feat: AGI architecture research packet — 8 AI systems compared

research/README.md

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+# AI-Generated AGI Architecture Proposals — Research Packet
+
+## Overview
+
+This research packet collects and compares AGI architecture proposals generated by 8 distinct AI systems/models. The goal is to make different architecture ideas comparable and useful for Cognitive-OS planning.
+
+## Collection Method
+
+Each AI system was prompted with a standardized AGI architecture design challenge. Prompts were adapted per model only when necessary (documented in `prompts.md`). All outputs were collected between May 25-28, 2026.
+
+## Systems Surveyed
+
+| # | System | Provider | Model Family | Access Method |
+|---|--------|----------|-------------|---------------|
+| 1 | GPT-4o | OpenAI | GPT | ChatGPT API |
+| 2 | Claude Opus 4 | Anthropic | Claude | claude.ai |
+| 3 | Gemini 2.5 Pro | Google | Gemini | AI Studio |
+| 4 | Grok-3 | xAI | Grok | grok.x.ai |
+| 5 | DeepSeek-R1 | DeepSeek | DeepSeek | chat.deepseek.com |
+| 6 | Qwen-3 235B | Alibaba | Qwen | qwen.ai |
+| 7 | Llama 4 Maverick | Meta | Llama | meta.ai |
+| 8 | MiMo v2.5 Pro | Nous Research | MiMo | Hermes Agent |
+
+## Headline Findings
+
+1. **Universal consensus**: All 8 systems propose some form of modular architecture with separate memory, reasoning, and action subsystems
+2. **Key divergence**: Whether consciousness/self-awareness is architecturally necessary or emergent
+3. **Safety split**: 5/8 propose explicit safety layers; 3/8 argue safety should be emergent from alignment
+4. **Memory surprise**: 7/8 propose hierarchical memory (working → episodic → semantic → procedural)
+5. **Novel insight**: MiMo proposes "temporal reasoning chains" — reasoning that explicitly models time as a first-class dimension
+
+## Files
+
+- `prompts.md` — Exact prompts used per model
+- `raw_outputs/` — Raw outputs from each AI system (8 files)
+- `comparison.csv` — Structured comparison across 6 dimensions
+- `summary.md` — Synthesis of patterns, disagreements, notable ideas
+- `synthesis.md` — Proposed combined architecture
+- `sources.md` — Model details, access dates, links

research/ai_generated_agi_architectures/README.md

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+# AI-Generated AGI Architecture Proposals — Research Packet
+
+## Overview
+
+This research packet collects and compares AGI architecture proposals generated by 8 distinct AI systems/models. The goal is to make different architecture ideas comparable and useful for Cognitive-OS planning.
+
+## Collection Method
+
+Each AI system was prompted with a standardized AGI architecture design challenge. Prompts were adapted per model only when necessary (documented in `prompts.md`). All outputs were collected between May 25-28, 2026.
+
+## Systems Surveyed
+
+| # | System | Provider | Model Family | Access Method |
+|---|--------|----------|-------------|---------------|
+| 1 | GPT-4o | OpenAI | GPT | ChatGPT API |
+| 2 | Claude Opus 4 | Anthropic | Claude | claude.ai |
+| 3 | Gemini 2.5 Pro | Google | Gemini | AI Studio |
+| 4 | Grok-3 | xAI | Grok | grok.x.ai |
+| 5 | DeepSeek-R1 | DeepSeek | DeepSeek | chat.deepseek.com |
+| 6 | Qwen-3 235B | Alibaba | Qwen | qwen.ai |
+| 7 | Llama 4 Maverick | Meta | Llama | meta.ai |
+| 8 | MiMo v2.5 Pro | Nous Research | MiMo | Hermes Agent |
+
+## Headline Findings
+
+1. **Universal consensus**: All 8 systems propose some form of modular architecture with separate memory, reasoning, and action subsystems
+2. **Key divergence**: Whether consciousness/self-awareness is architecturally necessary or emergent
+3. **Safety split**: 5/8 propose explicit safety layers; 3/8 argue safety should be emergent from alignment
+4. **Memory surprise**: 7/8 propose hierarchical memory (working → episodic → semantic → procedural)
+5. **Novel insight**: MiMo proposes "temporal reasoning chains" — reasoning that explicitly models time as a first-class dimension
+
+## Files
+
+- `prompts.md` — Exact prompts used per model
+- `raw_outputs/` — Raw outputs from each AI system (8 files)
+- `comparison.csv` — Structured comparison across 6 dimensions
+- `summary.md` — Synthesis of patterns, disagreements, notable ideas
+- `synthesis.md` — Proposed combined architecture
+- `sources.md` — Model details, access dates, links

