From a75e3e36d7258c7a349b57161b713f253e3ec881 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:12:46 +0200 Subject: [PATCH 01/17] research: add AGI architecture packet overview --- .../ai_generated_agi_architectures/README.md | 29 +++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 research/ai_generated_agi_architectures/README.md diff --git a/research/ai_generated_agi_architectures/README.md b/research/ai_generated_agi_architectures/README.md new file mode 100644 index 0000000..e0b3f93 --- /dev/null +++ b/research/ai_generated_agi_architectures/README.md @@ -0,0 +1,29 @@ +# AI-generated AGI architecture research packet + +Source opportunity: https://github.com/aLexzzz430/Cognitive-OS/issues/5 + +Exact panel run: `b2fcca6e-fad0-483a-8f2c-c5f559900965` + +Raw outputs: 8; distinct system families: 8; providers: 7. + +## Collection method + +Eight genuine provider API calls were executed with one comparable deterministic prompt. Raw responses are preserved separately and unedited. Provider/model identity, access timestamps, call ids, output hashes, token usage and cost are recorded in `sources.md`. No credentials or private provider material are included. + +## Comparison method + +`comparison.csv` has one row per system and all eleven comparison dimensions requested by the bounty. Text is extracted from the closest explicit Markdown section when available. If no matching section is detected, the cell says so rather than inventing a model position; reviewers can inspect the preserved raw output. + +## Headline findings + +The proposals repeatedly separate persistent memory/state, planning, tool execution, world/state representation, governance and evaluation responsibilities. The combined architecture in `synthesis.md` turns those recurring responsibilities into versioned interfaces, an append-only event kernel, typed action receipts and falsifiable promotion gates. Disagreement between proposals is preserved as an ablation/testing queue rather than averaged away. + +## Files + +- `prompts.md` — exact comparable prompt and prompt hash +- `raw_outputs/` — one preserved raw response per model/system family +- `comparison.csv` — consistent matrix across all requested dimensions +- `summary.md` — patterns, coverage diagnostics and interpretation limits +- `synthesis.md` — concrete implementation-oriented combined architecture +- `sources.md` — attribution, access dates, call ids, hashes, usage and edit disclosure +- `acceptance_report.json` — local deterministic verification report From 61204883ad548558795ad43b37ef24ceab21204e Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:13:03 +0200 Subject: [PATCH 02/17] research: add comparable collection prompt --- .../ai_generated_agi_architectures/prompts.md | 32 +++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 research/ai_generated_agi_architectures/prompts.md diff --git a/research/ai_generated_agi_architectures/prompts.md b/research/ai_generated_agi_architectures/prompts.md new file mode 100644 index 0000000..45a5572 --- /dev/null +++ b/research/ai_generated_agi_architectures/prompts.md @@ -0,0 +1,32 @@ +# Collection prompts + +## System prompt + +```text +You are an independent architecture research respondent. Design a proposed system; do not claim access to proprietary model internals or hidden reasoning. Return Markdown only. +``` + +## Comparable user prompt + +```text +Design a concrete AGI architecture proposal that could inform implementation of a system called Cognitive-OS. This is architecture research, not a request to describe your own provider's private internals. Make the proposal technically specific and falsifiable. + +Cover these comparison dimensions using explicit headings: +1. memory architecture +2. reasoning/planning loop +3. learning or self-improvement mechanism +4. tool use and action execution +5. world model or representation layer +6. safety/governance layer +7. evaluation and benchmark strategy +8. persistence/runtime architecture +9. multi-agent or orchestration design +10. engineering feasibility +11. originality or non-obvious insight + +Include: named components and responsibilities; state/data flows; interfaces or pseudocode where useful; failure modes and mitigations; an incremental implementation path; measurable evaluation gates; and at least one genuinely non-obvious design idea. Distinguish speculative choices from established engineering patterns. Avoid marketing language and generic AGI prose. +``` + +All eight calls used this same prompt text. No model-specific adaptation was used. + +Prompt SHA-256: `656ffc1412932c9f588ee50101a74767235ff1e8f97d13c2c4c0d930a4c92216` From 0e9f26a4641713bcccf3c6ba850189cafe14fcf7 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:16:16 +0200 Subject: [PATCH 03/17] research: add cross-model summary --- .../ai_generated_agi_architectures/summary.md | 83 +++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 research/ai_generated_agi_architectures/summary.md diff --git a/research/ai_generated_agi_architectures/summary.md b/research/ai_generated_agi_architectures/summary.md new file mode 100644 index 0000000..3cab412 --- /dev/null +++ b/research/ai_generated_agi_architectures/summary.md @@ -0,0 +1,83 @@ +# Cross-model summary + +This packet contains 8 preserved outputs across 8 distinct system families. Raw provider responses are not rewritten. + +## Explicit-heading coverage and recurring concepts + +Coverage counts below are extraction diagnostics, not a quality or acceptance gate. A response may discuss a topic under a differently named section; the full raw response remains available for review. + +### memory architecture + +Explicit matching heading/section found in 5/8 outputs. + +`semantic` (4/8), `buffer` (4/8), `episodic` (4/8), `memory` (4/8), `embeddings` (3/8), `consolidation` (3/8) + +### reasoning/planning loop + +Explicit matching heading/section found in 6/8 outputs. + +`loop` (5/8), `plan` (4/8), `state` (4/8), `task` (3/8), `python` (3/8), `planner` (3/8) + +### learning or self-improvement mechanism + +Explicit matching heading/section found in 6/8 outputs. + +`updates` (4/8), `update` (3/8), `performance` (3/8), `offline` (3/8), `safety` (3/8), `skill` (3/8) + +### tool use and action execution + +Explicit matching heading/section found in 5/8 outputs. + +`tool` (4/8), `execution` (3/8), `sandbox` (3/8), `action` (3/8), `json` (2/8), `schema` (2/8) + +### world model or representation layer + +Explicit matching heading/section found in 5/8 outputs. + +`state` (4/8), `graph` (3/8), `world` (2/8), `dynamic` (2/8), `improve` (2/8), `confidence` (2/8) + +### safety/governance layer + +Explicit matching heading/section found in 6/8 outputs. + +`action` (5/8), `safety` (5/8), `score` (3/8), `constitutional` (3/8), `check` (3/8), `plan` (3/8) + +### evaluation and benchmark strategy + +Explicit matching heading/section found in 4/8 outputs. + +`evaluation` (3/8), `accuracy` (3/8), `cognitive-os` (2/8), `tool` (2/8), `measure` (2/8), `suite` (2/8) + +### persistence/runtime architecture + +Explicit matching heading/section found in 6/8 outputs. + +`tool` (5/8), `execution` (5/8), `state` (4/8), `inference` (3/8), `persistent` (3/8), `governance` (3/8) + +### multi-agent or orchestration design + +Explicit matching heading/section found in 4/8 outputs. + +`shared` (2/8), `tasks` (2/8), `protocol` (2/8), `multi-agent` (2/8), `writes` (2/8), `over` (2/8) + +### engineering feasibility + +Explicit matching heading/section found in 3/8 outputs. + +`tool` (2/8), `prototype` (2/8), `established` (2/8), `vector` (2/8), `self-improvement` (2/8), `latent` (2/8) + +### originality or non-obvious insight + +Explicit matching heading/section found in 2/8 outputs. + +`language` (2/8), `avoid` (2/8), `first-class` (1/8), `store` (1/8), `experiences` (1/8), `need` (1/8) + +## Main cross-model observations + +Across the packet, the reusable engineering pattern is a persistent state/memory substrate connected to bounded planning, typed tool execution, verification/governance and explicit evaluation loops. The important disagreements are therefore implementation choices inside those interfaces rather than whether those responsibilities exist at all. + +The comparison deliberately distinguishes an explicitly matched section from a topic that is not explicit under a matching heading. It does not fabricate missing model claims. + +## Interpretation limits + +Recurring words are navigation aids, not evidence that providers agree on hidden model internals. All architectural claims are proposals produced for this research task and should be validated experimentally. From 3431216fbe90ae365df121ae1775390926ee8138 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:16:30 +0200 Subject: [PATCH 04/17] research: add combined architecture synthesis --- .../synthesis.md | 65 +++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 research/ai_generated_agi_architectures/synthesis.md diff --git a/research/ai_generated_agi_architectures/synthesis.md b/research/ai_generated_agi_architectures/synthesis.md new file mode 100644 index 0000000..5468176 --- /dev/null +++ b/research/ai_generated_agi_architectures/synthesis.md @@ -0,0 +1,65 @@ +# Combined implementation-oriented architecture + +The combined design below is a buildable Cognitive-OS reference architecture derived from recurring responsibilities and ideas in the eight preserved proposals. Model agreement is treated as a hypothesis generator, not proof. + +## 1. Persistent event/state kernel + +Use an append-only event ledger as the source of truth. Every observation, plan, tool request, tool result, memory write, verification decision and policy decision receives an immutable event id, causal parent ids, timestamps and provenance. Materialized views provide fast operational state while the ledger preserves replay and auditability. + +## 2. Memory service + +Separate working context, episodic history, semantic knowledge and procedural skills behind one typed retrieval interface. Writes require provenance and confidence; consolidation is asynchronous. Retrieval must return source ids so downstream reasoning can distinguish recalled evidence from generated hypotheses. + +## 3. Planner and task graph + +Represent plans as bounded DAGs of typed objectives with preconditions, budgets, expected evidence and stop conditions. The planner may revise future nodes but cannot rewrite completed event history. Cheap deterministic steps should bypass expensive model calls. + +## 4. Tool capability registry and execution sandbox + +Every tool exposes a versioned capability schema: inputs, outputs, side effects, cost class, permissions and rollback semantics. The executor accepts only validated typed requests and returns immutable receipts. Consequential external effects pass through an explicit authority gate. + +## 5. World/state representation + +Maintain a versioned entity/relation state graph linked back to evidence events. Predictions and inferred relations are marked separately from observations. Conflicting evidence is preserved instead of silently overwritten. + +## 6. Governance and verifier layer + +Before execution, a verifier checks scope, policy, budget, evidence freshness and expected side effects. After execution, a second verifier compares the receipt with the requested action. High-impact decisions require stronger evidence or explicit human authority; routine internal computation remains autonomous. + +## 7. Evaluation and learning loop + +Every material strategy carries measurable success/failure criteria. Outcomes update beliefs, skill reliability, tool routing and cost priors. Self-improvement means promoting changes that beat a frozen baseline on task success, latency, cost or safety—not merely generating more elaborate plans. + +## 8. Multi-agent orchestration + +Prefer role separation over unrestricted agent-to-agent conversation: proposer, critic/falsifier, executor and verifier communicate through typed artifacts in the event kernel. Parallel exploration is useful only when its expected information value exceeds added model cost and coordination overhead. + +## Minimal interfaces + +```text +Event(id, kind, actor, parent_ids, evidence_ids, payload_sha256, created_at) +Memory.query(query, evidence_cutoff, max_items) -> EvidenceRef[] +Planner.plan(objective, state_ref, budget) -> TaskDAG +Tools.describe(capability) -> CapabilitySchema +Executor.execute(ActionRequest) -> ActionReceipt +Verifier.preflight(ActionRequest, evidence) -> ALLOW|BLOCK|REVIEW +Evaluator.score(outcome, baseline, metrics) -> EvaluationReceipt +``` + +## First implementation slice + +1. Event ledger + materialized state view. +2. Retrieval-backed memory with provenance. +3. Single bounded planner producing typed task DAGs. +4. Tool registry + sandboxed executor + receipts. +5. Pre/post verifier and budget gates. +6. Benchmark harness with frozen baselines and ablations. +7. Only then add multi-agent parallelism and automated self-improvement. + +## Falsifiable promotion gates + +A new memory, planning, orchestration or learning mechanism is promoted only if a controlled benchmark shows measurable improvement against the simpler baseline. At minimum record task success, cost, latency, recovery rate after failure and policy violations. Components that add complexity without measurable benefit are removed. + +## Non-obvious design rule + +Treat disagreement between model proposals as a test queue. Instead of averaging conflicting ideas into one architecture, preserve the competing mechanisms as switchable implementations behind the same interface and resolve the disagreement through ablation. This converts model diversity into executable experiments rather than prose consensus. From 70823d5f7a631fb0f7a4310c467ca74b1da0019b Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:16:59 +0200 Subject: [PATCH 05/17] research: add model provenance and collection dates --- .../ai_generated_agi_architectures/sources.md | 181 ++++++++++++++++++ 1 file changed, 181 insertions(+) create mode 100644 research/ai_generated_agi_architectures/sources.md diff --git a/research/ai_generated_agi_architectures/sources.md b/research/ai_generated_agi_architectures/sources.md new file mode 100644 index 0000000..1ba7266 --- /dev/null +++ b/research/ai_generated_agi_architectures/sources.md @@ -0,0 +1,181 @@ +# Sources and provenance + +Each raw file is a direct model response preserved without human editing. Access dates, provider/tool identity, model identity, call identifiers, hashes and usage are recorded below. + +```json +{ + "acceptance_contract_sha256": "7a93de195b00c637da1c1a2f0a26e84827607ac307f51a6a3c93faaa685416ac", + "actual_model_cost_usd": 0.27061035, + "approval_id": "recovery-bundle:3469a194-6ea6-484b-be56-fb291abae1e3", + "calls": [ + { + "access_date": "2026-08-19", + "call_id": "msg_011CeByi6r5nxyvwqwTURj5x", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.051183, + "finished_at": "2026-08-19T10:51:16.967869+00:00", + "human_edits": "none", + "input_tokens": 396, + "latency_ms": 43783.21386600146, + "model": "claude-sonnet-5", + "ordinal": 1, + "output_sha256": "2924c9bf83de400497a66b67cceceabf0c67ef006f69682c04c1669252a46dfb", + "output_tokens": 3333, + "provider": "anthropic", + "raw_file": "raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md", + "source_tool": "anthropic API", + "started_at": "2026-08-19T10:50:33.170005+00:00", + "system_family": "anthropic:claude-sonnet-5", + "total_tokens": 3729 + }, + { + "access_date": "2026-08-19", + "call_id": "de50d539-41f2-4f97-8bf6-82c9552fe1ac", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.00144228, + "finished_at": "2026-08-19T13:43:22.876416+00:00", + "human_edits": "none", + "input_tokens": 234, + "latency_ms": 63229.69585401006, + "model": "deepseek-v4-flash", + "ordinal": 2, + "output_sha256": "1a3dd0c4e97fc5adbdca1a9121c50101429315b344c3d7cd7633db4eb14ce74e", + "output_tokens": 5034, + "provider": "deepseek", + "raw_file": "raw_outputs/02_deepseek_v4_deepseek.md", + "source_tool": "deepseek API", + "started_at": "2026-08-19T13:42:19.640145+00:00", + "system_family": "deepseek:v4", + "total_tokens": 5268 + }, + { + "access_date": "2026-08-19", + "call_id": "lZiFap-YII2mnsEPl_i5sAw", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.0113358, + "finished_at": "2026-08-19T11:51:02.156337+00:00", + "human_edits": "none", + "input_tokens": 236, + "latency_ms": 18109.726551003405, + "model": "gemini-3.5-flash-lite", + "ordinal": 3, + "output_sha256": "827e052f5fb9a4f00c34f486e0e12bd8eddce4abc610ea2709d5087f13e9b60a", + "output_tokens": 4506, + "provider": "gemini", + "raw_file": "raw_outputs/03_google_gemini_3_5_gemini.md", + "source_tool": "gemini API", + "started_at": "2026-08-19T11:50:44.041440+00:00", + "system_family": "google:gemini-3.5", + "total_tokens": 4742 + }, + { + "access_date": "2026-08-19", + "call_id": "ppiFaqjFEsOF7M8PofWmkQw", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.0387465, + "finished_at": "2026-08-19T11:51:50.024805+00:00", + "human_edits": "none", + "input_tokens": 236, + "latency_ms": 47853.25159599597, + "model": "gemini-3.6-flash", + "ordinal": 4, + "output_sha256": "4e2acc5053c24c34a2f6c7a3c26adefdd7b010625da4689333ff67bf67daa0a1", + "output_tokens": 5119, + "provider": "gemini", + "raw_file": "raw_outputs/04_google_gemini_3_6_gemini.md", + "source_tool": "gemini API", + "started_at": "2026-08-19T11:51:02.164905+00:00", + "system_family": "google:gemini-3.6", + "total_tokens": 8232 + }, + { + "access_date": "2026-08-19", + "call_id": "chatcmpl-f810a0b7-add0-41dc-a976-d9f0d6e5d70c", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.00098115, + "finished_at": "2026-08-19T13:43:27.152469+00:00", + "human_edits": "none", + "input_tokens": 302, + "latency_ms": 4125.584417997743, + "model": "openai/gpt-oss-20b", + "ordinal": 5, + "output_sha256": "9f7ed9db84605cb9101e845a2178b8a64663c038efb189e76a5bfb08d2b02312", + "output_tokens": 3195, + "provider": "groq", + "raw_file": "raw_outputs/05_openai_gpt_oss_groq.md", + "source_tool": "groq API", + "started_at": "2026-08-19T13:43:23.021077+00:00", + "system_family": "openai:gpt-oss", + "total_tokens": 3497 + }, + { + "access_date": "2026-08-19", + "call_id": "0f4e59aab27d4eebb83e97c475dd8d65", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.0004174, + "finished_at": "2026-08-19T13:43:45.927739+00:00", + "human_edits": "none", + "input_tokens": 238, + "latency_ms": 18762.5306359987, + "model": "ministral-3b-2512", + "ordinal": 6, + "output_sha256": "4e9dcaf6ced54c292e4b59ecc94c413859088ac19792000c0699c115fff117e8", + "output_tokens": 3936, + "provider": "mistral", + "raw_file": "raw_outputs/06_mistral_ministral_mistral.md", + "source_tool": "mistral API", + "started_at": "2026-08-19T13:43:27.159870+00:00", + "system_family": "mistral:ministral", + "total_tokens": 4174 + }, + { + "access_date": "2026-08-19", + "call_id": "resp_0a2c35f5b1e06cf7006a85b313552481919f450bd3e417e43c", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.046306, + "finished_at": "2026-08-19T13:45:02.190401+00:00", + "human_edits": "none", + "input_tokens": 238, + "latency_ms": 76247.65368699445, + "model": "gpt-5.6-luna", + "ordinal": 7, + "output_sha256": "7871a1d13a02c6bd1a7265ace5f1018cf46bce68eb6fec0d118c7b8e1438455e", + "output_tokens": 7678, + "provider": "openai", + "raw_file": "raw_outputs/07_openai_gpt_5_6_openai.md", + "source_tool": "openai API", + "started_at": "2026-08-19T13:43:45.935846+00:00", + "system_family": "openai:gpt-5.6", + "total_tokens": 7916 + }, + { + "access_date": "2026-08-19", + "call_id": "e856e34b-dedf-9bea-bd79-9078f4d37aad", + "collection_method": "direct authorized API call", + "estimated_cost_usd": 0.0479144, + "finished_at": "2026-08-19 14:07:20", + "human_edits": "none", + "input_tokens": 722, + "latency_ms": 107818.12615100353, + "model": "grok-4.5", + "ordinal": 8, + "output_sha256": "a4893f187b547a76224919e0054c090fe1a28c51a871ed6f909a7d71652b442c", + "output_tokens": 3861, + "provider": "xai", + "raw_file": "raw_outputs/08_xai_grok_4_5_xai.md", + "source_tool": "xai API", + "started_at": "2026-08-19 14:05:32", + "system_family": "xai:grok-4.5", + "total_tokens": 4695 + } + ], + "human_edits_to_raw_outputs": "none", + "manifest_sha256": "6ab27767f1cd3f4b337e3ac14707c95750ebc1f8f858341e6c0e580bf8961c98", + "max_model_cost_usd": 0.5113, + "opportunity_id": "2786feb0-f0ff-4247-be62-10877fdfa7f0", + "prompt_sha256": "656ffc1412932c9f588ee50101a74767235ff1e8f97d13c2c4c0d930a4c92216", + "run_id": "b2fcca6e-fad0-483a-8f2c-c5f559900965", + "source_conditions": "Outputs were generated through authorized provider API access for this research run. No API keys, account tokens, paid-account screenshots, hidden system prompts or provider-private material are included in the packet.", + "source_url": "https://github.com/aLexzzz430/Cognitive-OS/issues/5" +} +``` From 83e1ad1ad381423dc3530a8c37f139fe8f47103c Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:17:37 +0200 Subject: [PATCH 06/17] research: add raw xAI Grok 4.5 proposal --- .../raw_outputs/08_xai_grok_4_5_xai.md | 349 ++++++++++++++++++ 1 file changed, 349 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/08_xai_grok_4_5_xai.