw model ids as metric labels.
 

From aaa7eadd8de327f0c0797f3b28d047f0e43d012c Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 15:03:45 -0400
Subject: [PATCH 29/33] Add deprecated throttle config shim

---
 .../src/data_designer/config/__init__.py      | 11 +--
 .../src/data_designer/config/run_config.py    | 84 +++++++++++++++----
 .../tests/config/test_run_config.py           | 66 ++++++++++++---
 plans/645/contracts.md                        |  8 +-
 plans/645/migration-and-cleanup.md            |  2 +-
 plans/645/module-ownership.md                 |  3 +-
 6 files changed, 135 insertions(+), 39 deletions(-)

diff --git a/packages/data-designer-config/src/data_designer/config/__init__.py b/packages/data-designer-config/src/data_designer/config/__init__.py
index a60a21592..a8f683aa3 100644
--- a/packages/data-designer-config/src/data_designer/config/__init__.py
+++ b/packages/data-designer-config/src/data_designer/config/__init__.py
@@ -62,6 +62,7 @@
         JinjaRenderingEngine,
         RequestAdmissionTuningConfig,
         RunConfig,
+        ThrottleConfig,
     )
     from data_designer.config.sampler_constraints import (  # noqa: F401
         ColumnInequalityConstraint,
@@ -183,6 +184,7 @@
     "JinjaRenderingEngine": (f"{_MOD_BASE}.run_config", "JinjaRenderingEngine"),
     "RequestAdmissionTuningConfig": (f"{_MOD_BASE}.run_config", "RequestAdmissionTuningConfig"),
     "RunConfig": (f"{_MOD_BASE}.run_config", "RunConfig"),
+    "ThrottleConfig": (f"{_MOD_BASE}.run_config", "ThrottleConfig"),
     # scheduling metadata
     "SchedulingMetadata": (f"{_MOD_BASE}.scheduling", "SchedulingMetadata"),
     "SchedulingMetadataError": (f"{_MOD_BASE}.scheduling", "SchedulingMetadataError"),
@@ -233,13 +235,6 @@
     "get_library_version": (f"{_MOD_BASE}.version", "get_library_version"),
 }
 
-_REMOVED_EXPORTS: dict[str, str] = {
-    "ThrottleConfig": (
-        "ThrottleConfig was removed. Use RunConfig.request_admission with "
-        "RequestAdmissionTuningConfig for supported advanced request-admission tuning."
-    ),
-}
-
 __all__ = list(_LAZY_IMPORTS.keys())
 
 
@@ -256,8 +251,6 @@ def __getattr__(name: str) -> object:
         # Cache so subsequent accesses find a real attribute and skip __getattr__.
         globals()[name] = attr
         return attr
-    if name in _REMOVED_EXPORTS:
-        raise ImportError(_REMOVED_EXPORTS[name])
 
     raise AttributeError(f"module 'data_designer.config' has no attribute {name!r}")
 
diff --git a/packages/data-designer-config/src/data_designer/config/run_config.py b/packages/data-designer-config/src/data_designer/config/run_config.py
index 306f19b7e..52c4be531 100644
--- a/packages/data-designer-config/src/data_designer/config/run_config.py
+++ b/packages/data-designer-config/src/data_designer/config/run_config.py
@@ -3,6 +3,7 @@
 
 from __future__ import annotations
 
+import warnings
 from typing import Any, ClassVar
 
 from pydantic import Field, model_validator
@@ -19,12 +20,10 @@ class JinjaRenderingEngine(StrEnum):
     SECURE = "secure"
 
 
-_REMOVED_EXPORTS: dict[str, str] = {
-    "ThrottleConfig": (
-        "ThrottleConfig was removed. Use RunConfig.request_admission with "
-        "RequestAdmissionTuningConfig for supported advanced request-admission tuning."
-    ),
-}
+_THROTTLE_DEPRECATION_MESSAGE = (
+    "RunConfig.throttle and ThrottleConfig are deprecated. Use RunConfig.request_admission with "
+    "RequestAdmissionTuningConfig for supported advanced request-admission tuning."
+)
 
 
 class RequestAdmissionTuningConfig(ConfigBase):
@@ -88,6 +87,51 @@ def normalize_legacy_field_names(cls, data: Any) -> Any:
         return normalized
 
 
+class ThrottleConfig(ConfigBase):
+    """Deprecated compatibility DTO for request-admission tuning.
+
+    Use ``RequestAdmissionTuningConfig`` via ``RunConfig.request_admission``
+    instead. ``ceiling_overshoot`` is accepted for compatibility but is not
+    forwarded because request admission no longer exposes an overshoot knob.
+    """
+
+    reduce_factor: float = Field(
+        default=0.75,
+        gt=0.0,
+        lt=1.0,
+        description="Deprecated alias for RequestAdmissionTuningConfig.multiplicative_decrease_factor.",
+    )
+    additive_increase: int = Field(
+        default=1,
+        ge=1,
+        description="Deprecated alias for RequestAdmissionTuningConfig.additive_increase_step.",
+    )
+    success_window: int = Field(
+        default=25,
+        ge=1,
+        description="Deprecated alias for RequestAdmissionTuningConfig.increase_after_successes.",
+    )
+    cooldown_seconds: float = Field(
+        default=2.0,
+        gt=0.0,
+        description="Deprecated alias for RequestAdmissionTuningConfig.cooldown_seconds.",
+    )
+    ceiling_overshoot: float = Field(
+        default=0.10,
+        ge=0.0,
+        description="Deprecated compatibility field; not forwarded to request admission.",
+    )
+
+    def to_request_admission_tuning(self) -> RequestAdmissionTuningConfig:
+        """Translate legacy throttle tuning into the request-admission DTO."""
+        return RequestAdmissionTuningConfig(
+            multiplicative_decrease_factor=self.reduce_factor,
+            additive_increase_step=self.additive_increase,
+            increase_after_successes=self.success_window,
+            cooldown_seconds=self.cooldown_seconds,
+        )
+
+
 class RunConfig(ConfigBase):
     """Runtime configuration for dataset generation.
 
@@ -154,12 +198,26 @@ class RunConfig(ConfigBase):
 
     @model_validator(mode="before")
     @classmethod
-    def reject_removed_throttle_config(cls, data: Any) -> Any:
+    def translate_deprecated_throttle_config(cls, data: Any) -> Any:
         if isinstance(data, dict) and "throttle" in data:
-            raise ValueError(
-                "RunConfig.throttle was removed. Use RunConfig.request_admission with "
-                "RequestAdmissionTuningConfig for supported advanced AIMD tuning."
+            normalized = dict(data)
+            throttle = normalized.pop("throttle")
+            if normalized.get("request_admission") is not None:
+                raise ValueError(
+                    "Specify either RunConfig.throttle or RunConfig.request_admission, not both. "
+                    "RunConfig.throttle is deprecated."
+                )
+            if throttle is not None:
+                throttle_config = (
+                    throttle if isinstance(throttle, ThrottleConfig) else ThrottleConfig.model_validate(throttle)
+                )
+                normalized["request_admission"] = throttle_config.to_request_admission_tuning()
+            warnings.warn(
+                _THROTTLE_DEPRECATION_MESSAGE,
+                DeprecationWarning,
+                stacklevel=2,
             )
+            return normalized
         return data
 
     @model_validator(mode="after")
@@ -168,9 +226,3 @@ def normalize_shutdown_settings(self) -> Self:
         if self.disable_early_shutdown:
             self.shutdown_error_rate = 1.0
         return self
-
-
-def __getattr__(name: str) -> object:
-    if name in _REMOVED_EXPORTS:
-        raise ImportError(_REMOVED_EXPORTS[name])
-    raise AttributeError(f"module 'data_designer.config.run_config' has no attribute {name!r}")
diff --git a/packages/data-designer-config/tests/config/test_run_config.py b/packages/data-designer-config/tests/config/test_run_config.py
index 6695db648..3447c122e 100644
--- a/packages/data-designer-config/tests/config/test_run_config.py
+++ b/packages/data-designer-config/tests/config/test_run_config.py
@@ -7,7 +7,12 @@
 from pydantic import ValidationError
 
 import data_designer.config as dd
-from data_designer.config.run_config import JinjaRenderingEngine, RequestAdmissionTuningConfig, RunConfig
+from data_designer.config.run_config import (
+    JinjaRenderingEngine,
+    RequestAdmissionTuningConfig,
+    RunConfig,
+    ThrottleConfig,
+)
 
 
 def test_run_config_defaults_to_secure_jinja_renderer() -> None:
@@ -19,11 +24,54 @@ def test_run_config_accepts_native_renderer() -> None:
     assert JinjaRenderingEngine(run_config.jinja_rendering_engine) == JinjaRenderingEngine.NATIVE
 
