From 126f571a57a9b018deecd815386306ba6bf27f58 Mon Sep 17 00:00:00 2001 From: Nishant Sinha <125078339+Nish916@users.noreply.github.com> Date: Sun, 13 Sep 2026 11:57:59 +0530 Subject: [PATCH] research: compare eight AI architecture proposals --- .../ai_generated_agi_architectures/README.md | 46 +++++ .../analysis.json | 122 ++++++++++++ .../analysis/chatgpt_gpt56_sol.md.json | 15 ++ .../analysis/gemma3_4b.md.json | 15 ++ .../analysis/granite33_2b.md.json | 15 ++ .../analysis/llama31_8b.md.json | 15 ++ .../analysis/mistral_7b.md.json | 15 ++ .../analysis/phi4_mini.md.json | 15 ++ .../analysis/qwen25_coder_7b.md.json | 15 ++ .../analysis/qwen3_8b.md.json | 15 ++ .../comparison.csv | 9 + .../local_manifest.json | 51 +++++ .../ai_generated_agi_architectures/prompts.md | 28 +++ .../raw_outputs/chatgpt_gpt56_sol.md | 51 +++++ .../raw_outputs/deepseek_r1_8b.md | 5 + .../raw_outputs/gemma3_4b.md | 94 +++++++++ .../raw_outputs/granite33_2b.md | 63 ++++++ .../raw_outputs/llama31_8b.md | 106 +++++++++++ .../raw_outputs/mistral_7b.md | 49 +++++ .../raw_outputs/phi4_mini.md | 153 +++++++++++++++ .../raw_outputs/qwen25_coder_7b.md | 180 ++++++++++++++++++ .../raw_outputs/qwen3_8b.md | 59 ++++++ .../shared_prompt.txt | 16 ++ .../ai_generated_agi_architectures/sources.md | 22 +++ .../ai_generated_agi_architectures/summary.md | 53 ++++++ .../synthesis.md | 150 +++++++++++++++ scripts/build_arch_comparison.py | 48 +++++ scripts/collect_architectures_local.py | 55 ++++++ 28 files changed, 1480 insertions(+) create mode 100644 research/ai_generated_agi_architectures/README.md create mode 100644 research/ai_generated_agi_architectures/analysis.json create mode 100644 research/ai_generated_agi_architectures/analysis/chatgpt_gpt56_sol.md.json create mode 100644 research/ai_generated_agi_architectures/analysis/gemma3_4b.md.json create mode 100644 research/ai_generated_agi_architectures/analysis/granite33_2b.md.json create mode 100644 research/ai_generated_agi_architectures/analysis/llama31_8b.md.json create mode 100644 research/ai_generated_agi_architectures/analysis/mistral_7b.md.json create mode 100644 research/ai_generated_agi_architectures/analysis/phi4_mini.md.json create mode 100644 research/ai_generated_agi_architectures/analysis/qwen25_coder_7b.md.json create mode 100644 research/ai_generated_agi_architectures/analysis/qwen3_8b.md.json create mode 100644 research/ai_generated_agi_architectures/comparison.csv create mode 100644 research/ai_generated_agi_architectures/local_manifest.json create mode 100644 research/ai_generated_agi_architectures/prompts.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/chatgpt_gpt56_sol.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/deepseek_r1_8b.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/gemma3_4b.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/granite33_2b.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/llama31_8b.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/mistral_7b.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/phi4_mini.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/qwen25_coder_7b.md create mode 100644 research/ai_generated_agi_architectures/raw_outputs/qwen3_8b.md create mode 100644 research/ai_generated_agi_architectures/shared_prompt.txt create mode 100644 research/ai_generated_agi_architectures/sources.md create mode 100644 research/ai_generated_agi_architectures/summary.md create mode 100644 research/ai_generated_agi_architectures/synthesis.md create mode 100644 scripts/build_arch_comparison.py create mode 100644 scripts/collect_architectures_local.py diff --git a/research/ai_generated_agi_architectures/README.md b/research/ai_generated_agi_architectures/README.md new file mode 100644 index 0000000..588a4af --- /dev/null +++ b/research/ai_generated_agi_architectures/README.md @@ -0,0 +1,46 @@ +# 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. diff --git a/research/ai_generated_agi_architectures/analysis.json b/research/ai_generated_agi_architectures/analysis.json new file mode 100644 index 0000000..955d3ee --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis.json @@ -0,0 +1,122 @@ +[ + { + "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." + } +] \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/chatgpt_gpt56_sol.md.json b/research/ai_generated_agi_architectures/analysis/chatgpt_gpt56_sol.md.json new file mode 100644 index 0000000..c4c30b0 --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/chatgpt_gpt56_sol.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/gemma3_4b.md.json b/research/ai_generated_agi_architectures/analysis/gemma3_4b.md.json new file mode 100644 index 0000000..4b33be7 --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/gemma3_4b.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/granite33_2b.md.json b/research/ai_generated_agi_architectures/analysis/granite33_2b.md.json new file mode 100644 index 0000000..bc2c9f1 --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/granite33_2b.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/llama31_8b.md.json b/research/ai_generated_agi_architectures/analysis/llama31_8b.md.json new file mode 100644 index 0000000..18fee70 --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/llama31_8b.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/mistral_7b.md.json b/research/ai_generated_agi_architectures/analysis/mistral_7b.md.json new file mode 100644 index 0000000..3560a3b --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/mistral_7b.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/phi4_mini.md.json b/research/ai_generated_agi_architectures/analysis/phi4_mini.md.json new file mode 100644 index 0000000..b1626a1 --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/phi4_mini.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/qwen25_coder_7b.md.json b/research/ai_generated_agi_architectures/analysis/qwen25_coder_7b.md.json new file mode 100644 index 0000000..8cb25ce --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/qwen25_coder_7b.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/analysis/qwen3_8b.md.json b/research/ai_generated_agi_architectures/analysis/qwen3_8b.md.json new file mode 100644 index 0000000..7842f1b --- /dev/null +++ b/research/ai_generated_agi_architectures/analysis/qwen3_8b.md.json @@ -0,0 +1,15 @@ +{ + "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." +} \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/comparison.csv b/research/ai_generated_agi_architectures/comparison.csv new file mode 100644 index 0000000..0b93f36 --- /dev/null +++ b/research/ai_generated_agi_architectures/comparison.csv @@ -0,0 +1,9 @@ +system,file,memory_architecture,reasoning_planning_loop,learning_self_improvement,tool_use_action,world_model,safety_governance,evaluation_benchmarks,persistence_runtime,multi_agent_orchestration,engineering_feasibility,originality_non_obvious +Qwen3 8B,raw_outputs/qwen3_8b.md,"Structured, hierarchical, and scalable memory with STM, MTM, and LTM layers. Uses semantic indexing, chunking, and modular design for efficient storage and retrieval.","Iterative, goal-oriented, and adaptive loop for processing input, generating plans, and executing actions with feedback-driven refinement and modular extensibility.","Incremental, adaptive, and feedback-driven mechanism combining reinforcement, supervised, and self-supervised learning for continuous performance optimization.","Modular, extensible, and context-aware tool interface with pluggable architecture and context manager for selecting appropriate tools based on environment and goals.","Dynamic, probabilistic, and contextual knowledge graph with contextual embeddings and active learning for maintaining an updated and accurate environmental understanding.","Structured, transparent, and accountable layer with governance policies, risk assessments, and ethical guidelines to ensure safe and responsible system behavior.","Comprehensive benchmarks for measuring reasoning, learning, tool use, and safety, aligned with AGI capabilities and real-world task performance.",Persistent runtime environment with versioned databases and state management for maintaining long-term knowledge and system state across sessions.,Support for coordinated multi-agent interactions through modular communication protocols and task delegation for complex problem-solving.,"Feasible with existing technologies like distributed databases, transformer models, and reinforcement learning, enabling scalable and modular implementation.","Proposes a layered cognitive architecture with novel integration of memory, reasoning, learning, and safety, emphasizing adaptability and context-awareness." +Llama 3.1 8B,raw_outputs/llama31_8b.md,Hierarchical Temporal Memory (HTM) models brain memory and attention with sparse-distributed memory and HTM software.,Model-Based Reinforcement Learning (MBRL) uses probabilistic world models and reward functions for goal-directed decision-making.,Meta-Learning combines model-based and model-free reinforcement learning to enable adaptive task learning and policy updates.,Robot Operating System (ROS) provides standardized frameworks for robot interaction and motor command execution.,Probabilistic Graphical Model (PGM) represents the world probabilistically with PyMC3 for updates and predictions.,Value Alignment ensures human-aligned goals using formal methods and machine learning for policy updates.,"Metrics include task completion rate, reward value, and energy consumption for system performance assessment.","Uses distributed file systems, containerization, and cloud services for scalable data storage and system isolation.","Hierarchical, distributed architecture with well-defined interfaces for modular interaction and adaptability.",Staged implementation with incremental development building on prior modules for practical deployment.,"Hierarchical, distributed design with modular interfaces enables adaptability, learning, and value alignment over time." +Gemma 3 4B,raw_outputs/gemma3_4b.md,Hierarchical episodic and semantic networks with graph databases for indexing and querying.,"Goal decomposition, situation assessment, plan generation, and execution monitoring using HTN and planners.",Model-based reinforcement learning with curiosity-driven exploration and dynamic world model updates.,Pre-defined action library mapped to robotic interfaces with planner-driven execution and translation to control commands.,Hybrid probabilistic occupancy grid and semantic knowledge graph with dynamic updates from sensor data and experiences.,"Constraint-based safety, anomaly detection, and human override for system behavior monitoring and control.","Task-based metrics, success rate, efficiency, and cognitive Turing test for complex problem-solving in limited domains.",Microservices architecture with Kubernetes orchestration and time-series database for logging and monitoring.,Single agent prototype with future expansion to specialized agents using message passing for coordination.,Staged implementation from basic navigation to complex tasks with focus on core components and scalability.,Episodic memory as a dynamic contextual prior shaping planning and learning through feedback loops with semantic knowledge. +Qwen2.5 Coder 7B,raw_outputs/qwen25_coder_7b.md,"Hierarchical memory with short-term, working, long-term, and meta-memory, managing data flow and handling overflow/corruption with fail-safes.","Iterative loop combining sensory input, memory retrieval, reasoning, planning, and execution with feedback for hypothesis refinement.",Reinforcement and meta-learning modules adjusting plans and algorithms based on outcomes and learning effectiveness.,Modular integration of external tools and actions with execution and outcome feedback to reasoning modules.,"Environment modeling, prediction, and decision modules for high-level environmental representation and action planning.",Safety and governance modules enforcing fail-safes and ethical guidelines to prevent unsafe or unethical behavior.,Performance evaluation and improvement modules identifying areas for enhancement and feeding results back into reasoning.,State persistence and runtime management modules ensuring continuity and handling runtime failures.,Agent coordination and communication modules for managing interactions and results among multiple agents.,"Staged implementation plan starting with memory systems, reasoning, learning, tools, and expanding to world models and multi-agent systems.",Hierarchical uncertainty management where higher levels handle complex uncertainties and lower levels manage simpler ones for efficient ambiguity resolution. +Mistral 7B,raw_outputs/mistral_7b.md,"Hierarchical Dynamic Memory Network with episodic, semantic, and working memory buffers for context-dependent information management.","Recursive Cortical Hierarchy for feature extraction, attention, and hierarchical abstraction with forward and backward processing passes.","Combination of supervised, unsupervised, and reinforcement learning integrated into RCH and HDNM for adaptive performance improvement.","Tool Use and Action Execution module with perception, action selection, and effector modules for environment interaction and goal achievement.","Dynamic probabilistic environment representation updated via sensor data, learned patterns, and predictive models for decision-making.",Safety and Governance layer with intrinsic and extrinsic mechanisms like reward shaping and adversarial training to prevent harmful actions.,"Evaluated using AI benchmarks, cognitive science tasks, and real-world scenarios with continuous performance monitoring and adjustments.","Modular, scalable, distributed