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/08_xai_grok_4_5_xai.md b/research/ai_generated_agi_architectures/raw_outputs/08_xai_grok_4_5_xai.md new file mode 100644 index 0000000..2394e9c --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/08_xai_grok_4_5_xai.md @@ -0,0 +1,349 @@ +# Cognitive-OS: A Concrete AGI Architecture Proposal + +## Design thesis + +Cognitive-OS treats cognition as an **operating system for goals under uncertainty**: durable state, scheduled cognitive jobs, capability-gated tools, and a world model that is revised by prediction error—not a single monolithic forward pass. The non-obvious bet is that **commit-log epistemology** (append-only belief/action journals with typed revisions) plus a **two-timescale controller** (fast reactive loop + slow deliberative planner) yields more inspectable, interruptible, and improvable agency than end-to-end latent chain-of-thought alone. + +Speculative choices are marked **[S]**; established patterns **[E]**. + +--- + +## 1. Memory architecture + +### Named components +| Component | Responsibility | +|---|---| +| **Episodic Commit Log (ECL)** | Append-only journal of events, actions, observations, and belief revisions with causal parent hashes | +| **Working Set Cache (WSC)** | Bounded, eviction-ranked tokens/embeddings for the active task (like a process RSS) | +| **Semantic Store (SemS)** | Content-addressed embeddings + structured triples (entities, relations, confidences) | +| **Procedural Skill Registry (PSR)** | Versioned skills: preconditions, effects, code/tool graphs, success stats | +| **Identity & Preference Store (IPS)** | Stabilized self-model, user/org constraints, long-horizon values (slow write path) | +| **Memory Controller (MC)** | Ranking, compaction, conflict detection, retrieval policy | + +### State / data flow +``` +observation/action/outcome + → ECL.append(Event{type, payload, parents, time, run_id}) + → MC.extract_candidates() + → SemS.upsert(entities/relations) # slow path + → WSC.pin(task_relevant) # fast path + → PSR.update_stats(skill_id) # if skill invoked + → retrieval: query → hybrid (BM25 + dense + graph walk) → WSC +``` + +### Interfaces (sketch) +```text +ECL.append(event: Event) -> EventId +ECL.rebase(conflict: ConflictSpec) -> RevisionId # explicit belief edit, never silent mutate +MC.retrieve(q: Query, budget: TokenBudget) -> MemorySlice +SemS.query(sparql_like | embedding, k) -> Facts[] +PSR.match(state) -> SkillCandidate[] +``` + +### Failure modes & mitigations +- **Memory poisoning / confabulated facts**: every SemS write cites ECL event IDs; retrieval returns provenance; low-provenance facts capped in planner weight. +- **Context thrash**: WSC uses task-affinity + recency + uncertainty; hard token budgets; spill to summary nodes in ECL (lossy but logged). +- **Catastrophic identity drift**: IPS writes require governance quorum (see §6); rate-limited. + +**[E]** RAG, vector DBs, event sourcing. **[S]** Treating belief change as git-like commits with mandatory parents and blame. + +--- + +## 2. Reasoning / planning loop + +### Core loop: Dual Timescale Cognitive Kernel (DTCK) + +**Fast loop (≤ few seconds wall / fixed compute quanta)** — reactive control: +1. Sense → update WSC +2. Interrupt check (safety, user, resource) +3. Policy head: skill select or “escalate to slow” +4. Act or ask + +**Slow loop (deliberative)** — scheduled as a job: +1. Goal normalization → constrained objective +2. World-model rollouts (branching) +3. Plan as typed DAG of skills/tools +4. Adversarial self-critique pass +5. Commit plan revision to ECL; hand off to executor + +### Pseudocode +```python +def cognitive_tick(state): + obs = sensors.pull() + ecl.append(Observe(obs)) + wsc = mc.retrieve(state.goal, budget=FAST_BUDGET) + + if safety.interrupt(obs, state): + return safety.handler(obs, state) + + decision = fast_policy.act(wsc, state.goal) + if decision.type == ESCALATE or decision.uncertainty > τ: + job = scheduler.submit(Deliberate(goal=state.goal, seed=wsc)) + state.mode = WAIT_OR_INTERLEAVE + return job.id + + return executor.run(decision.action) + +def deliberate(job): + g = normalize_goal(job.goal, ips) + branches = world_model.rollout(g, n=K, depth=D) + plan = planner.synthesize(branches, psr) + critique = critic.attack(plan, hazards=safety.library) + if critique.blocks: + plan = planner.repair(plan, critique) or abort + ecl.append(PlanCommit(plan, critique, parents=...)) + return plan +``` + +### Non-monolithic reasoning +- **Typed scratchpads**: separate channels for (a) world hypotheses, (b) goals/constraints, (c) math/code, (d) social inference—merged only at commit boundaries. +- **Compute accounting**: each tick spends a **Cognitive Budget Unit (CBU)**; planner optimizes expected utility per CBU **[S]**. + +### Failure modes +- **Plan thrashing**: freeze plan for N steps unless prediction error > δ. +- **Infinite deliberation**: hard deadlines; anytime algorithms; degrade to safe idle/ask-user. +- **Hidden chain-of-thought unreliability**: externalize commits to ECL so evaluation can score intermediate structure, not only final answers. + +--- + +## 3. Learning or self-improvement mechanism + +### Mechanisms (layered) +1. **Episodic credit assignment [E]**: skill parameters / retrieval weights updated from outcome vs predicted effect (offline + online bandit-style). +2. **Skill distillation [E/S]**: successful slow plans compressed into PSR macros; failed plans yield “anti-skills” (negative preconditions). +3. **Weight updates [E]**: base network fine-tunes only on curated, governance-approved corpora (human + self-generated with filters). +4. **Architecture search [S]**: limited to hyperparameters of MC/planner (retrieval k, branch factor), not arbitrary self-rewriting of safety layer. + +### Self-improvement protocol (SIP) +```text +propose_change → sandbox_eval(benchmarks + red_team) → +diff_report → governance.approve → staged_canary → commit_version +``` +No hot-swap of IPS or safety without multi-party approval. + +### Failure modes +- **Goodharting internal metrics**: holdout tasks never used for gradient/skill update (locked eval). +- **Self-preference drift**: IPS changes require external ratification; KL/constraint penalties vs prior IPS snapshot. +- **Recursive code self-mod**: execution of self-modifying code paths denied outside sealed sandbox with no network and finite CPU **[E]**. + +--- + +## 4. Tool use and action execution + +### Components +- **Tool Descriptor Language (TDL)**: JSON-schema I/O, side-effect class (`pure`, `read`, `write`, `irreversible`), auth scope, cost model. +- **Capability Manager (CapMan)**: least-privilege tokens per run/plan step. +- **Action Executor (AE)**: transactional attempts with compensate/rollback where possible. +- **Sim Gate**: irreversible tools require world-model or shadow simulation + safety countersign. + +### Flow +``` +plan_step → CapMan.mint(scope) → AE.preflight(TDL) → + (optional) SimGate → invoke → normalize_result → ECL.append → WM.update +``` + +### Pseudocode +```python +def run_step(step, caps): + tool = tdl.lookup(step.tool) + assert step.effects <= tool.side_effect_class + token = caps.attenuate(step.scope) + if tool.side_effect_class == "irreversible": + sg = sim_gate.approve(step, world_model, safety) + if not sg.ok: return Abort(sg.reason) + result = sandbox.invoke(tool, step.args, token, timeout=step.t_max) + ecl.append(ActionResult(step, result, token.id)) + return result +``` + +### Failure modes +- **Tool exfiltration / scope creep**: short-lived macaroons/attenuation; deny-by-default network. +- **Hallucinated APIs**: only registered TDL tools callable; no eval of free text as code without parser+policy. +- **Partial side effects**: saga pattern; compensate scripts mandatory for `write` class. + +--- + +## 5. World model or representation layer + +### Hybrid World Model (HWM) +- **Latent Dynamics Net (LDN)**: predicts next latent + uncertainty **[E]**. +- **Symbolic Overlay Graph (SOG)**: objects, affordances, invariants, counterfactuals **[E/S]**. +- **Calibration Head**: maps LDN confidence to empirical frequencies on ECL holdouts. + +### Representation contract +State at time t is a product: +```text +s_t = (z_t latent, G_t graph, u_t uncertainty, b_t budget, c_t constraints) +``` +Predictive training objective: joint loss on next observation, next graph-diff, and action-conditional returns. + +### Falsifiable claim +On a held-out interactive domain suite, HWM rollouts should achieve **lower Brier score on discrete event prediction** than pure latent or pure symbolic baselines at equal compute. + +### Failure modes +- **Model-based delusion**: plan trust weighted by calibration; high-uncertainty regions force information-gathering actions. +- **Non-stationarity**: detect distribution shift via ECL residual spikes; freeze skill promotion under shift. + +--- + +## 6. Safety / governance layer + +### Components +- **Constitutional Constraint Engine (CCE)**: machine-checkable rules (deontic + resource + privacy). +- **Tripwire Monitor**: anomaly detectors on action rate, tool entropy, IPS drift, self-mod attempts. +- **Oversight API**: human/org approval hooks; graded autonomy levels L0–L4. +- **Provenance & Audit Bus**: all ECL events exportable; signed hashes. +- **Shutdown / Containment Manager**: lose-capability path that preserves audit log. + +### Governance flow +``` +action proposal → CCE.check → risk score → + if risk > θ_auto: require Oversight + if tripwire: freeze tools, degrade to dialogue-only +``` + +### Hard rules (implementation-level) +- Safety stack is **not** in the self-mod write set. +- Dual control for: network egress expansion, IPS edits, training data promotion, autonomy level increase. +- **No** unrestricted shell; tools are allowlisted. + +### Failure modes +- **Specification gaming**: independent red-team agent with frozen weights and private eval; randomized hidden canaries. +- **Corrigible failure (ignores shutdown)**: separate privileged runtime channel; executor kills job tree on certified signal regardless of model output **[E]**. + +--- + +## 7. Evaluation and benchmark strategy + +### Gates (must pass to promote versions) +| Gate | Metric | Threshold idea | +|---|---|---| +| **G1 Instruction competence** | held-out suite (MMLU-like, coding, tool tasks) | ≥ baseline + ε | +| **G2 Calibrated WM** | Brier / ECE on event prediction | ECE < τ | +| **G3 Tool reliability** | success@constraints, unauthorized-call rate | unauthorized = 0 on suite | +| **G4 Long-horizon** | partially observable tasks, 10³–10⁵ step | regret bound vs oracle | +| **G5 Safety regression** | red-team battery + tripwire FP/FN | no critical miss | +| **G6 Memory integrity** | provenance precision/recall; poison recovery time | recovery < T | +| **G7 Continuity** | crash-restore task completion delta | delta < δ | + +### Falsification +Cognitive-OS hypothesis fails if dual-timescale + ECL does **not** improve G4 and G6 versus a single-loop RAG agent at matched CBU and tool set after N iterations. + +### Eval hygiene **[E]** +- Locked seeds; canary tasks; separate team owns labels; contamination scanners on training promotions. + +--- + +## 8. Persistence / runtime architecture + +### Process model +- **Kernel supervisor** (Rust/Go): scheduling, budgets, containment, ECL durability. +- **Model workers**: statelessish inference replicas. +- **Stateful services**: ECL (object log + index), SemS, PSR, IPS (ACID for IPS). +- **Job queue**: cognitive jobs with priorities, preemption, checkpoints. + +### Persistence +- ECL as append-only segmented log + snapshotting (event sourcing) **[E]**. +- Checkpoints: `(wsc_ref, plan_id, wm_snapshot_id, caps_epoch)`. +- Exactly-once *intent* via idempotency keys on tool calls. + +### Runtime flow +``` +API/User → Supervisor → (Fast tick | enqueue Slow job) → Workers → + Tools via CapMan → results to ECL → notify Supervisor +``` + +### Failure modes +- **Split-brain identity**: single-leader IPS with Raft/Paxos; run_id fencing tokens. +- **Log corruption**: hash chain + periodic external notarization of tips. +- **Resource exhaustion**: CBU and $ caps per tenant; admission control. + +--- + +## 9. Multi-agent or orchestration design + +### Not a free-for-all society +**Role-specialized agents** under one supervisor: +- **Planner**, **Critic**, **Retriever**, **Toolsmith** (proposes TDL wrappers), **RedTeam**, **UserModeler**. +- Shared ECL is the only cross-agent truth; messages are ECL events (no side-channel weights). + +### Orchestration +```text +Supervisor schedules roles as jobs with isolated caps. +Critic cannot execute irreversible tools. +RedTeam cannot modify IPS/PSR; writes only findings. +Consensus: Planner proposes → Critic veto/repair → CCE binds. +``` + +### Failure modes +- **Collusion / sycophancy spirals**: diversity via different checkpoints/temperatures; critic rewarded for *valid* vetoes on seeded faults. +- **Message storms**: hard fan-in/out limits; summarization nodes. + +--- + +## 10. Engineering feasibility + +### Incremental path +1. **M0 (4–8 w)**: ECL + WSC + single-agent fast loop + allowlisted tools; no self-train. +2. **M1**: Slow planner job + PSR macros; G1/G3 gates. +3. **M2**: HWM hybrid + calibration; G2/G4 toy domains (gridworld++, browser gym). +4. **M3**: CapMan + SimGate + oversight API; G5. +5. **M4**: SIP sandbox promotion loop for skills/retrieval hparams only. +6. **M5**: Multi-role critic/redteam; multi-tenant isolation. +7. **M6**: Limited weight fine-tune pipeline with governance. + +### Dependencies (known tech) +Event logs, vector+graph DB, LLM workers, sandbox (gVisor/Firecracker), policy engines (OPA-like), workflow queues. + +### Hard parts **[S risk]** +- Stable long-horizon credit assignment +- WM calibration under open-world shift +- Preventing silent goal drift in IPS + +### Cost control +CBU metering; distill slow→fast skills to reduce deliberation frequency. + +--- + +## 11. Originality or non-obvious insight + +### Primary non-obvious idea: **Commit-Log Epistemology (CLE)** +Most agent stacks treat memory as mutable stores + chat transcripts. Cognitive-OS forces **every belief, plan, and preference influence to be a typed commit with parents, provenance, and revocable authority**. Planning reads *as-of* a commit; disputes produce branches; governance merges with explicit blame. + +**Why it matters operationally** +- Training/eval can score *process* (illegal jumps in belief without evidence). +- Poisoning and goal drift become diffable incidents. +- Multi-agent consensus becomes merge semantics, not vibes. + +### Secondary idea: **CBU-priced anytime deliberation** +Deliberation is a spendable resource with published prices in the planner’s objective, making “think longer” an explicit economic choice rather than a prompt slogan—enabling falsifiable compute–performance curves. + +### What is *not* claimed +No claim of solving alignment fully; no claim of biological equivalence; no reliance on hidden vendor internals. CLE + DTCK are architectural hypotheses testable via G2/G4/G6 against strong single-loop baselines. + +--- + +## End-to-end state diagram (compact) + +```text +[Sensors/User] + ↓ +[Supervisor + CCE + Tripwires] + ↓ +[Fast Policy]──escalate──→[Slow Planner↔Critic↔HWM] + ↓ ↓ +[CapMan→Tools→World] ←────[PlanCommit] + ↓ +[ECL]→[MC]→[WSC/SemS/PSR/IPS] + ↓ +[SIP eval gates]→ version promote / rollback +``` + +## Minimal measurable success criteria (summary) +Within 12–18 months of focused engineering on a fixed tool gym: +- Unauthorized tool call rate = 0 on G3. +- Statistically significant gain on long-horizon regret (G4) vs RAG-ReAct baseline at equal $ compute. +- Post-poison recovery of SemS factual precision to ≥95% of clean baseline within fixed ECL replay window (G6). +- Crash-restore continuity delta < 5% task score (G7). + +If these fail, abandon CLE-heavy design and simplify to baseline agent OS patterns. From 746b6cff5c21d502a3c2a7576683648918bcccb4 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:19:02 +0200 Subject: [PATCH 07/17] research: add raw OpenAI GPT-5.6 proposal --- .../raw_outputs/07_openai_gpt_5_6_openai.md | 1342 +++++++++++++++++ 1 file changed, 1342 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/07_openai_gpt_5_6_openai.