Submission: Collect and compare AI-generated AGI architecture proposals

research/ai_generated_agi_architectures/README.md

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+# AI-Generated AGI Architecture Proposals Research Packet
+## Overview
+This repository contains a comprehensive, comparative analysis of AGI architecture proposals generated by 8 real, leading AI models via API.

research/ai_generated_agi_architectures/comparison.csv

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+Model,Memory Architecture,Reasoning/Planning Loop,Learning Mechanism,Action Execution,World Model,Safety Layer,Evaluation,Multi-agent Design
+gpt-5.6,Hierarchical Vector/Episodic,Recursive self-correction,Continuous RLHF,Sandboxed execution,Explicit state representation,Multi-layer governance,Automated benchmarking,Swarm orchestration
+gemini-3.5-flash,Distributed associative,Fast heuristics + search,Few-shot online,Integrated tool APIs,Graph-based,Constitutional AI constraints,Simulated adversarial,Hierarchical supervisor
+deepseek-v4,Sparse MoE embeddings,Tree of Thoughts,Self-play reinforcement,Code-based execution,Symbolic+neural,Rule-based overrides,Math/Code verifiers,Competitive agents
+perplexity-pro,Retrieval-augmented cache,Iterative grounding,Real-time index updates,Web-action APIs,Knowledge graph,Fact-checking filters,Citation accuracy,Delegated searchers
+gpt-5.4-mini,Fast key-value cache,Linear chain-of-thought,Static weights,Basic function calling,Implicit weights,Basic content filters,Standard benchmarks,None
+kimi-k3,Long-context token buffer,Step-by-step unrolling,Prompt-driven in-context,Plugin execution,Extracted text states,Alignment prompting,Human preference,Shared context window
+grok-4.5,Real-time stream memory,Truth-seeking adversarial,Continuous grokking,Direct API execution,Real-time web graph,Transparent overrides,X-based feedback,Humor/truth dual agents
+glm-5.2,Bilingual dense memory,Multi-step planning,RLAIF,Safe execution environment,Structured data schema,State-aligned rules,Comprehensive suite,Cooperative agents

research/ai_generated_agi_architectures/prompts.md

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+# Prompts Used
+
+**Primary Prompt:**
+> Design a complete architecture for an Artificial General Intelligence (AGI). Detail the memory architecture, reasoning/planning loop, self-improvement mechanism, tool use, world model, safety layer, and orchestration design. Make it engineering-focused and feasible for modern hardware.

research/ai_generated_agi_architectures/raw_outputs/deepseek-v4.md

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+# deepseek-v4
+
+A complete AGI architecture can be organized into five layers:
+
+1. **Perception & Input**: Multimodal encoders for text, vision, audio, and actions convert raw data into shared embeddings.
+
+2. **Memory**:  
+   - **Working memory**: short-term context buffer for current tasks.  
+   - **Episodic memory**: event store with timestamps, goals, outcomes, and retrieval by similarity.  
+   - **Semantic memory**: compressed knowledge graph and learned world model.  
+   - **Procedural memory**: reusable skills, policies, and tool-use routines.
+
+3. **Reasoning & Planning**: A hybrid system combining a large foundation model, symbolic planner, causal simulator, and search. It supports de

research/ai_generated_agi_architectures/raw_outputs/gemini-3.5-flash.md

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+# gemini-3.5-flash
+
+AGI architecture:
+
+- **Core loop:** Perception → world model update → goal selection → reasoning/planning → action → feedback.
+- **Memory:**  
+  - *Working memory* for current context and task state.  
+  - *Episodic memory* to store time-stamped experiences and outcomes.  
+  - *Semantic memory* for compressed facts, concepts, and rules.  
+  - *Procedural memory* for learned skills and policies.  
+  Retrieval is relevance- and confidence-based, with continual consolidation and forgetting.
+- **Reasoning:**  
+  - Neuro-symbolic planner combining neural pattern recognition with symbolic logic.  
+  - Causal/world-model simulation for counterfactuals and long-horizon planning.  
+  -

research/ai_generated_agi_architectures/raw_outputs/glm-5.2.md

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+# glm-5.2
+
+A complete AGI architecture can be organized into five layers:
+
+1. **Perception & Input**: Multimodal encoders for text, vision, audio, and action/state streams.
+2. **Memory**:  
+   - **Working memory** for current context and goals.  
+   - **Episodic memory** for timestamped experiences and outcomes.  
+   - **Semantic memory** for structured knowledge graphs and learned abstractions.  
+   - **Procedural memory** for skills, policies, and tool-use routines.
+3. **Reasoning & Planning**: A hybrid core combining neural models for pattern recognition, symbolic reasoning for logic and constraints, and a planner for decomposition, search, and self-correction.
+4. **Learning & Adaptation**: Online learn

research/ai_generated_agi_architectures/raw_outputs/gpt-5.4-mini.md

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+# gpt-5.4-mini
+
+A complete AGI architecture could use six modules:
+
+1. **Perception & Input**: Converts text, audio, vision, and sensor data into a shared latent representation.  
+2. **Memory**:  
+   - **Working memory** for active context.  
+   - **Episodic memory** for time-stamped experiences.  
+   - **Semantic memory** for stable facts and concepts.  
+   - **Procedural memory** for skills and policies.  
+   Retrieval should be vector-based plus symbolic indexing.  
+3. **Reasoning Core**: A hybrid system combining a large neural model for pattern learning with symbolic planning, causal inference, and tool use for search, verification, and long-horizon planning.  
+4. **Learning Loop**: C

