Research: compare eight AI-generated AGI architecture proposals

research/ai_generated_agi_architectures/README.md

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+# AI-generated AGI architecture research packet
+
+This packet compares eight independently generated, implementation-oriented cognitive architecture proposals for Cognitive-OS issue #5.
+
+## Systems included
+
+1. Qwen3 8B
+2. Llama 3.1 8B
+3. Gemma 3 4B
+4. Qwen2.5 Coder 7B
+5. Mistral 7B
+6. Phi-4 Mini
+7. Granite 3.3 2B
+8. GPT-5.6 Sol
+
+An additional DeepSeek-R1 8B collection attempt produced no final-answer content and is preserved transparently as a failed collection incident rather than counted.
+
+## Method
+
+- A shared architecture prompt asked every system to address the same eleven comparison dimensions.
+- Local model runs were executed independently through Ollama; outputs were saved before comparative analysis.
+- Raw outputs remain separate from analysis.
+- A low-temperature extraction pass normalized each proposal into the same eleven structured dimensions for `comparison.csv`.
+- `summary.md` describes common patterns, disagreements, and notable ideas.
+- `synthesis.md` converts the strongest recurring ideas into an implementation-oriented combined architecture.
+
+## Headline findings
+
+The strongest cross-model convergence is around layered memory, explicit planning loops, tool-mediated action, persistent state, modular evaluation, and bounded specialist agents. The largest disagreements concern the mechanism of self-improvement, whether the world model should be primarily symbolic/neural/hybrid, and how much autonomy to grant the action layer. A repeated practical lesson is that verification should be structurally separated from generation: the system should not mark a goal complete merely because the planner or tool reports success.
+
+The synthesis therefore favors an event-sourced runtime with provenance-aware memory, a receding-horizon planner, capability-gated tools, independent outcome verification, regression-gated self-improvement, and specialist-agent routing used only when it measurably improves decisions.
+
+## Files
+
+- `prompts.md` — exact shared prompt and collection notes
+- `raw_outputs/` — preserved model outputs
+- `comparison.csv` — normalized 11-dimension comparison
+- `analysis.json` — machine-readable normalized comparison backing the CSV
+- `summary.md` — common patterns, disagreements, notable ideas
+- `synthesis.md` — proposed combined architecture
+- `sources.md` — provenance, access method, dates, edit policy
+- `analysis/` — per-output extraction records used to build the table
+
+## Reproducibility and limitations
+
+The local runs are reproducible in principle with the listed Ollama model identifiers, but stochastic generation means exact wording may vary. GPT-5.6 Sol is a hosted system and therefore cannot be reproduced solely from this repository. The analysis is comparative research, not a claim that any proposal constitutes AGI or that model self-descriptions are evidence of capability.

research/ai_generated_agi_architectures/analysis.json

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+[
+  {
+    "system": "Qwen3 8B",
+    "file": "raw_outputs/qwen3_8b.md",
+    "memory_architecture": "Structured, hierarchical, and scalable memory with STM, MTM, and LTM layers. Uses semantic indexing, chunking, and modular design for efficient storage and retrieval.",
+    "reasoning_planning_loop": "Iterative, goal-oriented, and adaptive loop for processing input, generating plans, and executing actions with feedback-driven refinement and modular extensibility.",
+    "learning_self_improvement": "Incremental, adaptive, and feedback-driven mechanism combining reinforcement, supervised, and self-supervised learning for continuous performance optimization.",
+    "tool_use_action": "Modular, extensible, and context-aware tool interface with pluggable architecture and context manager for selecting appropriate tools based on environment and goals.",
+    "world_model": "Dynamic, probabilistic, and contextual knowledge graph with contextual embeddings and active learning for maintaining an updated and accurate environmental understanding.",
+    "safety_governance": "Structured, transparent, and accountable layer with governance policies, risk assessments, and ethical guidelines to ensure safe and responsible system behavior.",
+    "evaluation_benchmarks": "Comprehensive benchmarks for measuring reasoning, learning, tool use, and safety, aligned with AGI capabilities and real-world task performance.",
+    "persistence_runtime": "Persistent runtime environment with versioned databases and state management for maintaining long-term knowledge and system state across sessions.",
+    "multi_agent_orchestration": "Support for coordinated multi-agent interactions through modular communication protocols and task delegation for complex problem-solving.",
+    "engineering_feasibility": "Feasible with existing technologies like distributed databases, transformer models, and reinforcement learning, enabling scalable and modular implementation.",
+    "originality_non_obvious": "Proposes a layered cognitive architecture with novel integration of memory, reasoning, learning, and safety, emphasizing adaptability and context-awareness."