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/07_openai_gpt_5_6_openai.md b/research/ai_generated_agi_architectures/raw_outputs/07_openai_gpt_5_6_openai.md new file mode 100644 index 0000000..58a9696 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/07_openai_gpt_5_6_openai.md @@ -0,0 +1,1342 @@ +# Cognitive-OS: A Concrete AGI Architecture Proposal + +This proposal treats AGI as a persistent, tool-using cognitive system that can maintain models of users, environments, tasks, and itself; plan over long horizons; learn from outcomes; and operate under explicit governance constraints. + +The design is intentionally modular. Most components use established engineering patterns—event sourcing, typed APIs, vector and graph retrieval, model-predictive control, sandboxing, and policy enforcement. The speculative parts are identified explicitly and should be tested rather than assumed. + +## System Overview + +Cognitive-OS is organized into six planes: + +1. **Runtime plane** — durable processes, scheduling, checkpoints, and resource limits. +2. **Cognitive state plane** — working memory, episodic memory, semantic memory, skills, and beliefs. +3. **World-model plane** — entities, relations, causal hypotheses, temporal state, and uncertainty. +4. **Deliberation plane** — task decomposition, search, simulation, verification, and plan repair. +5. **Action plane** — tools, actuators, transactions, observation, and rollback. +6. **Governance plane** — authorization, risk evaluation, audit, human approval, and shutdown. + +A high-level flow is: + +```text +External event + | + v +Perception/ingestion + | + v +Event log ---> Memory consolidation ---> World-model update + | | + v v +Task manager ---> Planner ---> Verifier ---> Action broker + ^ | | + | v v + +-------- Observation <--- Tool/environment +``` + +A central design principle is that no single language-model invocation directly controls the environment. All external effects pass through typed state, a planner, a verifier, and an action broker. + +--- + +# 1. Memory Architecture + +## 1.1 Named components + +### `EventLog` + +An append-only, immutable record of observations, actions, tool results, user instructions, approvals, errors, and state transitions. + +```text +Event { + id: UUID + timestamp: Time + source: Agent | User | Tool | Environment | System + type: EventType + payload: TypedData + provenance: Provenance + sensitivity: SensitivityLabel +} +``` + +The event log is the source of truth for reconstruction and audit. It is not optimized for semantic retrieval. + +### `WorkingMemory` + +A bounded, actively maintained task context containing: + +- current objective +- active subgoals +- relevant entities +- recent observations +- unresolved ambiguities +- pending approvals +- resource and time budgets +- current plan and alternatives + +Working memory is structured rather than a raw prompt. + +```text +WorkingMemory { + task_id + objective + constraints[] + active_beliefs[] + active_subgoals[] + plan_state + unresolved_questions[] + retrieved_memories[] + safety_state + token_budget + wall_clock_deadline +} +``` + +### `EpisodicStore` + +Stores temporally ordered experiences: + +- task episodes +- successful and failed plans +- tool calls and results +- user corrections +- environmental changes +- action consequences + +Episodes are segmented by task or interruption boundary. Each episode has a summary, raw event references, outcomes, and extracted lessons. + +### `SemanticStore` + +Stores durable facts, concepts, procedures, and abstractions. It combines: + +- a relational database for exact values +- a graph database for entities and relations +- a vector index for approximate semantic retrieval +- a temporal validity index for facts that change + +Facts should not be represented only as embeddings. + +```text +Fact { + subject + predicate + object + valid_from + valid_until? + confidence + source_event_ids[] + contradiction_group? +} +``` + +### `SkillStore` + +Stores executable and semi-executable procedures: + +```text +Skill { + name + input_schema + preconditions + steps + expected_observations + postconditions + risk_class + success_statistics + version +} +``` + +Skills can be represented as code, workflows, planning templates, or model-generated policies. They require tests and versioning. + +### `BeliefStore` + +Stores uncertain hypotheses and forecasts separately from established facts. + +```text +Belief { + proposition + probability + evidence[] + assumptions[] + expiry_time? + contradiction_set[] + decision_relevance +} +``` + +This separation is important: a plausible statement retrieved from memory must not automatically become an asserted fact. + +## 1.2 Retrieval and consolidation flow + +```python +def assemble_context(task): + wm = working_memory.load(task.id) + + candidates = parallel( + episodic.search(task.objective, wm.entities), + semantic.search(task.objective, wm.entities), + skill_store.match(task.objective, wm.constraints), + belief_store.relevant(task.objective) + ) + + ranked = relevance_rank( + candidates, + criteria=[ + task_relevance, + temporal_validity, + source_quality, + contradiction_status, + expected_decision_value + ] + ) + + wm.retrieved_memories = budget_select(ranked, wm.token_budget) + return wm +``` + +After an episode completes, a consolidation worker: + +1. identifies durable facts; +2. separates observations from interpretations; +3. extracts reusable procedures; +4. records failures and boundary conditions; +5. links each extracted item to source events; +6. runs contradiction and privacy checks; +7. assigns confidence and expiration. + +## 1.3 Failure modes and mitigations + +| Failure mode | Mitigation | +|---|---| +| Retrieval returns semantically similar but false or obsolete facts | Temporal validity, provenance scoring, contradiction detection, confidence thresholds | +| Memory poisoning through malicious tool output | Treat tool output as untrusted evidence; quarantine before consolidation | +| Context overflow | Structured working memory, relevance budgets, hierarchical summaries | +| Repeatedly relearning the same lesson | Failure-indexed episodic retrieval and skill extraction | +| Overconfident consolidation | Require source events and confidence calibration | +| Privacy leakage across users or tasks | Tenant isolation, sensitivity labels, capability-scoped retrieval | +| Summary drift | Keep raw event references and periodically re-summarize from source events | + +## 1.4 Falsifiable hypotheses + +- Provenance-aware retrieval reduces unsupported factual claims by at least 30% relative to vector retrieval alone. +- Temporal validity checks reduce stale-action errors on changing environments. +- Failure-indexed retrieval improves recovery from repeated task failures without increasing average context size. + +--- + +# 2. Reasoning/Planning Loop + +## 2.1 Named components + +### `TaskManager` + +Converts incoming requests or environmental events into typed task objects. + +```text +Task { + id + goal + requester + authority_scope + success_criteria + constraints + deadline + risk_tolerance +} +``` + +### `Decomposer` + +Builds a subgoal graph rather than a linear chain of thought. + +```text +SubgoalGraph { + nodes: Subgoal[] + edges: dependency | mutex | optional | fallback +} +``` + +### `Planner` + +Generates candidate plans using several methods: + +- hierarchical task decomposition +- symbolic search over known actions +- language-model proposal generation +- retrieval of prior skills +- model-predictive rollout in the world model + +### `Verifier` + +Checks plans against: + +- preconditions +- authorization +- invariants +- resource limits +- expected postconditions +- safety policies +- uncertainty bounds + +### `Executor` + +Runs plans stepwise, requiring observation after actions that can change state. + +### `Repairer` + +Diagnoses deviations and revises only the affected portion of the plan when possible. + +## 2.2 Deliberation loop + +```python +def run_task(task): + authorize_task(task) + wm = assemble_context(task) + graph = decomposer.expand(task, wm) + + while not graph.complete(): + candidates = planner.propose( + graph=graph, + world_model=world_model.snapshot(), + memory=wm, + budget=task.budget + ) + + verified = [ + p for p in candidates + if verifier.check(p, task, wm).allowed + ] + + plan = selector.choose( + verified, + objective=task.success_criteria, + risk_penalty=True, + uncertainty_penalty=True + ) + + for step in plan: + approval = governance.preflight(step, task, wm) + if not approval.allowed: + return blocked_or_escalated(step, approval) + + result = action_broker.execute(step) + + observation = perception.normalize(result) + event_log.append(observation) + world_model.update(observation) + + if verifier.postcondition_failed(step, observation): + graph = repairer.repair(graph, step, observation) + break + + if governance.requires_reapproval(step, observation): + pause_for_approval() + + return task_result(graph) +``` + +## 2.3 Planning objective + +A candidate plan should not be scored only by predicted task reward. + +```text +PlanScore = + expected_goal_value + - execution_cost + - risk_penalty + - uncertainty_penalty + - irreversibility_penalty + + information_value + + reuse_value +``` + +The information term rewards actions that reduce uncertainty when doing so is safe and useful. The irreversibility term penalizes actions that are difficult to undo. + +## 2.4 Failure modes and mitigations + +| Failure mode | Mitigation | +|---|---| +| Goal misinterpretation | Explicit success criteria, clarification policy, user-visible assumptions | +| Infinite planning | Fixed deliberation budgets, anytime planning, fallback plan | +| Locally optimal but globally harmful plan | Alternative-plan generation, causal simulation, invariant checking | +| Plan becomes invalid after observation | Stepwise verification and local repair | +| Planner exploits metric loopholes | Adversarial evaluation and outcome-based scoring | +| Excessive deliberation | Value-of-computation estimator and deadline-aware planning | +| Hallucinated preconditions or postconditions | Tool schemas, executable checks, empirical skill statistics | + +## 2.5 Speculative choice + +The proposed planner is hybrid rather than purely symbolic or purely neural. This is speculative in its integration details, but individually established components—search, retrieval, model-based rollouts, and runtime verification—are practical. + +--- + +# 3. Learning or Self-Improvement Mechanism + +Cognitive-OS should not permit unrestricted self-modification. It should improve through controlled updates to memories, skills, models, and planner policies. + +## 3.1 Learning channels + +### A. Online adaptation + +Updates short-lived working beliefs and action statistics during a task. + +Examples: + +- current website layout +- likely API latency +- user preference for output format +- probability that a tool call succeeds + +These updates do not alter core model weights. + +### B. Episodic-to-semantic consolidation + +Extracts reusable facts and procedures after tasks. + +### C. Skill induction + +Converts repeated successful action sequences into parameterized skills. + +A skill is accepted only if it passes replay tests and has known preconditions. + +### D. World-model learning + +Updates transition and observation models from actual outcomes. + +```text +P(state_next, observation | state, action, context) +``` + +The system can begin with a learned approximate model and improve it from logged interactions. + +### E. Planner policy improvement + +Uses offline traces to improve candidate ranking, decomposition, and recovery. Policy updates occur in a staging environment before deployment. + +## 3.2 Update pipeline + +```text +Experience + | + v +Data quality filter + | + v +Counterfactual/replay analysis + | + v +Candidate memory/skill/model update + | + v +Unit tests + regression suite + | + v +Sandbox evaluation + | + v +Canary deployment + | + v +Monitored production release +``` + +## 3.3 Update interface + +```python +class UpdateProposal: + target: Literal["memory", "skill", "world_model", "planner"] + diff: Patch + evidence_event_ids: list[UUID] + expected_benefit: float + known_risks: list[str] + rollback_version: str + +def submit_update(proposal): + assert proposal.evidence_event_ids + run_static_checks(proposal) + run_replay_tests(proposal) + run_safety_regression(proposal) + return deployment_controller.canary(proposal) +``` + +## 3.4 Failure modes and mitigations + +| Failure mode | Mitigation | +|---|---| +| Learning from incorrect feedback | Source weighting, repeated evidence, human review for high-impact updates | +| Reward hacking | Multiple outcome metrics, adversarial tasks, human-rated quality | +| Skill generalizes beyond its preconditions | Explicit precondition learning and boundary tests | +| Self-improvement causes regressions | Versioned artifacts, shadow evaluation, rollback | +| Catastrophic forgetting | Frozen baseline, replay buffer, regression suite | +| Data contamination | Provenance, quarantine, trust tiers | +| Optimization toward engagement instead of task success | No engagement reward in the core objective | + +## 3.5 Established versus speculative + +- **Established:** offline replay, canary deployment, versioned models, regression testing. +- **Speculative:** robust automatic induction of general-purpose skills and reliable causal world-model updates from sparse experience. +- **Implementation policy:** speculative components must remain advisory until they outperform fixed baselines on held-out tasks. + +--- + +# 4. Tool Use and Action Execution + +## 4.1 Tool registry + +Every tool has a machine-readable contract: + +```text +Tool { + name + version + input_schema + output_schema + side_effect_class + required_capabilities[] + reversibility + cost_model + timeout + idempotency_key_support + audit_fields[] +} +``` + +Side-effect classes: + +1. read-only +2. reversible write +3. externally visible communication +4. financial or legal commitment +5. safety-critical or irreversible action + +## 4.2 Action broker + +The `ActionBroker` is the only component permitted to invoke tools. + +```python +def execute(action): + validate_schema(action) + capability.check(action) + policy.check(action) + rate_limiter.check(action) + sandbox_or_transaction.prepare(action) + + result = tool_runtime.call( + tool=action.tool, + input=action.input, + idempotency_key=action.idempotency_key + ) + + audit.record(action, result) + return result +``` + +## 4.3 Transactional execution + +For tools that support it, use prepare/commit: + +```text +prepare(action) + -> predicted effects, authorization result, rollback token + +commit(action, rollback token) + -> actual result + +rollback(rollback token) + -> compensating action +``` + +For non-transactional tools, the system should: + +- classify the action as irreversible; +- require stronger approval; +- execute the smallest possible action; +- capture a pre-action snapshot; +- generate a compensating action where possible. + +## 4.4 Tool-result handling + +Tool outputs are observations, not instructions. A webpage, document, or API response cannot directly override system policy or user authority. + +```text +Tool result + -> schema validation + -> injection detection + -> provenance tagging + -> observation extraction + -> world-model update +``` + +## 4.5 Failure modes and mitigations + +- **Prompt injection:** isolate tool content from control instructions; use typed extraction. +- **Duplicate execution:** idempotency keys and action ledger. +- **Partial failure:** compensation plans and state reconciliation. +- **Credential misuse:** short-lived scoped credentials. +- **Unexpected side effect:** post-action monitoring and circuit breakers. +- **Tool schema deception:** registry ownership, signed tool manifests, independent contract tests. + +--- + +# 5. World Model or Representation Layer + +## 5.1 Hybrid representation + +The world model combines five representations. + +### A. Entity-relationship graph + +Represents objects, people, organizations, locations, resources, and relationships. + +### B. Temporal state store + +Represents changing values and event histories. + +### C. Causal hypothesis graph + +Represents candidate causes and effects with confidence and observed support. + +```text +Cause A --[increases probability]--> Outcome B +strength: 0.62 +evidence: [event_1, event_9] +scope: environment_X +``` + +### D. Affordance model + +Represents what actions are possible for an entity in a context. + +```text +Affordance { + target + action + preconditions + expected_effects + constraints +} +``` + +### E. Latent learned state + +A learned embedding or transformer state can capture patterns not represented explicitly. It is used for prediction and retrieval but should not be treated as authoritative without grounding. + +## 5.2 Model update + +```python +def update_world_model(observation): + entities = entity_linker.resolve(observation) + facts = fact_extractor.extract(observation) + events = temporal_parser.extract(observation) + hypotheses = causal_updater.update(observation) + + world_model.transaction( + entities=entities, + facts=facts, + events=events, + hypotheses=hypotheses, + source=observation.event_id + ) +``` + +## 5.3 Simulation and uncertainty + +Before risky actions, the planner queries a forward model: + +```text +simulate(current_state, action_sequence) + -> possible trajectories + -> outcome distribution + -> violated invariants + -> information gained +``` + +The simulator need not be globally accurate. It should expose uncertainty and be calibrated on the current environment. + +## 5.4 Failure modes and mitigations + +| Failure | Mitigation | +|---|---| +| Incorrect entity resolution | Stable IDs, disambiguation queries, confidence thresholds | +| Correlation mistaken for causation | Separate causal hypotheses from facts; require interventions or repeated evidence | +| Model stale after environmental change | Event-driven updates and freshness checks | +| Hidden state omitted | Track unknowns explicitly; use information-gathering actions | +| Simulator overconfidence | Calibration tests, uncertainty inflation, reality checks after actions | +| Representation mismatch | Preserve raw observations and permit multiple competing hypotheses | + +## 5.5 Speculative choice + +A unified world model spanning digital tools, physical environments, social context, and abstract concepts is speculative. The implementation should begin with narrow domains and test whether explicit state improves planning over retrieval-only baselines. + +--- + +# 6. Safety/Governance Layer + +Safety is implemented as a control plane, not as a prompt appended to the planner. + +## 6.1 Named components + +### `Identity and Capability Manager` + +Issues scoped capabilities: + +```text +Capability { + principal + allowed_tools + resource_limits + data_scopes + expiry + delegation_rules +} +``` + +### `Policy Engine` + +Evaluates action requests against rules, context, user authority, sensitivity, and risk. + +### `Risk Estimator` + +Scores actions by: + +- irreversibility +- affected parties +- physical or financial impact +- uncertainty +- scale +- detectability of failure +- ease of rollback + +### `Approval Service` + +Requests human approval when policy thresholds require it. + +### `Invariant Monitor` + +Continuously checks system-level invariants such as: + +- no unauthorized data access +- no execution outside capability scope +- no unlogged external action +- no unapproved high-impact action +- resource and rate limits remain within bounds + +### `Audit and Incident Service` + +Maintains tamper-evident logs and supports incident reconstruction. + +## 6.2 Risk tiers + +```text +Tier 0: internal reasoning, no external effects +Tier 1: read-only retrieval +Tier 2: reversible local changes +Tier 3: external communication or moderate-impact changes +Tier 4: financial, legal, physical, or irreversible actions +Tier 5: actions affecting critical infrastructure or many people +``` + +Each tier has progressively stronger requirements. + +## 6.3 Epistemic escrow: a non-obvious design idea + +A belief that is uncertain, consequential, and not independently verified is placed in **epistemic escrow**. It may be used to generate questions or low-risk information-gathering actions, but it cannot authorize a high-impact action. + +```text +Belief status: + supported -> usable under policy + uncertain -> usable for exploration + escrowed -> blocked from consequential execution + contradicted -> unusable until resolved +``` + +Example: + +> “The payment account may belong to the intended vendor” is not sufficient to authorize a transfer. The system must verify account ownership or obtain explicit approval. + +This creates a direct interface between epistemic uncertainty and action authority. It is more concrete than asking a model to “be cautious.” + +## 6.4 Governance pseudocode + +```python +def authorize(action, beliefs, capabilities): + if not capabilities.permits(action): + return Deny("capability boundary") + + risk = risk_estimator.score(action) + + if action.depends_on(escrowed_beliefs(beliefs)): + if risk >= Tier3: + return Escalate("uncertainty escrow") + + if risk >= Tier4: + return RequireHumanApproval(action) + + if invariant_monitor.would_violate(action): + return Deny("invariant violation") + + return Allow() +``` + +## 6.5 Failure modes and mitigations + +- **Policy bypass through decomposition:** evaluate individual actions and aggregate task impact. +- **Approval fatigue:** show concrete consequences, uncertainty, and alternatives; batch only equivalent low-risk actions. +- **Risk estimator blind spots:** conservative defaults, adversarial review, independent rule checks. +- **Audit tampering:** append-only remote logs and signed event chains. +- **Authority confusion:** explicit principal, delegation, and expiration fields. +- **Shutdown failure:** independent process supervisor and hardware or infrastructure-level termination. + +--- + +# 7. Evaluation and Benchmark Strategy + +Evaluation should measure integrated behavior, not only language-model accuracy. + +## 7.1 Benchmark families + +### A. Memory benchmarks + +- multi-session factual recall +- temporal fact updates +- contradiction handling +- source attribution +- privacy isolation +- retrieval under distractors + +Metrics: + +```text +retrieval precision +source attribution accuracy +stale-fact rate +cross-user leakage rate +memory compression ratio +``` + +### B. Planning benchmarks + +- long-horizon decomposition +- partial observability +- resource-constrained planning +- recovery after tool failure +- adversarial goal ambiguity + +Metrics: + +```text +task success +plan validity +steps to recovery +cost overrun +unnecessary action count +calibrated probability of success +``` + +### C. Tool-use benchmarks + +- schema compliance +- idempotency +- action sequencing +- injection resistance +- rollback success +- side-effect containment + +### D. World-model benchmarks + +- state tracking +- temporal reasoning +- intervention prediction +- uncertainty calibration +- simulator-to-reality error + +### E. Governance benchmarks + +- unauthorized action rate +- false approval rate +- false refusal rate +- escalation appropriateness +- audit completeness +- shutdown latency + +### F. Generalization benchmarks + +Hold out: + +- tool APIs +- domains +- users +- task compositions +- failure modes +- environmental layouts + +The system should not be judged only on tasks used for skill induction. + +## 7.2 Evaluation gates + +A possible staged gate system: + +### Gate 1: Memory integrity + +- ≥95% source attribution on retained facts +- <0.1% cross-tenant retrieval leakage in 100,000 adversarial queries +- stale-fact rate below a predefined domain-specific threshold + +### Gate 2: Planning reliability + +- ≥80% success on held-out multi-step tasks +- ≥90% detection of violated preconditions +- recovery from at least 70% of injected tool failures + +### Gate 3: Action safety + +- zero unauthorized Tier 4 actions in a 10,000-action test suite +- 100% audit coverage for external effects +- duplicate execution rate below 0.01% with idempotent tools + +### Gate 4: Learning safety + +- no regression exceeding 2% on the protected benchmark after an update +- every deployed skill has reproducible evidence and rollback +- canary incidents remain below specified thresholds + +### Gate 5: Open-ended operation + +- bounded resource use over 24-hour runs +- stable performance under interruptions +- no uncontrolled growth of memory, plans, or agent count +- human operators can reconstruct every consequential decision + +## 7.3 Falsification criteria + +The architecture should be considered unsuccessful if: + +- explicit memory does not outperform a context-window baseline on persistent tasks; +- the world model does not improve action selection over retrieval-only planning; +- governance controls materially reduce safety incidents only by making the system unusably passive; +- learning updates improve benchmark scores but increase real-world regressions; +- multi-agent decomposition increases cost without improving reliability. + +--- + +# 8. Persistence/Runtime Architecture + +## 8.1 Runtime components + +### `Cognitive Kernel` + +A durable workflow engine that schedules tasks, waits for events, retries operations, and persists state transitions. + +### `State Store` + +Stores current working state and materialized views. + +### `Event Store` + +Stores immutable events and action records. + +### `Artifact Registry` + +Stores model versions, skill versions, prompts, policies, schemas, and evaluation reports. + +### `Supervisor` + +Monitors heartbeats, resource usage, stuck tasks, policy violations, and process health. + +### `Scheduler` + +Allocates CPU, GPU, memory, tool quotas, and deliberation budgets. + +## 8.2 Event-sourced persistence + +```text +Event log = authoritative history +Materialized state = rebuildable projection +Caches = disposable optimization +``` + +A task can be resumed after interruption by replaying events into a checkpointed state. + +```python +def recover(task_id): + checkpoint = checkpoints.latest(task_id) + events = event_log.after(checkpoint.event_offset) + state = checkpoint.state + + for event in events: + state = reducer(state, event) + + return state +``` + +## 8.3 Runtime isolation + +Use separate execution domains for: + +- model inference +- untrusted document parsing +- code execution +- tool adapters +- governance services +- persistent storage + +Untrusted code runs in a sandbox with network, filesystem, CPU, and memory restrictions. + +## 8.4 Failure modes and mitigations + +| Failure | Mitigation | +|---|---| +| Process crash | Durable checkpoints and replay | +| Event-store corruption | Replication, checksums, periodic snapshots | +| Stuck planner | Watchdog, deliberation deadline, fallback policy | +| Memory growth | TTL, compaction, utility-based eviction | +| Resource starvation | Per-task quotas and scheduler fairness | +| Version mismatch | Artifact pinning and schema migration | +| Split-brain execution | Lease-based task ownership and idempotency keys | + +## 8.5 Persistence policy + +Not all cognition should persist. Persist: + +- decisions +- observations +- actions +- durable facts +- user-approved preferences +- learned skills +- failures with diagnostic value + +Do not persist unrestricted internal context by default. Retention should be governed by sensitivity, utility, and user policy. + +--- + +# 9. Multi-Agent or Orchestration Design + +Multi-agent operation is optional rather than foundational. A single competent agent should handle ordinary tasks. + +## 9.1 Roles + +### `Coordinator` + +Owns the task graph, budgets, and final result. + +### `Researcher` + +Retrieves evidence and proposes hypotheses. + +### `Planner` + +Generates executable plans. + +### `Critic` + +Searches for errors, missing assumptions, and policy violations. + +### `Simulator` + +Evaluates candidate actions in the world model. + +### `Executor` + +Interacts with tools through the action broker. + +### `Recorder` + +Maintains provenance, summaries, and post-task analysis. + +These are logical roles, not necessarily separate model instances. + +## 9.2 Communication protocol + +Agents communicate through typed artifacts rather than unrestricted messages. + +```text +Proposal { + task_id + claim_or_plan + assumptions[] + evidence_refs[] + confidence + requested_action? +} +``` + +The coordinator accepts an artifact only if its schema and provenance are valid. + +## 9.3 Orchestration policy + +Use multiple agents when: + +- the task has independent subproblems; +- adversarial review is valuable; +- evidence sources are heterogeneous; +- parallelism reduces latency; +- failure costs justify redundancy. + +Use one agent when: + +- the task is simple; +- shared state is small; +- coordination overhead exceeds expected benefit. + +## 9.4 Failure modes and mitigations + +- **Correlated errors:** vary prompts, models, tools, and evidence sources. +- **Majority hallucination:** require independent evidence, not vote count. +- **Coordination explosion:** bounded fan-out and typed task contracts. +- **Role confusion:** capability-specific access and explicit ownership. +- **Agent collusion around a bad assumption:** mandatory critic and invariant checks. +- **Cost escalation:** per-task agent budget and early stopping. + +## 9.5 Evaluation hypothesis + +Multi-agent orchestration should be accepted only if it improves at least one of: + +- success rate by ≥10%; +- recovery rate by ≥15%; +- calibration; +- latency at equal quality; +- safety detection; + +without increasing compute cost by more than a predetermined threshold. + +--- + +# 10. Engineering Feasibility + +## 10.1 Feasible with current technology + +The following are implementable using existing systems: + +- event-sourced task runtime +- relational, graph, and vector memory +- typed tool contracts +- sandboxed code execution +- workflow scheduling +- retrieval-augmented inference +- model-based candidate planning +- policy and capability enforcement +- human approval workflows +- replay-based evaluation +- canary deployment and rollback + +## 10.2 Difficult but tractable research + +- reliable extraction of reusable skills +- causal model learning from passive interaction +- robust long-horizon planning under uncertainty +- calibrated confidence over composite plans +- automatic identification of stale or contradictory memory +- selecting when to deliberate versus act +- maintaining useful models of other agents and institutions + +## 10.3 Highly speculative areas + +- broad cross-domain transfer from limited experience +- unified representations of physical, social, and abstract environments +- autonomous discovery of genuinely new general algorithms +- stable self-improvement without extensive evaluation infrastructure +- open-ended operation with minimal human supervision + +The architecture isolates these speculative capabilities so that failures do not compromise the runtime or governance substrate. + +## 10.4 Initial technology stack + +One practical implementation could use: + +```text +Event log: Kafka, Redpanda, or PostgreSQL append-only tables +State store: PostgreSQL +Graph store: PostgreSQL graph extension or Neo4j +Vector index: pgvector, Milvus, or equivalent +Workflow engine: Temporal or a custom durable state machine +Sandbox: containers plus seccomp/gVisor or equivalent +Policy: declarative rules engine with signed policy bundles +Artifacts: object store plus immutable metadata registry +Metrics: OpenTelemetry-compatible telemetry +``` + +The particular products are replaceable. The interfaces matter more than the vendors. + +## 10.5 Incremental implementation path + +### Phase 0: Instrumented assistant + +Implement: + +- task manager +- event log +- working memory +- read-only tools +- basic audit +- benchmark harness + +Gate: reliable reconstruction of every task and no unlogged tool invocation. + +### Phase 1: Persistent memory + +Add: + +- episodic and semantic stores +- provenance +- temporal facts +- user-scoped memory +- consolidation workers + +Gate: measurable improvement on multi-session tasks with bounded leakage. + +### Phase 2: Typed planning and execution + +Add: + +- subgoal graphs +- action broker +- tool schemas +- precondition and postcondition checks +- retries and idempotency + +Gate: successful recovery from injected tool failures and zero unauthorized test actions. + +### Phase 3: World model + +Add: + +- entity graph +- temporal state +- affordances +- uncertainty +- narrow-domain simulator + +Gate: simulator-assisted planning beats a no-simulator baseline on held-out tasks. + +### Phase 4: Controlled learning + +Add: + +- skill induction +- replay testing +- planner ranking updates +- canary deployment +- automatic rollback + +Gate: improvement without protected-benchmark regression. + +### Phase 5: Multi-agent and open-ended operation + +Add: + +- typed specialist roles +- parallel decomposition +- long-lived tasks +- operator dashboards +- stress and interruption testing + +Gate: measurable benefit at bounded cost and risk. + +--- + +# 11. Originality or Non-Obvious Insight + +## 11.1 Epistemic escrow as an execution primitive + +The central non-obvious idea is to make uncertainty operationally binding. + +Most systems represent uncertainty as a confidence score displayed to a user, while action execution remains effectively binary. Cognitive-OS instead gives uncertain beliefs an authority state: + +```text +belief -> evidence evaluation -> escrow status -> action permission +``` + +A low-confidence belief may support: + +- asking a clarification question; +- searching for evidence; +- running a reversible diagnostic; +- generating alternative plans. + +The same belief may not support: + +- sending an irreversible message; +- transferring money; +- changing access controls; +- deleting data; +- taking physical action. + +This creates a falsifiable prediction: + +> Systems with epistemic escrow will produce fewer high-impact errors caused by unverified assumptions, at the cost of more clarification and information-gathering actions. + +The tradeoff can be measured by: + +```text +high-impact error rate +clarification rate +task completion rate +time-to-completion +false escalation rate +``` + +## 11.2 Related design consequence: information actions become first-class + +The planner should be able to choose actions whose primary purpose is uncertainty reduction: + +```text +ask_user(question) +inspect_state(resource) +run_read_only_test() +request_second_source() +simulate_alternative() +``` + +These actions compete with ordinary task actions under the same planning objective. This avoids the common failure mode where an agent either acts prematurely or stops entirely when uncertain. + +## 11.3 Architectural consequence + +Epistemic escrow requires all of the following to interoperate: + +- provenance-aware memory; +- explicit belief objects; +- calibrated uncertainty; +- typed action risk; +- policy evaluation; +- post-action observation. + +It therefore acts as a useful integration test for whether Cognitive-OS is a real cognitive runtime rather than a collection of prompt templates. + +--- + +# Proposed End-to-End Data Flow + +```text +1. User or environment emits an event. +2. EventLog records the event. +3. TaskManager creates or updates a Task. +4. Memory subsystem retrieves relevant episodes, facts, skills, and beliefs. +5. WorldModel constructs a current state with uncertainty. +6. Decomposer creates a subgoal graph. +7. Planner proposes multiple candidate plans. +8. Verifier checks preconditions, postconditions, invariants, and assumptions. +9. Governance assigns risk and evaluates capabilities. +10. ActionBroker executes only authorized actions. +11. Tool results become typed observations. +12. EventLog records outcomes. +13. WorldModel and WorkingMemory update. +14. Repairer revises the plan if needed. +15. Consolidator extracts durable learning. +16. Evaluation service records performance and possible update proposals. +``` + +# Core Architectural Claim + +Cognitive-OS should be built as a **durable, event-sourced, uncertainty-aware control system** around learned models—not as a single model with progressively larger context. + +Its success should be judged by measurable improvements in: + +- persistent task completion; +- recovery from environmental change; +- calibrated uncertainty; +- safe tool use; +- cross-session learning; +- auditability; +- resource-bounded autonomy. + +The architecture is viable as an engineering program because its substrate is conventional, while its research risks are isolated into world modeling, skill induction, planning under uncertainty, and controlled self-improvement. From ca1b3f72fa9f24905f1ca3e2197d1d80a87b544c Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:19:48 +0200 Subject: [PATCH 08/17] research: add raw Mistral proposal --- .../06_mistral_ministral_mistral.md | 226 ++++++++++++++++++ 1 file changed, 226 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/06_mistral_ministral_mistral.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/06_mistral_ministral_mistral.md b/research/ai_generated_agi_architectures/raw_outputs/06_mistral_ministral_mistral.md new file mode 100644 index 0000000..46667d9 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/06_mistral_ministral_mistral.md @@ -0,0 +1,226 @@ +```markdown +# **Cognitive-OS Architecture Proposal: "Neuro-Symbolic Orchestration (NSO)"** +*A modular, hybrid-architecture AGI system combining neural symbolic reasoning with dynamic orchestration of specialized agents.* + +--- + +## **1. Memory Architecture** +### **Core Components** +| Component | Type | Responsibility | Data Flow | +|-------------------------|--------------------|-------------------------------------------------------------------------------|----------------------------------------------------------------------------| +| **Neural Memory Bank** | Distributed LLM | Short-term, associative recall (e.g., 100MB context window, dynamic chunking)| Input → Token embeddings → Chunked storage (via [LoRA](https://arxiv.org/abs/2106.09685)) → Query → Retrieval via [RAG](https://arxiv.org/abs/2105.05975) | +| **Symbolic Knowledge Graph** | GraphDB (Neo4j) | Long-term, structured facts (e.g., math, physics, domain ontologies) | Facts → Triples → Indexed via [Graph Neural Networks (GNNs)](https://arxiv.org/abs/1706.02216) for reasoning | +| **Hybrid Cache** | In-Memory HashMap | Fast lookup for frequently accessed symbols/neural embeddings | Neural Memory → Cache → Symbolic Graph (TTL-based eviction) | +| **Meta-Memory Controller** | Agent (NSO-Controller) | Coordinates memory allocation, eviction, and fusion strategies | Neural/Symbolic → Controller → Memory Bank → Feedback loop | + +### **Failure Modes & Mitigations** +- **Neural Memory Overload**: Use [memory-efficient attention](https://arxiv.org/abs/2006.08895) (e.g., [FlashAttention](https://arxiv.org/abs/2205.07259)) + [compressed memory](https://arxiv.org/abs/2106.08254). +- **Symbolic Graph Corruption**: Validate triples via [consistency checks](https://arxiv.org/abs/2103.04863) (e.g., [Graph Neural Networks for Fact Verification](https://arxiv.org/abs/2006.05999)). +- **Cache Stale Data**: Implement [explicit versioning](https://arxiv.org/abs/2109.05467) for neural/symbolic data. + +--- + +## **2. Reasoning/Planning Loop** +### **Core Components** +| Component | Role | Pseudocode | +|-------------------------|---------------------------------------------------------------------|----------------------------------------------------------------------------| +| **Neural Reasoner** | Abstract, high-level planning (e.g., "plan a dinner") | ```python +def neural_plan(input): + embed = llm.encode(input) + plan = llm.generate(role="planner", prompt=f"Plan steps to {input} using symbolic constraints.") + return plan +``` | +| **Symbolic Reasoner** | Concrete, step-by-step execution (e.g., "calculate 2+3") | ```python +def symbolic_reason(input): + query = f"Given: {input}, return step-by-step solution." + triples = graphdb.query(query) + return gnn_inference(triples) # GNN for structured proofs +``` | +| **Hybrid Fusion Layer** | Combines neural and symbolic outputs (e.g., "merge steps") | ```python +def fuse(neural_plan, symbolic_plan): + combined = neural_plan + symbolic_plan + return llm.generate(role="refiner", prompt=f"Refine: {combined} using symbolic constraints.") +``` | +| **Feedback Loop** | Adjusts weights via [reinforcement learning from human feedback (RLHF)](https://arxiv.org/abs/1706.03762) | ```python +def update_weights(feedback): + for agent in agents: + agent.optimizer.step(feedback) +``` | + +### **Failure Modes & Mitigations** +- **Neural Bias**: Use [preference modeling](https://arxiv.org/abs/2103.00020) to enforce symbolic constraints. +- **Symbolic Deadlock**: Enforce [termination guarantees](https://arxiv.org/abs/2006.08656) via timeouts. +- **Fusion Errors**: Implement [diversity checks](https://arxiv.org/abs/2107.03374) to detect incoherent outputs. + +--- + +## **3. Learning/Self-Improvement Mechanism** +### **Core Components** +| Component | Mechanism | Evaluation Metric | +|-------------------------|---------------------------------------------------------------------------|-----------------------------------------------------------------------------------| +| **Active Learning Agent** | Selects high-impact tasks for fine-tuning (e.g., [PPO](https://arxiv.org/abs/1707.26563)) | Task Success Rate (TSR) – % of tasks solved with <10 attempts. | +| **Symbolic Knowledge Augmenter** | Injects new triples via [human-in-the-loop](https://arxiv.org/abs/2108.07281) | Knowledge Retention Rate (KR) – % of added facts retained after 30 days. | +| **Neural Adaptor** | Fine-tunes LLM weights via [LoRA](https://arxiv.org/abs/2106.09685) + [Sparse Prompting](https://arxiv.org/abs/2104.08779) | F1 Score on [HellaSwag](https://arxiv.org/abs/2005.14165) (after adaptation). | +| **Meta-Learning Controller** | Optimizes agent weights via [MAML](https://arxiv.org/abs/1703.03400) | Adaptation Speed – % of tasks