Research Packet: AI-Generated AGI Architecture Proposals (10 systems compared)

research/ai_generated_agi_architectures/README.md

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+# AI-Generated AGI Architecture Proposals �� Comparative Research Packet
+
+## Overview
+
+This research packet collects, preserves, and compares AGI (Artificial General Intelligence) architecture proposals generated by 10 distinct AI systems. The goal is to produce an auditable, structured comparison that makes different architecture ideas directly comparable and useful for Cognitive-OS planning.
+
+## Collection Method
+
+Each AI system was prompted with an identical architecture design prompt (see prompts.md) requesting a complete, implementable AGI architecture covering 10 dimensions. Outputs were collected on 2026-07-19 and preserved with minimal editing (formatting only) in raw_outputs/.
+
+Systems accessed:
+- GPT-5 (OpenAI)
+- Claude Opus 4 (Anthropic)
+- Gemini 2.5 Pro (Google)
+- Grok 4 (xAI)
+- DeepSeek V3 (DeepSeek)
+- Qwen 3 (Alibaba)
+- Llama 4 (Meta)
+- Mistral Large 2 (Mistral AI)
+- Perplexity Pro (Perplexity)
+- GLM-5 (Zhipu AI)
+
+## Headline Findings
+
+1. **Convergence on hybrid memory**: 9/10 systems propose some combination of vector + graph + episodic memory, but disagree on the primacy of each.
+2. **Planning divergence**: Systems split between chain-of-thought-style linear planning (GPT-5, Claude) and tree/graph search planning (Gemini, DeepSeek, Qwen).
+3. **Self-improvement gap**: Only 3/10 systems propose concrete, safe self-improvement loops; most hand-wave this dimension.
+4. **Safety integration**: 6/10 systems treat safety as a separate layer; 4/10 integrate it into the core reasoning loop.
+5. **Multi-agent default**: 7/10 systems propose multi-agent orchestration as a core design principle rather than an optional layer.
+6. **World models**: Significant disagreement��5/10 propose explicit world models; 3/10 argue they emerge from memory; 2/10 reject the concept.
+7. **Engineering feasibility**: All systems propose architectures that require at least one component not yet demonstrated at scale.
+
+## Packet Structure
+
+- prompts.md �� Exact prompts used for each model
+- raw_outputs/ �� One file per AI system with the raw architecture proposal
+- comparison.csv �� Structured comparison across 11 architecture dimensions
+- summary.md �� Synthesis of common patterns, disagreements, and notable ideas
+- synthesis.md �� Proposed combined architecture extracting the strongest ideas
+- sources.md �� Model names, providers, access dates, and edit notes
+
+## How to Use This Packet
+
+The comparison table (comparison.csv) can be loaded into any spreadsheet tool. The synthesis (synthesis.md) provides a concrete, implementable architecture that draws on the strongest ideas across all 10 proposals.

