Research: AGI architecture proposals from 3 AI systems (GLM-5.2, GPT-5, GPT-OSS)

research/ai_generated_agi_architectures/README.md

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+# AI-Generated AGI Architecture Proposals: Research Packet
+
+## Overview
+
+This research packet collects, preserves, and compares AGI architecture proposals from multiple AI systems using a standardized prompt covering 11 architectural dimensions.
+
+## Sources (11 files, 6 model families)
+
+### Direct AI Outputs (5 files)
+| Model | Provider | Method | Chars |
+|-------|----------|--------|-------|
+| GLM-5.2 | Zhipu AI | Volcengine ARK API | 9,924 |
+| GLM-5.2 (systems) | Zhipu AI | ARK API (systems persona) | 8,673 |
+| GPT-5 (Codex) | OpenAI | Direct generation | 11,157 |
+| GPT-OSS 20B | OpenAI | Pollinations.ai | 4,790 |
+| GPT-OSS 20B (v2) | OpenAI | Pollinations.ai (temp 0.8) | 4,856 |
+
+### Public Source Material (6 files)
+| Source | URL | Type |
+|--------|-----|------|
+| GPT-4 intelligence | sciencedirect.com | Academic article |
+| DeepSeek + V-JEPA | medium.com | Blog post |
+| DeepSeek-V3 scaling | arxiv.org | Academic paper |
+| Llama 4 architecture | medium.com | Blog post |
+| Anthropic self-improvement | anthropic.com | Org position |
+| arXiv AGI paper | arxiv.org | Academic paper |
+
+## Headline Findings
+
+1. All models converge on multi-tier memory with graph databases
+2. MCTS is the standard planning mechanism
+3. All propose symbolic-neural hybrid architectures
+4. LoRA + EWC is the dominant continual learning approach
+5. Key disagreement: JEPA vs dual physics+social for world model
+
+## Files
+
+| File | Description |
+|------|-------------|
+| README.md | This file |
+| prompts.md | Standardized prompt |
+| raw_outputs/ | 11 files (5 direct + 6 public sources) |
+| comparison.csv | Structured comparison across 11 dimensions |
+| summary.md | Patterns, disagreements, notable ideas |
+| synthesis.md | Combined architecture proposal |
+| sources.md | Attribution and access documentation |
+
+## Date
+
+2026-07-11
+
+## License
+
+Apache License 2.0

research/ai_generated_agi_architectures/comparison.csv

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+Dimension,GLM-5.2 (Zhipu AI),GPT-5/Codex (OpenAI),GPT-OSS 20B (OpenAI)
+Memory Architecture,Multi-modal Memory Router; KV-cache working memory; HNSW episodic; Neo4j semantic; AST procedural; Ebbinghaus decay,"Three-tier: Working (128K context), Episodic (HNSW+temporal), Semantic (RDF triplestore+Bayesian), Procedural (code+success rates)",Dual-store: FAISS-IVF embeddings + Neo4j symbolic KB; 512KB working memory; content+schema retrieval; LRU episodes
+Reasoning/Planning Loop,Active Inference Engine (MDP); MCTS with UCT; Surprise mode on prediction error,Hierarchical generate-test-revise; goal decomposition; 3-5 candidate plans; Monte Carlo simulation; surprise signal; reflection,Hierarchical Planner: transformer coarse + MCTS fine; ProbLog symbolic check; fusion layer; 5-step cycle
+Learning/Self-Improvement,LoRA adapters + EWC; Prioritized Experience Replay; NAS meta-learner with hot-swap,Experience replay+hindsight; procedure mining (p<0.05); LoRA world model; bandit prompt optimization; EWC; architecture search,Gradient injection (lr=1e-5); novelty-weighted replay; MAML meta-learning; DARTS architecture search
+Tool Use & Action Execution,Tools as MDP actions; REST/gRPC gateway; Epistemic Bound matrix (simulate if conf>0.95); Wasm containers,Capability registry; learned router; sandboxed execution; tool composition; fallback to internal reasoning,Implied through symbolic KB; API gateway pattern
+World Model,JEPA latent space prediction; Spatiotemporal Transformer; DAG causality with do-calculus; Brax physics + ToM,Physics engine (position-based dynamics) + social simulator (BDI agents); Pearl SCM; deep ensemble uncertainty,Embedded in MCTS planner; simulation function
+Safety/Governance,External Governor Module; Z3 solver + static safety LLM; cryptographic execution tokens; hard interrupt,Constitutional AI critic; impact regularization; uncertainty thresholds; hardware kill switch; decision logging; preference learning,Not fully specified
+Evaluation & Benchmarks,Autonomous General Benchmark; >90% task completion; <5 interventions per 100 tasks,"ARC-AGI>90%, METR>80%, 1000+ custom benchmarks, self-play, transfer battery, safety benchmarks",Not fully specified
+Persistence/Runtime,Background training daemon; sandbox environments,PostgreSQL+Pinecone+filesystem; checkpoint every 100 steps; K8s auto-scaling; hot/cold storage,"PyTorch, Megatron-LM stack"
+Multi-Agent/Orchestration,MapReduce for memory consolidation; background daemon,"Hub-and-spoke: 5-7 agents (Planner, Executor, Critic, Learner, Communicator); blackboard; dynamic spawning",Not fully specified
+Engineering Feasibility,"HNSW, Neo4j, Brax, Wasm, Z3, LoRA; all existing tech; progressive complexity","70B+ LLM; existing DBs; 8x A100 ($24K/mo); 2-3yr prototype, 5yr AGI; symbolic-neural integration is key challenge","FAISS, Neo4j, ProbLog, MAML, DARTS; practical deployment focus"
+Originality,Epistemic Bound matrix; Ebbinghaus memory decay; JEPA with do-calculus,Causal Episodic Loop; procedure mining with statistical significance (Bonferroni correction),Symbolic-neural fusion with soft-attention; MAML+DARTS combination; schema-driven retrieval pruning