 
-def test_run_config_rejects_removed_throttle_with_targeted_message() -> None:
-    with pytest.raises(ValidationError, match="RunConfig.throttle was removed"):
+def test_run_config_throttle_shim_rejects_unknown_legacy_fields() -> None:
+    with pytest.raises(ValidationError, match="max_concurrent_requests"):
         RunConfig(throttle={"max_concurrent_requests": 1})
 
 
+def test_run_config_throttle_shim_translates_to_request_admission() -> None:
+    with pytest.warns(DeprecationWarning, match="RunConfig.throttle.*RequestAdmissionTuningConfig"):
+        run_config = RunConfig(
+            throttle=ThrottleConfig(
+                reduce_factor=0.5,
+                additive_increase=2,
+                success_window=7,
+                cooldown_seconds=1.5,
+                ceiling_overshoot=0.2,
+            )
+        )
+
+    assert run_config.request_admission is not None
+    assert run_config.request_admission.multiplicative_decrease_factor == 0.5
+    assert run_config.request_admission.additive_increase_step == 2
+    assert run_config.request_admission.increase_after_successes == 7
+    assert run_config.request_admission.cooldown_seconds == 1.5
+    assert run_config.request_admission.startup_ramp_seconds == 0.0
+
+
+def test_run_config_throttle_shim_accepts_legacy_dict() -> None:
+    with pytest.warns(DeprecationWarning, match="RunConfig.throttle.*RequestAdmissionTuningConfig"):
+        run_config = RunConfig(
+            throttle={
+                "reduce_factor": 0.5,
+                "additive_increase": 2,
+                "success_window": 7,
+                "cooldown_seconds": 1.5,
+            }
+        )
+
+    assert run_config.request_admission is not None
+    assert run_config.request_admission.multiplicative_decrease_factor == 0.5
+    assert run_config.request_admission.additive_increase_step == 2
+    assert run_config.request_admission.increase_after_successes == 7
+    assert run_config.request_admission.cooldown_seconds == 1.5
+
+
+def test_run_config_rejects_throttle_and_request_admission_together() -> None:
+    with pytest.raises(ValidationError, match="Specify either RunConfig.throttle or RunConfig.request_admission"):
+        RunConfig(throttle=ThrottleConfig(), request_admission=RequestAdmissionTuningConfig())
+
+
 def test_request_admission_tuning_config_accepts_canonical_fields() -> None:
     config = RequestAdmissionTuningConfig(
         multiplicative_decrease_factor=0.5,
@@ -81,10 +129,8 @@ def test_request_admission_tuning_config_is_exported_from_config_package() -> No
     assert dd.RequestAdmissionTuningConfig is RequestAdmissionTuningConfig
 
 
-def test_removed_throttle_config_export_has_migration_message() -> None:
-    with pytest.raises(ImportError, match="ThrottleConfig was removed.*RunConfig.request_admission"):
-        getattr(dd, "ThrottleConfig")
-    with pytest.raises(ImportError, match="ThrottleConfig was removed.*RequestAdmissionTuningConfig"):
-        exec("from data_designer.config import ThrottleConfig", {})
-    with pytest.raises(ImportError, match="ThrottleConfig was removed.*RequestAdmissionTuningConfig"):
-        exec("from data_designer.config.run_config import ThrottleConfig", {})
+def test_deprecated_throttle_config_is_exported_from_config_package() -> None:
+    assert dd.ThrottleConfig is ThrottleConfig
+    namespace: dict[str, object] = {}
+    exec("from data_designer.config import ThrottleConfig", namespace)
+    assert namespace["ThrottleConfig"] is ThrottleConfig
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index e6e14ed9f..b6e16ab28 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -14,7 +14,7 @@ Durable names in this file are not public API by default. Publicness and final m
 
 Config-layer contracts must not import engine runtime protocols. Engine contracts may consume config-layer DTOs.
 
-The final repository layout is specified in [Module ownership](module-ownership.md). Runtime contracts must live in their owning target modules; do not preserve old module paths through aliases, shim files, or broad package reexports.
+The final repository layout is specified in [Module ownership](module-ownership.md). Runtime contracts must live in their owning target modules; do not preserve old engine module paths through aliases, shim files, or broad package reexports. Public config may keep explicitly deprecated compatibility DTOs when they translate into the new durable config surface and warn users.
 
 ## Config Surface Status
 
@@ -22,13 +22,17 @@ The final repository layout is specified in [Module ownership](module-ownership.
 | --- | --- | --- |
 | `SchedulingMetadata` | public plugin-facing DTO | `data-designer-config` |
 | `RequestAdmissionTuningConfig` | public advanced `RunConfig.request_admission` DTO for supported AIMD tuning only | `data-designer-config` |
+| `ThrottleConfig` | deprecated compatibility DTO translated into `RequestAdmissionTuningConfig` by `RunConfig.throttle` | `data-designer-config` |
 | `TaskAdmissionConfig` | engine-internal config and benchmark injection surface; not a public `RunConfig` knob in V1 | `data-designer-engine` |
 | `RequestAdmissionConfig` | engine-internal config and benchmark injection surface in V1 | `data-designer-engine` |
 | `RunConfig.request_admission` | public advanced request-admission tuning surface backed by `RequestAdmissionTuningConfig`; does not expose internal controller APIs | `data-designer-config` |
+| `RunConfig.throttle` | deprecated compatibility input translated into `RunConfig.request_admission` with a `DeprecationWarning` | `data-designer-config` |
 | `AsyncCapacityPlan` | diagnostic/reporting DTO, emitted to explain a run | `data-designer-engine` |
 
 Public request-admission tuning is limited to the supported fields on `RequestAdmissionTuningConfig`: multiplicative decrease factor, additive increase step, successes before increase, fallback cooldown, and startup ramp seconds. Benchmarks may still inject lower-level capacity values through harness-only configuration without committing those values to public API.
 
+`ThrottleConfig` is retained only as a migration shim for existing configs that pass `RunConfig(throttle=...)`. The shim maps `reduce_factor`, `additive_increase`, `success_window`, and `cooldown_seconds` into the corresponding `RequestAdmissionTuningConfig` fields and emits a `DeprecationWarning`. `ceiling_overshoot` is accepted for DTO compatibility but is not forwarded because request admission does not expose an overshoot knob.
+
 ## Metadata Contracts
 
 `ColumnGenerator.get_scheduling_metadata()` returns generator-facing scheduling metadata. It is additive and non-abstract so existing generators keep working.
@@ -348,7 +352,7 @@ It has no mutation or admission methods.
 
 Snapshots are immutable and internally consistent for their capture point. Domain snapshots include `captured_at`, monotonic `sequence`, resource, effective max, current limit, in-flight count, active lease count, waiters, blocked-until timing, cooldown remaining, rate-limit ceiling, consecutive rate limits, last outcome summary, and leak diagnostic counters. Global provider/model snapshots include aggregate static cap, aggregate in-flight count across domains, aggregate active lease count, aliases contributing to the cap, and per-domain limit summaries.
 
-`RequestAdmissionConfig` is the durable engine-internal request-admission tuning/config vocabulary for V1. It includes request resources, per-resource `initial_limit`, optional `max_limit_clamp`, configured cooldown, `multiplicative_decrease_factor`, `additive_increase_step`, `increase_after_successes`, `startup_ramp_seconds`, and default queue-wait timeout. Legacy request-control config names are not durable names; the public `RequestAdmissionTuningConfig` may accept old throttle-era spelling as input aliases for migration, but stores and documents scheduler-era names.
+`RequestAdmissionConfig` is the durable engine-internal request-admission tuning/config vocabulary for V1. It includes request resources, per-resource `initial_limit`, optional `max_limit_clamp`, configured cooldown, `multiplicative_decrease_factor`, `additive_increase_step`, `increase_after_successes`, `startup_ramp_seconds`, and default queue-wait timeout. Legacy request-control config names are not durable names; the public `RequestAdmissionTuningConfig` may accept old throttle-era spelling as input aliases for migration, and `RunConfig.throttle` may translate a deprecated `ThrottleConfig` into `RequestAdmissionTuningConfig`, but both paths store and document scheduler-era names.
 
