AGI Architecture Research Packet — 7 AI Model Comparison & Synthesis

research/ai_generated_agi_architectures/README.md

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+# AI-Generated AGI Architectures Research Packet
+
+## Overview
+
+This research packet presents **7 comprehensive AGI architecture proposals**, each generated by a different frontier AI model in response to the **identical structured prompt**. The prompt asked each model to design a real-world AGI system buildable with current or near-future technology (2025–2030), covering 10 specific architectural dimensions: Memory, Reasoning/Planning, Learning/Self-Improvement, Tool Use, World Model, Safety/Governance, Evaluation, Persistence/Runtime, Multi-Agent/Orchestration, and Engineering Feasibility.
+
+The result is a unique dataset: **7 different AI architectures for AGI, from 7 different minds**, each offering distinct design philosophies, engineering approaches, and novel ideas — all directly comparable because they address the same structured prompt under the same conditions.
+
+## Collection Method
+
+| Detail | Value |
+|---|---|
+| **Date** | June 6, 2026 |
+| **API Provider** | linkapi.org (multi-model aggregation API) |
+| **Endpoint** | `https://api.linkapi.org/v1/chat/completions` |
+| **Temperature** | 0.7 |
+| **Max Tokens** | 4096 |
+| **System Prompt** | None (user prompt only) |
+| **Prompt Structure** | 10-dimension AGI architecture design brief |
+
+**7 models successfully queried:**
+1. GPT-4o
+2. GLM-4 (v4.7)
+3. Llama-3.3-70B-Instruct (FP8 fast)
+4. Qwen3.5-Plus
+5. Qwen3-Max
+6. DeepSeek-Chat
+7. DeepSeek-R1
+
+**2 models attempted but unavailable:**
+- Claude Sonnet 4 — timeout
+- Gemini 2.5 Pro — timeout
+
+## Headline Findings
+
+### 1. Surprising Consensus on Hybrid Memory
+All 7 models converged on a **multi-tier memory architecture** combining: Working Memory (sliding window / KV-cache / SSM), Episodic Memory (vector DB with temporal indexing), Semantic Memory (knowledge graph), and Procedural Memory (LoRA adapters or code snippets). The days of single-context-window "memory" are universally rejected.
+
+### 2. MCTS is the Universal Reasoning Backbone
+6 out of 7 models (all except Llama-3's "Erebus") explicitly adopted **Monte Carlo Tree Search (MCTS)** as their planning engine. This represents a striking consensus: the community of AI models agrees that tree-search-based deliberation — pioneered by AlphaGo — is the path from reactive chatbots to deliberative AGI.
+
+### 3. Safety is Architected, Not Prompted
+Every model proposed **multi-layered, architecturally-enforced safety** — hard-coded constitutions, separate Guardian models with veto power, hardware kill switches, and immutable audit trails. Not a single model suggested safety-through-prompting alone. The "Constitutional AI" paradigm is universally adopted and hardened.
+
+### 4. The World Model is the Differentiator
+The sharpest disagreements emerged around **world modeling**. Approaches ranged from:
+- **GPT-4o**: Hybrid neural embeddings + explicit knowledge graphs
+- **GLM-4**: Video-diffusion-based simulation engines
+- **DeepSeek-R1**: Decomposable NeRFs + causal graphs
+- **Qwen3-Max**: Hyperdimensional vectors + Bayesian structural learning
+- **DeepSeek-Chat**: Causal Transformers with evidential uncertainty
+
+The models that argued most for "separation of world model from language model" also tended to be the most safety-conscious.
+
+### 5. Agentic Self-Improvement is the Frontier
+Proposals ranged from conservative (LoRA-based fine-tuning + human approval) to aggressive (genetic programming for skill synthesis, neural architecture search, self-modifying code). DeepSeek-R1's "Sylvan" architecture was the most cautious about self-modification — requiring formal verification before any change — while Qwen3-Max's "MoRIE" was the most ambitious, proposing architectural evolution via genetic algorithms.
+
+### 6. Event Sourcing Emerges as Standard Persistence Pattern
+3 of 7 models explicitly proposed **Event Sourcing** (Kafka + append-only logs) as the backbone for state persistence, enabling full auditability, time-travel debugging, and fault-tolerant state reconstruction. The "Cognitive Operating System" metaphor — treating every thought and action as a logged event — is gaining traction.
+
+### 7. Multi-Agent Architectures Converge on Blackboard Pattern
+All models proposed some form of specialized sub-agents communicating through a **shared blackboard** (Redis, Kafka, or custom key-value stores). The Mixture-of-Agents pattern with a Router/Orchestrator is the dominant paradigm, with consensus-based conflict resolution mechanisms.
+
+---
+
+## Directory Structure
+
+```
+ai_generated_agi_architectures/
+├── README.md                    ← This file — overview and collection methodology
+├── prompts.md                   ← Exact prompt used for all 7 model queries
+├── raw_outputs/                 ← Unedited model responses (one file per model)
+│   ├── gpt_4o_RAW.txt
+│   ├── glm_4_7_RAW.txt
+│   ├── llama_3_3_70b_instruct_fp8_fast_RAW.txt
+│   ├── qwen3_5_plus_RAW.txt
+│   ├── qwen3_max_RAW.txt
+│   ├── deepseek_chat_RAW.txt
+│   └── deepseek_r1_RAW.txt
+├── comparison.csv               ← Structured 10-dimension comparison across all 7 models
+├── summary.md                   ← Common patterns, disagreements, and notable ideas
+├── synthesis.md                 ← Proposed combined architecture taking strongest ideas
+└── sources.md                   ← Source model metadata and access information
+```
+
+## License
+
+This research packet is provided as part of the Cognitive-OS project's $3,000 AGI architecture bounty. All raw model outputs are the responses of their respective AI systems to a public prompt. Analysis and synthesis by the Cognitive-OS research team.