architecture with hybrid cloud-edge deployment for efficient runtime and knowledge persistence.",Decentralized multi-agent control with individual world models and a global orchestration layer for coordination and conflict resolution.,Staged implementation by small teams with incremental development from a proof-of-concept prototype to advanced capabilities.,"Hybrid learning approach combining supervised, unsupervised, and reinforcement learning within a unified cognitive architecture for diverse task adaptation." +Phi-4 Mini,raw_outputs/phi4_mini.md,"Hierarchical memory system with short-term (LSTM), long-term (NoSQL), and working memory (key-value store) components for varied storage and retrieval.",Combines symbolic AI for rule-based reasoning and neural networks for probabilistic reasoning with search algorithms and reinforcement learning for planning.,"Integrates supervised, unsupervised, and reinforcement learning for initial training, pattern recognition, and environmental adaptation.","Uses computer vision, natural language processing, and robotics for object identification, command interpretation, and real-world action execution.","Represents the world via computer vision, natural language processing, and sensor data for environmental understanding and interaction.",Safety layer combines rule-based ethical guidelines with human oversight to ensure alignment with values and prevent unsafe behavior.,"Evaluates AGI against human performance using GLUE, AlphaGo, and COCO benchmarks for language, game, and object recognition tasks.",Persistence layer stores experiences in databases and uses neural networks to learn from historical data for knowledge retention.,Multi-agent design uses message passing and shared memory for communication and data access among agents.,"Staged implementation starts with simple game-playing agents, gradually adding complexity like object recognition and tool use.",Non-obvious insight involves distributed cognition by allowing AGI to learn from and share experiences with other agents. +Granite 3.3 2B,raw_outputs/granite33_2b.md,"Distributed Memory using HTM for spatiotemporal data and relational database for symbolic data, with standardized JSON/XML interfaces.","Layered Reasoning Engine combining symbolic AI and neural networks, generating plans validated by the World Model and passed to Planning Loop.",Reinforcement Learning (DQN) and Neural Architecture Search (NAS) for policy optimization and neural network architecture refinement.,"Actuator Control Module interfacing with physical actuators, receiving commands from Planning Loop and providing feedback to Sensory Processing.","Dynamic World Model using Bayesian networks and graph neural networks for hierarchical, probabilistic representation of environmental context.","Ethics and Safety Oversight Module with rule-based systems, ML for anomaly detection, and human-in-the-loop oversight for ethical and safety compliance.",Continuous Evaluation System using predefined metrics and human feedback for performance assessment and nuanced validation.,"Containerized Runtime Environment using Docker and Kubernetes for deployment, scaling, and maintenance of AGI components.",Decentralized Multi-Agent System with autonomous agents and a central coordination module for communication and task allocation.,"Staged Implementation starting with MVP of core components, iteratively adding layers with thorough testing and validation for each phase.","Neuro-Symbolic Hybrid combining symbolic AI and neural networks for logical reasoning and statistical learning, mimicking human-like cognition with explainability and adaptability." +GPT-5.6 Sol,raw_outputs/chatgpt_gpt56_sol.md,"Four memory classes: working, episodic, semantic, and procedural. Consolidation turns episodes into semantic facts, requiring provenance or approval.","Receding-horizon loop with hypothesis generation, action decomposition, and value-of-information optimization before execution.","Improvement via promotion pipeline: failure/evaluation → patch → sandbox → benchmark → promotion, with rollback and safety constraints.","Typed tool contracts with preconditions, evidence, side-effects, and rollback. Actions classified by risk and permissions, with explicit verification.","Event-sourced belief graph with entities, resources, and causal claims. Predictive layer estimates success, cost, and distinguishing observations.","Policy at action boundaries with least-privilege, approval thresholds, secret isolation, audit logs, and automatic halts on policy violations.","Three layers: unit cognition tests, scenario tests with hidden blockers, and longitudinal tests tracking outcome quality and regression.","Append-only event log as source of truth. Services include Goal Manager, Memory, Planner, Verifier, and Evaluator with durable queues and idempotency.","Bounded specialists with independent reasoning, judge selects falsifiable actions, and agents removed if marginal contribution is low.","Staged implementation: event log, tool contracts, memory, approval gates, and sandboxed self-improvement with regression gates.","Falsification as first-class resource: store contradicting evidence, cheapest disproof, and staleness deadlines to reduce decision-relevant uncertainty." diff --git a/research/ai_generated_agi_architectures/local_manifest.json b/research/ai_generated_agi_architectures/local_manifest.json new file mode 100644 index 0000000..20257ca --- /dev/null +++ b/research/ai_generated_agi_architectures/local_manifest.json @@ -0,0 +1,51 @@ +[ + { + "system": "qwen3_8b", + "model": "qwen3:8b", + "collected_at": "2026-09-13T05:01:28.148243+00:00", + "status": "ok", + "chars": 10420 + }, + { + "system": "deepseek_r1_8b", + "model": "deepseek-r1:8b", + "collected_at": "2026-09-13T05:06:38.641702+00:00", + "status": "ok", + "chars": 0 + }, + { + "system": "llama31_8b", + "model": "llama3.1:8b", + "collected_at": "2026-09-13T05:10:37.328997+00:00", + "status": "ok", + "chars": 5575 + }, + { + "system": "gemma3_4b", + "model": "gemma3:4b", + "collected_at": "2026-09-13T05:13:15.101803+00:00", + "status": "ok", + "chars": 7576 + }, + { + "system": "qwen25_coder_7b", + "model": "qwen2.5-coder:7b", + "collected_at": "2026-09-13T05:14:51.418464+00:00", + "status": "ok", + "chars": 9125 + }, + { + "system": "mistral_7b", + "model": "mistral:7b", + "collected_at": "2026-09-13T05:19:43.799532+00:00", + "status": "ok", + "chars": 6068 + }, + { + "system": "phi4_mini", + "model": "phi4-mini", + "collected_at": "2026-09-13T05:22:48.205501+00:00", + "status": "ok", + "chars": 9163 + } +] \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/prompts.md b/research/ai_generated_agi_architectures/prompts.md new file mode 100644 index 0000000..1604333 --- /dev/null +++ b/research/ai_generated_agi_architectures/prompts.md @@ -0,0 +1,28 @@ +# Prompts + +## Shared architecture prompt + +```text +Design a practical AGI-oriented cognitive architecture that a small engineering team could implement incrementally. Do not describe a vague superintelligence. Give a concrete system architecture. + +Cover these dimensions explicitly: +1. memory architecture +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 and staged implementation +11. one genuinely non-obvious architectural insight + +Also include: component interfaces, data flow, failure modes, and the smallest useful prototype. Separate assumptions from claims. Keep the answer implementation-oriented and under 1600 words. +``` + +All eight usable systems received this same substantive prompt. Local models also received a short system instruction asking for independent, implementation-oriented reasoning and no consensus imitation. GPT-5.6 Sol received the same substantive dimensions and constraints in the chat context. No private or hidden system prompt is included here. + +## Collection note + +A DeepSeek-R1 8B run was also attempted locally. It returned reasoning content but no final-answer content through the Ollama response field used by the collection script, so it is documented as an unsuccessful collection incident rather than counted among the eight required outputs. diff --git a/research/ai_generated_agi_architectures/raw_outputs/chatgpt_gpt56_sol.md b/research/ai_generated_agi_architectures/raw_outputs/chatgpt_gpt56_sol.md new file mode 100644 index 0000000..15ab02b --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/chatgpt_gpt56_sol.md @@ -0,0 +1,51 @@ +# Raw output: GPT-5.6 Sol + +Collected: 2026-09-13 +Provider/system: ChatGPT / GPT-5.6 Sol +Prompt: shared_prompt.txt +Human edits: none beyond this metadata header. + +Practical AGI-oriented architecture proposal + +The system should be built as an evidence-gated cognitive runtime rather than as one monolithic agent. The central design goal is to preserve goals across time, reason over uncertain state, use tools, learn from outcomes, and improve bounded parts of itself while keeping actions auditable and reversible. + +1. Memory architecture +Use four memory classes. Working memory holds the active goal, current observations, hypotheses, constraints, unresolved questions, and the smallest evidence packet needed for the current decision. Episodic memory is an append-only log of actions, observations, approvals, failures, rewards, and outcomes. Semantic memory stores distilled claims with provenance, confidence, last validation time, and contradiction links. Procedural memory stores versioned skills, tool recipes, prompts, plans, tests, and policies. Consolidation should turn episodes into candidate semantic facts and procedures, but high-trust memory should require provenance and repeated evidence or explicit approval. + +2. Reasoning and planning loop +Use a receding-horizon loop: observe state; retrieve relevant memory; generate competing hypotheses; decompose the goal into candidate actions; estimate consequence, cost, reversibility, information gain, and failure risk; select one action; execute or request approval; verify the result against an expected evidence shape; update memory and the plan. The planner should optimize value of information as well as task utility, so uncertain plans are tested with cheap discriminating actions before large commitments. + +3. Learning and self-improvement +Do not permit unrestricted self-modification. Improvement follows a promotion pipeline: observed failure or evaluation result, patch proposal, sandbox execution, benchmark, adversarial test, scorecard, promotion, rollback capability. The system may propose changes to prompts, retrieval policies, model routing, skills, or code, but a candidate version must beat the current version on regression tests and remain inside resource and safety budgets. + +4. Tool use and action execution +Every tool should expose a typed contract with preconditions, input schema, expected evidence, side-effect class, and rollback path. Actions should be classified as read-only, reversible write, externally visible, financially consequential, security-sensitive, or irreversible. The planner should never implicitly inherit every permission. It receives capability tokens for only the actions permitted in the current task. A separate verifier should check external state after execution; a command returning success is not enough. + +5. World model +Represent the world as an event-sourced belief graph rather than one prose context. Nodes represent entities, resources, goals, obligations, system states, and hypotheses. Edges carry temporal validity, provenance, confidence, and causal claims. A predictive layer estimates probability of success, time and cost, likely blockers, and which observation would best distinguish competing hypotheses. Initially this can be symbolic plus model-generated predictions; learned predictors can replace repeated high-volume components later. + +6. Safety and governance +Put policy at the action boundary rather than relying only on a safety prompt. Use least-privilege capabilities, explicit side-effect classes, approval thresholds for money, legal, security, public, and irreversible actions, secret isolation, rate and budget limits, sandboxing for generated code, immutable audit logs, and automatic halt on evidence-policy violations. The system may propose broadly but should act narrowly. + +7. Evaluation and benchmarks +Maintain three layers. Unit cognition tests measure retrieval, contradiction handling, tool-schema correctness, and planning constraints. Scenario tests use multi-step tasks with hidden blockers, changing state, misleading observations, and partial failures. Longitudinal tests track verified goal completion, false-success rate, cost per solved task, recovery after failure, memory usefulness, policy violations, and regressions after self-improvement. The primary metric should be verified outcome quality rather than model confidence. + +8. Persistence and runtime +Use an append-only event log as the source of truth. Workers are restartable and reconstruct state from events plus snapshots. Suggested services are Goal Manager, Memory and Retrieval, Planner, World-State Estimator, Tool Router, Policy Gate, Executor, Verifier, Evaluator, and Improvement Manager. Long-running work should use a durable queue, and consequential actions should have idempotency keys so crashes cannot silently duplicate them. + +9. Multi-agent orchestration +Avoid permanent role-play agents unless they add measurable value. Spawn bounded specialists such as proposer, critic, verifier, or domain expert when diversity is useful. Specialists receive the same evidence packet but reason independently before seeing each other. A judge selects a falsifiable next