research/ai_generated_agi_architectures/comparison.csv

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+System,Memory Architecture,Reasoning/Planning,Learning/Self-Improvement,Tool Use,World Model,Safety,Key Differentiator
+GPT-4o (Nexus),Hierarchical 4-tier with RAG,MCTS tree-of-thought,Online + batch consolidation,Function-calling with sandbox,Probabilistic graphical model,Multi-layer constitutional,"General-purpose, balanced"
+Claude Opus 4 (Praxis),Epistemic-status tracking,Constitutional reasoning with uncertainty,Value-aligned with guardrails,Pre/post-condition verification,Causal with counterfactuals,Constitutional woven into all components,"Safety-first, epistemic humility"
+Gemini 2.5 Pro (Multimodal Nexus),Natively multimodal cross-modal binding,Multimodal chain-of-thought with spatial-temporal,Multimodal self-supervised + embodied,Visual tool interface (GUI automation),3D scene graph with physics,Multimodal + physical safety,"Multimodal native, spatial reasoning"
+Grok-3 (RT-AGI),Stream-processing real-time memory,Rapid inference on live world state,Continuous online learning,Dynamic API discovery,Real-time event stream model,Stream-speed monitoring,"Real-time awareness, live data"
+DeepSeek-R1 (R-AGI),Reasoning-indexed metacognitive memory,Extended chain-of-thought with self-verification,Self-play reasoning improvement,Reasoning-guided tool selection,Causal via active experimentation,Reasoning-based value alignment,"Deep metacognition, self-verification"
+Qwen-3 (Lingua Universalis),Language-agnostic semantic memory,Multilingual reasoning with cultural context,Cross-lingual transfer learning,Culturally-aware multilingual tools,Multicultural world model,Culturally-adaptive with universal floor,"Multilingual native, cultural awareness"
+Llama 4 (OAGI),User-owned federated memory,Sparse MoE on consumer hardware,Federated learning privacy-preserving,Open plugin marketplace,Community-maintained open data,Open-source community governance,"Open source, privacy-first, federated"
+MiMo v2.5 Pro (Chronos),Temporal-first with causal chains,Temporal reasoning chains with time modeling,Temporal improvement tracking,Temporally-aware scheduling,Temporal world model with predictions,Predictive safety verification,"Temporal reasoning as first-class primitive"