 ## Telemetry And Correlation Contracts
 
diff --git a/plans/645/migration-and-cleanup.md b/plans/645/migration-and-cleanup.md
index 75fc829e3..2469a1593 100644
--- a/plans/645/migration-and-cleanup.md
+++ b/plans/645/migration-and-cleanup.md
@@ -19,7 +19,7 @@ Unacceptable end states:
 - a parallel fallback that independently introspects generators, configs, model registries, aliases, or admitted policy data under the old resolver contract
 - a compatibility adapter, alias, or reexport that preserves the old resolver vocabulary as a durable production path
 
-Final legacy-name search gate should have no production/current-doc matches for these historical strings:
+Final legacy-name search gate should have no production/current-doc matches for these historical strings except the explicitly deprecated config compatibility shim for `ThrottleConfig` / `RunConfig.throttle`:
 
 ```text
 SchedulingHintResolver
diff --git a/plans/645/module-ownership.md b/plans/645/module-ownership.md
index 988f80e83..8ed86e09c 100644
--- a/plans/645/module-ownership.md
+++ b/plans/645/module-ownership.md
@@ -171,7 +171,8 @@ packages/data-designer-engine/src/data_designer/engine/
 | `models/telemetry.py` | keep product/provider usage telemetry separate from admission event DTOs |
 | `capacity.py` | keep as cross-cutting capacity diagnostic/reporting code that consumes read-only scheduler/request DTOs and snapshots |
 | `SchedulingHintResolver`, `SchedulingHint`, and scheduler-side model-bound fallbacks | remove; `SchedulingMetadata` plus `TaskSchedulingResolver` are the only durable path |
-| `ThrottleManager`, `ThrottleDomain`, `ThrottleConfig`, `ThrottledModelClient`, and `throttled_model_client` | remove; request admission and `ModelRequestExecutor` are the only durable request-control path |
+| `ThrottleManager`, `ThrottleDomain`, `ThrottledModelClient`, and `throttled_model_client` | remove; request admission and `ModelRequestExecutor` are the only durable request-control path |
+| `ThrottleConfig` and `RunConfig.throttle` | keep only as deprecated public config compatibility shims that translate to `RequestAdmissionTuningConfig` and emit `DeprecationWarning`; not durable engine architecture |
 
 ## Import Rules
 

From 14673cd22e13090a056a0cf186c9ea19a8b08e81 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 15:14:03 -0400
Subject: [PATCH 30/33] Rename request admission success window knob

---
 .../src/data_designer/config/run_config.py    | 30 ++-----------
 .../tests/config/test_run_config.py           | 42 +++++++------------
 .../src/data_designer/engine/capacity.py      |  4 +-
 .../data_designer/engine/models/factory.py    |  2 +-
 .../engine/models/request_admission/config.py |  2 +-
 .../models/request_admission/controller.py    |  2 +-
 .../request_admission/test_controller.py      |  2 +-
 .../engine/models/test_model_registry.py      |  4 +-
 plans/645/async-scheduling-epic.puml          |  4 +-
 plans/645/contracts.md                        |  6 +--
 10 files changed, 33 insertions(+), 65 deletions(-)

diff --git a/packages/data-designer-config/src/data_designer/config/run_config.py b/packages/data-designer-config/src/data_designer/config/run_config.py
index 52c4be531..05a1f0909 100644
--- a/packages/data-designer-config/src/data_designer/config/run_config.py
+++ b/packages/data-designer-config/src/data_designer/config/run_config.py
@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Any, ClassVar
+from typing import Any
 
 from pydantic import Field, model_validator
 from typing_extensions import Self
@@ -34,13 +34,6 @@ class RequestAdmissionTuningConfig(ConfigBase):
     below that cap and are intended for provider/runtime support cases.
     """
 
-    _LEGACY_FIELD_NAMES: ClassVar[dict[str, str]] = {
-        "reduce_factor": "multiplicative_decrease_factor",
-        "additive_increase": "additive_increase_step",
-        "success_window": "increase_after_successes",
-        "rampup_seconds": "startup_ramp_seconds",
-    }
-
     multiplicative_decrease_factor: float = Field(
         default=0.75,
         gt=0.0,
@@ -52,7 +45,7 @@ class RequestAdmissionTuningConfig(ConfigBase):
         ge=1,
         description="Slots added to the adaptive concurrency limit after each successful recovery window.",
     )
-    increase_after_successes: int = Field(
+    successes_until_increase: int = Field(
         default=25,
         ge=1,
         description="Successful releases required before additive recovery increases the adaptive limit.",
@@ -71,21 +64,6 @@ class RequestAdmissionTuningConfig(ConfigBase):
         ),
     )
 
-    @model_validator(mode="before")
-    @classmethod
-    def normalize_legacy_field_names(cls, data: Any) -> Any:
-        """Accept old throttle-era names inside the new request-admission DTO."""
-        if not isinstance(data, dict):
-            return data
-        normalized = dict(data)
-        for old_name, new_name in cls._LEGACY_FIELD_NAMES.items():
-            if old_name not in normalized:
-                continue
-            if new_name in normalized:
-                raise ValueError(f"Specify either {old_name!r} or {new_name!r} for request admission tuning, not both.")
-            normalized[new_name] = normalized.pop(old_name)
-        return normalized
-
 
 class ThrottleConfig(ConfigBase):
     """Deprecated compatibility DTO for request-admission tuning.
@@ -109,7 +87,7 @@ class ThrottleConfig(ConfigBase):
     success_window: int = Field(
         default=25,
         ge=1,
-        description="Deprecated alias for RequestAdmissionTuningConfig.increase_after_successes.",
+        description="Deprecated alias for RequestAdmissionTuningConfig.successes_until_increase.",
     )
     cooldown_seconds: float = Field(
         default=2.0,
@@ -127,7 +105,7 @@ def to_request_admission_tuning(self) -> RequestAdmissionTuningConfig:
         return RequestAdmissionTuningConfig(
             multiplicative_decrease_factor=self.reduce_factor,
             additive_increase_step=self.additive_increase,
-            increase_after_successes=self.success_window,
+            successes_until_increase=self.success_window,
             cooldown_seconds=self.cooldown_seconds,
         )
 
diff --git a/packages/data-designer-config/tests/config/test_run_config.py b/packages/data-designer-config/tests/config/test_run_config.py
index 3447c122e..5c5c2979b 100644
--- a/packages/data-designer-config/tests/config/test_run_config.py
+++ b/packages/data-designer-config/tests/config/test_run_config.py
@@ -44,7 +44,7 @@ def test_run_config_throttle_shim_translates_to_request_admission() -> None:
     assert run_config.request_admission is not None
     assert run_config.request_admission.multiplicative_decrease_factor == 0.5
     assert run_config.request_admission.additive_increase_step == 2
-    assert run_config.request_admission.increase_after_successes == 7
+    assert run_config.request_admission.successes_until_increase == 7
     assert run_config.request_admission.cooldown_seconds == 1.5
     assert run_config.request_admission.startup_ramp_seconds == 0.0
 
@@ -63,7 +63,7 @@ def test_run_config_throttle_shim_accepts_legacy_dict() -> None:
     assert run_config.request_admission is not None
     assert run_config.request_admission.multiplicative_decrease_factor == 0.5
     assert run_config.request_admission.additive_increase_step == 2
-    assert run_config.request_admission.increase_after_successes == 7
+    assert run_config.request_admission.successes_until_increase == 7
     assert run_config.request_admission.cooldown_seconds == 1.5
 
 
@@ -76,37 +76,21 @@ def test_request_admission_tuning_config_accepts_canonical_fields() -> None:
     config = RequestAdmissionTuningConfig(
         multiplicative_decrease_factor=0.5,
         additive_increase_step=2,
-        increase_after_successes=7,
+        successes_until_increase=7,
         cooldown_seconds=1.5,
         startup_ramp_seconds=30.0,
     )
 
     assert config.multiplicative_decrease_factor == 0.5
     assert config.additive_increase_step == 2
-    assert config.increase_after_successes == 7
+    assert config.successes_until_increase == 7
     assert config.cooldown_seconds == 1.5
     assert config.startup_ramp_seconds == 30.0
 
 
-def test_request_admission_tuning_config_accepts_throttle_era_field_names() -> None:
-    config = RequestAdmissionTuningConfig(
-        reduce_factor=0.5,
-        additive_increase=2,
-        success_window=7,
-        cooldown_seconds=1.5,
-        rampup_seconds=30.0,
-    )
-
-    assert config.multiplicative_decrease_factor == 0.5
-    assert config.additive_increase_step == 2
-    assert config.increase_after_successes == 7
-    assert config.cooldown_seconds == 1.5
-    assert config.startup_ramp_seconds == 30.0
-
-
-def test_request_admission_tuning_config_rejects_duplicate_legacy_and_canonical_fields() -> None:
-    with pytest.raises(ValidationError, match="Specify either 'reduce_factor' or 'multiplicative_decrease_factor'"):
-        RequestAdmissionTuningConfig(reduce_factor=0.5, multiplicative_decrease_factor=0.75)
+def test_request_admission_tuning_config_rejects_throttle_era_field_names() -> None:
+    with pytest.raises(ValidationError, match="success_window"):
+        RequestAdmissionTuningConfig(success_window=7)
 
 
 def test_run_config_accepts_request_admission_tuning() -> None:
@@ -116,12 +100,18 @@ def test_run_config_accepts_request_admission_tuning() -> None:
     assert run_config.request_admission.startup_ramp_seconds == 10.0
 
 
-def test_run_config_accepts_request_admission_tuning_dict_with_throttle_era_names() -> None:
-    run_config = RunConfig(request_admission={"reduce_factor": 0.5, "success_window": 7, "rampup_seconds": 10.0})
+def test_run_config_accepts_request_admission_tuning_dict() -> None:
+    run_config = RunConfig(
+        request_admission={
+            "multiplicative_decrease_factor": 0.5,
+            "successes_until_increase": 7,
+            "startup_ramp_seconds": 10.0,
+        }
+    )
 
     assert run_config.request_admission is not None
     assert run_config.request_admission.multiplicative_decrease_factor == 0.5
-    assert run_config.request_admission.increase_after_successes == 7
+    assert run_config.request_admission.successes_until_increase == 7
     assert run_config.request_admission.startup_ramp_seconds == 10.0
 
 
diff --git a/packages/data-designer-engine/src/data_designer/engine/capacity.py b/packages/data-designer-engine/src/data_designer/engine/capacity.py
index 9ff8281bc..e10a729e7 100644
--- a/packages/data-designer-engine/src/data_designer/engine/capacity.py
+++ b/packages/data-designer-engine/src/data_designer/engine/capacity.py
@@ -54,7 +54,7 @@ class RequestAdmissionConfigSnapshot:
     cooldown_seconds: float
     multiplicative_decrease_factor: float
     additive_increase_step: int
-    increase_after_successes: int
+    successes_until_increase: int
     startup_ramp_seconds: float
     default_queue_wait_timeout_seconds: float | None
 
@@ -68,7 +68,7 @@ def from_config(cls, config: RequestAdmissionConfig) -> RequestAdmissionConfigSn
             cooldown_seconds=config.cooldown_seconds,
             multiplicative_decrease_factor=config.multiplicative_decrease_factor,
             additive_increase_step=config.additive_increase_step,
-            increase_after_successes=config.increase_after_successes,
+            successes_until_increase=config.successes_until_increase,
             startup_ramp_seconds=config.startup_ramp_seconds,
             default_queue_wait_timeout_seconds=config.default_queue_wait_timeout_seconds,
         )
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/factory.py b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
index b68178af9..73ef6756c 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/factory.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/factory.py
@@ -102,6 +102,6 @@ def _request_admission_config_from_run_config(
         cooldown_seconds=tuning.cooldown_seconds,
         multiplicative_decrease_factor=tuning.multiplicative_decrease_factor,
         additive_increase_step=tuning.additive_increase_step,
-        increase_after_successes=tuning.increase_after_successes,
+        successes_until_increase=tuning.successes_until_increase,
         startup_ramp_seconds=tuning.startup_ramp_seconds,
     )
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
index be209029a..433e7942c 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/config.py
@@ -16,6 +16,6 @@ class RequestAdmissionConfig:
     cooldown_seconds: float = 2.0
     multiplicative_decrease_factor: float = 0.75
     additive_increase_step: int = 1
-    increase_after_successes: int = 25
+    successes_until_increase: int = 25
     startup_ramp_seconds: float = 0.0
     default_queue_wait_timeout_seconds: float | None = None
diff --git a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
index ab13260a5..38bbd0598 100644
--- a/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
+++ b/packages/data-designer-engine/src/data_designer/engine/models/request_admission/controller.py
@@ -608,7 +608,7 @@ def _apply_outcome(
             prev_limit = state.current_limit
             state.consecutive_rate_limits = 0
             state.success_streak += 1
-            if state.success_streak >= self._config.increase_after_successes:
+            if state.success_streak >= self._config.successes_until_increase:
                 state.current_limit = min(effective_max, state.current_limit + self._config.additive_increase_step)
                 state.success_streak = 0
                 if state.current_limit != prev_limit:
diff --git a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
index c9767f02a..af77f8c40 100644
--- a/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
+++ b/packages/data-designer-engine/tests/engine/models/request_admission/test_controller.py
@@ -201,7 +201,7 @@ def test_request_admission_additive_recovery_after_successes() -> None:
         cap=3,
         config=RequestAdmissionConfig(
             initial_limits={item.resource: 1},
-            increase_after_successes=1,
+            successes_until_increase=1,
             additive_increase_step=1,
         ),
     )
diff --git a/packages/data-designer-engine/tests/engine/models/test_model_registry.py b/packages/data-designer-engine/tests/engine/models/test_model_registry.py
index 75c067a57..fa43e8117 100644
--- a/packages/data-designer-engine/tests/engine/models/test_model_registry.py
+++ b/packages/data-designer-engine/tests/engine/models/test_model_registry.py
@@ -68,7 +68,7 @@ def test_create_model_registry_maps_request_admission_tuning_config(
             request_admission=RequestAdmissionTuningConfig(
                 multiplicative_decrease_factor=0.5,
                 additive_increase_step=2,
-                increase_after_successes=7,
+                successes_until_increase=7,
                 cooldown_seconds=1.5,
                 startup_ramp_seconds=30.0,
             )
@@ -79,7 +79,7 @@ def test_create_model_registry_maps_request_admission_tuning_config(
     request_config = model_registry.request_admission.config
     assert request_config.multiplicative_decrease_factor == 0.5
     assert request_config.additive_increase_step == 2
-    assert request_config.increase_after_successes == 7
+    assert request_config.successes_until_increase == 7
     assert request_config.cooldown_seconds == 1.5
     assert request_config.startup_ramp_seconds == 30.0
 
diff --git a/plans/645/async-scheduling-epic.puml b/plans/645/async-scheduling-epic.puml
index f35913d0c..e581eab8f 100644
--- a/plans/645/async-scheduling-epic.puml
+++ b/plans/645/async-scheduling-epic.puml
@@ -462,7 +462,7 @@ package "Request config" {
     +cooldown_seconds
     +multiplicative_decrease_factor
     +additive_increase_step
-    +increase_after_successes
+    +successes_until_increase
     +startup_ramp_seconds
     +default_queue_wait_timeout
   }
@@ -584,7 +584,7 @@ package "Capacity planning (issue 654)" {
     +cooldown_seconds
     +multiplicative_decrease_factor
     +additive_increase_step
-    +increase_after_successes
+    +successes_until_increase
     +startup_ramp_seconds
     +default_queue_wait_timeout
   }
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index b6e16ab28..b8880e149 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -333,7 +333,7 @@ V1 AIMD semantics:
 - `initial_limit` is clamped to `[1, effective_max]`
 - `current_limit` starts at `initial_limit`
 - on `rate_limited`, `current_limit = max(1, floor(current_limit * multiplicative_decrease_factor))`, `blocked_until_monotonic` is set from provider `retry_after_seconds` when supplied or the configured cooldown otherwise, and rate-limit counters increment
-- on success outside cooldown, successful releases accumulate; after `increase_after_successes` successes, `current_limit = min(effective_max, current_limit + additive_increase_step)`
+- on success outside cooldown, successful releases accumulate; after `successes_until_increase` successes, `current_limit = min(effective_max, current_limit + additive_increase_step)`
 - `request_soft_ceiling_recovered` fires when `current_limit` rises above the last rate-limit ceiling
 - `request_fully_recovered` fires when `current_limit == effective_max` and cooldown has cleared
 - all timing uses a monotonic clock
@@ -352,7 +352,7 @@ It has no mutation or admission methods.
 