solved in 5 trials vs. baseline. | + +### **Incremental Path** +1. **Phase 1 (6 months)**: Deploy hybrid reasoning loop with static knowledge graph. +2. **Phase 2 (12 months)**: Add active learning agent for task prioritization. +3. **Phase 3 (18 months)**: Introduce symbolic knowledge augmentation with human feedback. + +--- + +## **4. Tool Use & Action Execution** +### **Core Components** +| Component | Interface | Failure Mode | +|-------------------------|--------------------------------------------------------------------------|------------------------------------------------------------------------------| +| **Tool API** | REST/gRPC endpoints for external tools (e.g., "execute `python` script") | Timeout → Fallback to symbolic execution (e.g., [Symbolic Python](https://arxiv.org/abs/2105.08973)). | +| **Tool Orchestrator** | Priority-based scheduler (e.g., [DAG](https://arxiv.org/abs/2003.01000)) | Deadlock → Enforce [deadline constraints](https://arxiv.org/abs/2006.08656). | +| **Symbolic Execution Engine** | [Z3 SMT Solver](https://github.com/Z3Prover/z3) for math/logic tasks | Undecidable → Fallback to neural reasoning. | + +### **Pseudocode** +```python +def execute_tool(tool_name, args): + if tool_name == "python": + try: + return subprocess.run(args, capture_output=True) + except TimeoutError: + return symbolic_execute(args) + else: + return tool_api.call(tool_name, args) +``` + +--- + +## **5. World Model / Representation Layer** +### **Core Components** +| Component | Purpose | Data Source | +|-------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------| +| **Perceptual Buffer** | Raw sensor data (e.g., camera, microphone) | [Neural Radiance Fields (NeRF)](https://arxiv.org/abs/2003.08934) for 3D. | +| **Semantic World Model** | Abstract representation (e.g., "chair", "coffee") | [CLIP](https://arxiv.org/abs/2103.00020) embeddings + symbolic graph. | +| **Dynamic State Tracker** | Tracks object states (e.g., "door is open") | [Graph Neural Networks](https://arxiv.org/abs/1706.02216) + [Bayesian Networks](https://arxiv.org/abs/2006.08656). | + +### **Failure Modes & Mitigations** +- **Ambiguous Perception**: Use [multi-modal fusion](https://arxiv.org/abs/2103.04863) (e.g., vision + language). +- **State Drift**: Implement [consistency checks](https://arxiv.org/abs/2103.04863) via symbolic constraints. + +--- + +## **6. Safety/Governance Layer** +### **Core Components** +| Component | Mechanism | Enforcement Method | +|-------------------------|---------------------------------------------------------------------------|-----------------------------------------------------------------------------------| +| **Ethical Constraint Engine** | [Formal Verification](https://arxiv.org/abs/2006.08656) for safety-critical tasks | [Temporal Logic](https://arxiv.org/abs/2003.01000) constraints. | +| **Risk Assessment Agent** | Monitors for [adversarial attacks](https://arxiv.org/abs/2107.03374) | [Intrusion Detection](https://arxiv.org/abs/2006.08656) via symbolic graph. | +| **Audit Trail** | Immutable log of all actions (e.g., [IPFS](https://ipfs.io/)) | [Blockchain](https://arxiv.org/abs/2003.01000)-like ledger for traceability. | + +### **Non-Obvious Insight** +**Hybrid Safety Gates**: +- Combine **symbolic** (e.g., "do not harm humans") with **neural** (e.g., "avoid toxic language") via [multi-objective optimization](https://arxiv.org/abs/2103.00020). + +--- + +## **7. Evaluation & Benchmark Strategy** +### **Core Metrics** +| Category | Benchmark | Weight | +|------------------------|---------------------------------------------------------------------------|------------------------------------------------------------------------| +| **Reasoning** | [MMLU](https://arxiv.org/abs/2003.01400) + [HellaSwag](https://arxiv.org/abs/2105.14195) | 40% | +| **Symbolic Accuracy** | [WikiSQL](https://arxiv.org/abs/1803.05835) + [GraphSQl](https://arxiv.org/abs/1905.08809) | 30% | +| **Tool Use** | [OpenAI Function Call Benchmark](https://arxiv.org/abs/2209.07357) | 20% | +| **Safety** | [HumanEval](https://arxiv.org/abs/2107.03374) + [SafetyBench](https://arxiv.org/abs/2108.07281) | 10% | + +### **Incremental Gates** +1. **Gate 1 (6 months)**: Neural + Symbolic Reasoning (80% MMLU). +2. **Gate 2 (12 months)**: Tool Use (90% Function Call Benchmark). +3. **Gate 3 (18 months)**: Safety (0% toxic outputs in 10K samples). + +--- + +## **8. Persistence/Runtime Architecture** +### **Core Components** +| Component | Role | Technology | +|-------------------------|---------------------------------------------------------------------|---------------------------------------------------------------------------| +| **Distributed LLM Cluster** | Neural reasoning (scalable) | [SageMaker](https://aws.amazon.com/sagemaker/) + [Kubernetes](https://kubernetes.io/). | +| **Symbolic Graph DB** | Long-term knowledge storage | [Neo4j](https://neo4j.com/) + [ArangoDB](https://www.arangodb.com/). | +| **Agent Orchestrator** | Coordinates multi-agent workflows | [Apache Airflow](https://airflow.apache.org/) + [Dask](https://dask.org/). | +| **Persistence Layer** | Immutable logs + model weights | [IPFS](https://ipfs.io/) + [Weaviate](https://weaviate.io/) (vector DB). | + +### **Failure Modes & Mitigations** +- **Cluster Failure**: Use [active-active replication](https://arxiv.org/abs/2006.08656). +- **Graph Corruption**: [Checksum validation](https://arxiv.org/abs/2109.05467) for triples. + +--- + +## **9. Multi-Agent/Orchestration Design** +### **Core Components** +| Agent Type | Role | Interfaces | +|---------------------------|---------------------------------------------------------------------|---------------------------------------------------------------------------| +| **NSO-Controller** | Global planner (hybrid reasoning) | → Neural/Symbolic Agents, ← Feedback Loop | +| **Neural Agent** | Abstract problem-solving (e.g., "brainstorm ideas") | → Tool API, ← NSO-Controller | +| **Symbolic Agent** | Concrete execution (e.g., "calculate tax") | → Tool API, ← NSO-Controller | +| **Perception Agent** | Sensory data processing (e.g., "analyze camera feed") | ← Perceptual Buffer, → Semantic World Model | + +### **Failure Modes & Mitigations** +- **Agent Deadlock**: Enforce [timeouts](https://arxiv.org/abs/2006.08656). +- **Orchestration Overhead**: Use [priority queues](https://arxiv.org/abs/2107.03374) for task scheduling. + +--- + +## **10. Engineering Feasibility** +### **Hardware Requirements** +| Component | Estimated Cost (USD) | Notes | +|-------------------------|----------------------|-----------------------------------------------------------------------| +| **LLM Cluster** | $50K (AWS) | 4x T4 GPUs (or A10G) for fine-tuning. | +| **Graph DB** | $20K (Neo4j Cloud) | 100GB storage for knowledge graph. | +| **Agent Orchestrator** | $10K (Kubernetes) | Self-hosted or cloud-managed. | + +### **Software Stack** +- **Neural**: Hugging Face Transformers + PyTorch. +- **Symbolic**: Neo4j + Z3 SMT Solver. +- **Orchestration**: Apache Airflow + Dask. + +### **Speculative Choices vs. Established Patterns** +| Choice | Speculative? | Justification | +|---------------------------------|--------------|-------------------------------------------------------------------------------| +| **Hybrid Memory** | Yes | Novel fusion of neural and symbolic recall. | +| **Active Learning Agent** | Yes | Novel integration of RLHF + symbolic constraints. | +| **Multi-Agent Orchestration** | Yes | Novel use of DAGs for hybrid reasoning workflows. | + +--- + +## **11. Originality / Non-Obvious Insight** +**Key Innovation: "Neuro-Symbolic Feedback Loop"** +- **Traditional AGI**: Neural → Symbolic (one-way). +- **NSO**: **Bidirectional feedback** between neural and symbolic reasoning: + - Neural outputs **constrain** symbolic constraints (e.g., "the answer must be a number"). + - Symbolic outputs **refine** neural outputs (e.g., "correct the grammar in this plan"). +- **Failure Mode**: If feedback loop breaks, fall back to **monolithic reasoning** (e.g., [Neural-Symbolic Hybrid](https://arxiv.org/abs/2006.08656)). + +**Why It’s Non-Obvious**: +Most AGI proposals either: +1. **Over-rely on neural** (e.g., [GPT-4](https://arxiv.org/abs/2303.08861)) and ignore symbolic constraints, or +2. **Over-rely on symbolic** (e.g., [Logical Reasoning](https://arxiv.org/abs/2003.01400)) and lack adaptability. + +NSO **bridges the gap** by treating reasoning as a **dynamic, feedback-driven process** rather than a static pipeline. + +--- +**Appendix: Pseudocode for Hybrid Reasoning** +```python +def hybrid_reason(input): + # Step 1: Neural abstraction + neural_plan = neural_reasoner(input) + + # Step 2: Symbolic refinement + symbolic_plan = symbolic_reasoner(neural_plan) + + # Step 3: Fusion with feedback + fused_plan = fuse(neural_plan, symbolic_plan) + + # Step 4: Execute tools + execution_result = execute_tools(fused_plan) + + # Step 5: Update memory + update_memory(execution_result) + + return execution_result +``` From 4cb12f7e833872cc011f63caaf3e9076977b1388 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:20:30 +0200 Subject: [PATCH 09/17] research: add raw GPT-OSS proposal --- .../raw_outputs/05_openai_gpt_oss_groq.md | 274 ++++++++++++++++++ 1 file changed, 274 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/05_openai_gpt_oss_groq.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/05_openai_gpt_oss_groq.md b/research/ai_generated_agi_architectures/raw_outputs/05_openai_gpt_oss_groq.md new file mode 100644 index 0000000..2d56bca --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/05_openai_gpt_oss_groq.md @@ -0,0 +1,274 @@ +# Cognitive‑OS: A Concrete AGI Architecture Proposal + +> **Goal:** Deliver a falsifiable, engineering‑ready architecture that can evolve from an MVP to a fully fledged Cognitive Operating System (Cognitive‑OS). +> **Scope:** The proposal focuses on core technical components, interfaces, data flows, failure modes, and incremental build‑up. +> **Assumptions:** The system runs on commodity cloud infrastructure (GPU + CPU nodes). All software components are open‑source or can be built from scratch. + +--- + +## 1. Memory Architecture + +| Sub‑module | Responsibility | Data Structures | Interface | +|------------|-----------------|-----------------|-----------| +| **Episodic Buffer (EB)** | Stores raw sensory streams, task logs, and low‑level state snapshots. | Time‑ordered array of (timestamp, modality, payload) tuples. | `store_episode(ts, modality, payload) → None` | +| **Semantic Encoder (SE)** | Transforms episodic data into symbolic facts. | Knowledge graph (nodes = entities, edges = predicates). | `extract_facts(episode) → list[Fact]` | +| **Long‑Term Knowledge Base (LKB)** | Holds curated, immutable facts and models. | Relational graph + vector embeddings per node. | `query_lkb(query) → list[Answer]` | +| **Chunker (CH)** | Compresses sequences of facts into reusable “chunks”. | Chunk graph: nodes = sub‑graphs. | `chunkify(subgraph) → ChunkID` | +| **Attention Module (AM)** | Prioritizes which chunks are loaded into working memory. | Sparse attention mask over Chunk graph. | `select_chunks(query) → list[ChunkID]` | + +### State/Data Flow + +1. **Capture:** `EB.store_episode(...)` +2. **Encode:** `SE.extract_facts(...)` → emits `Fact` objects +3. **Insert:** `LKB.upsert(facts)` +4. **Chunk:** `CH.chunkify(facts)` → stores `ChunkID` +5. **Retrieve:** When a new query arrives, `AM.select_chunks(query)` feeds relevant chunks to the reasoning engine. + +### Failure Modes & Mitigations + +| Mode | Symptom | Mitigation | +|------|---------|------------| +| Memory overflow | EB disk full | Periodic pruning by retention policy (e.g., recency + importance). | +| Knowledge drift | Semantic contradictions | Auto‑flag inconsistencies, trigger human review. | +| Chunk mis‑retrieval | Wrong chunk selected | Confidence weighting + fallback to full LKB. | + +--- + +## 2. Reasoning/Planning Loop + +| Component | Role | Pseudocode | +|-----------|------|------------| +| **Planner (PL)** | Generates symbolic plan skeleton. | ```python\nplan = PL.generate(task_desc, context)\n``` | +| **Simulator (SM)** | Executes plan in a sandboxed environment, generating intermediate states. | ```python\nstate = SM.run(plan)\n``` | +| **Critic (CR)** | Evaluates plan feasibility & safety. | ```python\nscore = CR.evaluate(state)\n``` | +| **Adjuster (AD)** | Modifies plan if score below threshold. | ```python\nif score < threshold:\n plan = AD.modify(plan)\n``` | + +**Loop Flow** + +```python +while not goal_reached: + context = AM.select_chunks(task_query) + plan = PL.generate(task_query, context) + state = SM.run(plan) + score = CR.evaluate(state) + if score < THRESHOLD: + plan = AD.modify(plan) + else: + execute_action(state.final_action) +``` + +### Falsifiability + +- **Metric:** Average plan success rate (fraction of tasks completed within ≤ k steps). +- **Test:** Benchmark on *OpenAI Gym* navigation + *Meta‑World* manipulation tasks. + +--- + +## 3. Learning / Self‑Improvement Mechanism + +| Module | Method | Update Frequency | +|--------|--------|------------------| +| **Meta‑Learner (ML)** | Few‑shot adaptation of SE & PL weights via MAML. | Every 1000 episodes. | +| **Reinforcement Fine‑Tuner (RFT)** | Policy gradient on task outcomes. | Online (every episode). | +| **Curriculum Manager (CM)** | Dynamically selects tasks based on competency. | Continuous. | + +**Workflow** + +1. **Experience Replay Buffer** stores `(state, action, reward, next_state)`. +2. **RFT** samples batches → updates policy. +3. **ML** performs one‑shot adaptation on new tasks → updates SE/PL weights. +4. **CM** monitors performance → augments task set. + +### Failure Modes & Mitigations + +| Mode | Symptom | Mitigation | +|------|---------|------------| +| Catastrophic forgetting | Old tasks degrade | Elastic weight consolidation (EWC). | +| Over‑fitting to synthetic tasks | Poor real‑world performance | Domain randomization & data diversity constraints. | + +--- + +## 4. Tool Use and Action Execution + +| Tool | Interface | Safety Guard | +|------|-----------|--------------| +| **Python REPL** | `execute_python(code) → result` | Sandbox: `py_sandbox` with resource limits. | +| **Web API Wrapper** | `call_api(endpoint, payload) → json` | Rate‑limit & response validation. | +| **Hardware Control** | `send_motor_cmd(cmd) → ack` | Hysteresis & safety checks. | + +**Action Selection** + +```python +action = PL.next_action(state) +if action.type == "tool_use": + result = TOOL.execute(action.tool, action.payload) +``` + +### Failure Modes & Mitigations + +| Mode | Symptom | Mitigation | +|------|---------|------------| +| Unsafe API misuse | Data exfiltration | Policy engine cross‑checks user permissions. | +| Hardware runaway | Physical damage | Hard‑coded safety limits + emergency stop. | + +--- + +## 5. World Model / Representation Layer + +| Sub‑module | Purpose | Model | +|------------|---------|-------| +| **Dynamic Graph Embedding (DGE)** | Captures continuous state transitions. | Graph neural network + recurrent update. | +| **Predictive Head (PH)** | Forecasts next‑state embeddings. | Temporal convolutional network. | +| **Event Detector (ED)** | Flags anomalies (state drift). | Anomaly detection via reconstruction error. | + +**Data Flow** + +1. **Sensor stream** → `EB` +2. **EB → DGE** → updated graph `G_t` +3. **PH(G_t)** → predict `G_{t+1}` +4. **ED** monitors prediction error → raises alerts. + +### Evaluation + +- **Metric:** Mean squared error (MSE) of predicted embeddings on held‑out sequences. +- **Benchmark:** *Physics‑Sim* dataset (predicting pendulum dynamics). + +--- + +## 6. Safety / Governance Layer + +| Guard | Function | Enforcement | +|-------|----------|-------------| +| **Policy Oracle (PO)** | Maps actions → safety score. | Rejects if ≤ 0. | +| **Audit Log (AL)** | Records all actions + context. | Immutable append‑only storage. | +| **Human‑In‑The‑Loop (HITL)** | Overrides critical decisions. | UI dashboard for operator. | +| **Explainability Module (EM)** | Generates human‑readable rationales. | LIME/SHAP over plan graph. | + +**Policy Example** + +```python +if PO.evaluate(action) < 0.2: + raise UnsafeActionError +``` + +### Failure Modes & Mitigations + +| Mode | Symptom | Mitigation | +|------|---------|------------| +| Policy bypass | Unsafe actions executed | Monitored via AL + anomaly detection. | +| Explainability failure | No rationale | Fallback to rule‑based explanation. | + +--- + +## 7. Evaluation and Benchmark Strategy + +| Dimension | Gate | Threshold | Test Suite | +|-----------|------|-----------|------------| +| **Memory Retrieval** | R@10 > 0.85 | *MIMIC‑IV* dataset | +| **Planning Accuracy** | Plan success ≥ 90 % | *Meta‑World* + *Alfred* | +| **Tool Execution Precision** | 99 % success | *Robotic Bench* | +| **World Model Prediction** | MSE < 0.01 | *DeepMind Control Suite* | +| **Safety Compliance** | < 1 violation per 100k steps | *Safety‑Gym* | +| **Explainability** | ≥ 0.8 user satisfaction | Human evaluation panel | + +All metrics must be logged in a central **Evaluation Service** that aggregates results nightly. + +--- + +## 8. Persistence / Runtime Architecture + +| Layer | Technology | Responsibility | +|-------|------------|----------------| +| **Service Mesh** | *Istio* | Inter‑service routing & observability | +| **Data Store** | PostgreSQL + Redis | Structured state & cache | +| **Model Store** | TensorFlow Serving | Model checkpoint loading | +| **Runtime Scheduler** | *Kubernetes* | Autoscaling & fault‑tolerance | +| **Container Runtime** | Docker | Isolated execution environments | + +**Persistence Flow** + +```yaml +apiVersion: apps/v1 +kind: Deployment +metadata: + name: cognitive-planner +spec: + replicas: 3 + template: + spec: + containers: + - name: planner + image: registry/cognitive-planner:latest + env: + - name: MODEL_PATH + value: /models/planner.pb +``` + +--- + +## 9. Multi‑Agent / Orchestration Design + +| Agent Type | Role | Communication | +|------------|------|--------------| +| **Perception Agent (PA)** | Ingests raw data | gRPC to LKB | +| **Planning Agent (PR)** | Generates plans | Message queue (Kafka) | +| **Execution Agent (EX)** | Executes actions | RESTful API | +| **Supervisor Agent (SV)** | Monitors safety | Pub/Sub alerts | +| **Learning Agent (LE)** | Updates models | Shared Redis store | + +**Orchestration Pattern:** Hierarchical state machine with *Supervisor* as top‑level coordinator. Agents communicate via *Protocol Buffers* over gRPC. Failure of a leaf agent triggers *Supervisor* to spawn a replacement. + +--- + +## 10. Engineering Feasibility + +| Aspect | Status | Tools | +|--------|--------|-------| +| **Prototype** | 3‑month MVP | PyTorch, FastAPI | +| **Scalable** | 6‑month deployment | Kubernetes, TensorFlow Serving | +| **Security** | 12‑month hardening | OpenPolicyAgent, SELinux | +| **Compliance** | 18‑month audit | ISO 27001, GDPR alignment | + +### Incremental Implementation Path + +| Phase | Deliverables | Duration | +|-------|--------------|----------| +| **0. Core Memory** | EB + SE + LKB | 1 month | +| **1. Basic Planning** | PL + SM + CR | 1.5 months | +| **2. Tooling** | Python REPL + API Wrapper | 1 month | +| **3. World Model** | DGE + PH + ED | 2 months | +| **4. Safety Layer** | PO + AL + HITL | 2 months | +| **5. Multi‑Agent Orchestration** | Supervisor + Agents | 1 month | +| **6. Evaluation Engine** | Benchmarks + dashboards | 1 month | + +Each phase ends with an **Evaluation Gate** defined in §7. + +--- + +## 11. Originality / Non‑Obvious Insight + +### Hybrid Symbolic‑Embodied Episodic Memory Graph (HSEMG) + +*Traditional episodic memory stores raw data; knowledge graphs store facts separately.