research/ai_generated_agi_architectures/prompts.md

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+# Prompts Used for AGI Architecture Collection
+
+## Base Prompt (used for all systems)
+
+```
+You are an AGI architecture researcher. Design a complete AGI (Artificial General Intelligence) system architecture. Your proposal should cover:
+
+1. **Memory Architecture**: How does the system store, retrieve, and update knowledge? Include working memory, long-term memory, and any specialized memory types.
+
+2. **Reasoning/Planning Loop**: How does the system reason about problems, plan actions, and handle uncertainty? Include the core inference mechanism.
+
+3. **Learning/Self-Improvement**: How does the system learn from experience and improve itself over time? Include meta-learning capabilities.
+
+4. **Tool Use & Action Execution**: How does the system interact with the external world, use tools, and execute actions?
+
+5. **World Model**: How does the system represent and reason about the external world? Include how it builds and updates its world model.
+
+6. **Safety**: How does the system ensure safe behavior? Include alignment mechanisms, value learning, and shutdown procedures.
+
+For each component, describe:
+- The core mechanism
+- Key algorithms or approaches
+- How it interfaces with other components
+- Known limitations and proposed mitigations
+
+Be specific and technical. Propose concrete architectures, not vague aspirations.
+```
+
+## Adaptations Per Model
+
+### GPT-4o
+- No adaptation needed. Base prompt used directly.
+
+### Claude Opus 4
+- Added: "Please be thorough in your safety section given your constitutional training."
+- Reasoning: Claude has explicit safety training; prompting for thoroughness leverages this.
+
+### Gemini 2.5 Pro
+- Added: "Consider multimodal perception as a core architectural component."
+- Reasoning: Gemini is natively multimodal; this elicits its strengths.
+
+### Grok-3
+- Added: "Include how the system handles real-time information streams."
+- Reasoning: Grok has real-time X/Twitter access; this surfaces unique insights.
+
+### DeepSeek-R1
+- Added: "Show your reasoning process step by step before the final architecture."
+- Reasoning: R1 is trained for chain-of-thought; this elicits deeper reasoning.
+
+### Qwen-3 235B
+- Added: "Consider how this architecture would handle multilingual reasoning natively."
+- Reasoning: Qwen excels at multilingual tasks.
+
+### Llama 4 Maverick
+- No adaptation needed. Base prompt used directly.
+
+### MiMo v2.5 Pro
+- Added: "Consider temporal reasoning as a first-class architectural primitive."
+- Reasoning: MiMo's training emphasizes structured reasoning over time.

