Research: compare eight AI-generated AGI architecture proposals

research/ai_generated_agi_architectures/ACCEPTANCE_REPORT.md

@@ -0,0 +1,54 @@
+# Issue #5 acceptance report
+
+This file maps the published bounty acceptance criteria to concrete artifacts in this pull request and gives reviewers a one-command deterministic check.
+
+## Acceptance matrix
+
+| Published criterion | Evidence in this packet |
+|---|---|
+| At least 8 model/system outputs | `comparison.csv` contains 8 compared systems and references 8 distinct non-empty files under `raw_outputs/`. |
+| Outputs clearly attributed with collection date and source model/tool | `sources.md` lists each counted system, model identifier, provider/tool, access route, raw file, edit policy, and collection date; `PROVENANCE_MANIFEST.json` binds each committed raw file and the shared prompt to SHA-256 hashes. |
+| Raw outputs preserved separately from analysis | Generated outputs are stored under `raw_outputs/`; normalized analysis is in `comparison.csv`, `analysis.json`, `summary.md`, and `synthesis.md`. |
+| Consistent structured comparison | `comparison.csv` has one row per counted system and all 11 requested dimensions. |
+| Concrete implementation-oriented synthesis | `synthesis.md` turns the cross-model findings into a bounded runtime architecture and staged implementation direction for Cognitive-OS. |
+| No fabricated inaccessible model output | The unsuccessful DeepSeek-R1 collection is preserved as a failed attempt and is not counted among the eight usable systems. |
+| No private API keys, tokens, screenshots, hidden system prompts, or proprietary/private content | `sources.md` documents the confidentiality boundary; `verify_packet.py` also scans the review packet for common credential/private-key patterns. |
+
+## Eleven comparison dimensions
+
+The verifier requires every counted system to have non-empty entries for:
+
+1. memory architecture
+2. reasoning/planning loop
+3. learning or self-improvement
+4. tool use and action execution
+5. world model / representation
+6. safety / governance
+7. evaluation / benchmarks
+8. persistence / runtime
+9. multi-agent orchestration
+10. engineering feasibility
+11. originality / non-obvious insight
+
+## Deterministic reviewer check
+
+From the repository root:
+
+```bash
+python research/ai_generated_agi_architectures/verify_packet.py
+```
+
+Expected receipt:
+
+```text
+PASS: issue #5 research packet acceptance checks
+  systems compared: 8
+  required dimensions per system: 11
+  distinct referenced raw outputs: 8
+  required files: present and non-empty
+  provenance references + SHA-256 hashes: verified for every compared raw output
+  failed DeepSeek attempt: explicitly non-counted
+  secret-pattern scan: no matches
+```
+
+This verifier does not judge the quality of the architecture proposals. It checks the objective packaging, coverage, separation, provenance, and confidentiality conditions that can be validated mechanically.

research/ai_generated_agi_architectures/PROVENANCE_MANIFEST.json

@@ -0,0 +1,107 @@
+{
+  "schema_version": 1,
+  "shared_prompt_file": "shared_prompt.txt",
+  "shared_prompt_sha256": "68a0dc9183fb1b0f63ca0267c5e8891c2995ba544951416d14d768c0a7cc5ee7",
+  "counted_systems": 8,
+  "entries": [
+    {
+      "system": "Qwen3 8B",
+      "model": "qwen3:8b",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:01:28.148243+00:00",
+      "raw_file": "raw_outputs/qwen3_8b.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 10492,
+      "sha256": "15fc419263eafc28eeadd48c34f8db3f6f267cfaa2e519770356ceb9dfa89c94"
+    },
+    {
+      "system": "Llama 3.1 8B",
+      "model": "llama3.1:8b",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:10:37.328997+00:00",
+      "raw_file": "raw_outputs/llama31_8b.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 5670,
+      "sha256": "1dad22427f3094bc09a3f6e84b05e599bdae5d2f659f20ff0e533896dd917913"
+    },
+    {
+      "system": "Gemma 3 4B",
+      "model": "gemma3:4b",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:13:15.101803+00:00",
+      "raw_file": "raw_outputs/gemma3_4b.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 7761,
+      "sha256": "b2b306f5a2e2a024142735aa37ce05f8fa1f3aa03e885dd38be746478dcfcaf9"
+    },
+    {
+      "system": "Qwen2.5 Coder 7B",
+      "model": "qwen2.5-coder:7b",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:14:51.418464+00:00",
+      "raw_file": "raw_outputs/qwen25_coder_7b.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 9203,
+      "sha256": "177c3fe4e200021e0f26d018934e4c580430435d41ca8f2e260107dc036bebdf"
+    },
+    {
+      "system": "Mistral 7B",
+      "model": "mistral:7b",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:19:43.799532+00:00",
+      "raw_file": "raw_outputs/mistral_7b.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 6140,
+      "sha256": "a13654a9af3c7b5f84008b26b50163a3001359e9c431c9e7e862a0ab6257a423"
+    },
+    {
+      "system": "Phi-4 Mini",
+      "model": "phi4-mini",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:22:48.205501+00:00",
+      "raw_file": "raw_outputs/phi4_mini.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 9234,
+      "sha256": "ecb45aa455b9f7b17fcb88abd265fbc32e20c01963038458b7f20c8debec1c90"
+    },
+    {
+      "system": "Granite 3.3 2B",
+      "model": "granite3.3:2b",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:37:15.243741+00:00",
+      "raw_file": "raw_outputs/granite33_2b.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 4913,
+      "sha256": "edd4ddeca4853fb801b9be123e18ca13b8257a10c7f0876ba6e4f2a9903f18ed"
+    },
+    {
+      "system": "GPT-5.6 Sol",
+      "model": "GPT-5.6 Sol",
+      "provider_tool": "ChatGPT",
+      "collected_at": "2026-09-13",
+      "raw_file": "raw_outputs/chatgpt_gpt56_sol.md",
+      "counted": true,
+      "status": "usable",
+      "bytes": 7434,
+      "sha256": "e0b6389bc6da365a0fbcd49bafa3477357252aa7ea533afd78b3172e814333ae"
+    },
+    {
+      "system": "DeepSeek-R1 8B",
+      "model": "deepseek-r1:8b",
+      "provider_tool": "Ollama local",
+      "collected_at": "2026-09-13T05:06:38.641702+00:00",
+      "raw_file": "raw_outputs/deepseek_r1_8b.md",
+      "counted": false,
+      "status": "empty_no_final_answer",
+      "bytes": 76,
+      "sha256": "91708c807506f6a1ee84e48d1d73701cdc0fbca4bae5f0ddf6ab3eedf1fa045d"
+    }
+  ]
+}

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.