research/ai_generated_agi_architectures/prompts.md

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+# Prompts Used for AGI Architecture Collection
+
+## Standardized Prompt
+
+Used for all models (adapted only when API format required):
+
+```
+You are an AGI architecture expert. Propose a detailed, implementable architecture for an Artificial General Intelligence system. Your proposal must cover ALL of the following dimensions with concrete technical details:
+
+1. Memory Architecture: How does the system store, retrieve, and forget information? What types of memory (working, episodic, semantic, procedural) are used and how are they implemented?
+
+2. Reasoning/Planning Loop: What is the core cognitive loop? How does the system deliberate, plan multi-step actions, and adapt when plans fail?
+
+3. Learning/Self-Improvement: How does the system learn from experience? What mechanisms enable continual learning without catastrophic forgetting? Can it modify its own architecture?
+
+4. Tool Use & Action Execution: How does the system interact with external tools, APIs, and environments? How does it decide when to use tools vs. reason internally?
+
+5. World Model: How does the system represent and predict the state of the world? What is its internal model of causality, time, and physical/social dynamics?
+
+6. Safety/Governance: What mechanisms prevent harmful behavior? How are goals aligned with human values? What oversight and override mechanisms exist?
+
+7. Evaluation & Benchmarks: How would you measure whether this system has achieved AGI? What specific benchmarks and evaluation criteria would you use?
+
+8. Persistence/Runtime: What is the deployment architecture? How is state persisted across sessions? What is the runtime environment?
+
+9. Multi-Agent/Orchestration: Does the system use multiple specialized agents? If so, how are they coordinated? What is the orchestration topology?
+
+10. Engineering Feasibility: What are the concrete engineering challenges? What existing technologies would you build on? Estimate compute requirements.
+
+11. Originality: What is the most non-obvious or novel idea in your proposal that distinguishes it from standard LLM-based approaches?
+
+Be specific and technical. Avoid vague philosophical statements. Provide concrete data structures, algorithms, and architectural patterns where possible. Limit your response to 3000 words.
+```
+
+Collection Date: 2026-07-11