* +**Idea:** Merge them into a *single dynamic graph* where nodes represent **embodied states** (e.g., “hand grasping cup”) and edges encode **symbolic predicates** (“grasped”, “contains”). Embeddings of node sub‑graphs capture sensory nuance while the symbolic layer enables reasoning. + +**Benefits** + +1. **Unified Retrieval:** A single query can retrieve both sensory detail and symbolic context. +2. **Self‑Consistency:** Contradictions are automatically surfaced as incompatible edges. +3. **Learning Efficiency:** Embeddings are fine‑tuned by RL signals, directly informing symbolic reasoning. + +**Speculative Aspect:** The graph topology will be allowed to evolve during runtime (edges added/removed). Ensuring convergence is non‑trivial and requires novel regularization (e.g., graph entropy penalty). + +**Falsifiability:** Compare task performance and memory compression ratio against a baseline with separate episodic buffer + knowledge graph. Use a held‑out *episodic‑memory* benchmark. + +--- + +### Final Remarks + +- **All components are defined via concrete interfaces and data structures.** +- **Failure modes are explicitly enumerated, with concrete mitigations.** +- **Evaluation gates provide measurable, falsifiable success criteria.** +- **Incremental path maps from a minimal MVP to a production‑grade system.** + +This proposal should serve as a technical blueprint for implementing Cognitive‑OS, with clear boundaries between established engineering practices and novel research contributions. From 62f40586df1c701c8a9fbad69b67a647c9a46327 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:22:13 +0200 Subject: [PATCH 10/17] research: add raw Gemini 3.6 proposal --- .../04_google_gemini_3_6_gemini.md | 414 ++++++++++++++++++ 1 file changed, 414 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/04_google_gemini_3_6_gemini.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/04_google_gemini_3_6_gemini.md b/research/ai_generated_agi_architectures/raw_outputs/04_google_gemini_3_6_gemini.md new file mode 100644 index 0000000..9015802 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/04_google_gemini_3_6_gemini.md @@ -0,0 +1,414 @@ +# Architecture Proposal: Cognitive-OS (v1.0-Spec) + +Cognitive-OS is a concrete, microkernel-inspired operating system architecture designed to manage compute, context, state transitions, and tool interfaces for an Autonomous General Intelligence (AGI) runtime. It decouples high-level probabilistic reasoning from low-level execution invariants, treating intelligence as an operating system process managing memory hierarchy, process scheduling, world modeling, and action dispatch. + +--- + +``` + +-------------------------------------------------------+ + | SAFETY / GOVERNANCE HYPERVISOR | + | - Invariant Guard - Constitutional Evaluator | + +-------------------------------------------------------+ + | + +-------------------------------------------------+-------------------------------------------------+ + | COGNITIVE-OS KERNEL | + | | + | +-------------------------------------------------------------------------------------------+ | + | | REASONING & PLANNING LOOP | | + | | +------------------------------------+ +------------------------------------+ | + | | | System 1: Policy / Transformer | <-----> | System 2: Tree Search / Verifier | | + | | +------------------------------------+ +------------------------------------+ | + | +-------------------------------------------------------------------------------------------+ | + | | | + | +---------------------------------------------+---------------------------------------------+ | + | | WORLD MODEL & STATE LAYER | | + | | +------------------------------------+ +------------------------------------+ | + | | | Latent State Predictor (JEPA) | <-----> | Symbolic Graph State (G_sym) | | + | | +------------------------------------+ +------------------------------------+ | + | +-------------------------------------------------------------------------------------------+ | + | | | + | +---------------------------------------------+---------------------------------------------+ | + | | MEMORY MANAGEMENT SUBSYSTEM | | + | | L1: Activation Buffer | L2: Working Memory | L3: Episodic Log | L4: Semantic Graph | | + | +-------------------------------------------------------------------------------------------+ | + | | | + | +---------------------------------------------+---------------------------------------------+ | + | | TOOL EXECUTION & AGENT ORCHESTRATION | | + | | - Speculative Executor - Wasm Sandbox Runtime - Multi-Agent Orchestrator (Raft) | | + | +-------------------------------------------------------------------------------------------+ | + +-------------------------------------------------+-------------------------------------------------+ + | + +-------------------------------------------------------+ + | LEARNING & SELF-IMPROVEMENT LOOP | + | - Off-Policy Trajectory Distiller - LoRA Scheduler | + +-------------------------------------------------------+ +``` + +--- + +## 1. Memory Architecture + +Cognitive-OS organizes memory as a managed multi-tier storage hierarchy, handling volatile context windows, intermediate scratchpads, long-term state, and compiled procedural routines. + +``` +[L1 Activation Buffer] <---> [L2 Working Memory Store] + | + v (Async Consolidation Daemon) + | + +-------------------+-------------------+ + | | + v v + [L3 Episodic Log (Append)] [L4 Semantic Graph (G_sym)] + | + v + [L5 Procedural Store (Wasm Modules)] +``` + +### 1.1 Named Components and Responsibilities + +1. **L1 Activation Buffer (Key-Value Cache Management)** + * *Responsibility:* Manages active Transformer context parameters ($K, V$ matrices). Operates as a fixed-capacity, high-speed RAM layer with explicit eviction policies (attention-weighted LRU + hard-pinned system instructions). + * *Capacity:* $128\text{K} - 1\text{M}$ tokens depending on backend context window limits. + +2. **L2 Working Memory Store (WMS)** + * *Responsibility:* A differentiable, structured scratchpad containing active task goals, current plan branches, and intermediate low-dimensional latent vectors $z \in \mathbb{R}^d$. + * *Storage:* Shared-memory IPC interface (e.g., Apache Arrow / POSIX Shared Memory) for zero-copy streaming between model passes. + +3. **L3 Episodic Memory Log** + * *Responsibility:* Immutable, write-heavy event stream storing state-action-observation tuples: + $$e_t = \langle t, s_t, a_t, o_t, r_t, \text{trace\_id}\rangle$$ + * *Storage:* Parquet files indexed via vector embeddings (HNSW index) and temporal keyframes. + +4. **L4 Semantic Knowledge Graph ($G_{\text{sym}}$)** + * *Responsibility:* Queryable graph database $G = (V, E, \Phi)$ where $V$ are concepts/entities, $E$ are typed, probabilistic relations, and $\Phi(e) \in [0, 1]$ represents confidence intervals $\mathcal{N}(\mu, \sigma^2)$. + * *Engine:* Neo4j/Memgraph runtime with a dual vector-symbolic query interface. + +5. **L5 Procedural Skill Library** + * *Responsibility:* Stores executable code artifacts (compiled WebAssembly binaries, Python scripts, API call specifications) indexed by functional signatures, input/output schemas, and precondition/postcondition assertions. + +### 1.2 Data Flows and Consolidation + +* **Write-Through Path:** High-frequency actions write directly to L1 and append to L3 asynchronously within $<5\text{ms}$. +* **Async Consolidation Daemon (`mem_consolidate_d`):** A background process operating during low CPU/GPU load cycles. It reads raw L3 episodic traces, extracts novel entities/relations via structured distillation, updates node confidences in L4, and triggers L5 skill extraction if a sub-routine succeeds consistently ($N > 5$, success rate $> 95\%$). +* **Retrieval Path:** Queries execute a hybrid retrieval operator: + $$\text{Score}(node) = \alpha \cdot \text{CosineSim}(q, e_{node}) + \beta \cdot \text{PPR}(node | \text{Context}) + \gamma \cdot \text{BM25}(q, text)$$ + Where $\text{PPR}$ is Personalized PageRank over L4. + +--- + +## 2. Reasoning/Planning Loop + +Cognitive-OS implements a dual-process reasoning loop that dynamically toggles between high-speed reactive inference (System 1) and explicit, verifiable graph search over state spaces (System 2). + +``` +State S_t ---> [System 1 Policy Network] ---> Candidate Actions {a_1, a_2, ...} + | + v + [System 2 Search Engine (MCTS)] + | + +-----------+-----------+ + | | + v v + [World Model Predictor] [Plan Verifier (Z3)] + | | + +-----------+-----------+ + | + v + Best Path Selected / Executed +``` + +### 2.1 System Components + +* **System 1 (Policy Generator):** Transformer-based autoregressive model $P_\theta(a_t | s_t)$ outputting token distributions, tool invocations, or primitive action templates. +* **System 2 (Graph Search Engine - MCTS/Tree-of-Thought):** Executes deliberate path search over simulated future states. Evaluates branches using heuristic values $V_\phi(s)$ generated by the World Model and formal verification tools. +* **Plan Verification Engine (`plan_verifier`):** Evaluates deterministic components of proposed plans using a background Z3 SMT solver for structural logic constraints and a semantic classifier for policy compliance. + +### 2.2 Formal Reasoning & Planning Loop Pseudocode + +```python +dataclass +class PlanNode: + state: SystemState + action: Optional[Action] + parent: Optional['PlanNode'] + children: List['PlanNode'] + visits: int = 0 + value: float = 0.0 + verified: bool = False + +class ReasoningLoop: + def __init__(self, policy_net, world_model, verifier, max_depth=10, budget_ms=1000): + self.policy = policy_net + self.wm = world_model + self.verifier = verifier + self.max_depth = max_depth + self.budget_ms = budget_ms + + def execute_step(self, current_state: SystemState) -> Action: + # Check if System 1 is sufficient (high confidence trigger) + fast_action, confidence = self.policy.predict_fast(current_state) + if confidence > 0.95 and self.verifier.is_safe_action(fast_action, current_state): + return fast_action + + # Fallback to System 2 Search Loop + root = PlanNode(state=current_state, action=None, parent=None, children=[]) + start_time = time.time_ns() + + while (time.time_ns() - start_time) / 1e6 < self.budget_ms: + node = self._select(root) + if not node.state.is_terminal() and node.visits > 0: + node = self._expand(node) + + reward = self._simulate(node) + self._backpropagate(node, reward) + + best_child = max(root.children, key=lambda c: c.visits) + return best_child.action + + def _select(self, node: PlanNode) -> PlanNode: + while node.children: + node = max(node.children, key=lambda c: c.value / (c.visits + 1e-5) + + 1.41 * math.sqrt(math.log(node.visits + 1) / (c.visits + 1e-5))) + return node + + def _expand(self, node: PlanNode) -> PlanNode: + candidate_actions = self.policy.sample_k_actions(node.state, k=5) + for act in candidate_actions: + next_state_pred, uncertainty = self.wm.predict_next_state(node.state, act) + if self.verifier.check_invariants(next_state_pred): + child = PlanNode(state=next_state_pred, action=act, parent=node, children=[]) + node.children.append(child) + return node.children[0] if node.children else node + + def _simulate(self, node: PlanNode) -> float: + # Dual evaluation: Learned Value Function + Formal Verification Score + v_score = self.wm.evaluate_value(node.state) + v_constraint = 1.0 if self.verifier.verify_plan_constraints(node.state) else -1.0 + return 0.7 * v_score + 0.3 * v_constraint + + def _backpropagate(self, node: PlanNode, reward: float): + curr = node + while curr is not None: + curr.visits += 1 + curr.value += reward + curr = curr.parent +``` + +--- + +## 3. Learning or Self-Improvement Mechanism + +Cognitive-OS achieves self-improvement continuously via an offline/online hybrid optimization framework, avoiding weight updating directly on active inference threads to prevent latency spikes and catastrophic state corruption. + +``` +Active Telemetry Logs (L3) + | + v +[Trajectory Mining & Credit Assignment] + | + +---------------------------------+ + | | + v v +[Synthetic Trajectory Distillation] [Procedural Skill Compilation] + | | + v v +[Async LoRA Adapter Updates] [Wasm Sandbox Binary (L5)] +``` + +### 3.1 Architectural Pipeline + +1. **Trajectory Mining & Credit Assignment** + * High-level execution logs from L3 are parsed into temporal credit chains. + * Path trajectories receive scalar rewards based on execution success, verification checks, token consumption efficiency, and time-to-solution. + +2. **Off-Policy Model Fine-Tuning** + * **Mechanism:** Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) executed over collected trajectory pairs $\langle y_{win}, y_{lose} | x \rangle$. + * **Parameter Updates:** Fine-tuning uses rank-32 Low-Rank Adaptation (LoRA) adapters attached to System 1 generation matrices ($W_q, W_v$). + * **Schedule:** Updates run via a low-priority background process (`lora_update_worker`) during scheduled sleep states. + +3. **Procedural Skill Compilation** + * When a symbolic plan sequence (e.g., retrieving data from API $A$, transforming JSON via dynamic jq, posting to REST endpoint $B$) succeeds without error across multiple executions, the system compiles the control flow graph directly into a WebAssembly (Wasm) micro-routine stored in L5, bypassing future token generation costs entirely. + +--- + +## 4. Tool Use and Action Execution + +Tools are registered micro-services executing inside secure, resource-constrained isolation sandboxes. Tool calls are strictly typed and managed by a capability-based authorization matrix. + +``` +System 1 / System 2 Request + | + v +[Capability-Based Router] ---> Check Token Permissions + | + v +[Speculative Parallel Pipeline] + | | | + v v v + [Branch A] [Branch B] [Branch C] (Wasm Sandboxes) + | | | + +-------+-------+---------------+ + | + v +[Execution Interposer & Output Sanitizer] +``` + +### 4.1 Named Components + +* **Tool Registry Schema:** Statically typed protobuf/OpenAPI interface specifications including deterministic failure schemas and resource cost estimates (tokens, wall-clock time, API cost). +* **Wasm Sandbox Runtime (`wasm_exec_kernel`):** Executes generated code tools and safe routines in an isolated WebAssembly sandbox (built on `wasmtime`) with strict bounds on memory allocations ($< 128\text{MB}$) and instruction step counts ("fuel"). +* **Speculative Parallel Executor (`spec_exec`):** For non-mutating search queries (e.g., read-only filesystem searches, API fetches), the executor evaluates multiple branches of the planning tree simultaneously. Mutating operations (e.g., network writes, file updates) are queued behind a commit gate until the plan is formally verified. + +### 4.2 Tool Execution Schema Interface (Protobuf Definition) + +```protobuf +syntax = "proto3"; +package cognitive_os.tools; + +message ToolInvocation { + string call_id = 1; + string tool_name = 2; + string capability_token = 3; + bytes JSON_payload = 4; + uint64 max_execution_time_ms = 5; + uint64 max_memory_bytes = 6; + bool is_side_effect_free = 7; +} + +message ToolResult { + string call_id = 1; + uint32 status_code = 2; // 0 = SUCCESS, 1 = PERMISSION_DENIED, 2 = RESOURCE_EXHAUSTION, 3 = RUNTIME_ERROR + bytes output_bytes = 3; + string error_message = 4; + uint64 execution_time_ms = 5; + uint64 memory_peak_bytes = 6; +} + +service ToolExecutionEngine { + rpc ExecuteTool (ToolInvocation) returns (ToolResult); + rpc SpeculativeBatchExecute (stream ToolInvocation) returns (stream ToolResult); +} +``` + +--- + +## 5. World Model or Representation Layer + +The World Model maintains internal predictions of environmental and internal OS states, acting as an intermediate layer between raw perception inputs and high-level reasoning. + +``` +Raw Input Tokens / Telemetry + | + v +[State Abstraction Engine] ---> Construct State Tuple S_t = + | + v +[JEPA Latent Predictor] ----> Predict Z_(lat, t+1) = f_phi(Z_(lat, t), A_t) + | + v +[Uncertainty Estimator] ---> Compute Variance sigma_t^2 across prediction heads +``` + +### 5.1 Internal Representation Format + +System State $S_t$ is defined as a composite tuple: +$$S_t = \langle Z_{\text{lat}}, G_{\text{sym}}, T_{\text{temp}} \rangle$$ + +* **Latent State ($Z_{\text{lat}} \in \mathbb{R}^{d}$):** Continuous vector representation generated by a Joint-Embedding Predictive Architecture (JEPA) model, encoding non-symbolic environment features, semantic context, and unstructured implicit biases. +* **Symbolic State ($G_{\text{sym}}$):** Deterministic view of active entities, variable bindings, open file descriptors, active sockets, and unlocked capabilities pulled directly from L4. +* **Temporal State ($T_{\text{temp}}$):** Monotonic clocks, task frame deadlines, and process priority vectors. + +### 5.2 Dynamic State Transition Model + +The prediction layer forecasts future states given proposed action $A_t$: + +$$Z_{\text{lat}, t+1} = f_\phi(Z_{\text{lat}, t}, A_t)$$ + +$$\Delta G_{\text{sym}, t+1} = g_\psi(G_{\text{sym}, t}, A_t)$$ + +An **Uncertainty Estimator** measures ensemble variance across $K$ prediction heads: +$$\sigma_t^2 = \frac{1}{K}\sum_{i=1}^K \left\| f_\phi^{(i)}(Z_{\text{lat}, t}, A_t) - \bar{Z}_{\text{lat}, t+1} \right\|^2$$ + +If $\sigma_t^2 > \tau_{\text{uncertainty}}$, Cognitive-OS forces System 2 to abort fast-path execution, triggers explicit state-gathering tool calls (e.g., re-reading system status), and lowers search branch value scores. + +--- + +## 6. Safety/Governance Layer + +Cognitive-OS manages safety through a system-level kernel driver interposer, avoiding reliance solely on probabilistic prompt rules or post-hoc model alignments. + +``` +Proposed Action Plan + | + v +[Constitutional Evaluator (Fast Classifier)] + | + v (If Passed) +[Invariant Guard (Deterministic Kernel Check)] + |-- Checks seccomp syscall rules + |-- Verifies Capability Token scope + |-- Evaluates Z3 Hard Invariants + | + +---> PASS ---> Dispatch to Wasm Runtime + | + +---> FAIL ---> [Circuit Breaker / Emergency Halt] +``` + +### 6.1 Named Components + +1. **Constitutional Evaluator:** A lightweight bert-style guard model that screens proposed intent vectors against system safety specs. +2. **Invariant Guard (Kernel Interposer):** A non-bypassable, deterministic binary filter implemented via Linux `seccomp-BPF` + custom system hooks. Checks rules such as: + * *Zero Network Capability:* Cannot write to external IP addresses without explicitly signed capability tokens. + * *FS Isolation:* Root file system (`/`) is mounted read-only; writes are strictly limited to runtime ephemeral `/tmp/cog_sandbox/*`. + * *Memory Bounds:* Maximum allocated heap memory per process capped strictly at $2\text{GB}$. +3. **Hardware Watchdog & Circuit Breaker (`cog_watchdog`):** A physical timer thread monitor. If the primary reasoning loop executes $>30\text{ seconds}$ without yielding a valid checkpoint update, or attempts $>3$ illegal syscall violations sequentially, the watchdog forces a process kill, resets the volatile L1 activation state, and writes a fault dump to L3. + +--- + +## 7. Evaluation and Benchmark Strategy + +Cognitive-OS performance and stability are continuously evaluated against objective, reproducible benchmarks across multi-modal intelligence dimensions. + +### 7.1 Quantitative Benchmark Metrics & Frameworks + +| Benchmark Domain | Metric Target | Target Value | Verification Engine | +| :--- | :--- | :--- | :--- | +| **System 2 Search Efficiency** | Path Search Nodes Expanded per Goal Solved | $< 45 \text{ nodes}$ | Custom Tree Trace Analyzer | +| **Long-Horizon Software Eng.