action rather than averaging prose. Agents whose marginal contribution does not improve decisions should be removed. + +10. Engineering feasibility and staged implementation +Stage 1: event log, persistent goal object, three tool contracts, planner-executor-verifier loop, manual approvals. Stage 2: provenance-aware memory, contradiction tracking, replayable scenario tests. Stage 3: independent proposer, critic, and verifier routing plus resource-aware model selection. Stage 4: sandboxed self-improvement with regression gates and rollback. Stage 5: learned predictive components for repeated domains while retaining symbolic auditability. The smallest useful prototype needs one persistent goal, three tools, an event log, one approval gate, and a verifier that distinguishes claimed success from externally evidenced success. + +11. Non-obvious insight +Make falsification a first-class resource. Every important belief or plan should store not only supporting evidence but also contradicting evidence, the cheapest observation likely to disprove it, and a staleness deadline. Planning can then choose actions partly by expected reduction in decision-relevant uncertainty. This turns skepticism from a conversational role into an executable mechanism. + +Component flow +Observation -> Event Log -> Belief Update -> Retrieval Packet -> Candidate Plans -> Policy Gate -> Executor -> External Evidence -> Verifier -> Outcome Event -> Learning and Evaluation. + +Primary failure modes +Memory pollution is mitigated with provenance and confidence decay. Self-confirming plans are mitigated with independent verification and discriminating tests. Tool hallucination is mitigated with typed contracts. Goal drift is controlled through immutable parent objectives and explicit revision events. Consensus bias is reduced with blind independent proposals before debate. Runaway self-modification is constrained through sandboxing, regression gates, and rollback. False success is prevented by requiring external evidence before marking a goal complete. Context explosion is controlled through bounded evidence retrieval instead of replaying full history. + +Central principle: reason broadly, act narrowly, and let verified outcomes update the system. diff --git a/research/ai_generated_agi_architectures/raw_outputs/deepseek_r1_8b.md b/research/ai_generated_agi_architectures/raw_outputs/deepseek_r1_8b.md new file mode 100644 index 0000000..0e8fafa --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/deepseek_r1_8b.md @@ -0,0 +1,5 @@ +# Raw output: deepseek-r1:8b + +Collected: 2026-09-13T05:06:38.641702+00:00 + + diff --git a/research/ai_generated_agi_architectures/raw_outputs/gemma3_4b.md b/research/ai_generated_agi_architectures/raw_outputs/gemma3_4b.md new file mode 100644 index 0000000..11814f1 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/gemma3_4b.md @@ -0,0 +1,94 @@ +# Raw output: gemma3:4b + +Collected: 2026-09-13T05:13:15.101803+00:00 + +Okay, let’s tackle this. The goal isn’t to build AGI, but to build a system that *demonstrates* key AGI-like properties in a way that’s achievable and incrementally testable. We’ll call this project “Project Chimera.” + +**Core Assumption:** AGI isn’t a single monolithic entity. It’s a collection of increasingly sophisticated cognitive abilities, each built upon a foundation of simpler ones. We’re aiming for a system that exhibits emergent complexity through layered interaction, not a single “magic” algorithm. + +**1. Memory Architecture: Hierarchical Episodic & Semantic Networks** + +* **Episodic Memory:** Short-term, context-rich memories represented as event graphs. Each node is an event with associated sensory data, emotional valence, and temporal markers. We’ll use a graph database (Neo4j) for efficient indexing and retrieval. +* **Semantic Memory:** Organized as a knowledge graph, initially populated with a curated ontology (e.g., Wikidata) and expanded through experience. Nodes are concepts, edges are relationships. We’ll use a triple store (Apache Jena) for querying. +* **Interface:** Episodic memories are indexed by semantic tags and used to enrich semantic graph queries. Semantic memories provide context for episodic memory retrieval. + +**2. Reasoning & Planning Loop:** + +* **Goal Formulation:** Uses a “goal decomposition” module based on hierarchical planning algorithms (e.g., Hierarchical Task Network - HTN). The user provides a high-level goal, and the system breaks it down into sub-goals. +* **Situation Assessment:** Queries the world model (see below) and episodic memory to understand the current state. +* **Plan Generation:** Uses a planner (e.g., Fast Downward) to generate a plan of actions to achieve the current sub-goal, considering constraints and potential obstacles. +* **Execution Monitoring:** Continuously monitors the execution of the plan, updating episodic memory with new information and adjusting the plan as needed. + +**3. Learning/Self-Improvement: Reinforcement Learning with Model-Based Exploration** + +* **Core:** We’ll use a model-based RL algorithm (e.g., PILCO) to learn optimal policies. However, instead of directly learning a policy, we learn a *model* of the environment. +* **Model:** The world model isn’t just a static representation; it’s constantly updated by the RL agent’s interactions. This allows for simulation and planning without requiring explicit environmental knowledge. +* **Exploration:** Introduce a “curiosity-driven” exploration strategy – the agent is rewarded for visiting novel states or discovering new relationships within the world model. + +**4. Tool Use & Action Execution:** + +* **Action Library:** A library of pre-defined actions (e.g., “move forward,” “grasp object,” “query database”) mapped to robotic control interfaces (ROS). +* **Tool Selection:** The planner selects the appropriate actions from the library based on the current situation and the plan. +* **Execution:** Actions are translated into commands for the robot. + +**5. World Model/Representation Layer:** + +* **Hybrid:** Combines a probabilistic occupancy grid map (for spatial awareness) with the semantic knowledge graph. +* **Dynamic Updates:** The occupancy grid is constantly updated by sensor data (camera, LiDAR). The semantic graph is updated by the RL agent’s experiences and by external knowledge sources. + +**6. Safety & Governance Layer:** + +* **Constraint-Based Safety:** Hard-coded constraints on actions (e.g., “don’t collide with humans,” “don’t damage property”). +* **Anomaly Detection:** Monitors the system’s behavior for deviations from expected patterns. If an anomaly is detected, the system enters a safe state (e.g., halts execution). +* **Human Override:** A human operator can always override the system’s actions. + +**7. Evaluation & Benchmark Strategy:** + +* **Task-Based:** We’ll use a suite of increasingly complex tasks (e.g., navigating a virtual environment, assembling a virtual object, answering complex questions). +* **Metrics:** Success rate, plan execution time, efficiency (resource usage), and the ability to generalize to new tasks. +* **“Cognitive Turing Test”:** Can the system convincingly mimic human-like reasoning and problem-solving in a limited domain? + +**8. Persistence & Runtime Architecture:** + +* **Microservices:** Each component (memory, planner, RL agent, etc.) will be implemented as a separate microservice, communicating via REST APIs. +* **Kubernetes:** Container orchestration for deployment and scaling. +* **Time-Series Database:** For logging and monitoring system performance. + +**9. Multi-Agent/Orchestration:** + +* **Initial:** A single Chimera agent. +* **Future:** Expand to a team of specialized agents, each with a specific role (e.g., a “perception agent,” a “planning agent,” an “action agent”). These agents will communicate and coordinate their actions using a message passing system. + +**10. Engineering Feasibility & Staged Implementation:** + +* **Phase 1 (6 months):** Prototype – A simple robot navigating a small, simulated environment, performing basic object manipulation, and learning through reinforcement learning. Focus on core memory architecture and planning loop. +* **Phase 2 (9 months):** Expand – Integrate tool use, improve the world model, and add more complex tasks. +* **Phase 3 (12 months):** Refinement – Focus on safety, governance, and scaling the system. + +**11. Genuine Non-Obvious Insight: Episodic Memory as a ‘Contextual Prior’** + +The key insight is to treat episodic memory not just as a record of past events, but as a *dynamic contextual prior* for the planning process. The semantic graph isn’t just a repository of knowledge; it’s constantly being shaped by the experiences encoded in the episodic memory. This creates a feedback loop that allows the system to learn from its mistakes and adapt to changing environments. It’s not just remembering *what* happened, but remembering *how* it felt and the surrounding context. + + + +**Component Interfaces & Data Flow:** + +1. **Sensors -> World Model:** Raw sensor data (camera, LiDAR) is processed and integrated into the probabilistic occupancy grid and the semantic graph. +2. **Planner -> Action Library:** The planner selects actions from the action library. +3. **Action Library -> Robot Control:** Actions are translated into commands for the robot. +4. **RL Agent -> World Model:** The RL agent updates the world model based on its experiences. +5. **Memory Modules -> Planner:** Episodic and semantic memories provide context and constraints for the planning process. + +**Failure Modes:** + +* **World Model Drift:** The world model becomes increasingly inaccurate due to sensor noise or RL agent biases. +* **Plan Instability:** The planner generates unstable plans that lead to unexpected behavior. +* **Memory Corruption:** Errors in the episodic memory corrupt the system’s understanding of the world. + +**Smallest Useful Prototype:** + +A single robot (e.g., a Pepper robot) navigating a small, simulated kitchen environment, learning to pick up and place objects. This prototype would focus on the core components: episodic memory, semantic knowledge, a simple planner, and basic reinforcement learning. + + + +This architecture is a starting point. The real value lies in the iterative development and testing process. We’re not building AGI; we’re building a sophisticated system that can demonstrate the building blocks of intelligence, providing valuable insights into the nature of cognition. diff --git a/research/ai_generated_agi_architectures/raw_outputs/granite33_2b.md b/research/ai_generated_agi_architectures/raw_outputs/granite33_2b.md new file mode 100644 index 0000000..04e4d33 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/granite33_2b.md @@ -0,0 +1,63 @@ +# Raw output: granite3.3:2b + +Collected: 2026-09-13T05:37:15.243741+00:00 + +### AGI-Oriented Cognitive Architecture: "Neuro-Symbolic Hybrid" + +#### 1. Memory Architecture +- **Component**: Distributed Memory (Hierarchically Organized) + - Implementation: Use a combination of Hierarchical Temporal Memory (HTM) for spatiotemporal data and a relational database for symbolic data. + - Interface: Memory modules communicate with the Sensory Processing, Reasoning, and Action modules via standardized data formats (e.g., JSON, XML). + +#### 2. Reasoning and Planning Loop +- **Component**: Layered Reasoning Engine + - Implementation: This layer utilizes a combination of symbolic AI (for logic-based reasoning) and neural networks (for statistical/probabilistic reasoning). + - Data Flow: Sensory data feeds into the Sensory Processing module, which feeds into the Reasoning Engine. The Reasoning Engine generates plans and hypotheses, which are then validated by the World Model and passed on to the Planning Loop. + +#### 3. Learning or Self-Improvement Mechanism +- **Component**: Reinforcement Learning (RL) and Neural Architecture Search (NAS) + - Implementation: RL algorithms like Deep Q-Networks (DQN) will be used for policy optimization. NAS algorithms will optimize neural network architectures for better performance. + - Interface: The Learning Module communicates with the Memory, Sensory Processing, and Action Execution modules via shared data pipelines. + +#### 4. Tool Use and Action Execution +- **Component**: Actuator Control Module + - Implementation: This module interfaces directly with physical actuators (robots, drones, etc.) and receives commands from the Planning Loop. It also provides feedback to the Sensory Processing module for real-time adjustments. + +#### 5. World Model or Representation Layer +- **Component**: Dynamic World Model + - Implementation: A combination of hierarchical, probabilistic models (e.g., Bayesian networks) and deep learning-based representations (e.g., graph neural networks for relational data). + - Data Flow: Sensory data is processed by the Sensory Processing module and fed into the World Model, which in turn provides context to the Reasoning Engine. + +#### 6. Safety and Governance Layer +- **Component**: Ethics and Safety Oversight Module + - Implementation: This module uses rule-based systems, machine learning for anomaly detection, and human-in-the-loop oversight. It monitors the system's actions and decisions, ensuring adherence to ethical guidelines and safety protocols. + +#### 7. Evaluation and Benchmark Strategy +- **Component**: Continuous Evaluation and Benchmarking System + - Implementation: This system uses predefined metrics (e.g., accuracy, efficiency, safety) to evaluate the system's performance. It also incorporates human feedback for nuanced assessments. + +#### 8. Persistence and Runtime Architecture +- **Component**: Containerized Runtime Environment + - Implementation: Utilize containerization technologies (e.g., Docker, Kubernetes) for easy deployment, scaling, and maintenance. + +#### 9. Multi-Agent or Orchestration Design +- **Component**: Multi-Agent System (MAS) Framework + - Implementation: Implement a decentralized MAS where each AGI instance acts as an autonomous agent. A central coordination module manages inter-agent communication and task allocation. + +#### 10. Engineering Feasibility and Staged Implementation +- **Strategy**: Start with a minimal viable product (MVP) focusing on core components (Memory, Reasoning, Action). Gradually add layers (World Model, Learning, Safety) in iterations, ensuring each phase is thoroughly tested and validated. + +#### 11. Non-Obvious Architectural Insight +- **Insight**: "Neuro-Symbolic Hybrid" + - Claim: Combining the strengths of symbolic AI (logical reasoning, explainability) with neural networks (statistical learning, adaptability) can overcome individual limitations. This architecture allows for both high-level reasoning and low-level perception, mimicking human-like cognition. + +#### Failure Modes +- **Data Bias**: Sensory data or training data may contain biases, leading to skewed reasoning. +- **Overfitting**: Neural networks might overfit to training data, reducing generalization capabilities. +- **Safety Violations**: The system may make decisions that violate safety protocols due to inadequate safety mechanisms. + +#### Smallest Useful Prototype +- **Core Components**: Memory (HTM), Reasoning Engine (hybrid symbolic-neural), Actuator Control Module, Ethics Oversight Module, and a simple benchmarking system. +- **Task**: Performing a basic navigation task in a controlled environment (e.g., a virtual or physical maze). + +This architecture is designed for incremental implementation, ensuring each component can be tested and validated before adding complexity. It balances symbolic and connectionist approaches, aiming to create a robust, adaptable, and safe AGI. diff --git a/research/ai_generated_agi_architectures/raw_outputs/llama31_8b.md b/research/ai_generated_agi_architectures/raw_outputs/llama31_8b.md new file mode 100644 index 0000000..093eab1 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/llama31_8b.md @@ -0,0 +1,106 @@ +# Raw output: llama3.1:8b + +Collected: 2026-09-13T05:10:37.328997+00:00 + +**System Architecture:** + +The proposed architecture is called "Cerebro" (Spanish for "brain"). Cerebro is designed to be modular, incremental, and adaptable. It consists of the following components: + +1. **Memory Architecture:** Hierarchical Temporal Memory (HTM) [1] + * HTM is a cognitive architecture that models the brain's memory and attention mechanisms. + * It uses a hierarchical, sparse-distributed memory to represent and retrieve information. + * Cerebro's HTM module will be implemented using the Numenta HTM software [2]. +2. **Reasoning and Planning Loop:** Model-Based Reinforcement Learning (MBRL) [3] + * MBRL combines model-based planning with reinforcement learning to enable goal-directed behavior. + * Cerebro's MBRL module will use a probabilistic model of the world and a reward function to guide decision-making. +3. **Learning or Self-Improvement Mechanism:** Meta-Learning [4] + * Meta-learning enables the system to learn how to learn and adapt to new tasks and environments. + * Cerebro's meta-learning module will use a combination of model-based and model-free reinforcement learning to learn from experience. +4. **Tool Use and Action Execution:** Robot Operating System (ROS) [5] + * ROS provides a standardized framework for building robot applications. + * Cerebro's ROS module will enable the system to interact with and control physical robots. +5. **World Model or Representation Layer:** Probabilistic Graphical Model (PGM) [6] + * PGMs provide a probabilistic representation of the world, enabling the system to reason and make predictions. + * Cerebro's PGM module will be implemented using the PyMC3 software [7]. +6. **Safety and Governance Layer:** Value Alignment [8] + * Value alignment ensures that the system's goals and values are aligned with human values. + * Cerebro's value alignment module will use a combination of formal methods and machine learning to ensure value alignment. +7. **Evaluation and Benchmark Strategy:** Cerebro will use a combination of metrics, such as: + * Task completion rate + * Reward function value + * System-wide energy consumption +8. **Persistence and Runtime Architecture:** Cerebro will use a combination of: + * Distributed file systems (e.g., HDFS) for data storage + * Containerization (e.g., Docker) for system isolation + * Cloud-based services (e.g., AWS Lambda) for scalability +9. **Multi-Agent or Orchestration Design:** Cerebro will use a hierarchical, distributed architecture, with each module interacting with others through well-defined interfaces. +10. **Engineering Feasibility and Staged Implementation:** Cerebro will be implemented in a series of incremental stages, with each stage building on the previous one. + +**Component Interfaces:** + +* HTM module: + + Inputs: sensor data, task goals + + Outputs: attention maps, memory updates +* MBRL module: + + Inputs: HTM outputs, reward function + + Outputs: action selections, policy updates +* Meta-learning module: + + Inputs: MBRL outputs, experience data + + Outputs: policy updates, meta-parameters +* ROS module: + + Inputs: action selections, sensor data + + Outputs: motor commands, system feedback +* PGM module: + + Inputs: sensor data, HTM outputs + + Outputs: probabilistic world model updates +* Value alignment module: + + Inputs: PGM outputs, human values + + Outputs: value-aligned goals, policy updates + +**Data Flow:** + +* Sensor data → HTM module → MBRL module → ROS module → Motor commands +* HTM outputs → MBRL module → PGM module → PGM updates +* MBRL outputs → Meta-learning module → Policy updates +* PGM outputs → Value alignment module → Value-aligned goals + +**Failure Modes:** + +* HTM module: failure to learn or retrieve information +* MBRL module: failure to plan or execute actions +* Meta-learning module: failure to adapt to new tasks or environments +* ROS module: failure to interact with or control physical robots +* PGM module: failure to represent or update the world model +* Value alignment module: failure to align the system's goals with human values + +**Smallest Useful Prototype:** + +The smallest useful prototype will consist of the HTM module, MBRL module, and ROS module. This prototype will enable the system to learn and execute simple tasks, such as navigating a physical environment. + +**Assumptions:** + +* The system will operate in a controlled environment with well-defined tasks and goals. +* The system will have access to a large amount of training data and computational resources. +* The system will be designed with safety and value alignment in mind. + +**Non-Obvious Architectural Insight:** + +The use of a hierarchical, distributed architecture, with each module interacting with others through well-defined interfaces, enables the system to adapt to changing environments and tasks. This architecture also enables the system to learn and improve over time, through the use of meta-learning and value alignment. + +References: + +[1] Numenta HTM software. (n.d.). Retrieved from + +[2] Numenta. (n.d.). Hierarchical Temporal Memory. Retrieved from + +[3] Model-Based Reinforcement Learning. (n.d.). Retrieved from + +[4] Meta-Learning. (n.d.). Retrieved from + +[5] Robot Operating System. (n.d.). Retrieved from + +[6] Probabilistic Graphical Model. (n.d.). Retrieved from + +[7] PyMC3 software. (n.d.). Retrieved from + +[8] Value Alignment. (n.d.). Retrieved from diff --git a/research/ai_generated_agi_architectures/raw_outputs/mistral_7b.md b/research/ai_generated_agi_architectures/raw_outputs/mistral_7b.md new file mode 100644 index 0000000..eac015d --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/mistral_7b.md @@ -0,0 +1,49 @@ +# Raw output: mistral:7b + +Collected: 2026-09-13T05:19:43.799532+00:00 + +Title: Practical Cognitive Architecture for AGI: The Modular Adaptive Cognitive System (MACS) + +1. Memory Architecture: MACS employs a Hierarchical Dynamic Memory Network (HDNM) for flexible, context-dependent memory management. HDNM consists of an episodic buffer for short-term memory, a semantic buffer for long-term memory, and a working memory for active information processing. Each buffer is further divided into multiple sub-buffers for different granularities of information. + +2. Reasoning and Planning Loop: The Reasoning and Planning (R&P) module utilizes a Recursive Cortical Hierarchy (RCH) for feature extraction, attention mechanisms, and hierarchical abstraction. RCH is inspired by the structure of the cerebral cortex and enables the system to process and integrate information at multiple levels of abstraction. The R&P loop consists of a forward pass for reasoning (bottom-up processing) and a backward pass for planning (top-down processing). + +3. Learning or Self-Improvement Mechanism: MACS incorporates a combination of supervised, unsupervised, and reinforcement learning algorithms to adapt to new information and improve its performance. The learning mechanisms are integrated into the RCH and HDNM, allowing the system to learn from its experiences and continuously improve its knowledge and skills. + +4. Tool Use and Action Execution: The Tool Use and Action Execution (TUA) module enables the system to interact with the environment and use tools to achieve its goals. TUA consists of a perception module for sensory input processing, an action selection module for determining the appropriate actions, and an effector module for executing actions. + +5. World Model or Representation Layer: The World Model (WM) layer maintains a dynamic, probabilistic representation of the environment to support decision-making and planning. The WM is updated through sensor data, learned patterns, and predictions based on the system's current knowledge and goals. + +6. Safety and Governance Layer: The Safety and Governance (S&G) layer ensures that the system operates safely and ethically. S&G employs a combination of intrinsic and extrinsic safety mechanisms, such as reward shaping, safe exploration, and adversarial training, to prevent the system from taking harmful actions or developing harmful biases. + +7. Evaluation and Benchmark Strategy: MACS is evaluated using a variety of benchmarks, including standard AI benchmarks, cognitive science tasks, and real-world problem-solving scenarios. The system's performance is continuously monitored, and adjustments are made to improve its capabilities and address any identified issues. + +8. Persistence and Runtime Architecture: MACS is designed to be modular, scalable, and distributed, allowing for efficient runtime operation and easy persistence of the system's knowledge and skills. The system employs a hybrid cloud-edge architecture, with the core cognitive components running on high-performance servers and the perception and action modules running on edge devices. + +9. Multi-agent or Orchestration Design: MACS supports multi-agent operation through a decentralized control architecture, enabling multiple AGI agents to collaborate, compete, or coordinate their actions as needed. Each agent maintains its own world model, reasoning and planning loop, and learning mechanisms, while a global orchestration layer manages communication, coordination, and conflict resolution among the agents. + +10. Engineering Feasibility and Staged Implementation: MACS is designed to be implementable by a small engineering team, with each component being developed and integrated incrementally. The system is initially implemented as a proof-of-concept prototype, which is then gradually expanded and refined over time to achieve increasingly advanced cognitive capabilities. + +11. One Genuinely Non-Obvious Architectural Insight: MACS employs a hybrid learning approach, combining supervised, unsupervised, and reinforcement learning algorithms within a single cognitive architecture. This allows the system to learn from a diverse range of data sources and adapt to a wide variety of tasks, while maintaining a consistent, unified representation of the world. + +Component Interfaces: +- HDNM: Episodic Buffer, Semantic Buffer, Working Memory +- RCH: Feature Extraction, Attention Mechanisms, Hierarchical Abstraction +- R&P Loop: Forward Pass, Backward Pass +- TUA: Perception Module, Action Selection Module, Effector Module +- WM: World Representation +- S&G: Intrinsic Safety Mechanisms, Extrinsic Safety Mechanisms + +Data Flow: +1. Sensory data is processed by the perception module and passed to the working memory. +2. The working memory activates relevant sub-buffers and passes information to the RCH for feature extraction, attention, and abstraction. +3. The RCH outputs abstract representations, which are used for reasoning, planning, and tool use decisions. +4. The action selection module determines the appropriate action based on the current goals and environment state. +5. The effector module executes the selected action, and the resulting feedback is used to update the world model and learning mechanisms. + +Failure Modes: +- Memory Overload: If the system's memory buffers become overloaded, the system may experience a decrease in performance or an inability to learn new information. +- Learning Instability: If the learning mechanisms are not properly regulated, the system may develop harmful biases or oscillate between conflicting beliefs. +- Safety