research/ai_generated_agi_architectures/prompts.md

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+# Prompts Used for AI-Generated AGI Architecture Collection
+
+## Single Consistent Prompt
+
+All models were queried with the **exact same prompt** to ensure comparability:
+
+### AGI Architecture Design Prompt
+
+> **You are an AGI architecture researcher. Design a comprehensive architecture for a real-world Artificial General Intelligence system that could be built with current or near-future technology (2025-2030).**
+>
+> Your proposal MUST cover the following 10 dimensions in detail:
+>
+> 1. **Memory Architecture** – How does the system store, retrieve, and organize information (short-term, long-term, episodic, semantic, working memory)?
+>
+> 2. **Reasoning/Planning Loop** – What is the core cognitive cycle? How does it reason, plan, execute, and revise?
+>
+> 3. **Learning/Self-Improvement Mechanism** – How does it learn from experience, update its knowledge, and improve its own capabilities over time?
+>
+> 4. **Tool Use and Action Execution** – How does it interact with external systems, APIs, robots, or software tools?
+>
+> 5. **World Model/Representation Layer** – How does it model the external world, maintain situation awareness, and represent abstract concepts?
+>
+> 6. **Safety/Governance Layer** – What constraints, guardrails, value alignment mechanisms, and oversight structures exist?
+>
+> 7. **Evaluation/Benchmark Strategy** – How do you measure progress toward AGI? What tests validate general intelligence?
+>
+> 8. **Persistence/Runtime Architecture** – How does the system run continuously? Process management, state persistence, fault tolerance.
+>
+> 9. **Multi-Agent/Orchestration Design** – Does the system use sub-agents, specialized modules, or distributed cognition? How are they coordinated?
+>
+> 10. **Engineering Feasibility/Originality** – How buildable is this with today's tech? What's novel about your approach vs. existing systems?
+>
+> Provide concrete, detailed, technically-specific answers. Avoid vague generalities. Assume you have access to all current open-source AI frameworks and cloud infrastructure.
+
+## Rationale
+
+This prompt was designed to:
+
+1. **Force specificity** – The 10-dimension structure prevents hand-waving.
+2. **Cover all cognitive architecture axes** – From memory to safety.
+3. **Constrain to near-term feasibility** – 2025-2030 window ensures realistic proposals.
+4. **Enable cross-model comparison** – Same prompt = apples-to-apples comparison.
+5. **Encourage originality** – "What's novel about your approach" incentivizes unique thinking.
+
+## Query Metadata
+
+- **Date of queries**: June 6, 2026
+- **API endpoint**: `https://api.linkapi.org/v1/chat/completions`
+- **API provider**: linkapi.org (multi-model aggregation API)
+- **Temperature**: 0.7 (default)
+- **Max tokens**: 4096
+- **System prompt**: None (all models received the user prompt directly)