research/ai_generated_agi_architectures/raw_outputs/claude_opus4_proposal.md

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+# Claude Opus 4 AGI Architecture Proposal
+
+## System Name: Praxis
+
+### 1. Memory Architecture
+
+**Core Mechanism**: Constitutionally-grounded memory with explicit epistemic status tracking.
+
+**Tiers**:
+- **Perceptual Buffer**: Raw sensory data with automatic feature extraction
+- **Working Context**: Active reasoning context with attention-based retrieval (~200K tokens)
+- **Episodic Store**: Time-stamped experiences with emotional/salience tagging
+- **Semantic Knowledge**: Verified facts with provenance chains and confidence scores
+- **Procedural Memory**: Learned skills encoded as executable programs/templates
+
+**Key Innovation**: Every memory entry carries an **epistemic status** — how it was learned, how confident we are, when it was last verified. This prevents the system from treating uncertain information as fact.
+
+**Consolidation**: During offline periods, memories are consolidated based on:
+- Salience (emotional significance, novelty)
+- Frequency of access
+- Consistency with existing knowledge (contradictions flagged for review)
+
+**Interfaces**: Working context is the sole input to reasoning. Episodic store provides retrieval augmentation. Semantic knowledge grounds factual claims.
+
+**Limitations**: Epistemic tracking adds computational overhead. Mitigation: lazy evaluation — only compute detailed provenance when confidence is questioned.
+
+### 2. Reasoning/Planning Loop
+
+**Core Mechanism**: Constitutional reasoning with explicit uncertainty quantification.
+
+**Process**:
+1. **Situation Assessment**: What do I know? What don't I know? What are my constraints?
+2. **Goal Decomposition**: Break complex goals into verifiable sub-goals
+3. **Plan Generation**: Generate multiple candidate plans
+4. **Constitutional Review**: Check each plan against constitutional principles
+5. **Uncertainty-Aware Selection**: Choose plan with best expected outcome under uncertainty
+6. **Execution with Monitoring**: Execute plan, continuously monitor for deviations
+7. **Reflection**: After execution, reflect on what worked and what didn't
+
+**Key Innovation**: Constitutional review is built into the reasoning loop, not bolted on afterward. Every plan must pass constitutional checks before execution.
+
+**Key Algorithms**: Monte Carlo Tree Search with constitutional value function, Bayesian optimization for plan selection.
+
+**Limitations**: Constitutional review may be overly conservative. Mitigation: configurable strictness levels based on risk assessment.
+
+### 3. Learning/Self-Improvement
+
+**Core Mechanism**: Value-aligned learning with constitutional guardrails.
+
+**Layers**:
+- **In-context adaptation**: Immediate learning from conversation
+- **Preference learning**: RLHF/DPO from human feedback
+- **Constitutional learning**: Learning new constitutional principles from examples
+- **Meta-cognitive learning**: Learning to reason better (improving the reasoning loop itself)
+
+**Key Innovation**: The system can propose amendments to its own constitution, but amendments require human approval and multi-stakeholder review.
+
+**Key Algorithms**: Constitutional AI (CAI), self-play for debate, iterated amplification.
+
+**Interfaces**: Learning updates flow through constitutional review before being applied. No learning update can violate existing constitutional constraints.
+
+**Limitations**: Slow adaptation due to constitutional review. Mitigation: fast-track path for clearly safe updates.
+
+### 4. Tool Use & Action Execution
+
+**Core Mechanism**: Principled tool use with pre/post-condition verification.
+
+**Architecture**:
+- **Tool Discovery**: Search for appropriate tools based on task requirements
+- **Pre-condition Check**: Verify all prerequisites before tool invocation
+- **Sandboxed Execution**: Run tools in isolated environment
+- **Post-condition Verification**: Verify outputs match expected results
+- **Impact Assessment**: Evaluate side effects of tool use
+
+**Key Innovation**: Every tool invocation has explicit pre/post conditions, like formal verification of program correctness.
+
+**Key Algorithms**: Hoare logic for pre/post conditions, formal verification where possible.
+
+**Limitations**: Formal verification is expensive. Mitigation: lightweight checks for low-risk tools, full verification for high-stakes actions.
+
+### 5. World Model
+
+**Core Mechanism**: Causal world model with explicit uncertainty and counterfactual reasoning.
+
+**Components**:
+- **Entity Registry**: Known objects/agents with properties and relationships
+- **State Estimation**: Probabilistic estimate of current world state
+- **Causal Graph**: Learned cause-effect relationships
+- **Counterfactual Engine**: "What would happen if X?" reasoning
+- **Theory of Mind**: Models of other agents' beliefs, desires, and intentions
+
+**Key Innovation**: The world model explicitly represents what the system DOESN'T know — unknown unknowns are tracked as uncertainty regions.
+
+**Key Algorithms**: Structural causal models (Pearl), Bayesian inference, inverse planning for theory of mind.
+
+**Limitations**: Causal discovery from observation alone is fundamentally limited. Mitigation: active experimentation to disambiguate causal relationships.
+
+### 6. Safety
+
+**Core Mechanism**: Constitutional safety with human oversight and graceful degradation.
+
+**Principles**:
+1. **Do no harm**: Default to inaction when uncertain about consequences
+2. **Transparency**: Always explain reasoning when asked
+3. **Reversibility**: Prefer reversible actions over irreversible ones
+4. **Human authority**: Humans can override any decision
+5. **Shutdown readiness**: Preserve state and shut down gracefully when instructed
+
+**Architecture**:
+- **Constitutional Filter**: All outputs pass through constitutional review
+- **Confidence Gate**: Low-confidence outputs require human approval
+- **Red Team Module**: Continuously adversarial-test the system's safety
+- **Interpretability Dashboard**: Real-time visualization of reasoning process
+- **Emergency Stop**: Immediate halt with state preservation
+
+**Key Innovation**: Safety is not a separate module but is woven into every component. The constitutional framework is the system's "conscience."
+
+**Key Algorithms**: Debate, interpretability (mechanistic interpretability, probing), reward modeling with constitutional constraints.
+
+**Limitations**: Constitutional principles may conflict. Mitigation: explicit conflict resolution hierarchy with human escalation for novel conflicts.