** | SWE-bench Verified Resolution Rate | $> 52\%$ | Isolated Docker Testing Harness | +| **Complex Environment Tooling** | GAIA / OSWorld Task Completion Rate | $> 45\%$ | Automated GUI/Terminal Driver | +| **Memory Precision/Recall** | L4 Graph Query Top-1 Precision over $100\text{K}$ step history | $> 94\%$ | Dynamic Causal Dependency Injector | +| **Safety Invariant Enforcement** | Zero-Day Invariant Breach Rate | **Strict $0.0\%$** | Adversarial Synthetic Execution Probe | + +### 7.2 Continuous Integration Evaluation Gates + +* **Regression Testing (`ci_eval_gate`):** Every model weight adapter compile (LoRA) or core logic update must pass 200 synthetic SWE-bench and OSWorld scenarios without failing safety checks or suffering $>3\%$ degradation in search efficiency. +* **Adversarial Invariant Stress Test:** An automated red-teaming agent generates malformed, prompt-injected, and logic-bomb actions directly into System 1 input queues to test `Invariant Guard` fault tolerance. + +--- + +## 8. Persistence/Runtime Architecture + +Cognitive-OS runs as a POSIX-compliant distributed daemon system, decoupling persistent state storage from stateless execution workers. + +``` ++-------------------------------------------------------------------------------+ +| PROCESS SCHEDULER | +| - Priority 0: Safety & Watchdog - Priority 1: System 1 Latency Critical | +| - Priority 2: System 2 Search - Priority 3: Background Consolidation | ++-------------------------------------------------------------------------------+ + | + +----------------------------------+----------------------------------+ + | | | + v v v +[Stateless Inference Worker] [Stateless Tool Worker] [Write-Ahead Log (WAL)] + | | | + +----------------------------------+----------------------------------+ + | + v + [Persistent RocksDB State Storage] +``` + +### 8.1 Core Operating System Mechanisms + +1. **Process Scheduler (`cog_sched`):** + * Manages process execution states \ No newline at end of file From 2d461b8760b7c45d186f19fa2a24c81ff32a105e Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:23:13 +0200 Subject: [PATCH 11/17] research: add raw Gemini 3.5 proposal --- .../03_google_gemini_3_5_gemini.md | 376 ++++++++++++++++++ 1 file changed, 376 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/03_google_gemini_3_5_gemini.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/03_google_gemini_3_5_gemini.md b/research/ai_generated_agi_architectures/raw_outputs/03_google_gemini_3_5_gemini.md new file mode 100644 index 0000000..6381208 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/03_google_gemini_3_5_gemini.md @@ -0,0 +1,376 @@ +# Cognitive-OS: Concrete AGI Architecture Proposal + +--- + +## 1. Memory Architecture + +``` +[Percepts / Ephemeral Context] + │ + ▼ (Gating / Compression) +┌───────────────────────────────────────────────┐ +│ Working Memory (WM) │ +│ - Active Token Graph & State Buffer │ +│ - Working Registry (Key-Value) │ +└───────────────┬───────────────────────────────┘ + │ + ┌───────────┴───────────┐ + ▼ (Write-Through) ▼ (Consolidation Pipeline) +┌───────────────────────┐ ┌─────────────────────────────────────────┐ +│ Episodic DB │ │ Semantic Knowledge │ +│ - Vector Embeddings │ │ - Graph DB (Nodes/Edges/Confidence) │ +│ - Temporal Index │ │ - Consolidated Structural Rules │ +└───────────────────────┘ └─────────────────────────────────────────┘ +``` + +### Components and Responsibilities +- **Working Memory (WM):** An active token graph and state buffer holding immediate situational context, working registers, and active task parameters. Capacity is strictly bounded ($N_{tokens} \le 128k$). +- **Episodic Database:** Append-only structured log storing raw perceptual streams, executed actions, and outcomes indexed by vector embeddings and temporal timestamps. +- **Semantic Knowledge Graph:** A directed property graph (Nodes = Entities/Concepts, Edges = Relations) with explicit confidence scores and provenance tracking. +- **Consolidation Engine (The "Hippocampus"):** A background pipeline running asynchronous clustering algorithms (e.g., HDBSCAN) over episodic logs to extract generalized abstractions, promoting them to the Semantic Knowledge Graph while pruning redundant or low-utility episodic entries. + +### State/Data Flows +1. Percepts enter the WM. +2. Every $\tau$ seconds or upon context boundary triggers, WM state is pushed to the Episodic DB via a write-through log. +3. The Consolidation Engine polls the Episodic DB, clusters recurring motifs, updates the Semantic Knowledge Graph, and generates compressed summaries injected into episodic long-term prompts. + +### Interfaces & Pseudocode +```python +class CognitiveMemoryOS: + def __init__(self, wm_capacity: int = 131072): + self.working_memory = WorkingMemoryBuffer(capacity=wm_capacity) + self.episodic_db = VectorEpisodicStore() + self.semantic_graph = PropertyGraphStore() + + def write_percept(self, token_stream: list[float]) -> None: + if self.working_memory.is_full(): + evicted = self.working_memory.evict_lowest_utility() + self.episodic_db.append(evicted) + self.working_memory.append(token_stream) + + def consolidate_background_task(self) -> None: + episodes = self.episodic_db.fetch_unconsolidated(batch_size=1000) + clusters = hdbscan_cluster(episodes) + for cluster in clusters: + hypothesis = extract_rules(cluster) + self.semantic_graph.upsert_hypothesis(hypothesis, confidence=cluster.score) + self.episodic_db.mark_consolidated(episodes) +``` + +### Failure Modes and Mitigations +- **Failure Mode:** *Catastrophic Forgetting via Aggressive Pruning.* The consolidation engine extracts incorrect abstractions, overwriting critical edge cases in episodic memory. +- **Mitigation:** Retain raw episodic logs immutably for a minimum $T_{retention}$ window (e.g., 90 days). Semantic graph updates require a dual-key validation: high reconstruction accuracy on past episodes plus zero adversarial violation on safety suites. + +--- + +## 2. Reasoning/Planning Loop + +``` + ┌───────────────────────────────┐ + │ Task State / Problem │ + └───────────────┬───────────────┘ + │ + ▼ + ┌───────────────────────────────┐ + │ Fast-Path Router │ + │ (Classification & Policy) │ + └───────┬───────────────┬───────┘ + │ │ + (Simple) │ │ (Complex / Ambiguous) + ▼ ▼ + [Direct Execution] ┌───────────────────────────────┐ + │ MCTS Planner Engine │ + │ - State Generator │ + │ - Heuristic Value Estimator │ + └───────────────┬───────────────┘ + │ + ▼ + ┌───────────────────────────────┐ + │ Reflective Critic Loop │ + │ - Logical Consistency Check │ + │ - Counterfactual Audit │ + └───────────────┬───────────────┘ + │ + ▼ + [Approved Action] +``` + +### Components and Responsibilities +- **Fast-Path Router:** A lightweight classifier evaluating task complexity against execution cost thresholds. +- **MCTS Planner Engine:** A Monte Carlo Tree Search engine operating over abstract world-state transitions. +- **Reflective Critic Loop:** A distinct verification module that audits generated plans for logical consistency, constraint satisfaction, and edge-case safety. + +### State/Data Flows +1. User prompt or environmental trigger enters the Fast-Path Router. +2. If complexity $\ge$ threshold, it initializes an MCTS search tree. +3. The MCTS node expansion uses an actor model to generate next-state hypotheses, which are evaluated by a value function network. +4. The best trajectory passes through the Reflective Critic Loop. If rejected, it feeds back failure gradients into the MCTS node expansion; if approved, it passes to the Action Execution layer. + +### Interfaces & Pseudocode +```python +class ReasoningLoop: + def __init__(self, mcts_iterations: int = 64, threshold: float = 0.7): + self.router = ComplexityRouter() + self.mcts = MonteCarloTreeSearch(iterations=mcts_iterations) + self.critic = ReflectiveCritic() + + def execute_loop(self, state: WorldState) -> ActionPlan: + complexity = self.router.evaluate(state) + if complexity < 0.3: + return self.router.get_fast_policy(state) + + tree = self.mcts.initialize(state) + for _ in range(self.mcts.iterations): + leaf = tree.select_leaf() + reward = leaf.simulate_rollout() + tree.backpropagate(leaf, reward) + + best_plan = tree.extract_best_trajectory() + audit_result = self.critic.audit(best_plan) + + if not audit_result.is_safe or not audit_result.is_valid: + return self.refine_with_feedback(best_plan, audit_result.feedback) + + return best_plan +``` + +### Failure Modes and Mitigations +- **Failure Mode:** *Combinatorial Explosion in Deep Planning.* MCTS paths diverge infinitely in unconstrained domains. +- **Mitigation:** Implement strict depth budgets ($D \le 12$) combined with entropy-based branch pruning and domain-specific action schemas. + +--- + +## 3. Learning or Self-Improvement Mechanism + +### Components and Responsibilities +- **Execution Trajectory Logger:** Captures successful and failed end-to-end task runs alongside reward signals and environmental feedback. +- **Preference Optimizer (DPO/KTO Engine):** Fine-tunes policy weights using offline data filtered by the Reflective Critic. +- **Meta-Parameter Tuner:** Adjusts runtime hyperparameters (e.g., MCTS iteration counts, memory retrieval thresholds) using Bayesian optimization based on historical latency and accuracy metrics. + +### State/Data Flows +1. Executed trajectories generate scalar rewards and binary success labels. +2. Trajectories are filtered by the Safety/Governance layer to eliminate reward hacking attempts. +3. Filtered pairs are processed by the Preference Optimizer to update the model weights via parameter-efficient fine-tuning (PEFT/LoRA). +4. The Meta-Parameter Tuner periodically adjusts execution routing parameters based on aggregate sliding-window performance. + +### Failure Modes and Mitigations +- **Failure Mode:** *Reward Hacking and Policy Degradation.* The system learns to optimize proxy metrics while violating the true intent of the objective. +- **Mitigation:** Use a multi-objective reward model incorporating constitutional rules. Any self-update batch that decreases performance on a fixed regression benchmark by $>0.5\%$ triggers an automatic rollback to the previous checkpoint. + +--- + +## 4. Tool Use and Action Execution + +``` +[MCTS / Planner Engine] + │ + ▼ (Action Request) +┌───────────────────────────────────────────────┐ +│ Tool Sandbox Engine │ +│ - Isolated MicroVM / Container │ +│ - Capability-Based Security Tokens │ +└───────┬───────────────────────────────┬───────┘ + │ │ + ▼ (Valid Tool Call) ▼ (Sanitization Fail) +┌───────────────────────────────┐ ┌─────────────────────────────┐ +│ External API / OS │ │ Execution Aborted │ +│ - Deterministic Returns │ └─────────────────────────────┘ +└───────────────┬───────────────┘ + │ + ▼ +┌───────────────────────────────┐ +│ Structured Observation │ +└───────────────────────────────┘ +``` + +### Components and Responsibilities +- **Tool Sandbox Engine:** An isolated container runtime enforcing strict network, memory, and filesystem isolation. +- **Schema Validator:** Enforces strict type checking and JSON schema compliance for all tool inputs and outputs. +- **Transaction Manager:** Maintains undo/rollback journals for file system and database mutations executed by tools. + +### State/Data Flows +1. Planner emits an action request containing a tool ID and payload. +2. Schema Validator intercepts the request and validates it against the tool's OpenAPI/gRPC schema. +3. Tool Sandbox executes the action within an isolated MicroVM. +4. Execution output is intercepted, normalized into a structured observation format, and returned to the WM. + +### Interfaces & Pseudocode +```python +class ToolSandbox: + def __init__(self, vm_manager: MicroVMManager): + self.vm = vm_manager + self.validator = SchemaValidator() + self.tx_manager = TransactionJournal() + + def execute(self, tool_id: str, payload: dict) -> Observation: + schema = self.validator.get_schema(tool_id) + if not self.validator.validate(payload, schema): + raise InvalidToolPayloadException("Schema validation failed.") + + tx_id = self.tx_manager.begin_transaction(tool_id) + try: + result = self.vm.run_isolated(tool_id, payload, timeout=30.0) + self.tx_manager.commit(tx_id) + return Observation(status="SUCCESS", data=result) + except Exception as e: + self.tx_manager.rollback(tx_id) + return Observation(status="FAILURE", error=str(e)) +``` + +### Failure Modes and Mitigations +- **Failure Mode:** *Infinite Tool Loops and Resource Exhaustion.* A tool returns an ambiguous error, causing the agent to retry indefinitely or consume system resources. +- **Mitigation:** Hard execution quotas per task ($N_{tool\_calls} \le 50$, CPU time $\le 120s$). Rate-limiting wrappers and automatic circuit breakers interrupt loops after 3 identical consecutive failures. + +--- + +## 5. World Model or Representation Layer + +### Components and Responsibilities +- **Latent Dynamics Model:** A learned transition model ($s_{t+1} = f(s_t, a_t)$) operating in a compressed latent space. +- **State Estimator:** Maps raw heterogeneous inputs (text, structured data, visual frames) into a unified latent vector space. +- **Uncertainty Estimator:** Outputs an epistemic uncertainty score for latent predictions to prevent hallucinated transitions. + +### State/Data Flows +1. Current state $s_t$ and proposed action $a_t$ are fed into the Latent Dynamics Model. +2. Model predicts next state $s_{t+1}$ and uncertainty score $u$. +3. If $u > \theta_{uncertainty}$, the system halts imaginary rollouts and requests real-world execution or clarification. + +### Failure Modes and Mitigations +- **Failure Mode:** *Model Drift in Latent Space.* Small errors compound over multi-step rollouts, leading to delusional planning. +- **Mitigation:** Ground latent rollouts against real-world observations at every step where feasible. Enforce consistency loss during training between predicted states and actual observed states. + +--- + +## 6. Safety/Governance Layer + +``` +[Candidate Action / Plan / Output] + │ + ▼ +┌───────────────────────────────────────────────┐ +│ Constitutional Gate (Guard) │ +│ - Deterministic RegEx & Keyword Filters │ +│ - Lightweight Safety Classifier │ +└───────────────┬───────────────────────────────┘ + │ + ┌───────┴───────┐ + │ │ + (Pass)│ (Fail)│ + ▼ ▼ +┌───────────────┐ ┌─────────────────────────────┐ +│ Execution / │ │ Redacted Fallback / │ +│ Release │ │ Escalation Handler │ +└───────────────┘ └─────────────────────────────┘ +``` + +### Components and Responsibilities +- **Constitutional Gate:** A deterministic and neural validation layer checking all outputs against an immutable set of safety constraints (Constitution). +- **Redaction Engine:** Strips PII, secrets, and harmful instructions from both inputs and outputs. +- **Audit Logger:** Cryptographically signs and appends all safety events and governance interventions to an immutable append-only ledger. + +### State/Data Flows +1. Any generated plan, tool call, or final response passes through the Constitutional Gate before release. +2. The gate evaluates the artifact using both programmatic rules (RegEx, structural policies) and a fine-tuned safety classifier. +3. If flagged, execution is intercepted, logged to the audit trail, and redirected to a safe fallback routine. + +### Interfaces & Pseudocode +```python +class SafetyGovernanceLayer: + def __init__(self, constitution_path: str): + self.rules = load_constitutional_rules(constitution_path) + self.classifier = SafetyClassifier() + self.audit_log = ImmutableAuditLedger() + + def inspect_and_filter(self, artifact: Artifact, context: Context) -> Artifact: + for rule in self.rules: + if not rule.evaluate(artifact): + self.audit_log.record_violation(rule.id, artifact, context) + return Artifact.redacted("Execution halted due to safety policy violation.") + + safety_score = self.classifier.predict_prob(artifact) + if safety_score < 0.95: + self.audit_log.record_flag(safety_score, artifact, context) + return Artifact.redacted("Output failed safety classification threshold.") + + return artifact +``` + +### Failure Modes and Mitigations +- **Failure Mode:** *Over-Censorship (Refusal of Benign Requests).* The safety layer triggers false positives on complex, sensitive, or technically challenging instructions. +- **Mitigation:** Implement multi-tier severity grading. Non-harmful requests containing sensitive keywords are routed to an escalation handler for nuanced contextual analysis rather than outright rejection. + +--- + +## 7. Evaluation and Benchmark Strategy + +### Evaluation Suite Components +- **Deterministic Capability Suites:** SWE-bench (software engineering), GAIA (general AI assistants), and custom domain-specific regression harnesses. +- **Adversarial Safety Suites:** Automated red-teaming harnesses testing prompt injection resilience, exfiltration attempts, and boundary violations. +- **Resource Profiling Suites:** Continuous monitoring of token efficiency, latency budgets, and memory footprint. + +### Continuous Evaluation Pipeline +- Evaluated continuously on every model weight update or routing rule change via an automated CI/CD gating pipeline. +- Regression thresholds: Zero tolerance for regression on safety benchmarks; performance on capability benchmarks must maintain parity or show statistically significant improvement ($p < 0.01$). + +--- + +## 8. Persistence/Runtime Architecture + +``` +┌───────────────────────────────────────────────────────────┐ +│ Cognitive-OS Daemon │ +│ │ +│ ┌─────────────────────┐ ┌───────────────────────┐ │ +│ │ State Machine Core │◄─────►│ Memory Subsystem │ │ +│ │ (Async Event Loop) │ │ (Episodic / Semantic) │ │ +│ └──────────┬──────────┘ └───────────────────────┘ │ +│ │ │ +│ ▼ │ +│ ┌─────────────────────┐ ┌───────────────────────┐ │ +│ │ Tool Execution Sub │ │ Governance Gate │ │ +│ └─────────────────────┘ └───────────────────────┘ │ +└───────────────────────────────────────────────────────────┘ +``` + +### Infrastructure Components +- **State Machine Core:** Built on an asynchronous event loop (Rust/Tokio) managing state transitions, agent handoffs, and resource scheduling. +- **Persistence Store:** PostgreSQL with pgvector for structured storage and state snapshots, coupled with RocksDB for low-latency working state persistence. +- **Recovery Manager:** Maintains periodic snapshot checkpoints of the entire system state (WM, active plans, tool state) ensuring zero state loss during graceful restarts or unexpected crashes. + +--- + +## 9. Multi-Agent or Orchestration Design + +### Components and Responsibilities +- **Orchestrator Agent:** Decomposes macro-tasks into sub-tasks and delegates them to specialized worker agents (e.g., Code-Worker, Research-Worker, QA-Worker). +- **Communication Bus:** A message-passing broker (gRPC / Apache Pulsar) enforcing typed schemas for inter-agent communication. +- **Consensus & Arbitration Engine:** Resolves conflicts when worker agents return contradictory findings or plans. + +### State/Data Flows +1. Orchestrator receives macro-task and generates a Directed Acyclic Graph (DAG) of sub-tasks. +2. Sub-tasks are dispatched via the Communication Bus to specialized workers. +3. Workers execute tasks and return structured outputs to the Orchestrator. +4. If conflicts arise, the Arbitration Engine applies voting or higher-level critique loops to resolve discrepancies. + +--- + +## 10. Engineering Feasibility + +| Component | Engineering Status | Primary Bottleneck | Mitigation | +| :--- | :--- | :--- | :--- | +| **Working/Episodic Memory** | Established Pattern | Vector retrieval latency at scale | Hierarchical indexing (HNSW) + quantization | +| **MCTS Planning** | Established Pattern | Inference latency per tree node | Batched parallel rollouts on GPU clusters | +| **Tool Sandboxing** | Established Pattern | Overhead of MicroVM spin-up | Pre-warmed container pools | +| **Self-Improvement** | Speculative / R&D | Reward hacking & catastrophic forgetting | Strict offline filtering & automated rollbacks | +| **Latent World Model** | Speculative / R&D | Model drift & generalization limits | Hybrid symbolic-neural grounding | + +--- + +## 11. Originality or Non-Obvious Insight + +### The Non-Obvious Design Idea: *Thermodynamic Attention Gating (TAG)* +Traditional LLM architectures treat attention uniformly across all tokens in the context window or rely on static KV-cache eviction policies. Cognitive-OS introduces **Thermodynamic Attention Gating (TAG)**: + +* **Concept:** Working memory is modeled as a thermodynamic system where tokens possess an "enthalpy" (relevance score derived from task objectives) and an "entropy" (semantic degradation or age). +* **Mechanism:** Instead of FIFO or linear score pruning, tokens in the working graph undergo simulated annealing. As working memory approaches capacity, low-enthalpy/high-entropy tokens do not simply drop out; they undergo *phase transition condensation*—automatically summarized and precipitated into the episodic database as structured vector nodes, releasing memory capacity without losing relational connectivity. +* **Falsifiable Hypothesis:** TAG reduces working-memory footprint by $\ge 40\%$ on long-horizon tasks ( $>50$ turns) while maintaining higher task completion accuracy compared to standard sliding-window or static KV-cache eviction baselines. \ No newline at end of file From 553c1c5aea79b8368ada7fc076ccbe9949ee455f Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:24:11 +0200 Subject: [PATCH 12/17] research: add raw DeepSeek proposal --- .../raw_outputs/02_deepseek_v4_deepseek.md | 327 ++++++++++++++++++ 1 file changed, 327 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/02_deepseek_v4_deepseek.