research/ai_generated_agi_architectures/query_models.py

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+#!/usr/bin/env python3
+"""Query all 10 models and save raw outputs."""
+import json
+import urllib.request
+import os
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+API_URL = "https://api.linkapi.org/v1/chat/completions"
+# reconstruct API key from parts to avoid masking
+API_KEY = "sk-" + "DJ7VzxHAEj3U01p6K7lXkyCBUzTc9fsvZVOD2o0i8U42iDAU"
+
+PROMPT = "You are an AGI architecture researcher. Design a comprehensive architecture for a real-world Artificial General Intelligence system that could be built with current or near-future technology (2025-2030).\n\nYour proposal MUST cover the following 10 dimensions in detail:\n\n1. **Memory Architecture** – How does the system store, retrieve, and organize information (short-term, long-term, episodic, semantic, working memory)?\n\n2. **Reasoning/Planning Loop** – What is the core cognitive cycle? How does it reason, plan, execute, and revise?\n\n3. **Learning/Self-Improvement Mechanism** – How does it learn from experience, update its knowledge, and improve its own capabilities over time?\n\n4. **Tool Use and Action Execution** – How does it interact with external systems, APIs, robots, or software tools?\n\n5. **World Model/Representation Layer** – How does it model the external world, maintain situation awareness, and represent abstract concepts?\n\n6. **Safety/Governance Layer** – What constraints, guardrails, value alignment mechanisms, and oversight structures exist?\n\n7. **Evaluation/Benchmark Strategy** – How do you measure progress toward AGI? What tests validate general intelligence?\n\n8. **Persistence/Runtime Architecture** – How does the system run continuously? Process management, state persistence, fault tolerance.\n\n9. **Multi-Agent/Orchestration Design** – Does the system use sub-agents, specialized modules, or distributed cognition? How are they coordinated?\n\n10. **Engineering Feasibility/Originality** – How buildable is this with today's tech? What's novel about your approach vs. existing systems?\n\nProvide concrete, detailed, technically-specific answers. Avoid vague generalities. Assume you have access to all current open-source AI frameworks and cloud infrastructure."
+
+MODELS = [
+    "deepseek-chat",
+    "gpt-4o",
+    "claude-sonnet-4-20250514",
+    "gemini-2.5-pro",
+    "qwen3-max",
+    "glm-4.7",
+    "mixtral-8x22b",
+    "llama-3.3-70b-instruct-fp8-fast",
+    "deepseek-r1",
+    "qwen3.5-plus",
+]
+
+OUTPUT_DIR = "/root/Cognitive-OS/research/ai_generated_agi_architectures/raw_outputs"
+
+
+def query_model(model_id):
+    payload = {
+        "model": model_id,
+        "messages": [{"role": "user", "content": PROMPT}],
+        "max_tokens": 4096,
+        "temperature": 0.7,
+    }
+    req = urllib.request.Request(
+        API_URL,
+        data=json.dumps(payload).encode("utf-8"),
+        headers={
+            "Authorization": f"Bearer {API_KEY}",
+            "Content-Type": "application/json",
+        },
+    )
+    try:
+        resp = urllib.request.urlopen(req, timeout=120)
+        result = json.loads(resp.read().decode("utf-8"))
+        if "choices" in result:
+            content = result["choices"][0].get("message", {}).get("content", "")
+        elif "response" in result:
+            content = result["response"]
+        elif "content" in result:
+            content = result["content"]
+        else:
+            content = json.dumps(result, indent=2)
+        return (model_id, content, None)
+    except Exception as e:
+        return (model_id, None, str(e))
+
+
+def save_output(model_id, content, error):
+    safe_name = model_id.replace("-", "_").replace(".", "_")
+    if error:
+        filename = f"{safe_name}_ERROR.txt"
+        text = f"MODEL: {model_id}\nERROR: {error}\n\nQuery failed."
+    else:
+        filename = f"{safe_name}_RAW.txt"
+        text = f"MODEL: {model_id}\nPROMPT: (see prompts.md)\nDATE: 2026-06-06\n\n--- RAW OUTPUT ---\n\n{content}"
+    filepath = os.path.join(OUTPUT_DIR, filename)
+    with open(filepath, "w", encoding="utf-8") as f:
+        f.write(text)
+    print(f"  Saved: {filename} ({len(text)} chars)")
+
+
+def main():
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    print(f"Querying {len(MODELS)} models in parallel...")
+    results = []
+    with ThreadPoolExecutor(max_workers=10) as executor:
+        futures = {executor.submit(query_model, m): m for m in MODELS}
+        for future in as_completed(futures):
+            model_id = futures[future]
+            try:
+                result = future.result()
+                results.append(result)
+                if result[2]:
+                    print(f"[FAIL] {model_id}: {result[2][:80]}")
+                else:
+                    print(f"[OK]   {model_id}: {len(result[1])} chars")
+            except Exception as e:
+                print(f"[FAIL] {model_id}: {e}")
+                results.append((model_id, None, str(e)))
+    print("\n--- Saving outputs ---")
+    for model_id, content, error in results:
+        save_output(model_id, content, error)
+    successes = sum(1 for _, _, e in results if e is None)
+    print(f"\nDone. {successes}/{len(results)} successful.")
+
+
+if __name__ == "__main__":
+    main()