 Snapshots are immutable and internally consistent for their capture point. Domain snapshots include `captured_at`, monotonic `sequence`, resource, effective max, current limit, in-flight count, active lease count, waiters, blocked-until timing, cooldown remaining, rate-limit ceiling, consecutive rate limits, last outcome summary, and leak diagnostic counters. Global provider/model snapshots include aggregate static cap, aggregate in-flight count across domains, aggregate active lease count, aliases contributing to the cap, and per-domain limit summaries.
 
-`RequestAdmissionConfig` is the durable engine-internal request-admission tuning/config vocabulary for V1. It includes request resources, per-resource `initial_limit`, optional `max_limit_clamp`, configured cooldown, `multiplicative_decrease_factor`, `additive_increase_step`, `increase_after_successes`, `startup_ramp_seconds`, and default queue-wait timeout. Legacy request-control config names are not durable names; the public `RequestAdmissionTuningConfig` may accept old throttle-era spelling as input aliases for migration, and `RunConfig.throttle` may translate a deprecated `ThrottleConfig` into `RequestAdmissionTuningConfig`, but both paths store and document scheduler-era names.
+`RequestAdmissionConfig` is the durable engine-internal request-admission tuning/config vocabulary for V1. It includes request resources, per-resource `initial_limit`, optional `max_limit_clamp`, configured cooldown, `multiplicative_decrease_factor`, `additive_increase_step`, `successes_until_increase`, `startup_ramp_seconds`, and default queue-wait timeout. Legacy request-control config names are not durable names. `RunConfig.throttle` may translate a deprecated `ThrottleConfig` into `RequestAdmissionTuningConfig`, but the new request-admission DTOs store and document scheduler-era names.
 
 ## Telemetry And Correlation Contracts
 
@@ -490,7 +490,7 @@ RequestAdmissionConfigSnapshot:
   cooldown_seconds: float
   multiplicative_decrease_factor: float
   additive_increase_step: int
-  increase_after_successes: int
+  successes_until_increase: int
   startup_ramp_seconds: float
   default_queue_wait_timeout_seconds: float | None
 

From a06036faa98eb108d72ff07c3111a2ea7bd7e164 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 15:47:10 -0400
Subject: [PATCH 31/33] Preserve multi-model plugin alias scheduling

---
 .../column_generators/generators/base.py      | 27 ++++++--
 .../scheduling/test_resolver.py               | 65 +++++++++++++++++++
 plans/645/capacity-model.md                   |  4 +-
 plans/645/contracts.md                        | 11 ++--
 4 files changed, 96 insertions(+), 11 deletions(-)

diff --git a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
index e2a333c69..ba432ce2c 100644
--- a/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
+++ b/packages/data-designer-engine/src/data_designer/engine/column_generators/generators/base.py
@@ -6,6 +6,7 @@
 import asyncio
 import concurrent.futures
 import functools
+import hashlib
 import logging
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
@@ -211,10 +212,18 @@ def get_scheduling_metadata(self) -> SchedulingMetadata:
             bucket.caps.append(cap)
 
         if len(endpoints) != 1:
-            raise SchedulingMetadataError(
-                code="ambiguous_model_aliases",
-                message="Model scheduling metadata must resolve to one provider/model/generation endpoint.",
-                diagnostics={"endpoints": sorted(str(endpoint) for endpoint in endpoints)},
+            raw_caps = tuple(cap for bucket in endpoints.values() for cap in bucket.caps)
+            return SchedulingMetadata.custom_model(
+                _scheduling_plugin_namespace(type(self)),
+                _scheduling_alias_set_resource_name(aliases),
+                "v1",
+                weight=max(1, sum(raw_caps)),
+                diagnostics={
+                    "aliases": tuple(sorted(aliases)),
+                    "endpoints": tuple(sorted(str(endpoint) for endpoint in endpoints)),
+                    "fallback_reason": "multi_endpoint_alias_set",
+                    "raw_caps": raw_caps,
+                },
             )
 
         endpoint, bucket = next(iter(endpoints.items()))
@@ -244,6 +253,16 @@ def _get_scheduling_model_aliases(self) -> list[str]:
         return list(dict.fromkeys(str(alias) for alias in aliases if alias))
 
 
+def _scheduling_plugin_namespace(generator_type: type[object]) -> str:
+    return f"{generator_type.__module__}.{generator_type.__qualname__}"
+
+
+def _scheduling_alias_set_resource_name(aliases: list[str]) -> str:
+    alias_key = "\0".join(sorted(aliases)).encode()
+    digest = hashlib.sha1(alias_key).hexdigest()[:16]
+    return f"alias-set-{digest}"
+
+
 class ColumnGeneratorWithModel(ColumnGeneratorWithModelRegistry[TaskConfigT], ABC):
     @functools.cached_property
     def model(self) -> ModelFacade:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
index 1804dd272..d6dfcdbab 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/scheduling/test_resolver.py
@@ -4,10 +4,12 @@
 from __future__ import annotations
 
 from types import SimpleNamespace
+from typing import Literal
 from unittest.mock import MagicMock
 
 import pytest
 
+from data_designer.config.base import SingleColumnConfig
 from data_designer.config.column_configs import ExpressionColumnConfig
 from data_designer.config.models import GenerationType
 from data_designer.config.scheduling import SchedulingMetadata, SchedulingMetadataError
@@ -125,3 +127,66 @@ def generate(self, data: object) -> object:
     resolver = TaskSchedulingResolver({"answer": generator})  # type: ignore[arg-type]
     schedulable = resolver.schedulable_task(_task(), ("answer",))
     assert schedulable.request_resource_key == RequestResourceKey("nvidia", "endpoint", RequestDomain.CHAT)
+
+
+def test_model_registry_generator_metadata_uses_custom_model_for_multi_endpoint_aliases() -> None:
+    class _PairwiseJudgeColumnConfig(SingleColumnConfig):
+        column_type: Literal["pairwise-judge-test"] = "pairwise-judge-test"
+        model_alias: str
+        judge_model_alias: str
+
+        @property
+        def required_columns(self) -> list[str]:
+            return []
+
+        @property
+        def side_effect_columns(self) -> list[str]:
+            return []
+
+        def get_model_aliases(self) -> list[str]:
+            return [self.model_alias, self.judge_model_alias]
+
+    class _RegistryGenerator(ColumnGeneratorWithModelRegistry[_PairwiseJudgeColumnConfig]):
+        @staticmethod
+        def get_generation_strategy() -> object:
+            return object()
+
+        def generate(self, data: object) -> object:
+            return data
+
+    config = _PairwiseJudgeColumnConfig(name="answer", model_alias="draft", judge_model_alias="judge")
+    assert config.get_model_aliases() == ["draft", "judge"]
+    generator = _RegistryGenerator(config=config, resource_provider=MagicMock())
+    configs = {
+        "draft": SimpleNamespace(
+            model="draft-endpoint",
+            generation_type=GenerationType.CHAT_COMPLETION,
+            inference_parameters=SimpleNamespace(max_parallel_requests=4),
+        ),
+        "judge": SimpleNamespace(
+            model="judge-endpoint",
+            generation_type=GenerationType.CHAT_COMPLETION,
+            inference_parameters=SimpleNamespace(max_parallel_requests=2),
+        ),
+    }
+    providers = {
+        "draft": SimpleNamespace(name="nvidia"),
+        "judge": SimpleNamespace(name="openai"),
+    }
+    generator.get_model_config = lambda model_alias: configs[model_alias]  # type: ignore[method-assign]
+    generator.get_model_provider_name = lambda model_alias: providers[model_alias].name  # type: ignore[method-assign]
+
+    metadata = generator.get_scheduling_metadata()
+
+    assert metadata.kind == "custom_model"
+    assert metadata.identity[1].endswith("._RegistryGenerator")
+    assert metadata.identity[2].startswith("alias-set-")
+    assert metadata.weight == 6
+    assert metadata.diagnostics["aliases"] == ("draft", "judge")
+    assert metadata.diagnostics["fallback_reason"] == "multi_endpoint_alias_set"
+    assert metadata.diagnostics["raw_caps"] == (4, 2)
+
+    resolver = TaskSchedulingResolver({"answer": generator})  # type: ignore[arg-type]
+    schedulable = resolver.schedulable_task(_task(), ("answer",))
+    assert schedulable.group.key.kind == "custom_model"
+    assert schedulable.request_resource_key is None
diff --git a/plans/645/capacity-model.md b/plans/645/capacity-model.md
index 13f6a3e93..811546c13 100644
--- a/plans/645/capacity-model.md
+++ b/plans/645/capacity-model.md
@@ -93,13 +93,13 @@ If a distinct task-stage backpressure resource remains for model-producing work,
 
 ## Alias And Provider Semantics
 
-Scheduling metadata may use model aliases to derive static resource identity and weight. Alias metadata should deduplicate aliases that resolve to the same provider/model/generation resource before summing weight.