+  },
+  {
+    "system": "Llama 3.1 8B",
+    "file": "raw_outputs/llama31_8b.md",
+    "memory_architecture": "Hierarchical Temporal Memory (HTM) models brain memory and attention with sparse-distributed memory and HTM software.",
+    "reasoning_planning_loop": "Model-Based Reinforcement Learning (MBRL) uses probabilistic world models and reward functions for goal-directed decision-making.",
+    "learning_self_improvement": "Meta-Learning combines model-based and model-free reinforcement learning to enable adaptive task learning and policy updates.",
+    "tool_use_action": "Robot Operating System (ROS) provides standardized frameworks for robot interaction and motor command execution.",
+    "world_model": "Probabilistic Graphical Model (PGM) represents the world probabilistically with PyMC3 for updates and predictions.",
+    "safety_governance": "Value Alignment ensures human-aligned goals using formal methods and machine learning for policy updates.",
+    "evaluation_benchmarks": "Metrics include task completion rate, reward value, and energy consumption for system performance assessment.",
+    "persistence_runtime": "Uses distributed file systems, containerization, and cloud services for scalable data storage and system isolation.",
+    "multi_agent_orchestration": "Hierarchical, distributed architecture with well-defined interfaces for modular interaction and adaptability.",
+    "engineering_feasibility": "Staged implementation with incremental development building on prior modules for practical deployment.",
+    "originality_non_obvious": "Hierarchical, distributed design with modular interfaces enables adaptability, learning, and value alignment over time."
+  },
+  {
+    "system": "Gemma 3 4B",
+    "file": "raw_outputs/gemma3_4b.md",
+    "memory_architecture": "Hierarchical episodic and semantic networks with graph databases for indexing and querying.",
+    "reasoning_planning_loop": "Goal decomposition, situation assessment, plan generation, and execution monitoring using HTN and planners.",
+    "learning_self_improvement": "Model-based reinforcement learning with curiosity-driven exploration and dynamic world model updates.",
+    "tool_use_action": "Pre-defined action library mapped to robotic interfaces with planner-driven execution and translation to control commands.",
+    "world_model": "Hybrid probabilistic occupancy grid and semantic knowledge graph with dynamic updates from sensor data and experiences.",
+    "safety_governance": "Constraint-based safety, anomaly detection, and human override for system behavior monitoring and control.",
+    "evaluation_benchmarks": "Task-based metrics, success rate, efficiency, and cognitive Turing test for complex problem-solving in limited domains.",
+    "persistence_runtime": "Microservices architecture with Kubernetes orchestration and time-series database for logging and monitoring.",
+    "multi_agent_orchestration": "Single agent prototype with future expansion to specialized agents using message passing for coordination.",
+    "engineering_feasibility": "Staged implementation from basic navigation to complex tasks with focus on core components and scalability.",
+    "originality_non_obvious": "Episodic memory as a dynamic contextual prior shaping planning and learning through feedback loops with semantic knowledge."
+  },
+  {
+    "system": "Qwen2.5 Coder 7B",
+    "file": "raw_outputs/qwen25_coder_7b.md",
+    "memory_architecture": "Hierarchical memory with short-term, working, long-term, and meta-memory, managing data flow and handling overflow/corruption with fail-safes.",
+    "reasoning_planning_loop": "Iterative loop combining sensory input, memory retrieval, reasoning, planning, and execution with feedback for hypothesis refinement.",
+    "learning_self_improvement": "Reinforcement and meta-learning modules adjusting plans and algorithms based on outcomes and learning effectiveness.",
+    "tool_use_action": "Modular integration of external tools and actions with execution and outcome feedback to reasoning modules.",
+    "world_model": "Environment modeling, prediction, and decision modules for high-level environmental representation and action planning.",
+    "safety_governance": "Safety and governance modules enforcing fail-safes and ethical guidelines to prevent unsafe or unethical behavior.",
+    "evaluation_benchmarks": "Performance evaluation and improvement modules identifying areas for enhancement and feeding results back into reasoning.",
+    "persistence_runtime": "State persistence and runtime management modules ensuring continuity and handling runtime failures.",
+    "multi_agent_orchestration": "Agent coordination and communication modules for managing interactions and results among multiple agents.",
+    "engineering_feasibility": "Staged implementation plan starting with memory systems, reasoning, learning, tools, and expanding to world models and multi-agent systems.",
+    "originality_non_obvious": "Hierarchical uncertainty management where higher levels handle complex uncertainties and lower levels manage simpler ones for efficient ambiguity resolution."