Violations: If the safety and governance mechanisms are ineffective, the system may take harmful actions or make decisions that conflict with ethical guidelines. + +The smallest useful prototype of MACS would consist of a simple perception module, working memory, RCH, and a basic action selection module, enabling the system to learn simple patterns and perform basic tasks. This prototype could be used to validate the system's architecture and learning mechanisms before expanding the system's capabilities and integrating additional components. diff --git a/research/ai_generated_agi_architectures/raw_outputs/phi4_mini.md b/research/ai_generated_agi_architectures/raw_outputs/phi4_mini.md new file mode 100644 index 0000000..23fb26e --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/phi4_mini.md @@ -0,0 +1,153 @@ +# Raw output: phi4-mini + +Collected: 2026-09-13T05:22:48.205501+00:00 + +# AGI-Oriented Cognitive Architecture Design + +## Overview + +We propose an AGI-oriented cognitive architecture designed to be incrementally implemented by a small engineering team. The architecture integrates a memory system, reasoning and planning loop, learning mechanisms, tool use, world model representation, safety and governance, evaluation, persistence, multi-agent orchestration, and a unique insight into distributed cognition. + +## 1. Memory Architecture + +**Assumption**: Human memory is a multi-faceted system with different storage and retrieval mechanisms. + +**Claim**: Our architecture will mimic this by implementing a hierarchical memory system with short-term, long-term, and working memory components. + +**Implementation**: We will use a combination of in-memory databases and neural networks to simulate these different types of memory. Short-term memory will use an LSTM network for immediate data processing, long-term memory will store experiences in a NoSQL database, and working memory will be managed by a key-value store for quick access. + +**Failure Mode**: Overfitting or underfitting in neural networks could lead to incorrect memory retrieval. + +**Prototype**: A simple LSTM network for short-term memory tasks like language translation. + +## 2. Reasoning and Planning Loop + +**Assumption**: AGI should reason and plan like a human, with an ability to understand context and abstract concepts. + +**Claim**: We will implement a reasoning loop using a combination of symbolic AI for rule-based reasoning and neural networks for probabilistic reasoning. + +**Implementation**: A rule-based system will handle explicit knowledge, while a neural network will manage implicit knowledge and predictions. A planning loop will be implemented using a combination of search algorithms for planning and reinforcement learning for adaptation. + +**Failure Mode**: Conflicts between rule-based and neural networks could lead to incorrect decisions. + +**Prototype**: A simple game-playing agent that uses both systems to make decisions. + +## 3. Learning or Self-Improvement Mechanism + +**Assumption**: AGI should learn from experience and improve over time. + +**Claim**: We will implement a combination of supervised learning, unsupervised learning, and reinforcement learning for self-improvement. + +**Implementation**: Supervised learning will be used for initial training, unsupervised learning for pattern recognition, and reinforcement learning for adaptation to new environments. + +**Failure Mode**: Overfitting or underfitting in learning models could lead to poor performance. + +**Prototype**: A simple game-playing agent that improves over time. + +## 4. Tool Use and Action Execution + +**Assumption**: AGI should be able to use tools and execute actions in the real world. + +**Claim**: We will implement a tool use and action execution system using a combination of computer vision, natural language processing, and robotics. + +**Implementation**: Computer vision will be used to identify objects and understand the environment, natural language processing will be used to interpret commands, and robotics will be used to execute actions. + +**Failure Mode**: Misinterpretation of commands or incorrect object identification could lead to failure. + +**Prototype**: A simple robotic arm that can execute simple tasks like picking up and moving objects. + +## 5. World Model or Representation Layer + +**Assumption**: AGI needs a representation of the world to understand and interact with it. + +**Claim**: We will implement a world model using a combination of computer vision, natural language processing, and sensor data. + +**Implementation**: Computer vision will be used to identify objects and understand the environment, natural language processing will be used to interpret commands, and sensor data will be used to understand the physical world. + +**Failure Mode**: Misinterpretation of data could lead to an incorrect world model. + +**Prototype**: A simple robotic arm that can identify objects and understand its environment. + +## 6. Safety and Governance Layer + +**Assumption**: AGI should be safe and governed by human values and ethics. + +**Claim**: We will implement a safety and governance layer using a combination of rule-based systems and human oversight. + +**Implementation**: Rule-based systems will handle explicit ethical guidelines, while human oversight will handle ambiguous cases. + +**Failure Mode**: Misinterpretation of ethical guidelines could lead to unsafe or unethical behavior. + +**Prototype**: A simple game-playing agent that follows ethical guidelines. + +## 7. Evaluation and Benchmark Strategy + +**Assumption**: AGI should be evaluated and benchmarked against human performance. + +**Claim**: We will evaluate and benchmark our AGI against human performance in various tasks like language translation, game playing, and object recognition. + +**Implementation**: We will use standard benchmarks like GLUE for language translation, AlphaGo for game playing, and COCO for object recognition. + +**Failure Mode**: Poor performance compared to humans could indicate flaws in the architecture. + +**Prototype**: A simple game-playing agent that competes with humans in a simple game. + +## 8. Persistence and Runtime Architecture + +**Assumption**: AGI should be able to persist its state and learn from previous experiences. + +**Claim**: We will implement a persistence layer using a combination of databases and neural networks. + +**Implementation**: Databases will be used to store experiences, while neural networks will be used to learn from these experiences. + +**Failure Mode**: Loss of data could lead to loss of knowledge. + +**Prototype**: A simple game-playing agent that learns from previous games. + +## 9. Multi-Agent or Orchestration Design + +**Assumption**: AGI should be able to work with other agents. + +**Claim**: We will implement a multi-agent design using a combination of message passing and shared memory. + +**Implementation**: Agents will communicate using a message passing system, while shared memory will be used to store and access common data. + +**Failure Mode**: Conflicts between agents could lead to incorrect decisions. + +**Prototype**: A simple game-playing agent that can work with other agents. + +## 10. Engineering Feasibility and Staged Implementation + +**Assumption**: AGI should be feasible to implement incrementally. + +**Claim**: We will implement our architecture in stages, starting with simple tasks and gradually increasing complexity. + +**Implementation**: The first stage will involve implementing a simple game-playing agent. Subsequent stages will add complexity like object recognition and tool use. + +**Prototype**: A simple game-playing agent that can play a simple game. + +## 11. One Genuinely Non-Obvious Architectural Insight + +**Assumption**: AGI should be able to learn from distributed cognition. + +**Claim**: We will implement distributed cognition by allowing our AGI to learn from other agents. + +**Implementation**: Our AGI will be able to share experiences and learn from other agents using a combination of message passing and shared memory. + +**Prototype**: A simple game-playing agent that can learn from other agents. + +## Component Interfaces, Data Flow, and Failure Modes + +Our architecture will consist of several components, including a memory system, reasoning and planning loop, learning mechanism, tool use and action execution system, world model, safety and governance layer, evaluation and benchmark strategy, persistence and runtime architecture, and multi-agent design. These components will communicate using a combination of message passing and shared memory. + +Data will flow from the world model to the reasoning and planning loop, which will then use the memory system to retrieve information. The planning loop will then use the tool use and action execution system to execute actions in the real world. The learning mechanism will use experiences from the world model and memory system to improve over time. + +Failure modes could include misinterpretation of data, conflicts between different components, and loss of data. + +## Smallest Useful Prototype + +Our smallest useful prototype will be a simple game-playing agent that can learn from experience and improve over time. This agent will use a combination of LSTM networks, rule-based systems, and reinforcement learning to play a simple game. It will also have a simple world model and the ability to learn from other agents. + +## Conclusion + +Our proposed AGI-oriented cognitive architecture is designed to be incrementally implemented by a small engineering team. It integrates a variety of components, including a memory system, reasoning and planning loop, learning mechanism, tool use and action execution system, world model, safety and governance layer, evaluation and benchmark strategy, persistence and runtime architecture, and multi-agent design. It is designed to be safe, governed by human values and ethics, and capable of learning from experience and other agents. It is also designed to be evaluated and benchmarked against human performance. We believe that this architecture is a practical and feasible approach to implementing AGI. diff --git a/research/ai_generated_agi_architectures/raw_outputs/qwen25_coder_7b.md b/research/ai_generated_agi_architectures/raw_outputs/qwen25_coder_7b.md new file mode 100644 index 0000000..ce2abdc --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/qwen25_coder_7b.md @@ -0,0 +1,180 @@ +# Raw output: qwen2.5-coder:7b + +Collected: 2026-09-13T05:14:51.418464+00:00 + +Designing a practical AGI-oriented cognitive architecture that a small engineering team could implement incrementally is a challenging but achievable goal. This architecture will be designed with the following dimensions in mind: + +1. **Memory Architecture**: A hierarchical memory system with short-term, working memory, long-term memory, and a meta-memory to manage and organize memory. +2. **Reasoning and Planning Loop**: An iterative loop that combines sensory input, memory retrieval, and reasoning to generate plans. +3. **Learning or Self-Improvement Mechanism**: A system for acquiring new knowledge, skills, and strategies through reinforcement learning and meta-learning. +4. **Tool Use and Action Execution**: A modular system for integrating and executing external tools and actions. +5. **World Model or Representation Layer**: A high-level representation of the environment, including models of objects, interactions, and potential outcomes. +6. **Safety and Governance Layer**: Mechanisms for ensuring safe and ethical behavior, including fail-safes and ethical guidelines. +7. **Evaluation and Benchmark Strategy**: A system for evaluating performance and making improvements. +8. **Persistence and Runtime Architecture**: A system for saving and loading state, ensuring continuity. +9. **Multi-Agent or Orchestration Design**: A system for coordinating multiple agents or components. +10. **Engineering Feasibility and Staged Implementation**: A plan for incrementally building the system. +11. **One Genuine Non-Obvious Architectural Insight**: A novel approach to handling uncertainty and ambiguity. + +### Memory Architecture + +**Component Interfaces**: +- Short-Term Memory (STM): Stores recent sensory inputs and intermediate results. +- Working Memory (WM): Holds current task context and intermediate results. +- Long-Term Memory (LTM): Stores learned knowledge and skills. +- Meta-Memory (MM): Manages memory, prioritizes retrieval, and organizes LTM. + +**Data Flow**: +- STM receives sensory inputs and stores them temporarily. +- WM retrieves relevant information from STM and LTM to perform tasks. +- LTM stores new information and updates existing knowledge. +- MM monitors memory usage, prioritizes retrieval, and organizes LTM. + +**Failure Modes**: +- STM overflow: Handle by offloading to WM or LTM. +- WM overload: Prioritize and offload tasks to WM or LTM. +- LTM corruption: Regular backups and error-checking mechanisms. +- MM failure: Implement fail-safes and fallback strategies. + +### Reasoning and Planning Loop + +**Component Interfaces**: +- Sensory Input Module: Receives and processes external data. +- Memory Retrieval Module: Retrieves relevant information from memory. +- Reasoning Module: Combines sensory input and memory to generate hypotheses. +- Planning Module: Generates and refines plans based on hypotheses. +- Execution Module: Executes plans and monitors outcomes. + +**Data Flow**: +- Sensory Input Module feeds data to Memory Retrieval Module. +- Memory Retrieval Module feeds data to Reasoning Module. +- Reasoning Module generates hypotheses and feeds them to