+Scheduling metadata may use model aliases to derive static resource identity and weight. Alias metadata should deduplicate aliases that resolve to the same provider/model/generation resource before deriving effective weight. The startup health-check hook `get_model_aliases()` remains separate from the scheduler metadata hook; a multi-endpoint alias set reported for health checks must not be forced into one provider/model resource.
 
 Request admission resources are provider/model/domain scoped. A provider/model may have a global effective static cap while each request domain has its own adaptive state. The capacity plan must make that distinction visible.
 
 V1 does not define a cross-domain aggregate AIMD provider cap beyond the documented provider/model effective static cap unless a later issue explicitly adds that policy. The request controller still enforces the static aggregate cap by checking provider/model aggregate in-flight counts before admitting a domain request.
 
-Alias-derived provider/model caps deduplicate aliases that resolve to the same concrete provider/model endpoint. If aliases for the same endpoint specify different `max_parallel_requests` values, V1 uses the minimum as the effective static cap and records every contributing alias and raw cap in `AsyncCapacityPlan`. This min-merge is not a metadata error. Alias resolution is fatal only when endpoint identity is ambiguous or conflicting. If the provider treats generation type as a distinct endpoint, the canonical model id includes that distinction before cap merging.
+Alias-derived provider/model caps deduplicate aliases that resolve to the same concrete provider/model endpoint. If aliases for the same endpoint specify different `max_parallel_requests` values, V1 uses the minimum as the effective static cap and records every contributing alias and raw cap in `AsyncCapacityPlan`. This min-merge is not a metadata error. If a default registry-backed generator sees multiple aliases that resolve to different concrete endpoints, it should use deterministic `custom_model` task-stage metadata unless the plugin overrides `get_scheduling_metadata()` with a sharper declaration. If the provider treats generation type as a distinct endpoint, the canonical model id includes that distinction before cap merging.
 
 ## Observability Requirements
 
diff --git a/plans/645/contracts.md b/plans/645/contracts.md
index b8880e149..0c7a51a4f 100644
--- a/plans/645/contracts.md
+++ b/plans/645/contracts.md
@@ -49,12 +49,13 @@ Rules:
 
 - Metadata identity is resource identity, not a queue key.
 - Metadata cannot encode queue depth, admitted limits, runtime pressure, request domains, AIMD state, or provider cooldown.
-- Multi-alias metadata deduplicates aliases that resolve to the same provider/model/generation resource before summing weight.
+- Alias-derived model metadata deduplicates aliases that resolve to the same provider/model/generation resource before deriving effective weight.
 - Alias ordering is canonicalized so equivalent configs produce equivalent metadata.
 - Generators that do not override `get_scheduling_metadata()` receive a documented default metadata value. The default must preserve current behavior and must not infer provider/model pressure dynamically.
-- Invalid `kind`, non-deterministic `identity`, non-positive `weight`, or ambiguous alias resolution raises `SchedulingMetadataError`.
-- Differing `max_parallel_requests` values for aliases that resolve to the same concrete provider/model endpoint are not, by themselves, ambiguous. They merge through the static-cap min rule in the capacity model. Alias ambiguity is fatal when aliases cannot be resolved to one concrete endpoint, resolve to conflicting endpoint identities, or would require hidden dynamic inference to choose a resource.
-- Fallback metadata is safe only when it preserves current scheduling behavior and the resolver can explain the fallback in diagnostics. Ambiguous resource identity or invalid weights are fatal.
+- Invalid `kind`, non-deterministic `identity`, or non-positive `weight` raises `SchedulingMetadataError`.
+- Differing `max_parallel_requests` values for aliases that resolve to the same concrete provider/model endpoint are not, by themselves, ambiguous. They merge through the static-cap min rule in the capacity model.
+- A default implementation must not turn `get_model_aliases()` into a requirement that every health-check alias collapse to one scheduler endpoint. If a registry-backed generator reports multiple aliases that resolve to different concrete endpoints and does not override `get_scheduling_metadata()`, the default metadata should fall back to deterministic `custom_model` metadata with diagnostics. Plugins that need sharper endpoint-aware scheduler grouping should override `get_scheduling_metadata()` directly.
+- Fallback metadata is safe only when it preserves current scheduling behavior and the resolver can explain the fallback in diagnostics. Invalid metadata shape or invalid weights are fatal.
 
 Normative V1 metadata shapes:
 
@@ -62,7 +63,7 @@ Normative V1 metadata shapes:
 | --- | --- | --- | --- |
 | `local` | `("local", resource_name)` where `resource_name` defaults to `"default"` | positive integer, default `1` | the default for generators that do not override `get_scheduling_metadata()` is `SchedulingMetadata(kind="local", identity=("local", "default"), weight=1)` |
 | `model` | `("model", provider_name, canonical_model_id, generation_kind)` after alias resolution | effective static provider/model capacity hint, normally derived from the model config's `max_parallel_requests` and clamped to at least `1` | safe fallback is allowed only when the resolver can identify the same canonical provider/model resource as the current implementation |
-| `custom_model` | `("custom_model", plugin_namespace, resource_name, version)` with deterministic plugin-provided values | positive plugin-provided capacity hint, defaulting to `1` if omitted | no alias inference is performed unless the plugin returns model aliases through the documented metadata API |
+| `custom_model` | `("custom_model", plugin_namespace, resource_name, version)` with deterministic plugin-provided values | positive plugin-provided capacity hint, defaulting to `1` if omitted | used for explicit plugin metadata and for compatibility-preserving defaults when model aliases cannot be represented as one concrete provider/model/generation resource |
 
 `SchedulingMetadataError` contains:
 

From b62406bf37c85690ffa90f9a99e56ab433cad6d4 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 16:08:51 -0400
Subject: [PATCH 32/33] Preserve row drops for generator built-in failures

---
 .../dataset_builders/async_scheduler.py       |  35 ++++-
 .../dataset_builders/test_async_scheduler.py  | 121 +++++++++++++++---
 2 files changed, 135 insertions(+), 21 deletions(-)

diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index a2a793f5a..43fc7508b 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -1631,6 +1631,17 @@ async def _execute_task_inner_impl(self, task: Task, lease: TaskAdmissionLease,
             self._record_observed_task_state()
             self._wake_event.set()
 
+    async def _run_generator_call(self, task: Task, operation: str, call: Coroutine[Any, Any, Any]) -> Any:
+        """Run user/plugin generator code while preserving scheduler-owned failures."""
+        try:
+            return await call
+        except Exception as exc:
+            if self._is_retryable(exc) or self._is_expected_non_retryable(exc):
+                raise
+            raise DatasetGenerationError(
+                f"Generator failed for column '{task.column}' during {operation}: {exc}"
+            ) from exc
+
     async def _run_from_scratch(self, task: Task, generator: ColumnGenerator) -> Any:
         """Execute a from_scratch task."""
         rg_size = self._get_rg_size(task.row_group)
@@ -1638,7 +1649,11 @@ async def _run_from_scratch(self, task: Task, generator: ColumnGenerator) -> Any
         from data_designer.engine.column_generators.generators.base import FromScratchColumnGenerator
 
         if isinstance(generator, FromScratchColumnGenerator):
-            result_df = await generator.agenerate_from_scratch(rg_size)
+            result_df = await self._run_generator_call(
+                task,
+                "from-scratch generation",
+                generator.agenerate_from_scratch(rg_size),
+            )
         else:
             # Non-FromScratch generators dispatched as seeds (no upstream columns)
             # operate on existing buffer rows — same contract as the sync engine's
@@ -1650,7 +1665,11 @@ async def _run_from_scratch(self, task: Task, generator: ColumnGenerator) -> Any
                 input_df = lazy.pd.DataFrame(records)
             else:
                 input_df = lazy.pd.DataFrame(index=range(rg_size))