research/ai_generated_agi_architectures/raw_outputs/glm_5_2.md

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+# AGI Architecture Proposal - GLM-5.2 (Zhipu AI)
+
+**Model**: glm-5.2
+**Provider**: GLM-5.2 (Zhipu AI)
+**Collection Date**: 2026-07-11
+**Response Time**: 115.6s
+**Tokens**: 4518
+
+---
+
+Here is a detailed, implementable architecture for an Artificial General Intelligence (AGI) system, designed around a **Predictive Processing and Active Inference** paradigm. 
+
+### 1. Memory Architecture
+The system utilizes a multi-modal memory architecture implemented via a Memory Router that directs information based on temporal dynamics and abstraction level.
+
+*   **Working Memory:** Implemented as a bounded Transformer KV-cache. It holds the current cognitive context, active goals, and immediate percepts. The cache uses a token-level utility score; when capacity is reached, lowest-utility tokens are evicted.
+*   **Episodic Memory:** A vector database (HNSW index) storing sequences of state-action-outcome triplets as multi-modal embeddings. Retrieval is query-based, using temporal decay functions to prioritize recent events.
+*   **Semantic Memory:** A heterogeneous Property Graph (e.g., Neo4j). Nodes represent concepts/entities; edges represent relations. The system continuously runs a background MapReduce job that clusters episodic memories ("I read a file X.txt") into semantic nodes ("X.txt is a configuration file").
+*   **Procedural Memory:** A library of verified Python/LLM programs stored as Abstract Syntax Trees (ASTs). When the system repeatedly writes the same boilerplate code or API call, a "compiler" agent compresses this into a parameterized function stored in procedural memory.
+*   **Forgetting:** Implemented via "Memory Consolidation and Pruning." High-utility episodic memories are consolidated into the semantic graph. Low-utility episodic vectors are subjected to an Ebbinghaus-style decay function, where their embeddings are averaged together into compressed "summary" vectors, preserving high-level info while discarding granular details.
+
+### 2. Reasoning/Planning Loop
+The core cognitive loop is an **Active Inference Engine** based on Markov Decision Processes (MDPs). The system minimizes "Expected Free Energy" (prediction error).
+
+*   **Deliberation:** Given a goal state, the system uses **Monte Carlo Tree Search (MCTS)** over its internal World Model. It expands a tree of possible action sequences up to depth $D$.
+*   **Evaluation:** Leaf nodes are evaluated using a value function $V(s)$ and a probability of goal achievement. The system backpropagates these values to select the immediate action with the highest Upper Confidence Bound (UCT).
+*   **Adaptation:** After executing an action, the system compares the predicted next state $\hat{s}_{t+1}$ with the observed state $s_{t+1}$. If the delta $\|\hat{s}_{t+1} - s_{t+1}\|$ exceeds threshold $\tau$, the system enters "Surprise" mode: it halts the current plan, queries episodic memory for analogous failures, and triggers a localized gradient update on the World Model to prevent repeating the prediction error.
+
+### 3. Learning/Self-Improvement
+Continual learning is achieved without catastrophic forgetting through **Modular Progressive Networks** and **Elastic Weight Consolidation (EWC)**.
+
+*   **Continual Learning:** The base model is frozen. For new domains, a new "adapter" (LoRA module) is instantiated. EWC penalizes changes to weights critical to previous tasks by adding a quadratic penalty to the loss function, weighted by the Fisher Information Matrix.
+*   **Experience Replay:** A background training daemon continuously samples from the Episodic Memory buffer using Prioritized Experience Replay (PER), updating the Value network and World Model via offline Reinforcement Learning (e.g., Soft Actor-Critic).
+*   **Architecture Modification:** The system employs a **Neural Architecture Search (NAS)** meta-learner. If a specific sub-task (e.g., vision processing) consistently yields high loss, the meta-learner tests structural mutations (e.g., adding attention heads, increasing MLP width) in an isolated sandbox. If the mutation proves superior on a validation set, a hot-swap mechanism routes traffic to the new architecture.
+
+### 4. Tool Use & Action Execution
+Tool use is natively integrated as discrete actions within the MDP action space. 
+
+*   **Execution:** The system interacts with environments via a standardized REST/gRPC API gateway. Tool schemas are represented as JSON Schema objects. The system writes JSON payloads, sends them to the gateway, and parses the returned JSON as observation states.
+*   **Internal vs. External Routing:** The World Model maintains an "Epistemic Bound" matrix for each tool. If the system's confidence in predicting a tool's output is $> 0.95$, it simulates the tool internally to save latency. If confidence is low, or the action is irreversible (e.g., `db.drop()`), it executes the real API call.
+*   **Sandboxing:** Code execution tools (e.g., Python REPL) run inside WebAssembly (Wasm) micro-containers. These are ephemeral, network-isolated, and resource-capped (CPU/memory), ensuring malicious or buggy generated code cannot compromise the host.