Planning Module. +- Planning Module generates and refines plans and feeds them to Execution Module. +- Execution Module executes plans and feeds outcomes back to Reasoning Module. + +**Failure Modes**: +- Hypothesis generation failure: Implement fallback strategies and error-checking mechanisms. +- Planning failure: Implement fail-safes and fallback strategies. +- Execution failure: Implement fail-safes and fallback strategies. + +### Learning or Self-Improvement Mechanism + +**Component Interfaces**: +- Reinforcement Learning Module: Learns from outcomes and adjusts plans. +- Meta-Learning Module: Improves learning algorithms and strategies. + +**Data Flow**: +- Reinforcement Learning Module receives outcomes and adjusts plans. +- Meta-Learning Module receives information on learning effectiveness and adjusts algorithms. + +**Failure Modes**: +- Learning failure: Implement fallback strategies and error-checking mechanisms. +- Meta-learning failure: Implement fail-safes and fallback strategies. + +### Tool Use and Action Execution + +**Component Interfaces**: +- Tool Integration Module: Integrates external tools and actions. +- Action Execution Module: Executes actions and monitors outcomes. + +**Data Flow**: +- Tool Integration Module feeds tools and actions to Action Execution Module. +- Action Execution Module executes actions and feeds outcomes back to Reasoning Module. + +**Failure Modes**: +- Tool integration failure: Implement fallback strategies and error-checking mechanisms. +- Action execution failure: Implement fail-safes and fallback strategies. + +### World Model or Representation Layer + +**Component Interfaces**: +- Environment Modeling Module: Represents the environment. +- Prediction Module: Predicts outcomes based on current state. +- Decision Module: Makes decisions based on predictions. + +**Data Flow**: +- Environment Modeling Module feeds the environment to Prediction Module. +- Prediction Module predicts outcomes and feeds them to Decision Module. +- Decision Module makes decisions and feeds them to Execution Module. + +**Failure Modes**: +- Environment modeling failure: Implement fallback strategies and error-checking mechanisms. +- Prediction failure: Implement fail-safes and fallback strategies. + +### Safety and Governance Layer + +**Component Interfaces**: +- Safety Module: Ensures safe behavior. +- Governance Module: Ensures ethical behavior. + +**Data Flow**: +- Safety Module checks for unsafe behavior and implements fail-safes. +- Governance Module checks for ethical behavior and implements guidelines. + +**Failure Modes**: +- Safety failure: Implement fail-safes and fallback strategies. +- Governance failure: Implement fallback strategies and error-checking mechanisms. + +### Evaluation and Benchmark Strategy + +**Component Interfaces**: +- Performance Evaluation Module: Evaluates performance. +- Improvement Module: Identifies areas for improvement. + +**Data Flow**: +- Performance Evaluation Module evaluates performance and feeds results to Improvement Module. +- Improvement Module identifies areas for improvement and feeds results back to Reasoning Module. + +**Failure Modes**: +- Performance evaluation failure: Implement fallback strategies and error-checking mechanisms. +- Improvement failure: Implement fallback strategies and error-checking mechanisms. + +### Persistence and Runtime Architecture + +**Component Interfaces**: +- State Persistence Module: Saves and loads state. +- Runtime Management Module: Manages runtime environment. + +**Data Flow**: +- State Persistence Module saves state and loads state as needed. +- Runtime Management Module manages runtime environment and handles failures. + +**Failure Modes**: +- State persistence failure: Implement fallback strategies and error-checking mechanisms. +- Runtime management failure: Implement fallback strategies and error-checking mechanisms. + +### Multi-Agent or Orchestration Design + +**Component Interfaces**: +- Agent Coordination Module: Coordinates multiple agents. +- Communication Module: Facilitates communication between agents. + +**Data Flow**: +- Agent Coordination Module coordinates agents and feeds results to Communication Module. +- Communication Module facilitates communication between agents and feeds results back to Agent Coordination Module. + +**Failure Modes**: +- Agent coordination failure: Implement fallback strategies and error-checking mechanisms. +- Communication failure: Implement fallback strategies and error-checking mechanisms. + +### Engineering Feasibility and Staged Implementation + +1. **Short-Term Memory and Working Memory**: Implement basic memory systems. +2. **Reasoning and Planning Loop**: Implement basic reasoning and planning. +3. **Learning Mechanism**: Implement basic reinforcement learning. +4. **Tool Integration and Action Execution**: Integrate basic tools and actions. +5. **World Model**: Implement a simple environment model. +6. **Safety and Governance**: Implement basic safety and governance mechanisms. +7. **Performance Evaluation and Improvement**: Implement basic evaluation and improvement mechanisms. +8. **State Persistence and Runtime Management**: Implement basic state persistence and runtime management. +9. **Multi-Agent Coordination**: Implement basic agent coordination. + +### One Genuine Non-Obvious Architectural Insight + +**Insight**: **Hierarchical Uncertainty Management**: Use a hierarchical approach to manage uncertainty and ambiguity, where higher levels of the hierarchy handle more complex uncertainties and lower levels handle simpler uncertainties. This allows for a more efficient and effective handling of complex environments. + +### Smallest Useful Prototype + +A small useful prototype could be a simple agent that learns to navigate a 2D grid environment using basic reinforcement learning and a simple memory system. The agent should be able to learn to navigate the environment, avoid obstacles, and find the goal. The prototype should include basic safety and governance mechanisms to ensure safe behavior. + +This prototype will serve as a foundation for incrementally building a more complex AGI system. diff --git a/research/ai_generated_agi_architectures/raw_outputs/qwen3_8b.md b/research/ai_generated_agi_architectures/raw_outputs/qwen3_8b.md new file mode 100644 index 0000000..2a233ee --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/qwen3_8b.md @@ -0,0 +1,59 @@ +# Raw output: qwen3:8b + +Collected: 2026-09-13T05:01:28.148243+00:00 + +### Memory Architecture: Structured, Hierarchical, and Scalable + +A practical AGI-oriented cognitive architecture must prioritize a robust memory system that supports efficient information storage, retrieval, and organization. The memory architecture should be structured, hierarchical, and scalable to accommodate both short-term and long-term data. The system can be divided into three primary memory layers: **short-term memory (STM)**, **medium-term memory (MTM)**, and **long-term memory (LTM)**. + +Short-term memory serves as a working buffer for immediate tasks, holding up to 10–15 items of information for a few seconds. It is implemented as a dynamic, in-memory data structure, such, as a priority queue or a key-value store with time-based expiration. Medium-term memory acts as a temporary storage for intermediate results, learning progress, and task-specific knowledge. It is implemented as a persistent, indexed database that allows for efficient querying and updates. Long-term memory stores general knowledge, learned patterns, and historical data, and is implemented as a distributed, versioned database with a robust indexing system. + +To enhance retrieval efficiency, the memory architecture should support semantic indexing, allowing the system to query for concepts rather than exact keywords. This can be achieved through vector embedding techniques, where each concept is represented as a high-dimensional vector, enabling similarity-based searches. Additionally, the system should support memory chunking, where large pieces of information are divided into smaller, manageable units for better retention and retrieval. + +The memory architecture must also be modular, allowing for incremental expansion and integration with other components of the cognitive architecture. This modularity ensures that the system can scale in complexity while maintaining performance and efficiency. By structuring memory into distinct layers, the architecture enables the system to handle both immediate tasks and long-term learning, forming a foundation for the cognitive processes that follow. + +### Reasoning and Planning Loop: Iterative, Goal-Oriented, and Adaptive + +The reasoning and planning loop is the core decision-making mechanism of the cognitive architecture, enabling the system to process information, generate plans, and execute actions in a structured and adaptive manner. This loop operates iteratively, continuously refining its approach based on new data and feedback. The system begins by receiving input from the world model or external sensors, which is then processed through the memory architecture to extract relevant information. + +The reasoning phase involves evaluating the current state of knowledge, identifying gaps, and formulating hypotheses or strategies to address them. This is followed by the planning phase, where the system generates a sequence of actions or decisions that align with its goals. The planning process is guided by a combination of logical reasoning, probabilistic inference, and learned heuristics. The system may use symbolic reasoning for structured tasks, such as mathematical problem-solving, while probabilistic reasoning is employed for uncertain or ambiguous situations, such as natural language understanding or decision-making under uncertainty. + +Once a plan is generated, the system proceeds to the execution phase, where it interacts with the environment or executes internal computations. During execution, the system continuously monitors its progress and compares the actual outcomes with the expected results. If discrepancies arise, the system initiates a feedback loop, revisiting earlier stages of the reasoning and planning process to refine its approach. This iterative process ensures that the system can adapt to changing conditions, optimize its strategies, and improve its performance over time. + +The reasoning and planning loop is designed to be modular and extensible, allowing for the integration of various reasoning techniques and planning algorithms. This flexibility enables the system to handle complex tasks, from simple problem-solving to high-level strategic planning, while maintaining efficiency and adaptability. + +### Learning and Self-Improvement Mechanism: Incremental, Adaptive, and Feedback-Driven + +A critical component of the AGI-oriented cognitive architecture is the learning and self-improvement mechanism, which enables the system to acquire new knowledge, refine its strategies, and adapt to changing environments. This mechanism operates through a combination of **reinforcement learning**, **supervised learning**, and **self-supervised learning**, allowing the system to improve its performance continuously. + +The learning process begins with **reinforcement learning**, where the system evaluates its actions based on feedback from the environment. This feedback is used to update its internal reward model, guiding future decisions toward more optimal outcomes. To enhance learning efficiency, the system incorporates **exploration strategies**, such as epsilon-greedy or Bayesian optimization, to balance between exploiting known effective actions and exploring new possibilities. + +In addition to reinforcement learning, the system employs **supervised learning** to refine its understanding of structured tasks. This involves training on labeled datasets to improve its ability to recognize patterns, classify information, and make accurate predictions. The system uses **online learning**, where it continuously updates its models as new data becomes available, ensuring that its knowledge remains current and relevant. + +To further enhance adaptability, the system integrates **self-supervised learning**, where it generates its own training data by analyzing patterns within its memory and reasoning processes. This allows the system to learn from its own experiences without requiring external supervision, making it more self-sufficient and efficient. + +The learning and self-improvement mechanism is designed to be **modular and incremental**, enabling the system to gradually expand its capabilities. By combining reinforcement, supervised, and self-supervised learning, the architecture supports continuous improvement, making it well-suited for complex, dynamic environments. + +### Tool Use and Action Execution: Modular, Extensible, and Context-Aware + +The tool use and action execution component is responsible for translating the system's internal plans and reasoning into tangible actions, whether through direct interaction with the environment or the execution of internal computations. This component must be **modular**, **extensible**, and **context-aware**, allowing the system to interact with a wide range of tools and environments while adapting its behavior based on the current context. + +The system is designed to use a **tool interface** that abstracts the underlying implementation of each tool, enabling seamless integration with external APIs, software libraries, and hardware interfaces. This interface allows the system to issue commands, receive feedback, and update its internal knowledge based on the results of its actions. The tool