+  },
+  {
+    "system": "Mistral 7B",
+    "file": "raw_outputs/mistral_7b.md",
+    "memory_architecture": "Hierarchical Dynamic Memory Network with episodic, semantic, and working memory buffers for context-dependent information management.",
+    "reasoning_planning_loop": "Recursive Cortical Hierarchy for feature extraction, attention, and hierarchical abstraction with forward and backward processing passes.",
+    "learning_self_improvement": "Combination of supervised, unsupervised, and reinforcement learning integrated into RCH and HDNM for adaptive performance improvement.",
+    "tool_use_action": "Tool Use and Action Execution module with perception, action selection, and effector modules for environment interaction and goal achievement.",
+    "world_model": "Dynamic probabilistic environment representation updated via sensor data, learned patterns, and predictive models for decision-making.",
+    "safety_governance": "Safety and Governance layer with intrinsic and extrinsic mechanisms like reward shaping and adversarial training to prevent harmful actions.",
+    "evaluation_benchmarks": "Evaluated using AI benchmarks, cognitive science tasks, and real-world scenarios with continuous performance monitoring and adjustments.",
+    "persistence_runtime": "Modular, scalable, distributed architecture with hybrid cloud-edge deployment for efficient runtime and knowledge persistence.",
+    "multi_agent_orchestration": "Decentralized multi-agent control with individual world models and a global orchestration layer for coordination and conflict resolution.",
+    "engineering_feasibility": "Staged implementation by small teams with incremental development from a proof-of-concept prototype to advanced capabilities.",
+    "originality_non_obvious": "Hybrid learning approach combining supervised, unsupervised, and reinforcement learning within a unified cognitive architecture for diverse task adaptation."
+  },
+  {
+    "system": "Phi-4 Mini",
+    "file": "raw_outputs/phi4_mini.md",
+    "memory_architecture": "Hierarchical memory system with short-term (LSTM), long-term (NoSQL), and working memory (key-value store) components for varied storage and retrieval.",
+    "reasoning_planning_loop": "Combines symbolic AI for rule-based reasoning and neural networks for probabilistic reasoning with search algorithms and reinforcement learning for planning.",
+    "learning_self_improvement": "Integrates supervised, unsupervised, and reinforcement learning for initial training, pattern recognition, and environmental adaptation.",
+    "tool_use_action": "Uses computer vision, natural language processing, and robotics for object identification, command interpretation, and real-world action execution.",
+    "world_model": "Represents the world via computer vision, natural language processing, and sensor data for environmental understanding and interaction.",
+    "safety_governance": "Safety layer combines rule-based ethical guidelines with human oversight to ensure alignment with values and prevent unsafe behavior.",
+    "evaluation_benchmarks": "Evaluates AGI against human performance using GLUE, AlphaGo, and COCO benchmarks for language, game, and object recognition tasks.",
+    "persistence_runtime": "Persistence layer stores experiences in databases and uses neural networks to learn from historical data for knowledge retention.",
+    "multi_agent_orchestration": "Multi-agent design uses message passing and shared memory for communication and data access among agents.",
+    "engineering_feasibility": "Staged implementation starts with simple game-playing agents, gradually adding complexity like object recognition and tool use.",
+    "originality_non_obvious": "Non-obvious insight involves distributed cognition by allowing AGI to learn from and share experiences with other agents."
+  },
+  {
+    "system": "Granite 3.3 2B",
+    "file": "raw_outputs/granite33_2b.md",
+    "memory_architecture": "Distributed Memory using HTM for spatiotemporal data and relational database for symbolic data, with standardized JSON/XML interfaces.",
+    "reasoning_planning_loop": "Layered Reasoning Engine combining symbolic AI and neural networks, generating plans validated by the World Model and passed to Planning Loop.",
+    "learning_self_improvement": "Reinforcement Learning (DQN) and Neural Architecture Search (NAS) for policy optimization and neural network architecture refinement.",
+    "tool_use_action": "Actuator Control Module interfacing with physical actuators, receiving commands from Planning Loop and providing feedback to Sensory Processing.",
+    "world_model": "Dynamic World Model using Bayesian networks and graph neural networks for hierarchical, probabilistic representation of environmental context.",
+    "safety_governance": "Ethics and Safety Oversight Module with rule-based systems, ML for anomaly detection, and human-in-the-loop oversight for ethical and safety compliance.",
+    "evaluation_benchmarks": "Continuous Evaluation System using predefined metrics and human feedback for performance assessment and nuanced validation.",
+    "persistence_runtime": "Containerized Runtime Environment using Docker and Kubernetes for deployment, scaling, and maintenance of AGI components.",
+    "multi_agent_orchestration": "Decentralized Multi-Agent System with autonomous agents and a central coordination module for communication and task allocation.",
+    "engineering_feasibility": "Staged Implementation starting with MVP of core components, iteratively adding layers with thorough testing and validation for each phase.",
+    "originality_non_obvious": "Neuro-Symbolic Hybrid combining symbolic AI and neural networks for logical reasoning and statistical learning, mimicking human-like cognition with explainability and adaptability."