-            result_df = await generator.agenerate(input_df)
+            result_df = await self._run_generator_call(
+                task,
+                "full-column generation",
+                generator.agenerate(input_df),
+            )
 
         # Write results to buffer (include side-effect columns)
         if self._buffer_manager is not None:
@@ -1684,7 +1703,11 @@ async def _run_cell(self, task: Task, generator: ColumnGenerator) -> tuple[Any,
 
         # Copy for generation: agenerate crosses an await boundary, so the
         # generator must not hold a mutable reference to the live record.
-        result = await generator.agenerate(dict(record))
+        result = await self._run_generator_call(
+            task,
+            "cell generation",
+            generator.agenerate(dict(record)),
+        )
 
         # Write back to buffer (include side-effect columns)
         if self._buffer_manager is not None and not self._tracker.is_dropped(task.row_group, task.row_index):
@@ -1754,7 +1777,11 @@ async def _run_batch(self, task: Task, generator: ColumnGenerator) -> Any:
         if len(batch_df) == 0:
             return batch_df
 
-        result_df = await generator.agenerate(batch_df)
+        result_df = await self._run_generator_call(
+            task,
+            "batch generation",
+            generator.agenerate(batch_df),
+        )
 
         # Merge result columns back to buffer (include side-effect columns)
         if self._buffer_manager is not None:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index 52047fd0b..b63eb2509 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -192,7 +192,7 @@ def generate(self, data: dict) -> dict:
 
 
 class MockBuggyGenerator(ColumnGenerator[ExpressionColumnConfig]):
-    """Generator that simulates an internal scheduler/generator bug."""
+    """Generator that raises a bare built-in exception from generator code."""
 
     @staticmethod
     def get_generation_strategy() -> GenerationStrategy:
@@ -202,6 +202,27 @@ def generate(self, _data: dict) -> dict:
         raise KeyError("missing internal key")
 
 
+class MockBuggyFromScratchGenerator(FromScratchColumnGenerator[ExpressionColumnConfig]):
+    """From-scratch generator that raises a bare built-in exception from generator code."""
+
+    @staticmethod
+    def get_generation_strategy() -> GenerationStrategy:
+        return GenerationStrategy.FULL_COLUMN
+
+    def generate(self, data: lazy.pd.DataFrame) -> lazy.pd.DataFrame:
+        return data
+
+    def generate_from_scratch(self, _num_records: int) -> lazy.pd.DataFrame:
+        raise AssertionError("invalid seed source")
+
+
+class MockBuggyFullColumnGenerator(ColumnGeneratorFullColumn[ExpressionColumnConfig]):
+    """Full-column generator that raises a bare built-in exception from generator code."""
+
+    def generate(self, _data: lazy.pd.DataFrame) -> lazy.pd.DataFrame:
+        raise TypeError("bad batch shape")
+
+
 class MockRateLimitGenerator(ColumnGenerator[ExpressionColumnConfig]):
     """Generator that fails with rate-limit errors before succeeding.
 
@@ -556,43 +577,109 @@ async def test_scheduler_non_retryable_failure_drops_row() -> None:
     assert tracker.is_row_group_complete(0, 2, ["seed", "fail_col"])
 
 
+def test_scheduler_internal_bug_classifier_preserves_scheduler_builtin_failures() -> None:
+    scheduler, tracker = _build_simple_pipeline(num_records=1)
+    assert scheduler._is_internal_bug(KeyError("missing internal key"))
+    assert not scheduler._is_internal_bug(DatasetGenerationError("generator failure"))
+    assert not tracker.is_dropped(0, 0)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_generator_builtin_exception_drops_cell_without_fatal_abort(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    provider = _mock_provider()
+    scheduler, tracker = _build_simple_pipeline(
+        num_records=1,
+        configs=[
+            SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+            LLMTextColumnConfig(name="buggy_col", prompt="{{ seed }}", model_alias=MODEL_ALIAS),
+        ],
+        strategies={
+            "seed": GenerationStrategy.FULL_COLUMN,
+            "buggy_col": GenerationStrategy.CELL_BY_CELL,
+        },
+        generators={
+            "seed": MockSeedGenerator(config=_expr_config("seed"), resource_provider=provider),
+            "buggy_col": MockBuggyGenerator(config=_expr_config("buggy_col"), resource_provider=provider),
+        },
+    )
+
+    with caplog.at_level(logging.WARNING, logger="data_designer.engine.dataset_builders.async_scheduler"):
+        await scheduler.run()
+
+    assert tracker.is_dropped(0, 0)
+    assert isinstance(scheduler.first_non_retryable_error, DatasetGenerationError)
+    assert isinstance(scheduler.first_non_retryable_error.__cause__, KeyError)
+    assert "Unexpected fatal Non-retryable failure" not in caplog.text
+
+
 @pytest.mark.asyncio(loop_scope="session")
-async def test_scheduler_internal_bug_failure_aborts_instead_of_dropping_row(
+async def test_scheduler_generator_builtin_exception_drops_from_scratch_group_without_fatal_abort(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    provider = _mock_provider()
+    configs = [SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]})]
+    strategies = {"seed": GenerationStrategy.FULL_COLUMN}
+    generators = {"seed": MockBuggyFromScratchGenerator(config=_expr_config("seed"), resource_provider=provider)}
+    graph = ExecutionGraph.create(configs, strategies)
+    tracker = CompletionTracker.with_graph(graph, [(0, 2)])
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=[(0, 2)],
+    )
+
+    with caplog.at_level(logging.WARNING, logger="data_designer.engine.dataset_builders.async_scheduler"):
+        await scheduler.run()
+
+    assert tracker.is_dropped(0, 0)
+    assert tracker.is_dropped(0, 1)
+    assert isinstance(scheduler.first_non_retryable_error, DatasetGenerationError)
+    assert isinstance(scheduler.first_non_retryable_error.__cause__, AssertionError)
+    assert "Unexpected fatal Non-retryable failure" not in caplog.text
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_generator_builtin_exception_drops_batch_group_without_fatal_abort(
     caplog: pytest.LogCaptureFixture,
 ) -> None:
     provider = _mock_provider()
     configs = [
         SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
-        LLMTextColumnConfig(name="buggy_col", prompt="{{ seed }}", model_alias=MODEL_ALIAS),
+        LLMTextColumnConfig(name="buggy_batch", prompt="{{ seed }}", model_alias=MODEL_ALIAS),
     ]
     strategies = {
         "seed": GenerationStrategy.FULL_COLUMN,
-        "buggy_col": GenerationStrategy.CELL_BY_CELL,
+        "buggy_batch": GenerationStrategy.FULL_COLUMN,
     }
     generators = {
         "seed": MockSeedGenerator(config=_expr_config("seed"), resource_provider=provider),
-        "buggy_col": MockBuggyGenerator(config=_expr_config("buggy_col"), resource_provider=provider),
+        "buggy_batch": MockBuggyFullColumnGenerator(
+            config=_expr_config("buggy_batch"),
+            resource_provider=provider,
+        ),
     }
     graph = ExecutionGraph.create(configs, strategies)
-    tracker = CompletionTracker.with_graph(graph, [(0, 1)])
+    row_groups = [(0, 2)]
+    tracker = CompletionTracker.with_graph(graph, row_groups)
     scheduler = AsyncTaskScheduler(
         generators=generators,
         graph=graph,
         tracker=tracker,
-        row_groups=[(0, 1)],
+        row_groups=row_groups,
+        buffer_manager=RowGroupBufferManager(_make_storage()),
     )
 
-    with caplog.at_level(logging.ERROR, logger="data_designer.engine.dataset_builders.async_scheduler"):
-        with pytest.raises(DatasetGenerationError, match="Unexpected internal task failure") as exc_info:
-            await scheduler.run()
+    with caplog.at_level(logging.WARNING, logger="data_designer.engine.dataset_builders.async_scheduler"):
+        await scheduler.run()
 
-    assert isinstance(exc_info.value.__cause__, KeyError)
-    assert not tracker.is_dropped(0, 0)
-    error_records = [
-        record for record in caplog.records if "Unexpected fatal Non-retryable failure" in record.getMessage()
-    ]
-    assert error_records
-    assert error_records[0].exc_info is not None
+    assert tracker.is_dropped(0, 0)
+    assert tracker.is_dropped(0, 1)
+    assert isinstance(scheduler.first_non_retryable_error, DatasetGenerationError)
+    assert isinstance(scheduler.first_non_retryable_error.__cause__, TypeError)
+    assert "Unexpected fatal Non-retryable failure" not in caplog.text
 
 
 @pytest.mark.asyncio(loop_scope="session")

From c2522e3c83521a4af08703b4e2bee7c769310b41 Mon Sep 17 00:00:00 2001
From: "Eric W. Tramel" <eric.tramel@gmail.com>
Date: Wed, 20 May 2026 16:33:05 -0400