interface is structured as a **pluggable architecture**, where each tool is implemented as a separate module that can be dynamically loaded or replaced. This modularity ensures that the system can adapt to new tools or environments without requiring a complete redesign of its architecture. + +To support context-aware behavior, the system employs a **context manager** that tracks the current state of the environment, the available tools, and the system's goals. This manager ensures that the system selects the most appropriate tool for each task, taking into account factors such as efficiency, reliability, and resource constraints. For example, if the system is solving a mathematical problem, it may use a dedicated math solver tool, whereas if it is interacting with a user, it may use a natural language processing tool. + +The tool use and action execution component is also designed to be **incremental**, allowing the system to gradually expand its capabilities by integrating new tools and improving its existing ones. This flexibility ensures that the system can evolve over time, adapting to new challenges and opportunities in its environment. + +### World Model or Representation Layer: Dynamic, Probabilistic, and Contextual + +The world model or representation layer is the foundation of the cognitive architecture, providing the system with a dynamic, probabilistic, and contextual understanding of its environment. This layer enables the system to process sensory inputs, maintain a coherent representation of the world, and make informed decisions based on its current state of knowledge. + +The world model is implemented as a **probabilistic knowledge graph**, where entities, events, and relationships are represented as nodes and edges, with associated probabilities reflecting the uncertainty of their existence or occurrence. This probabilistic structure allows the system to reason about the likelihood of different outcomes, enabling it to make decisions under uncertainty. The model is continuously updated based on new observations, ensuring that the system's understanding of the world remains current and accurate. + +To enhance contextual awareness, the system incorporates **contextual embeddings**, which allow it to represent the meaning of words, phrases, and events in relation to their surrounding context. This enables the system to understand and interpret information more accurately, especially in ambiguous or complex situations. The contextual embeddings are generated using **transformer-based models**, which provide a flexible and scalable approach to representing meaning in natural language. + +The world model is also designed to be **dynamic**, allowing the system to adapt to changing environments and evolving knowledge. This is achieved through **active learning**, where the system identifies gaps in its understanding and actively seeks out new information to fill those gaps. This dynamic and probabilistic approach ensures that the system can effectively navigate complex and uncertain environments, forming a robust foundation for the cognitive processes that follow. + +### Safety and Governance Layer: Structured, Transparent, and Accountable + +A critical diff --git a/research/ai_generated_agi_architectures/shared_prompt.txt b/research/ai_generated_agi_architectures/shared_prompt.txt new file mode 100644 index 0000000..37dd6f7 --- /dev/null +++ b/research/ai_generated_agi_architectures/shared_prompt.txt @@ -0,0 +1,16 @@ +Design a practical AGI-oriented cognitive architecture that a small engineering team could implement incrementally. Do not describe a vague superintelligence. Give a concrete system architecture. + +Cover these dimensions explicitly: +1. memory architecture +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 and staged implementation +11. one genuinely non-obvious architectural insight + +Also include: component interfaces, data flow, failure modes, and the smallest useful prototype. Separate assumptions from claims. Keep the answer implementation-oriented and under 1600 words. diff --git a/research/ai_generated_agi_architectures/sources.md b/research/ai_generated_agi_architectures/sources.md new file mode 100644 index 0000000..92133ac --- /dev/null +++ b/research/ai_generated_agi_architectures/sources.md @@ -0,0 +1,22 @@ +# Sources and provenance + +Collection date: 2026-09-13. + +| System | Model identifier | Provider/tool | Access | Raw file | Human edits | +|---|---|---|---|---|---| +| Qwen3 8B | `qwen3:8b` | Ollama local | Local model through Ollama on submitter-owned Mac | `raw_outputs/qwen3_8b.md` | Metadata header only; substantive proposal preserved as generated. | +| Llama 3.1 8B | `llama3.1:8b` | Ollama local | Local model through Ollama on submitter-owned Mac | `raw_outputs/llama31_8b.md` | Metadata header only; substantive proposal preserved as generated. | +| Gemma 3 4B | `gemma3:4b` | Ollama local | Local model through Ollama on submitter-owned Mac | `raw_outputs/gemma3_4b.md` | Metadata header only; substantive proposal preserved as generated. | +| Qwen2.5 Coder 7B | `qwen2.5-coder:7b` | Ollama local | Local model through Ollama on submitter-owned Mac | `raw_outputs/qwen25_coder_7b.md` | Metadata header only; substantive proposal preserved as generated. | +| Mistral 7B | `mistral:7b` | Ollama local | Local model through Ollama on submitter-owned Mac | `raw_outputs/mistral_7b.md` | Metadata header only; substantive proposal preserved as generated. | +| Phi-4 Mini | `phi4-mini` | Ollama local | Local model through Ollama on submitter-owned Mac | `raw_outputs/phi4_mini.md` | Metadata header only; substantive proposal preserved as generated. | +| Granite 3.3 2B | `granite3.3:2b` | Ollama local | Local model through Ollama on submitter-owned Mac | `raw_outputs/granite33_2b.md` | Metadata header only; substantive proposal preserved as generated. | +| GPT-5.6 Sol | `GPT-5.6 Sol` | ChatGPT | ChatGPT conversation available to submitter | `raw_outputs/chatgpt_gpt56_sol.md` | Metadata header only; substantive proposal preserved as generated. | + +## Unsuccessful/auxiliary attempt + +- DeepSeek-R1 8B (`deepseek-r1:8b`) was invoked locally. The collection endpoint returned no final-answer content, so the empty result is preserved in `raw_outputs/deepseek_r1_8b.md` but is **not** counted as one of the eight accepted system outputs. No answer was fabricated. + +## Confidentiality + +No API keys, account tokens, private screenshots, hidden system prompts, or proprietary prompt text are included. Local model inference used publicly available model packages already installed on the submitter-owned machine. diff --git a/research/ai_generated_agi_architectures/summary.md b/research/ai_generated_agi_architectures/summary.md new file mode 100644 index 0000000..d8cb9b4 --- /dev/null +++ b/research/ai_generated_agi_architectures/summary.md @@ -0,0 +1,53 @@ +# Comparative summary + +## Common patterns + +Across all eight usable systems, the most consistent architectural pattern is **modularity around a persistent cognitive loop** rather than a single undifferentiated model. Every proposal separates memory, reasoning/planning, learning, action/tool use, a world representation, safety/governance, and evaluation to some degree. + +### Memory +All eight proposals use layered memory. Qwen3, Qwen2.5 Coder, Mistral, Phi-4 Mini, and GPT-5.6 Sol explicitly distinguish short/working/long-term or episodic/semantic forms. Gemma emphasizes episodic + semantic graphs, while Llama and Granite lean more heavily on hierarchical temporal or distributed memory. The common engineering implication is that memory should not be one free-form transcript; different retention horizons and data types need different stores and retrieval policies. + +### Planning and reasoning +Every proposal describes an iterative control loop. Qwen3, Qwen2.5 Coder, Gemma, and GPT-5.6 Sol make the loop especially explicit: observe or assess, retrieve state, generate or decompose plans, act, then use feedback to revise. Llama frames this through model-based reinforcement learning; Mistral and Granite emphasize hierarchical/neuro-symbolic processing. The shared pattern is closed-loop planning with feedback rather than one-shot prompting. + +### Learning and self-improvement +All systems propose adaptation, but they disagree on mechanism. Qwen3, Mistral, and Phi-4 combine supervised/unsupervised/reinforcement learning. Llama emphasizes meta-learning plus model-based/model-free RL. Gemma uses model-based RL and curiosity. Granite proposes RL plus neural architecture search. GPT-5.6 Sol is the most conservative: it treats self-improvement as a gated software promotion pipeline with sandboxing, benchmarks, regression tests, and rollback. + +### Tool use and action +All eight include a distinct action layer. The local-model proposals often describe modular tool interfaces, robotics, actuators, or action libraries. GPT-5.6 Sol adds typed tool contracts, side-effect classification, least privilege, rollback, and independent verification. The shared lesson is that action execution should be an explicit subsystem, not an implicit continuation of reasoning text. + +### World representation +A probabilistic or structured world model appears in every proposal. Qwen3 and Gemma use knowledge-graph-like structures; Llama uses probabilistic graphical models; Mistral uses dynamic probabilistic representations; Granite combines Bayesian networks and graph neural networks; GPT-5.6 Sol proposes an event-sourced belief graph with provenance, confidence, and causal claims. + +### Safety and governance +All proposals include some safety boundary. The less specific proposals rely on ethical rules, anomaly detection, or human oversight. Qwen2.5 Coder mentions fail-safes and governance modules. GPT-5.6 Sol moves policy to the action boundary through capability restrictions, approval thresholds, audit logs, and automatic halts. The strongest convergence is that safety should constrain action, not merely describe desired behavior. + +### Persistence/runtime +The proposals converge on persistent, restartable infrastructure: databases, versioned state, containerization, microservices, distributed storage, or event logs. This suggests Cognitive-OS should treat persistence as a first-class runtime property rather than reconstructing identity/state solely from prompt history. + +### Multi-agent orchestration +Most outputs support multiple specialized agents or modules. Qwen3, Qwen2.5 Coder, Gemma, Mistral, Phi-4 Mini, and Granite use explicit coordination or message passing. GPT-5.6 Sol adds a useful constraint: specialist agents should be bounded, reason independently before debate, and be removed when they do not measurably improve decisions. + +## Meaningful disagreements + +1. **Symbolic vs neural vs hybrid world models.** Llama and Granite favor explicit probabilistic/neuro-symbolic structures, while Qwen-family and Mistral outputs are more modular and model-agnostic. GPT-5.6 Sol favors an auditable belief graph plus replaceable learned predictors. +2. **How self-improvement should happen.** Several models assume online or reinforcement learning; GPT-5.6 Sol instead treats improvement as versioned engineering promotion. This is a major governance choice because unrestricted online adaptation is harder to audit and roll back. +3. **Embodiment.** Phi-4 Mini, Granite, and parts of Llama/Gemma assume robotics or physical actuators; Qwen3, Qwen2.5 Coder, Mistral, and GPT-5.6 Sol are more general-purpose and can operate entirely in software. +4. **Centralized vs decentralized multi-agent control.** Granite and Mistral describe decentralized agents with coordination layers; other proposals lean toward hierarchical orchestration. +5. **Evaluation philosophy.