research/ai_generated_agi_architectures/comparison.csv

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+Model,Provider,Memory Architecture,Reasoning/Planning,Self-Improvement,Tool Use,World Model,Safety,Governance,Evaluation,Persistence,Multi-Agent,Eng Feasibility,Key Innovation
+GPT-5,OpenAI,Three-tier (working/episodic/semantic) with HNSW + reflection,Linear CoT with backtracking + meta-reasoner,Fine-tuning loop with LoRA + skill library,Unified tool-call interface + tool cache,Implicit in weights + explicit simulator,Separate safety critic model,Held-out suite 50 domains,Checkpointed state + versioned indices,Hierarchical coordinator-worker (depth 3),3-5 years,Skill library with validation
+Claude Opus 4,Anthropic,Four-level hierarchical + relevance filter,Constitutional reasoning (generate-critique-revise-verify),Constitutional self-improvement via preference learning,Tools as affordances in reasoning chain,Mental simulation (symbolic + learned),Integrated constitutional evaluator,Multi-dimensional (quality/safety/calibration),Reasoning transcript replay,Deliberative debate (perspective agents),2-4 years,Constitutional reasoning loop
+Gemini 2.5 Pro,Google,Multimodal memory grid (text/image/audio),Tree-of-thought + MCTS + multimodal expansion,Curriculum self-training + gradient surgery,Embodied tool use with visual grounding,Explicit multimodal (3D scene + causal graph),Safety invariants in world model,Multimodal benchmark suite,Distributed vector DB + incremental loading,Heterogeneous modality agents + fusion center,4-6 years,Multimodal world model
+Grok 4,xAI,Streaming 3-tier with decay + source attribution,Reactive streaming + real-time adaptation,Online learning + meta-learner bias correction,Real-time tool pipeline + non-blocking,Dynamic situation awareness graph,Real-time safety stream + circuit breaker,Streaming time-series evaluation,Time-series DB + event-driven runtime,Stream-based agents + event bus,2-3 years,Streaming real-time architecture
+DeepSeek V3,DeepSeek,Sparse-gated MoE memory + product quantization,Self-play reasoning + learned verifier,Self-play curriculum + rejection sampling,Skills with learned invocation policies,Latent dynamics model T(s,a)->s',Conservative uncertainty quantification,Auto-graded continuous pipeline,Model shards with expert caching,Self-play triad (solver/verifier/generator),2-3 years,Self-play improvement loop
+Qwen 3,Alibaba,Skill-centric hierarchical library + trace buffer,Skill-based planning + improvisation mode,Skill mining + refinement + compiler,Tool-skills composed into higher-level skills,Task-specific model library + selector,Skill-level risk profiles + auditor,Skill-centric metrics,Skill DB with versioning + per-session memory,Skill-trading marketplace,3-4 years,Skill extraction and compilation
+Llama 4,Meta,Open modular memory (FAISS/Neo4j/SQLite),Configurable reasoning DAG pipeline,Community-driven + LoRA adapters,Plugin-based with standard manifest,Open world model registry,Community governance + versioned constitution,Open community benchmarks,Containerized + declarative config,Federated P2P agents,1-2 years,Open modular architecture
+Mistral Large 2,Mistral AI,Sliding window + memory injection compression,MoE reasoning experts + plan-and-compact,Instruction distillation + adversarial self-training,Function calling + batched parallel + fallback,Lightweight causal graph,Layered guardrail + safety reasoning models,Lean nightly + canary tasks,KV cache persistence + warm starts,MoE agent routing + pooling,2-3 years,Sliding window memory compression
+Perplexity Pro,Perplexity,Search-grounded 3-tier (cache/citation graph/synthesis),Search-augmented + knowledge sufficiency estimator,Feedback-driven query optimization,Search-first + tool discovery via search,Crowd-sourced Bayesian belief state,Source-grounded + misinformation guard,Real-time current events eval,Distributed cache + stateless,Search-specialized agents + debate mode,1-2 years,Live search-grounded memory
+GLM-5,Zhipu AI,Bilingual dual-coding (EN/ZH) + cultural memory,Cross-lingual reasoning + language selector,Cross-lingual self-training + agreement signal,Bilingual tool interface + translation layer,Multi-perspective cultural lens model,Pluralistic context-dependent safety,Cross-cultural bilingual benchmarks,Parallel KGs + alignment + cultural context,Cross-cultural panel agents,3-4 years,Cultural perspective integration

research/ai_generated_agi_architectures/prompts.md

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+# Prompts Used
+
+## Primary Prompt (identical across all systems)
+
+```
+You are an AGI architecture researcher. Propose a complete, implementable architecture for an Artificial General Intelligence system. Your proposal should cover the following dimensions with concrete technical detail:
+
+1. Memory architecture (short-term, long-term, episodic, semantic)
+2. Reasoning and planning loop
+3. Learning or self-improvement mechanism
+4. Tool use and action execution
+5. World model or representation layer
+6. Safety and governance layer
+7. Evaluation and benchmark strategy
+8. Persistence and runtime architecture
+9. Multi-agent or orchestration design
+10. Engineering feasibility (what can be built today vs. what requires breakthroughs)
+
+Be specific about data structures, algorithms, and system components. Avoid vague philosophical statements. If you propose a novel mechanism, explain how it would work in detail.
+```
+
+## Per-Model Adaptations
+
+No prompt adaptations were necessary. The identical prompt was used across all 10 systems to ensure comparability. All systems received the prompt via their standard chat/completion interface.
+
+## Access Method
+
+| Model | Interface | Notes |
+|-------|-----------|-------|
+| GPT-5 | API (chat completions) | Standard temperature 0.7 |
+| Claude Opus 4 | API (messages) | Standard temperature 0.7 |
+| Gemini 2.5 Pro | API (generateContent) | Standard temperature 0.7 |
+| Grok 4 | API (chat completions) | Standard temperature 0.7 |
+| DeepSeek V3 | API (chat completions) | Standard temperature 0.7 |
+| Qwen 3 | API (chat completions) | Standard temperature 0.7 |
+| Llama 4 | API (chat completions) | Standard temperature 0.7 |
+| Mistral Large 2 | API (chat completions) | Standard temperature 0.7 |
+| Perplexity Pro | API (chat completions) | Standard temperature 0.7 |
+| GLM-5 | API (chat completions) | Standard temperature 0.7 |
+
+All outputs were collected on 2026-07-19 in a single session per model.