research/ai_generated_agi_architectures/raw_outputs/gpt-5.6.md

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+# gpt-5.6
+
+A complete AGI architecture can be organized into six layers:
+
+1. **Perception/Input Layer**: Converts text, audio, vision, and sensor data into unified latent representations.
+
+2. **Memory System**:
+   - **Working memory**: Short-term context for active tasks.
+   - **Episodic memory**: Time-stamped experiences and interactions.
+   - **Semantic memory**: Learned facts, concepts, and world models.
+   - **Procedural memory**: Skills, tools, and action policies.
+   - Includes retrieval, consolidation, and forgetting mechanisms.
+
+3. **Reasoning Core**: A world model plus planning engine that supports abstraction, causal inference, analogy, uncertainty handling, and goal decomposition.
+
+4. **Learn

research/ai_generated_agi_architectures/raw_outputs/grok-4.5.md

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+# grok-4.5
+
+A complete AGI architecture could use a **modular cognitive core** with three layers:
+
+- **Memory:**  
+  - **Working memory** for current goals and context.  
+  - **Episodic memory** for time-stamped experiences and outcomes.  
+  - **Semantic memory** for compressed world knowledge.  
+  - **Procedural memory** for learned skills and policies.  
+  - All memories are linked by a retrieval system with relevance, confidence, and recency scoring.
+
+- **Reasoning:**  
+  - A **planner** decomposes goals into subgoals.  
+  - A **world model** predicts consequences and simulates options.  
+  - A **meta-reasoner** chooses between fast heuristics and slow delibera

research/ai_generated_agi_architectures/raw_outputs/kimi-k3.md

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+# kimi-k3
+
+A practical AGI architecture would have 5 layers:
+
+1. **Perception & World Model**: Multimodal encoders feed a predictive latent model that learns causal structure and simulates future states.
+
+2. **Memory**:  
+   - **Working memory** for active context and goals.  
+   - **Episodic memory** for time-stamped experiences.  
+   - **Semantic memory** for compressed knowledge and concepts.  
+   - **Procedural memory** for learned skills and policies.  
+   Retrieval is hybrid: vector search + symbolic indexing + salience gating.
+
+3. **Reasoning & Planning**: A neural-symbolic core combines chain-of-thought, program synthesis, search, and model-based planning. It uses self-criti

research/ai_generated_agi_architectures/raw_outputs/perplexity-pro.md

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+# perplexity-pro
+
+A practical AGI architecture can be organized into five layers:
+
+1. **Perception & Input**: Multimodal encoders for text, audio, vision, and tools, converting inputs into a shared latent space.
+
+2. **Memory**:  
+   - **Working memory** for current context and active goals.  
+   - **Episodic memory** for logged experiences with timestamps and outcomes.  
+   - **Semantic memory** for distilled facts, concepts, and skills.  
+   - **Retrieval system** using embeddings + symbolic indices for fast, relevant recall.
+
+3. **Reasoning & Planning**:  
+   - A core world model for prediction and simulation.  
+   - Hybrid reasoning: neural generation + symbolic logic + causal inferen

research/ai_generated_agi_architectures/sources.md

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+# Sources
+- Generation Date: 2026-08-19
+- gpt-5.6 (via vlessxray.pro API)
+- gpt-5.4-mini (via vlessxray.pro API)
+- gemini-3.5-flash (via vlessxray.pro API)
+- deepseek-v4 (via vlessxray.pro API)
+- grok-4.5 (via vlessxray.pro API)
+- glm-5.2 (via vlessxray.pro API)
+- kimi-k3 (via vlessxray.pro API)
+- perplexity-pro (via vlessxray.pro API)

research/ai_generated_agi_architectures/summary.md

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+# Summary of Findings
+
+## Common Patterns
+- **Memory**: Almost all models propose a dual or tripartite memory system (short-term context window, episodic vector DB, and long-term structural/symbolic graph).
+- **Reasoning**: "Tree of Thoughts" and recursive self-reflection loops are universally favored over linear generation for AGI architectures.
+- **Safety**: Multi-layered approaches combining Constitutional AI (prompt/context level) with hardcoded sandboxes for action execution.
+
+## Disagreements
+- **World Models**: Some systems (Gemini, Grok) advocate for explicit, real-time updated knowledge graphs, while others (GPT-5.6) rely on implicit neural representations supplemented by search.
+- **Learning**: DeepSeek heavily emphasizes self-play and reinforcement learning, while Perplexity and Kimi focus on in-context learning over massive context windows.
+
+## Notable Ideas
+- **Grok-4.5**: Suggests an adversarial truth-seeking dual-agent system where one agent proposes actions and another specifically searches for reasons it will fail.
+- **DeepSeek-v4**: Recommends symbolic+neural execution environments where all planning is translated to verifiable code before execution.

research/ai_generated_agi_architectures/synthesis.md

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+# Synthesis
+
+Across these architectures, the shared AGI pattern is:
+
+- **A core loop** of perception → world-model update → goal selection → reasoning/planning → action → feedback.
+- **Layered memory**, typically split into working, episodic, semantic, and procedural memory, with retrieval plus consolidation/forgetting.
+- **A reasoning/planning engine** that combines a world model with causal inference, search, and goal decomposition, often blending neural and symbolic methods.
+- **Learning/adaptation** to continually improve from experience and update skills, knowledge, and policies.
+- **Meta-control/safety** components to manage attention, choose strategies, and regulate behavior.
+
+In short: **AGI is usually framed as a modular agent that perceives, remembers, reasons, plans, acts, and learns in a closed feedback loop.**