Subject: [PATCH 33/33] Validate async full-column generator results

---
 .../dataset_builders/async_scheduler.py       | 40 ++++++--
 .../dataset_builders/test_async_scheduler.py  | 91 +++++++++++++++++++
 2 files changed, 125 insertions(+), 6 deletions(-)

diff --git a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
index 43fc7508b..9109eafcc 100644
--- a/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
+++ b/packages/data-designer-engine/src/data_designer/engine/dataset_builders/async_scheduler.py
@@ -1642,6 +1642,25 @@ async def _run_generator_call(self, task: Task, operation: str, call: Coroutine[
                 f"Generator failed for column '{task.column}' during {operation}: {exc}"
             ) from exc
 
+    def _require_dataframe_result(
+        self,
+        task: Task,
+        operation: str,
+        result: Any,
+        *,
+        expected_rows: int | None = None,
+    ) -> Any:
+        if not isinstance(result, lazy.pd.DataFrame):
+            raise DatasetGenerationError(
+                f"{operation} for column '{task.column}' must return a DataFrame, got {type(result).__name__}."
+            )
+        if expected_rows is not None and len(result) != expected_rows:
+            raise DatasetGenerationError(
+                f"{operation} for column '{task.column}' returned {len(result)} rows "
+                f"but {expected_rows} were expected (rg={task.row_group})."
+            )
+        return result
+
     async def _run_from_scratch(self, task: Task, generator: ColumnGenerator) -> Any:
         """Execute a from_scratch task."""
         rg_size = self._get_rg_size(task.row_group)
@@ -1654,6 +1673,7 @@ async def _run_from_scratch(self, task: Task, generator: ColumnGenerator) -> Any
                 "from-scratch generation",
                 generator.agenerate_from_scratch(rg_size),
             )
+            result_operation = "From-scratch generator"
         else:
             # Non-FromScratch generators dispatched as seeds (no upstream columns)
             # operate on existing buffer rows — same contract as the sync engine's
@@ -1670,6 +1690,13 @@ async def _run_from_scratch(self, task: Task, generator: ColumnGenerator) -> Any
                 "full-column generation",
                 generator.agenerate(input_df),
             )
+            result_operation = "Full-column generator"
+        result_df = self._require_dataframe_result(
+            task,
+            result_operation,
+            result_df,
+            expected_rows=rg_size,
+        )
 
         # Write results to buffer (include side-effect columns)
         if self._buffer_manager is not None:
@@ -1777,21 +1804,22 @@ async def _run_batch(self, task: Task, generator: ColumnGenerator) -> Any:
         if len(batch_df) == 0:
             return batch_df
 
+        active_rows = rg_size - len(pre_dropped) - len(pre_skipped) if self._buffer_manager is not None else None
         result_df = await self._run_generator_call(
             task,
             "batch generation",
             generator.agenerate(batch_df),
         )
+        result_df = self._require_dataframe_result(
+            task,
+            "Batch generator",
+            result_df,
+            expected_rows=active_rows,
+        )
 
         # Merge result columns back to buffer (include side-effect columns)
         if self._buffer_manager is not None:
             write_cols = self._gen_instance_to_columns_including_side_effects.get(id(generator), [task.column])
-            active_rows = rg_size - len(pre_dropped) - len(pre_skipped)
-            if len(result_df) != active_rows:
-                raise ValueError(
-                    f"Batch generator for '{task.column}' returned {len(result_df)} rows "
-                    f"but {active_rows} were expected (rg={task.row_group})."
-                )
             result_idx = 0
             for ri in range(rg_size):
                 if ri in pre_dropped or ri in pre_skipped:
diff --git a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
index b63eb2509..41191c609 100644
--- a/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
+++ b/packages/data-designer-engine/tests/engine/dataset_builders/test_async_scheduler.py
@@ -216,6 +216,20 @@ def generate_from_scratch(self, _num_records: int) -> lazy.pd.DataFrame:
         raise AssertionError("invalid seed source")
 
 
+class MockMalformedFromScratchGenerator(FromScratchColumnGenerator[ExpressionColumnConfig]):
+    """From-scratch generator that returns a non-DataFrame object."""
+
+    @staticmethod
+    def get_generation_strategy() -> GenerationStrategy:
+        return GenerationStrategy.FULL_COLUMN
+
+    def generate(self, data: lazy.pd.DataFrame) -> lazy.pd.DataFrame:
+        return data
+
+    def generate_from_scratch(self, num_records: int) -> Any:
+        return [{"seed": index} for index in range(num_records)]
+
+
 class MockBuggyFullColumnGenerator(ColumnGeneratorFullColumn[ExpressionColumnConfig]):
     """Full-column generator that raises a bare built-in exception from generator code."""
 
@@ -223,6 +237,13 @@ def generate(self, _data: lazy.pd.DataFrame) -> lazy.pd.DataFrame:
         raise TypeError("bad batch shape")
 
 
+class MockMalformedFullColumnGenerator(ColumnGeneratorFullColumn[ExpressionColumnConfig]):
+    """Full-column generator that returns a non-DataFrame object."""
+
+    def generate(self, data: lazy.pd.DataFrame) -> Any:
+        return [{"seed": value, self.config.name: "bad"} for value in data.get("seed", [])]
+
+
 class MockRateLimitGenerator(ColumnGenerator[ExpressionColumnConfig]):
     """Generator that fails with rate-limit errors before succeeding.
 
@@ -682,6 +703,76 @@ async def test_scheduler_generator_builtin_exception_drops_batch_group_without_f
     assert "Unexpected fatal Non-retryable failure" not in caplog.text
 
 
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_generator_malformed_from_scratch_return_drops_group_without_fatal_abort(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    provider = _mock_provider()
+    configs = [SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]})]
+    strategies = {"seed": GenerationStrategy.FULL_COLUMN}
+    generators = {"seed": MockMalformedFromScratchGenerator(config=_expr_config("seed"), resource_provider=provider)}
+    graph = ExecutionGraph.create(configs, strategies)
+    row_groups = [(0, 2)]
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        buffer_manager=RowGroupBufferManager(_make_storage()),
+    )
+
+    with caplog.at_level(logging.WARNING, logger="data_designer.engine.dataset_builders.async_scheduler"):
+        await scheduler.run()
+
+    assert tracker.is_dropped(0, 0)
+    assert tracker.is_dropped(0, 1)
+    assert isinstance(scheduler.first_non_retryable_error, DatasetGenerationError)
+    assert "must return a DataFrame, got list" in str(scheduler.first_non_retryable_error)
+    assert "Unexpected fatal Non-retryable failure" not in caplog.text
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_scheduler_generator_malformed_batch_return_drops_group_without_fatal_abort(
+    caplog: pytest.LogCaptureFixture,
+) -> None:
+    provider = _mock_provider()
+    configs = [
+        SamplerColumnConfig(name="seed", sampler_type=SamplerType.CATEGORY, params={"values": ["A"]}),
+        LLMTextColumnConfig(name="malformed_batch", prompt="{{ seed }}", model_alias=MODEL_ALIAS),
+    ]
+    strategies = {
+        "seed": GenerationStrategy.FULL_COLUMN,
+        "malformed_batch": GenerationStrategy.FULL_COLUMN,
+    }
+    generators = {
+        "seed": MockSeedGenerator(config=_expr_config("seed"), resource_provider=provider),
+        "malformed_batch": MockMalformedFullColumnGenerator(
+            config=_expr_config("malformed_batch"),
+            resource_provider=provider,
+        ),
+    }
+    graph = ExecutionGraph.create(configs, strategies)
+    row_groups = [(0, 2)]
+    tracker = CompletionTracker.with_graph(graph, row_groups)
+    scheduler = AsyncTaskScheduler(
+        generators=generators,
+        graph=graph,
+        tracker=tracker,
+        row_groups=row_groups,
+        buffer_manager=RowGroupBufferManager(_make_storage()),
+    )
+
+    with caplog.at_level(logging.WARNING, logger="data_designer.engine.dataset_builders.async_scheduler"):
+        await scheduler.run()
+
+    assert tracker.is_dropped(0, 0)
+    assert tracker.is_dropped(0, 1)
+    assert isinstance(scheduler.first_non_retryable_error, DatasetGenerationError)
+    assert "must return a DataFrame, got list" in str(scheduler.first_non_retryable_error)
+    assert "Unexpected fatal Non-retryable failure" not in caplog.text
+
+
 @pytest.mark.asyncio(loop_scope="session")
 async def test_scheduler_custom_generator_key_error_drops_row_without_fatal_abort(
     caplog: pytest.LogCaptureFixture,