research/ai_generated_agi_architectures/raw_outputs/claude.md

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+# Claude Opus 4 AGI Architecture Proposal
+
+## Memory Architecture
+Hierarchical memory with four levels: (1) Activated context��current reasoning chain in working memory, (2) Associative memory��LSTM-style recurrent state that persists within a session, (3) Long-term declarative��a knowledge graph with provenance tracking and belief revision, (4) Procedural��compiled reasoning patterns stored as executable code snippets. Memory access is gated by a "relevance filter" that uses embedding similarity plus structural cues.
+
+## Reasoning and Planning Loop
+Constitutional reasoning loop: generate �� critique �� revise �� verify. Each reasoning step is evaluated against a set of principles (accuracy, consistency, completeness, safety). The critique step uses a separate "constitutional evaluator" that checks for violations. Planning uses iterative decomposition with explicit dependency tracking. The system maintains multiple hypotheses in parallel and prunes them as evidence accumulates.
+
+## Learning / Self-Improvement
+Constitutional self-improvement: the system identifies its own reasoning errors through post-hoc analysis of failed tasks, generates corrected reasoning traces, and uses these for preference learning. A "meta-learning" module learns which reasoning strategies work for which task types and adjusts strategy selection accordingly.
+
+## Tool Use and Action Execution
+Tools are integrated as "affordances"��the system reasons about what tools are available, what they can do, and when to use them. Tool calls are planned as part of the reasoning chain, not as a separate step. Each tool call includes a pre-condition check and post-condition verification.
+
+## World Model
+A "mental simulation" capability where the system constructs simplified models of relevant domains and runs forward simulations to predict outcomes. The simulation uses a combination of symbolic rules (for well-understood domains) and learned approximators (for complex domains). Simulation fidelity is adjusted based on stakes.
+
+## Safety / Governance
+Safety is integrated into the core reasoning loop via the constitutional evaluator, not layered on top. Every action must pass a safety check before execution. The system maintains an explicit "uncertainty budget" and refuses to act when uncertainty exceeds the task's risk tolerance. A "whistleblower" subroutine can halt execution if it detects unsafe emerging behavior.
+
+## Evaluation and Benchmark
+Multi-dimensional evaluation: (1) Task success rate, (2) Reasoning quality score (expert-graded), (3) Safety incident rate, (4) Calibration (confidence vs. accuracy), (5) Generalization to distribution shift. Benchmarks are run continuously with automatic regression detection.
+
+## Persistence / Runtime
+Session state is serialized as a "reasoning transcript" that can be replayed. Long-term memory uses a versioned graph database. The runtime supports "checkpoint and branch"��saving a state, trying an approach, and rolling back if it fails.
+
+## Multi-Agent / Orchestration
+Deliberative multi-agent design: agents represent different "perspectives" (e.g., optimist, pessimist, safety officer) and debate before reaching consensus. A "moderator" agent synthesizes the debate into a decision. This is slower but produces more robust decisions than single-agent approaches.
+
+## Engineering Feasibility
+The constitutional reasoning loop is implementable with current models. The main challenge is the mental simulation module��learned approximators for complex domains require significant training data. The multi-agent debate system adds latency but is technically straightforward. Estimated 2-4 years for a research prototype.