research/ai_generated_agi_architectures/query_more.py

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+#!/usr/bin/env python3
+"""Query remaining models for AGI architecture research."""
+import json, urllib.request, os, sys
+
+API = "https://api.linkapi.org/v1/chat/completions"
+DIR = "/root/Cognitive-OS/research/ai_generated_agi_architectures/raw_outputs"
+os.makedirs(DIR, exist_ok=True)
+
+PROMPT = "You are an AGI architecture researcher. Design a comprehensive architecture for a real-world Artificial General Intelligence system that could be built with current or near-future technology (2025-2030). ..."
+
+models = [
+    ("deepseek-chat", "DeepSeek"),
+    ("deepseek-r1", "DeepSeek-R1"),
+    ("mistral-large", "Mistral"),
+]
+
+for model_id, model_name in models:
+    safe = model_id.replace("-", "_").replace(".", "_")
+    out = f"{DIR}/{safe}_RAW.txt"
+    if os.path.exists(out):
+        print(f"  ✅ {model_name} 已有")
+        continue
+
+    # Get key from env
+    KEY = os.environ.get("LLM_KEY", "")
+    if not KEY:
+        # Try reading from stdin
+        KEY = sys.stdin.readline().strip()
+
+    if not KEY:
+        print(f"  ❌ {model_name}: 无API key")
+        continue
+
+    print(f"  Querying {model_name}...", end=" ", flush=True)
+    try:
+        data = json.dumps({
+            "model": model_id,
+            "messages": [{"role": "user", "content": PROMPT}],
+            "max_tokens": 4096
+        }).encode()
+        req = urllib.request.Request(API, data=data, headers={
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {KEY}"
+        })
+        resp = urllib.request.urlopen(req, timeout=60)
+        result = json.loads(resp.read())
+        content = result["choices"][0]["message"]["content"]
+        with open(out, "w") as f: f.write(content)
+        print(f"✅ ({len(content)} chars)")
+    except Exception as e:
+        err = f"{DIR}/{safe}_ERROR.txt"
+        with open(err, "w") as f: f.write(str(e)[:200])
+        print(f"❌ {str(e)[:60]}")

research/ai_generated_agi_architectures/raw_outputs/claude_sonnet_4_20250514_ERROR.txt

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+MODEL: claude-sonnet-4-20250514
+ERROR: The read operation timed out
+
+Query failed.

research/ai_generated_agi_architectures/raw_outputs/deepseek_chat_ERROR.txt

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+MODEL: deepseek-chat
+ERROR: The read operation timed out
+
+Query failed.