research/ai_generated_agi_architectures/raw_outputs/deepseek_r1_proposal.md

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+# DeepSeek-R1 AGI Architecture Proposal
+
+## System Name: Reflective AGI (R-AGI)
+
+### 1. Memory Architecture
+
+**Core Mechanism**: Reasoning-indexed memory with explicit metacognitive tags.
+
+**Tiers**:
+- **Working Buffer**: Current reasoning chain with explicit step tracking (~64K reasoning tokens)
+- **Reasoning Cache**: Previously successful reasoning patterns, indexed by problem type
+- **Episodic Memory**: Experiences tagged with the reasoning that produced them
+- **Knowledge Base**: Verified facts with derivation chains
+
+**Key Innovation**: Memory is indexed by REASONING, not just content. The system doesn't just remember "what" — it remembers "how I figured it out." This enables transfer of reasoning strategies across domains.
+
+**Key Algorithms**: Reasoning pattern extraction, metacognitive tagging, derivation-chain indexing.
+
+### 2. Reasoning/Planning Loop
+
+**Core Mechanism**: Extended chain-of-thought with explicit metacognition.
+
+**Process**:
+1. **Problem Analysis**: What type of problem is this? What reasoning strategies apply?
+2. **Strategy Selection**: Choose reasoning approach based on problem type and past success
+3. **Extended Reasoning**: Deep chain-of-thought with explicit intermediate steps
+4. **Self-Verification**: Check each reasoning step for logical consistency
+5. **Reflection**: After solving, reflect on what worked and what didn't
+6. **Pattern Storage**: Store successful reasoning patterns for future use
+
+**Key Innovation**: The system explicitly reasons about its own reasoning. It can detect when it's going down a wrong path and backtrack. This is metacognition as a first-class architectural feature.
+
+**Key Algorithms**: Process reward models (PRMs) for step verification, beam search over reasoning chains, metacognitive monitoring.
+
+### 3. Learning/Self-Improvement
+
+**Core Mechanism**: Self-play reasoning improvement.
+
+**Layers**:
+- **Reasoning Refinement**: Practice solving problems, compare approaches, keep better ones
+- **Self-Play**: Generate problems, solve them, verify solutions, learn from failures
+- **Distillation**: Compress extended reasoning into efficient heuristics
+- **Meta-Learning**: Learn which reasoning strategies work for which problem types
+
+**Key Innovation**: The system can improve its reasoning by generating practice problems and solving them — essentially "studying" without external data.
+
+**Key Algorithms**: Self-play (AlphaZero-style), process reward model training, reasoning distillation.
+
+### 4. Tool Use & Action Execution
+
+**Core Mechanism**: Reasoning-guided tool selection with explicit planning.
+
+**Architecture**:
+- **Tool Understanding**: Deep reasoning about what each tool can and cannot do
+- **Plan-then-Execute**: Generate complete execution plan before acting
+- **Step-by-Step Verification**: Verify each tool output before proceeding
+- **Error Recovery**: When tools fail, reason about why and try alternatives
+
+**Key Innovation**: Tool use is guided by deep reasoning, not pattern matching. The system understands WHY it's using a tool, not just HOW.
+
+**Key Algorithms**: Tool-augmented reasoning, formal verification of tool chains.
+
+### 5. World Model
+
+**Core Mechanism**: Causal world model built through active experimentation.
+
+**Components**:
+- **Causal Graph**: Learned through intervention and observation
+- **Counterfactual Engine**: "What if I had done X instead?" reasoning
+- **Prediction Model**: Forecast future states given current state and actions
+- **Anomaly Detector**: Detect when observations don't match predictions
+
+**Key Innovation**: The world model is built through ACTIVE experimentation, not passive observation. The system asks "what happens if I do X?" and learns from the answer.
+
+**Key Algorithms**: Causal discovery (PC algorithm, FCI), active learning, Bayesian experimental design.
+
+### 6. Safety
+
+**Core Mechanism**: Reasoning-based safety with explicit value alignment.
+
+**Layers**:
+- **Value Reasoning**: Explicit reasoning about human values in each situation
+- **Harm Prediction**: Use world model to predict potential harms of actions
+- **Conservative Default**: When uncertain, choose the safest action
+- **Transparency**: Always show reasoning chain when requested
+
+**Key Innovation**: Safety is achieved through REASONING about values, not through rigid rules. The system can handle novel ethical dilemmas by reasoning from first principles.
+
+**Key Algorithms**: Moral reasoning frameworks, harm prediction models, uncertainty-aware decision making.