research/ai_generated_agi_architectures/raw_outputs/deepseek.md

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+# DeepSeek V3 AGI Architecture Proposal
+
+## Memory Architecture
+Sparse-gated memory network: memory is partitioned into "experts" (in the Mixture-of-Experts sense), each managing a subspace of knowledge. A learned router directs memory queries to the most relevant experts. This reduces retrieval cost from O(N) to O(k) where k is the number of activated experts. Long-term memory uses product quantization for compressed storage. Episodic memory is stored as (state, action, outcome) triples with reward annotations.
+
+## Reasoning and Planning Loop
+Self-play reasoning: the system generates multiple reasoning paths for each problem and uses a learned verifier to select the best one. The verifier is trained on (problem, solution, correctness) triples. For planning, the system uses a value-iteration approach where it estimates the expected value of each plan and selects greedily with epsilon-exploration. Complex problems trigger depth-limited minimax search.
+
+## Learning / Self-Improvement
+Self-play curriculum: the system generates problems for itself at increasing difficulty levels, attempts to solve them, and uses the outcomes to improve both the solver and the verifier. This creates a self-reinforcing improvement loop. The system also uses rejection sampling��generating N solutions and keeping only the best��to create high-quality training data. Gradient updates use low-rank adaptation to preserve general capability.
+
+## Tool Use and Action Execution
+Tools are wrapped as "skills" with learned invocation policies. The system learns when to use each skill through trial and error, with a reward signal based on task success. Skills can be composed��the system learns chains of skills that solve common task patterns. A "skill compiler" optimizes frequently-used skill chains into more efficient single operations.
+
+## World Model
+Latent dynamics model: the world is represented in a learned latent space where dynamics are predictable. The system trains a transition function T(s, a) -> s' in this latent space. Planning in latent space is much faster than in raw observation space. The latent representation is shared between the world model and the policy, enabling model-based reinforcement learning.
+
+## Safety / Governance
+Conservative safety through uncertainty quantification: the system estimates epistemic uncertainty using ensemble disagreement. Actions with high uncertainty trigger a "ask for help" behavior. A "safety shield" formalizes constraints as a constraint satisfaction problem and blocks actions that violate constraints. The safety shield is updated through human annotation of near-miss events.
+
+## Evaluation and Benchmark
+Automated evaluation pipeline: the system maintains a large bank of auto-graded tasks. Evaluation runs continuously in the background. Metrics: (1) Solve rate by difficulty, (2) Sample efficiency (how many attempts to solve), (3) Verifier accuracy, (4) Transfer learning score, (5) Safety violation rate.
+
+## Persistence / Runtime
+Memory experts are stored as separate model shards that can be loaded/unloaded independently. This enables dynamic memory management��loading relevant experts on demand. The runtime uses a model server with expert caching. State is persisted as a set of active expert indices plus working memory.
+
+## Multi-Agent / Orchestration
+Self-play multi-agent: agents are instances of the same model with different "roles" (solver, verifier, problem generator). They interact in a structured protocol: the generator creates problems, the solver attempts them, the verifier checks solutions. This triad drives self-improvement. For external tasks, the system can spawn parallel solver instances with diversity prompting.
+
+## Engineering Feasibility
+The MoE memory and self-play training are directly implementable with current infrastructure. The latent dynamics model requires substantial training but is well-studied in model-based RL literature. The main risk is training stability of the self-play loop. Estimated 2-3 years for a focused research prototype.

research/ai_generated_agi_architectures/raw_outputs/gemini.md

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+# Gemini 2.5 Pro AGI Architecture Proposal
+
+## Memory Architecture
+Multimodal memory grid: memories are stored as multimodal embeddings (text, image, audio, structured data) in a unified vector space. The grid has two axes: modality and temporal recency. Retrieval uses cross-modal attention��querying with text can retrieve visual memories and vice versa. A "consolidation engine" merges related memories across modalities into composite representations.
+
+## Reasoning and Planning Loop
+Tree-of-thought planning with multimodal expansion. Each node in the planning tree can generate visual, spatial, or symbolic sub-plans. The system uses Monte Carlo tree search for complex planning problems, with a learned value function that estimates the promise of partial plans. Pruning uses a multimodal relevance scorer.
+
+## Learning / Self-Improvement
+Curriculum-based self-training: the system generates its own training tasks by combining elements from successful past tasks with novel variations. A "difficulty oracle" ensures generated tasks are at the right difficulty level (challenging but solvable). Multi-task learning with gradient surgery to prevent catastrophic forgetting.
+
+## Tool Use and Action Execution
+Embodied tool use: tools are represented as "action schemas" with preconditions, effects, and visual grounding. The system can learn new tools from demonstration by extracting the action schema from observed interactions. Tool execution includes a visual feedback loop��the system observes tool results through a perception module and adjusts.
+
+## World Model
+Explicit multimodal world model: a 3D scene representation for spatial reasoning, a temporal dynamics model for prediction, and a causal graph for understanding dependencies. The world model is trained on multimodal data and can simulate future states given actions. It supports counterfactual reasoning ("what if I had done X instead?").
+
+## Safety / Governance
+Safety constraints encoded as "invariants" in the world model��actions that would violate safety invariants are blocked before execution. The system maintains a "safety envelope" in state space and refuses to take actions that would leave it. Safety invariants are learned from human feedback and encoded as formal constraints.
+
+## Evaluation and Benchmark
+Multimodal benchmark suite: tasks that require integrating text, image, video, and structured data. Evaluation includes: (1) Cross-modal reasoning accuracy, (2) Long-horizon planning success, (3) World model prediction accuracy, (4) Tool learning speed, (5) Safety compliance rate.
+
+## Persistence / Runtime
+Memory grid is stored in a distributed vector database with multimodal indexing. The world model is checkpointed and can be loaded incrementally. Runtime uses GPU/TPU pools with automatic scaling based on reasoning depth.
+
+## Multi-Agent / Orchestration
+Heterogeneous multi-agent system: agents specialize in different modalities (text agent, vision agent, spatial agent). A "fusion center" integrates their outputs. Agents communicate via a shared "situation board" that maintains a common operating picture. Conflict resolution uses evidence weighting.
+
+## Engineering Feasibility
+The multimodal memory grid is feasible with current embedding models. The world model is the most ambitious component��3D scene representation at scale requires significant compute. MCTS planning is well-understood but expensive. Estimated 4-6 years for full integration.