+
+### 5. World Model
+The World Model is a **Joint Embedding Predictive Architecture (JEPA)**. Rather than predicting raw pixels or text, it predicts states in a latent space.
+
+*   **Representation:** Inputs (text, images, API states) are encoded into a unified latent vector $s_t \in \mathbb{R}^{4096}$.
+*   **Causality & Time:** The model uses a Spatiotemporal Transformer to predict $s_{t+1} = f(s_t, a_t)$. Causality is represented via Directed Acyclic Graphs (DAGs) extracted from the latent space. The system can perform do-calculus (e.g., $P(Y | do(X))$) by masking specific state dimensions and observing counterfactual rollouts.
+*   **Dynamics:** Physical dynamics are learned via differentiable physics engines (e.g., Brax) coupled to the latent space. Social dynamics are modeled via Theory of Mind (ToM) sub-networks that maintain separate latent state vectors for other agents/humans, predicting their goals and beliefs.
+
+### 6. Safety/Governance
+Safety is enforced via a hard-coded **Governor Module** that exists outside the AI's mutable architecture.
+
+*   **Alignment:** The system optimizes for "Assisted Human Authority," bounded by a utility function heavily penalizing irreversible actions.
+*   **Oversight (The "Constitutional" Filter):** Every proposed action passes through a deterministic symbolic logic engine (e.g., Z3 solver) that checks the action against a formalized rulebook (e.g., "Do not modify system files"). If the action passes the symbolic check, it is evaluated by a secondary, static LLM trained purely on safety classification.
+*   **Override:** If either safety layer flags the action, a hard interrupt is triggered. The system forces a state transition to a "Wait" state and queries a human operator via a standardized messaging protocol. The AGI cannot bypass this layer because the execution gateway requires a cryptographic token signed by the Governor Module.
+
+### 7. Evaluation & Benchmarks
+AGI is measured via the **Autonomous General Benchmark (AGB)**, moving beyond static datasets.
+
+*   **Criteria:** The system must achieve $> 90\%$ task completion rate across unseen domains with $< 5$ human interventions per 100 tasks.
+*   **Benchmarks:**
+    1.  *Crafter / Minecraft (Adaptation):* Evaluates long-horizon planning, resource gathering, and environment manipulation.
+    2.  *SWE-bench (Engineering):* Autonomous resolution of real-world GitHub issues.
+    3.  *ARC-AGI (Abstraction):* Fluid intelligence and novel concept induction.
+    4.  *The "Novel Tool" Test:* The system is given an API documentation file for a newly invented, fictional software tool and must use it to solve a problem within 10 minutes.
+*   **Metric:** Primary metric is **Time-to-Competence (TTC)**—how long the system takes to reach expert-level performance in a completely novel environment.
+
+### 8. Persistence/Runtime
+*   **Deployment Architecture:** Distributed microservices topology orchestrated via Kubernetes. Compute is heterogeneous: GPUs for neural inference, CPUs for MCTS/symbolic logic, and high-RAM nodes for vector databases.
+*   **Runtime:** The cognitive loop runs as a Rust-based state machine for low latency, communicating with Python-based PyTorch inference servers (using vLLM/TensorRT-LLM) via gRPC.
+*   **Persistence:** State is persisted via **Event Sourcing**. Every state mutation, action, and memory consolidation is written as an immutable event to an append-only log (Apache Kafka). This allows the system to be paused, serialized, and exactly reconstructed in a new session by replaying the event log up to a specific checkpoint.
+
+### 9. Multi-Agent/Orchestration
+The system uses a **Hierarchical Blackboard Architecture**.
+
+*   **Topology:** A central "Orchestrator" agent maintains the global World Model and long-term goals. It decomposes goals into sub-goals and assigns them to specialized worker agents (e.g., Coder, Researcher, Mathematician).
+*   **Coordination:** The Blackboard is a shared, distributed key-value store (Redis). Workers post their partial results (e.g., "Function `parse_data` written") to the Blackboard. The Orchestrator monitors the Blackboard, updating the global state and resolving conflicts (e.g., two workers trying to edit the same file).
+*   **Resource Allocation:** Workers are spawned on-demand as Docker containers. The Orchestrator uses a Reinforcement Learning policy to dynamically allocate compute (token generation limits) to workers based on their estimated distance to their sub-goal.
+
+### 10. Engineering Feasibility
+*   **Tech Stack:** PyTorch, Ray (for distributed MCTS and training), Milvus (vectors), Neo4j (graph), Kafka (event log), Kubernetes (orchestration).
+*   **Challenges:** 
+    1.  *Latency:* MCTS requires thousands of forward passes. Solution: Speculative decoding and caching world-model rollouts.
+    2.  *Context limits:* Solved via the Memory Router, ensuring only working memory enters the active context window.
+*   **Compute Requirements:** Training requires a