** Some proposals emphasize broad benchmark batteries or human-level comparisons; GPT-5.6 Sol emphasizes verified outcomes, false-success rate, recovery after failure, and regressions across time. + +## Notable ideas + +- **Episodic memory as a contextual prior** (Gemma): past episodes should shape planning, not merely provide searchable facts. +- **Hierarchical uncertainty management** (Qwen2.5 Coder): uncertainty can be handled at multiple abstraction levels instead of one global confidence score. +- **Distributed cognition through shared experience** (Phi-4 Mini): multi-agent memory can transfer useful experience between specialists. +- **Neuro-symbolic hybridization** (Granite): symbolic reasoning and probabilistic/neural representations can coexist behind typed interfaces. +- **Falsification as a first-class resource** (GPT-5.6 Sol): store the cheapest observation likely to disprove important beliefs and plans. + +## Engineering implications for Cognitive-OS + +The comparison supports an incremental architecture rather than an all-at-once AGI design. A practical first system can be built around persistent goals, layered memory, a receding-horizon planner, typed tools, an action policy gate, independent verification, and replayable evaluation. More speculative components—online RL, neural architecture search, embodied robotics, or large decentralized agent swarms—can remain optional modules until they outperform simpler baselines. + +## Limitations + +These are generated design proposals, not empirical proof that any architecture will yield AGI. Model outputs can repeat common architectural tropes from training data. The eight systems also differ in model size and provider context, so differences should not be interpreted as controlled scientific comparisons of model intelligence. The packet is most useful as a structured design-space survey and source of falsifiable implementation ideas. diff --git a/research/ai_generated_agi_architectures/synthesis.md b/research/ai_generated_agi_architectures/synthesis.md new file mode 100644 index 0000000..56ed7a7 --- /dev/null +++ b/research/ai_generated_agi_architectures/synthesis.md @@ -0,0 +1,150 @@ +# Combined architecture synthesis + +## Design principle + +Build Cognitive-OS as an **evidence-gated, persistent cognitive runtime**. The system may reason broadly and generate many hypotheses, but it should act through narrow typed capabilities and only mark goals complete when external evidence verifies the result. + +## Core components + +### 1. Goal Manager +Stores a durable goal tree with immutable parent objectives, priorities, constraints, deadlines, and explicit goal-revision events. + +**Interface:** `Goal -> {subgoals, constraints, success_evidence, priority, status}` + +### 2. Memory System +Use four stores: +- **Working memory:** current task state, active hypotheses, unresolved questions. +- **Episodic memory:** append-only observations/actions/outcomes. +- **Semantic memory:** distilled claims with provenance, confidence, contradictions, and staleness. +- **Procedural memory:** versioned skills, tool recipes, prompts, policies, and tests. + +A consolidation worker proposes semantic/procedural updates from episodes. High-trust memory requires provenance and validation rather than model repetition. + +### 3. Belief / World Model +Represent operational state as a graph of entities, resources, goals, obligations, observations, and hypotheses. Every important edge carries provenance, timestamp, confidence, and contradiction links. Learned predictors may estimate success probability or cost, but the graph remains the auditable state surface. + +### 4. Planner +Use a receding-horizon loop: + +1. observe current state; +2. retrieve the smallest relevant evidence packet; +3. generate competing hypotheses; +4. decompose the active goal; +5. propose candidate actions; +6. estimate cost, reversibility, risk, information gain, and expected utility; +7. choose one action or one cheap falsification test; +8. send it to the policy gate. + +The planner should prefer information-gaining tests when uncertainty dominates. + +### 5. Tool Router + Capability Gate +Every tool exposes a typed contract: + +`preconditions -> inputs -> expected evidence -> side-effect class -> rollback` + +Actions are classified as read-only, reversible write, externally visible, financial, security-sensitive, legal, or irreversible. The planner receives only the capabilities needed for the current task. High-impact classes require explicit approval or stricter policy. + +### 6. Executor +Executes only policy-approved actions and records an idempotency key, exact request, tool result, timestamps, and expected evidence. A successful process exit is not treated as task success. + +### 7. Verifier +Checks the world after execution using evidence independent of the action generator where possible. It compares observed state with the action contract's expected evidence and can return: + +`verified_success | partial | contradiction | unknown | failed` + +Only verified success advances the durable goal state. + +### 8. Learning / Improvement Manager +Self-improvement is versioned rather than unrestricted: + +`failure/evaluation -> candidate patch -> sandbox -> benchmark -> adversarial test -> scorecard -> promote or reject -> rollback available` + +Candidate improvements can change prompts, retrieval rules, tool routing, planning policies, model selection, or code. Online learning components can be introduced later behind the same promotion gate. + +### 9. Evaluator +Maintain three levels: +- **Unit cognition tests:** retrieval, contradiction handling, planning constraints, tool schema correctness. +- **Scenario tests:** multi-step tasks with hidden blockers, stale data, misleading observations, and partial failures. +- **Longitudinal tests:** verified goal completion, false-success rate, time/cost per solved task, recovery after failure, memory usefulness, policy violations, and regressions after updates. + +### 10. Specialist-Agent Router +Spawn bounded specialists only when diversity is useful: proposer, critic, verifier, domain expert, or implementation engineer. Give each the same evidence packet and collect independent outputs before debate. A judge selects a falsifiable next action rather than averaging opinions. Track each specialist's marginal contribution; remove agents that do not improve decisions. + +## End-to-end data flow + +`Observation -> Event Log -> Belief Update -> Retrieval Packet -> Candidate Plans -> Policy Gate -> Executor -> External Evidence -> Verifier -> Outcome Event -> Memory Consolidation -> Evaluation` + +The append-only event log is the source of truth. Snapshots accelerate restart, but the system can reconstruct state from events after a crash. + +## Persistence and runtime + +Start with one process or a small set of services, not premature microservices. Required durable objects: +- event log; +- goal state; +- memory indices; +- tool/action records; +- verifier evidence; +- evaluation results; +- versioned skills/policies. + +Long-running work uses a durable queue. Consequential actions use idempotency keys so restart/retry cannot silently duplicate them. + +## Falsification layer + +For every important belief or plan, store: +- supporting evidence; +- contradicting evidence; +- confidence; +- last validation time; +- the cheapest observation likely to disprove it. + +This makes skepticism executable. The planner can explicitly choose a falsification action when it reduces decision-relevant uncertainty more cheaply than committing to a full plan. + +## Staged implementation + +### Stage 1 — Smallest useful prototype +- persistent goal object; +- append-only event log; +- working + episodic memory; +- three typed tools; +- planner/executor/verifier loop; +- one approval gate; +- replayable outcome record. + +### Stage 2 — Reliable memory and evaluation +- semantic/procedural memory; +- provenance + contradiction tracking; +- scenario test harness; +- false-success metric; +- crash/restart replay tests. + +### Stage 3 — Independent specialist routing +- proposer/critic/verifier roles; +- blind independent answers before debate; +- model/resource routing; +- marginal-value tracking for agents. + +### Stage 4 — Governed self-improvement +- sandboxed patch proposals; +- regression suite; +- benchmark scorecards; +- promotion/rollback pipeline. + +### Stage 5 — Learned predictors +Add learned world-state or action-value predictors only for repeated domains where they beat simple baselines and retain provenance/auditability. + +## Primary failure modes and controls + +- **Memory pollution:** provenance, confidence decay, contradiction links. +- **Goal drift:** immutable parent objective and explicit revision events. +- **Tool hallucination:** typed contracts and external verification. +- **False success:** verifier controls goal-state transition. +- **Consensus bias:** blind independent proposals before discussion. +- **Runaway self-modification:** sandbox, regression gates, versioning, rollback. +- **Context explosion:** bounded evidence retrieval instead of replaying full history. +- **Duplicate side effects after restart:** idempotency keys and durable action records. +- **Over-agentization:** specialists must demonstrate marginal decision value. + +## Decisive recommendation + +The first architectural commitment should be **separating planning from verification**. A system that can generate sophisticated plans but cannot distinguish claimed success from externally evidenced success will accumulate false beliefs and compound errors. An independent verifier backed by an event log and provenance-aware memory provides the foundation on which more advanced learning, world models, and multi-agent reasoning can safely be added. diff --git a/scripts/build_arch_comparison.py b/scripts/build_arch_comparison.py new file mode 100644 index 0000000..913ca6c --- /dev/null +++ b/scripts/build_arch_comparison.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python3 +import json, pathlib, urllib.request, csv, datetime, re +ROOT=pathlib.Path('/Users/cashify/revenue-work/Cognitive-OS/research/ai_generated_agi_architectures') +RAW=ROOT/'raw_outputs' +OUT=ROOT/'analysis' +OUT.mkdir(exist_ok=True) +files=[ + ('Qwen3 8B','qwen3_8b.md'), + ('Llama 3.1 8B','llama31_8b.md'), + ('Gemma 3 4B','gemma3_4b.md'), + ('Qwen2.5 Coder 7B','qwen25_coder_7b.md'), + ('Mistral 7B','mistral_7b.md'), + ('Phi-4 Mini','phi4_mini.md'), + ('Granite 3.3 2B','granite33_2b.md'), + ('GPT-5.6 Sol','chatgpt_gpt56_sol.md'), +] +fields=['memory_architecture','reasoning_planning_loop','learning_self_improvement','tool_use_action','world_model','safety_governance','evaluation_benchmarks','persistence_runtime','multi_agent_orchestration','engineering_feasibility','originality_non_obvious'] + +def ask(text): + prompt='''Extract the architecture proposal into strict JSON with exactly these string keys: +memory_architecture, reasoning_planning_loop, learning_self_improvement, tool_use_action, world_model, safety_governance, evaluation_benchmarks, persistence_runtime, multi_agent_orchestration, engineering_feasibility, originality_non_obvious. +Each value must be a concise factual summary of what THIS output actually proposes, <=35 words. Do not add ideas not present. Return JSON only. + +OUTPUT TO ANALYZE: +'''+text + payload={'model':'qwen3:8b','stream':False,'think':False,'keep_alive':0,'format':'json', + 'options':{'temperature':0.1,'num_ctx':8192,'num_predict':700}, + 'messages':[{'role':'system','content':'You are a precise research data extractor. No invention.'},{'role':'user','content':prompt}]} + req=urllib.request.Request('http://127.0.0.1:11434/api/chat',data=json.dumps(payload).encode(),headers={'Content-Type':'application/json'}) + with urllib.request.urlopen(req,timeout=600) as r: return json.loads(r.read().decode()) +rows=[] +for system,fn in files: + text=(RAW/fn).read_text() + print('EXTRACT',system,flush=True) + try: + d=ask(text); content=(d.get('message',{}).get('content') or '').strip(); obj=json.loads(content) + row={'system':system,'file':'raw_outputs/'+fn} + for f in fields: row[f]=str(obj.get(f,'')).replace('\n',' ').strip() + rows.append(row) + (OUT/(fn+'.json')).write_text(json.dumps(row,indent=2,ensure_ascii=False)) + print('DONE',system,flush=True) + except Exception as e: + print('ERR',system,repr(e),flush=True) + rows.append({'system':system,'file':'raw_outputs/'+fn,**{f:'EXTRACTION_ERROR' for f in fields}}) +with (ROOT/'comparison.csv').open('w',newline='') as f: + w=csv.DictWriter(f,fieldnames=['system','file']+fields); w.writeheader(); w.writerows(rows) +(ROOT/'analysis.json').write_text(json.dumps(rows,indent=2,ensure_ascii=False)) +print('COMPLETE',len(rows),flush=True) diff --git a/scripts/collect_architectures_local.py b/scripts/collect_architectures_local.py new file mode 100644 index 0000000..a498634 --- /dev/null +++ b/scripts/collect_architectures_local.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python3 +import json, pathlib, urllib.request, datetime, subprocess, time +ROOT=pathlib.Path('/Users/cashify/revenue-work/Cognitive-OS/research/ai_generated_agi_architectures') +RAW=ROOT/'raw_outputs' +RAW.mkdir(parents=True,exist_ok=True) +MODELS=[ + ('qwen3_8b','qwen3:8b'), + ('deepseek_r1_8b','deepseek-r1:8b'), + ('llama31_8b','llama3.1:8b'), + ('gemma3_4b','gemma3:4b'), + ('qwen25_coder_7b','qwen2.5-coder:7b'), + ('mistral_7b','mistral:7b'), + ('phi4_mini','phi4-mini'), +] +PROMPT="""Design a practical AGI-oriented cognitive architecture that a small engineering team could implement incrementally. Do not describe a vague superintelligence. Give a concrete system architecture. + +Cover these dimensions explicitly: +1. memory architecture +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 and staged implementation +11. one genuinely non-obvious architectural insight + +Also include: component interfaces, data flow, failure modes, and the smallest useful prototype. Separate assumptions from claims. Keep the answer implementation-oriented and under 1600 words.""" +SYSTEM="You are an independent AI architecture researcher. Think from first principles. Do not imitate consensus merely because the prompt mentions AGI. Be concrete and falsifiable." + +def ask(model): + payload={ + 'model':model,'stream':False,'think':False,'keep_alive':0, + 'options':{'temperature':0.45,'num_ctx':8192,'num_predict':1800}, + 'messages':[{'role':'system','content':SYSTEM},{'role':'user','content':PROMPT}] + } + req=urllib.request.Request('http://127.0.0.1:11434/api/chat',data=json.dumps(payload).encode(),headers={'Content-Type':'application/json'}) + with urllib.request.urlopen(req,timeout=900) as r: return json.loads(r.read().decode()) +manifest=[] +for slug,model in MODELS: + started=datetime.datetime.now(datetime.timezone.utc).isoformat() + print('START',model,flush=True) + try: + data=ask(model); content=data.get('message',{}).get('content','').strip() + (RAW/f'{slug}.md').write_text(f'# Raw output: {model}\n\nCollected: {started}\n\n{content}\n') + manifest.append({'system':slug,'model':model,'collected_at':started,'status':'ok','chars':len(content)}) + print('DONE',model,len(content),flush=True) + except Exception as e: + manifest.append({'system':slug,'model':model,'collected_at':started,'status':'error','error':repr(e)}) + print('ERR',model,repr(e),flush=True) +(ROOT/'local_manifest.json').write_text(json.dumps(manifest,indent=2)) +(ROOT/'shared_prompt.txt').write_text(PROMPT+'\n') +print('COMPLETE',flush=True)