+  },
+  {
+    "system": "GPT-5.6 Sol",
+    "file": "raw_outputs/chatgpt_gpt56_sol.md",
+    "memory_architecture": "Four memory classes: working, episodic, semantic, and procedural. Consolidation turns episodes into semantic facts, requiring provenance or approval.",
+    "reasoning_planning_loop": "Receding-horizon loop with hypothesis generation, action decomposition, and value-of-information optimization before execution.",
+    "learning_self_improvement": "Improvement via promotion pipeline: failure/evaluation → patch → sandbox → benchmark → promotion, with rollback and safety constraints.",
+    "tool_use_action": "Typed tool contracts with preconditions, evidence, side-effects, and rollback. Actions classified by risk and permissions, with explicit verification.",
+    "world_model": "Event-sourced belief graph with entities, resources, and causal claims. Predictive layer estimates success, cost, and distinguishing observations.",
+    "safety_governance": "Policy at action boundaries with least-privilege, approval thresholds, secret isolation, audit logs, and automatic halts on policy violations.",
+    "evaluation_benchmarks": "Three layers: unit cognition tests, scenario tests with hidden blockers, and longitudinal tests tracking outcome quality and regression.",
+    "persistence_runtime": "Append-only event log as source of truth. Services include Goal Manager, Memory, Planner, Verifier, and Evaluator with durable queues and idempotency.",
+    "multi_agent_orchestration": "Bounded specialists with independent reasoning, judge selects falsifiable actions, and agents removed if marginal contribution is low.",
+    "engineering_feasibility": "Staged implementation: event log, tool contracts, memory, approval gates, and sandboxed self-improvement with regression gates.",
+    "originality_non_obvious": "Falsification as first-class resource: store contradicting evidence, cheapest disproof, and staleness deadlines to reduce decision-relevant uncertainty."
+  }
+]

research/ai_generated_agi_architectures/analysis/chatgpt_gpt56_sol.md.json

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+{
+  "system": "GPT-5.6 Sol",
+  "file": "raw_outputs/chatgpt_gpt56_sol.md",
+  "memory_architecture": "Four memory classes: working, episodic, semantic, and procedural. Consolidation turns episodes into semantic facts, requiring provenance or approval.",
+  "reasoning_planning_loop": "Receding-horizon loop with hypothesis generation, action decomposition, and value-of-information optimization before execution.",
+  "learning_self_improvement": "Improvement via promotion pipeline: failure/evaluation → patch → sandbox → benchmark → promotion, with rollback and safety constraints.",
+  "tool_use_action": "Typed tool contracts with preconditions, evidence, side-effects, and rollback. Actions classified by risk and permissions, with explicit verification.",
+  "world_model": "Event-sourced belief graph with entities, resources, and causal claims. Predictive layer estimates success, cost, and distinguishing observations.",
+  "safety_governance": "Policy at action boundaries with least-privilege, approval thresholds, secret isolation, audit logs, and automatic halts on policy violations.",
+  "evaluation_benchmarks": "Three layers: unit cognition tests, scenario tests with hidden blockers, and longitudinal tests tracking outcome quality and regression.",
+  "persistence_runtime": "Append-only event log as source of truth. Services include Goal Manager, Memory, Planner, Verifier, and Evaluator with durable queues and idempotency.",
+  "multi_agent_orchestration": "Bounded specialists with independent reasoning, judge selects falsifiable actions, and agents removed if marginal contribution is low.",
+  "engineering_feasibility": "Staged implementation: event log, tool contracts, memory, approval gates, and sandboxed self-improvement with regression gates.",
+  "originality_non_obvious": "Falsification as first-class resource: store contradicting evidence, cheapest disproof, and staleness deadlines to reduce decision-relevant uncertainty."
+}