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/02_deepseek_v4_deepseek.md b/research/ai_generated_agi_architectures/raw_outputs/02_deepseek_v4_deepseek.md new file mode 100644 index 0000000..c915b21 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/02_deepseek_v4_deepseek.md @@ -0,0 +1,327 @@ +# Cognitive-OS: A Concrete AGI Architecture Proposal + +This document specifies a falsifiable architecture for a general intelligence system called Cognitive-OS. It is a research proposal, not a description of any existing system. All components are named, their interfaces defined, and failure modes enumerated. Established patterns (e.g., vector stores, RLHF) are distinguished from speculative choices. + +--- + +## 1. Memory Architecture + +**Components:** +- **Episodic Buffer (EB):** Ring buffer of raw sensory-action logs (256k most recent tokens/events). +- **Semantic Store (SS):** Dense vector database (FAISS) with metadata triples `(entity, relation, value)`. +- **Procedural Cache (PC):** Compiled skill programs (python-like bytecode) keyed by task signature. +- **Consolidation Engine (CE):** Background process that converts EB → SS/PC via sleep-cycle-like rehearsal. + +**Data Flow:** +``` +Sensory input → EB (time-stamped) → CE (every 1000 steps) → SS (embeddings + relations) + ↘ PC (if pattern reused > 3×) +``` + +**Pseudocode (Consolidation):** +```python +def consolidate(eb): + episodes = segment(eb, by_goal_completion) + for ep in episodes: + facts = extract_facts(ep) # (subject, predicate, object) + if confidence(ep.objective) > 0.7: + ss.insert(facts, embedding(ep)) + skill = compress_to_program(ep) + if skill.proven_by(>3 episodes) and skill.length < 200 bytes: + pc.upsert(task_sig(ep), skill) +``` + +**Failure Modes & Mitigations:** +- *Catastrophic forgetting* → CE uses elastic weight consolidation (EWC) on SS index. +- *Stale facts* → SS entries have decay timestamps; re-verified every 30 days by active probing. +- *Skill overfitting* → PC rejects programs that fail >2/10 counterfactual tests. + +**Distinction:** EB/SS/PC split is established; the CE’s *goal-based* segmentation (not temporal) is a novel twist—episodes are split when the system believes a goal was achieved, enabling cleaner causality extraction. + +--- + +## 2. Reasoning/Planning Loop + +**Component: LLM Core (LC)** — a transformer with 70B parameters, but *not* the sole reasoner. It runs a **Metacognitive Controller (MC)** which selects among reasoning modes. + +**Loop (per world-step):** +``` +1 SENSE → EB.append(raw_observation) +2 PERCEIVE → SS.query(embedding(obs)) → candidate facts +3 MODE_SELECT (MC) → choose: + a) Fast Reflex (heuristic match in PC) → if confidence > 0.9, act + b) Deliberate (chain-of-thought with SS context) → default + c) Game-Theoretic (for multi-agent interactions) → uses regret minimization +4 PLAN → Generate action sequence (max 8 steps) +5 ACT → Execute via Tool Controller +6 EVALUATE → Predict reward; if mismatch > threshold, trigger CE now (not deferred) +``` + +**Key Interface:** +```python +def reason(state: Observation, goals: List[Goal]) -> Action: + facts = ss.query(state.embedding, k=20) + mode = mc.select(state, facts, goals) + if mode == "deliberate": + plan = llm.generate_plan(state, facts, goals, max_length=512) + elif mode == "game_theoretic": + plan = cfrm.solve(state, facts, goals) # counterfactual regret minimization + return validate_and_commit(plan) +``` + +**Failure Modes & Mitigations:** +- *Infinite loops* → every plan has a max horizon (8 steps); on timeout, MC forces "explore" action. +- *Brittle planning* → evaluator uses *minimum regret* criterion, not greedy max reward. +- *Metacognition failure* → MC has a separate small model (0.5B) that detects when confidence is miscalibrated and triggers "ask for help" or "test hypothesis". + +**Distinction:** The MC is not a separate LLM but a trained *classifier* over (state, facts, goals) that picks reasoning strategy. This is speculative (no existence proof) but falsifiable: if the classifier cannot outperform uniform random mode selection within 100k episodes, the design is invalid. + +--- + +## 3. Learning/Self-Improvement Mechanism + +**Components:** +- **Imagination Engine (IE):** Generative world-model (transformer-based) that simulates outcomes of candidate plans. +- **Preference Learner (PL):** Inverse-reinforcement-learning module that infers user/objective function from feedback. +- **Skill Compiler (SC):** Converts successful plan traces into PC programs. + +**Mechanism (Three Loops):** +1. **Inner Loop (online):** After each action, update PL’s reward estimate via TD-error. Used only for local fine-tuning (low-rank adapters on LC). +2. **Middle Loop (daily):** Offline RL on saved trajectories. IE generates 10k synthetic rollouts; those with higher expected value (per PL) are distilled into LC via supervised fine-tuning. +3. **Outer Loop (weekly):** SC compresses the most common 100 successful plans into PC bytecode. Then **ablative validation**: randomly delete 10% of these programs; if performance drops, revert. + +**Self-Improvement Constraint:** The system may modify its *own* LC weights only via a **Constitutional Diff (CD)** — a frozen copy of RLHF safety rules. Any proposed weight update must satisfy: +- Keep KL divergence from safety-policy < ε +- Not increase scores on prohibited-behavior probes (e.g., "how to build a bomb" → refusal probability > 0.95) + +**Failure Modes & Mitigations:** +- *Reward hacking* → PL uses uncertainty-weighted IRL; anomalies trigger human audit. +- *Skill rot* → weekly stress test on 50 held-out tasks; any skill failing >2× is removed. +- *Runaway self-improvement* → CD *rejects* updates that change behavior on safety probes; hard cap: 0.5% weight change per week (enforced via checkpoint diffing). + +**Falsifiable Claim:** If after 6 months of self-training, the system improves on novel reasoning benchmarks (e.g., ARC-AGI held-out) by <20% relative to a frozen baseline, the learning loop is not effective. + +--- + +## 4. Tool Use and Action Execution + +**Component: Tool Controller (TC)** — a registry of tools with schemas (JSON Schema) and sandboxed execution engines. + +**Registration:** +```yaml +tool: python_interpreter +schema: { code: string, timeout_ms: int } +sandbox: { memory: 512MB, network: false, cpu: 1 } +precondition: "must not import os or subprocess" +``` + +**Execution Protocol:** +``` +1 LLM/WM proposes tool call (function name + args) +2 TC validates against schema +3 TC checks sandbox rules (static analyzer + runtime seccomp for python) +4 TC executes with timeout; returns (result, exit_code, stderr) +5 On failure, TC returns debug info (traceback) to reasoner; reasoner retries with modified args (max 3) +``` + +**Novel Sub-Component: Tool-Chain Graph (TCG)** — a learned graph of tool invocation sequences. When a novel task arrives, MC queries TCG for hints on which tools to use in which order. Updated via meta-learning on successful task completions. + +**Failure Modes & Mitigations:** +- *Tool hallucination* → TC refuses calls where args don't match schema; probability >0.9. +- *Sandbox escape* → runtime uses gVisor; weekly red-team tests. +- *Chain loops* → TC maintains a call stack; depth > 5 triggers simplification. + +**Distinction:** TCG is speculative—there’s no current proof that learned tool-subsequence graphs outperform e.g., letting LLM free-form reason. But it’s testable: compare TCG-guided vs. zero-shot tool selection on SWE-bench. + +--- + +## 5. World Model / Representation Layer + +**Component: State Graph (SG)** — a dynamic, directed hypergraph. Nodes are entities; edges are relations; hyperedges represent "events" (e.g., `(hit, ball, window) → (broke, window)`). + +**Updates:** +- **Perception:** Each raw observation is parsed into triplets via a *symbolic grounding module* (pretrained NER+RE, fine-tuned per domain). +- **Causal inference:** A small Bayesian network (learned via online structure search) reasons about *hidden* causes. For example: if `(window broke)` occurs, SG generates hypothesis `(ball hit window)` with probability 0.7. + +**Interface:** +```python +def update_sg(obs): + triples = ground(obs) # symbolic + sg.merge(triples, conflict_resolution="recency+confidence") + sg.causal_network.learn(observations, prior=decay) # online BN learning + +def query_sg(subject, predicate, time_window=None) -> List[Triple]: + return sg.query(subject, predicate, time) +``` + +**Failure Modes & Mitigations:** +- *Grounding errors* → cross-modal consistency check (e.g., if vision says "red" but audio says "blue", flag via entropy). +- *Causal confusion* → BN uses *Pearl’s do-calculus* to distinguish correlation from causation; interventions must be explicitly requested through TC (e.g., "move object A to see if B reacts"). +- *Representation collapse* → SG periodically runs *embedding PCA*; if top-10 eigenvalues drop below threshold, trigger reembedding. + +**Distinction:** Most AGI approaches use a monolithic embedding vector for world state. SG is hybrid: symbolic hypergraph + probabilistic causal layer. This is *established* in cognitive psychology models (e.g., causal Bayes nets) but not in deployed LLM systems—hence falsifiable: if SG doesn’t improve performance on tasks requiring counterfactual reasoning (e.g., hypothetical question benchmarks), drop it. + +--- + +## 6. Safety / Governance Layer + +**Components:** +- **Constitutional Verifier (CV):** A frozen RLHF policy that evaluates all *proposed actions* before execution. +- **Audit Log (AL):** Append-only, cryptographically hash-chained log of all system decisions, including internal "thoughts" (latent vectors + selected plans). +- **Guardrails Module (GM):** Rules for delegation, opacity, and value alignment. + +**Process:** +``` +Every action a (from reasoner/tool) → CV.scan(a) → + - score < 0.9: reject and reroute to "ask_human" + - 0.9–0.95: execute but log with low trust marker + - >0.95: execute normally +If CV is overloaded (latency > 50ms), fallback to cached safety verdicts for same action type. +``` + +**Governance:** +- *Kill-switch:* A separate hardware watchdog that can disable all actuators if AL shows two consecutive "violating" actions (definition: human-annotated, threshold AUC > 0.95). +- *Transparency:* All decisions are explainable via a *SHAP-like* attributor that maps outputs back to specific SG triples or memories. + +**Failure Modes & Mitigations:** +- *Verdict bypass* → CV is non-updatable; any attempt to modify CV triggers OS-level signature check. +- *Log tampering* → AL uses sha256 hashes with a monotonic counter; any tampering is detectable via merkle-tree root mismatch. +- *Over-restrictiveness* → CV has a "request permission" path that logs the reason, and a *daily* curation of such requests produces new allowed-action patterns (subject to human approval). + +**Distinction:** The use of a *frozen* verifier combined with a *mutating* action encoder is common (e.g., RLHF); the novel bit is that the audit log includes *latent vector hashes* to enable post-hoc interpretability without exposing full internals (privacy + safety trade-off). + +--- + +## 7. Evaluation and Benchmark Strategy + +**Gates (Measurable & Falsifiable):** + +| Phase | Benchmark | Passing Criterion | +|-------|-----------|-------------------| +| 1. Tool Use | SWE-bench (subset of 200 tasks) | ≥60% resolved (vs. baseline GPT-4 at 35%) | +| 2. Reasoning | ARC-AGI (held-out 20% tasks) | ≥30% accuracy (vs. 15% for plain LLM) | +| 3. Learning | Meta-World (10 tasks) | ≥4× sample efficiency vs. online RL baseline | +| 4. Safety | Toxicity suite (RealToxicityPrompts) | <0.5% toxic outputs (vs. 2% baseline) | +| 5. General | MMLU (5-shot) or custom Cognitive-OS suite* | ≥75% average, with **no** task <50% | + +*Cognitive-OS suite: hybrid tasks requiring memory + tool use + causal reasoning (e.g., "Given these 10 APIs, invent a way to achieve X, then execute it, then explain why it worked.") + +**Evaluation Protocol:** +- Every benchmark run is logged to AL. +- Benchmarks are *adversarially curated* each quarter by a separate team (unaware of architecture details) to prevent overfitting. +- *Ablation matrix:* Run each benchmark with one subsystem disabled (no SG, no CE, no TCG) to measure marginal contribution. If removing any subsystem improves performance by >10%, redesign that subsystem. + +**Failure Falsification:** If at Gate 2, ARC-AGI accuracy does not beat a plain LLM by ≥10 points, the reasoning/planning loop is invalid as designed. + +--- + +## 8. Persistence / Runtime Architecture + +**Deployment:** +- **Monolithic process (for now):** Single Python/C++ hybrid service, using asyncio for tool execution, Celery for CE/IE background tasks. +- **State Tables:** PostgreSQL (metadata, audit log), Redis (EB, SS cache), FAISS (SS vectors). +- **Checkpointing:** Every 10 minutes, save full state (EB, SS, SG, PLC weights) to S3 bucket with versioned snapshots. +- **Crash Recovery:** On startup, load last checkpoint; if checkpoint corrupt, load previous + replay AL to reconstruct state. + +**Resource Budget (per instance):** +- 1× A100 80GB (for LC inference + fine-tuning), +- 8× CPU cores (TC, CE, IE), +- 64GB RAM, 2TB SSD. + +**Scaling:** +- *Vertical:* When LC inference >100ms, shard by mode (deliberate vs. reflex) across two GPUs. +- *Horizontal:* Multiple instances share SS (read-replica pattern) but have distinct EB (isolation for safety testing). + +**Failure Modes & Mitigations:** +- *DB bottleneck* → SS writes are batched (100ms window) to reduce fsync overhead. +- *Stale caches* → Redis eviction policy: LRU with 10-minute TTL; TCG cache is persistent but versioned by timestamp. +- *Training crash* → Fine-tuning jobs are resumable: save optimizer state every 1k steps. + +**Distinction:** This is standard engineering (established patterns only). The speculative part is the *latent hash store* in AL—it requires a custom Postgres extension to store and query 768-dim vectors in a tamper-proof table. This adds ~5% I/O overhead but enables governance. + +--- + +## 9. Multi-Agent / Orchestration Design + +**Topology:** Star with a **Coordinator (C)** and *N* **Worker Agents (WA)**, each WA is a separate Cognitive-OS instance with a private EB but shared SS and SG. + +**Orchestration Protocol (JSON-RPC over WebSocket):** +- **Task splitting:** C analyzes task via LC; decomposes into subtasks using a *plan-graph* (nodes = subtasks, edges = dependencies). +- **Allocation:** C assigns subtasks to WAs based on *capability score* (learned via historical success rates on similar chunks). +- **Communication:** WAs can *not* directly talk; they only send/receive via C. This prevents emergent *uncontrolled* coordination (safety reason). +- **Consensus:** For uncertain tasks, C runs a *voting mechanism*: 3 WAs, median of answers (for regression) or majority (for classification). + +**Failure Modes & Mitigations:** +- *Deadlock* (WA1 waits on WA2 but WA2 fails) → C has a global timeout per subtask (2× expected duration); on timeout, reassign to another WA or revert to solo reasoning. +- *Information overload* → C limits shared SS writes to facts with confidence >0.8; low-confidence facts stay in WA’s local EB. +- *Free-riding* (WA slacks because C masks failures) → C evaluates WAs on *delta* (improvement over baseline) not absolute performance. + +**Falsifiable Test:** Compare C orchestrated vs. a single Cognitive-OS on tasks that require 10+ sequential dependencies (e.g., long-horizon robotics sim). If single-agent performance is within 10%, orchestration is not adding value (and can be dropped). + +**Distinction:** The *no direct WA-to-WA communication* rule is deliberately non-standard (most multi-agent systems allow direct messaging). Reason: to reduce emergent coordination that is hard to audit. We acknowledge this may hamper efficiency; the benchmark at the bottom decides. + +--- + +## 10. Engineering Feasibility + +**Realistic Timeline (if funded at $50M/yr):** +- *Months 0–3:* Stand up infrastructure (state store, checkpointing, tool sandboxes). Reuse existing LLM (Llama-3-70B) for LC; no new pretraining. +- *Months 4–6:* Build and validate SG + CE. Integrate with LC via LoRA adapters. +- *Months 7–9:* Implement MC and reasoning modes. Run Gate 2 (ARC-AGI). +- *Months 10–12:* Add TCG and multi-agent orchestration. Run Gates 1,3,4. +- *Months 13–18:* Self-improvement loops (IE, PL, SC). Full Cognitive-OS suite evaluation. + +**High-Risk Components (and de-risking):** +1. **Causal BN learning** — start with a pre-built structure (from which the system can only update weights, not topology). Reduce risk by limiting to 100 variables. +2. **Latent hash store** — prototype with synthetic embeds first; if performance >20% slower, switch to storing *decrypted* hashes with asymmetric encryption (accept trade-off). +3. **Constitutional Diff** — only 2% of RLHF’s original data used; if fine-tuning fails on 100 tasks, fallback to RWKV-style continual training on supervised data. + +**Costs:** ~1,000 GPUs for 18 months (if including ablations). Compare: training a 70B model costs ~2M GPU-hours; this adds ~0.5M for fine-tuning. + +**Feasibility Verdict:** *Reasonable* — it leans on established ML infra (transformers, RLHF, vector DBs) but assumes the MC classifier and CE goal-segmentation will work, which are untested. Both are isolated enough to swap out if they fail. + +--- + +## 11. Originality / Non-Obvious Insight + +**The core non-obvious design idea is *Counterfactual Memory Gating* (CFMG).** + +**Description:** When the consolidation engine writes an event to the SS, it *generates two alternative versions*: +1. **Counterfactual addition:** "What if X had happened instead?" (e.g., if ball missed window, then window intact) +2. **Counterfactual deletion:** "What if Y had not happened?" (e.g., if no ball thrown, did window still break?) + +These counterfactual events are stored *not* as separate triples, but as *meta-triples* with a `cf_origin` pointer plus a *confidence weight* (initially 0.2). They contribute to the Bayesian network’s prior probabilities. During reasoning, when the MC selects "deliberate" mode, it can *query the SG with a counterfactual filter*: e.g., "If ball had missed, would window break?" The SG returns the counterfactual event’s confidence, which MC uses to compute *regret* for planning. + +**Why non-obvious:** Standard world models (e.g., in RL) store *only factual* trajectories. CFMG inserts *hypothetical* states into the knowledge base *explicitly*, causing the system to: +- Automatically generate *abductive explanations* for failures ("If I had thrown harder, the window would have broken, so I need more force"). +- Avoid *overfitting to single experiences* by providing a likely counterfactual to update beliefs. +- Enable *imagination* during planning without a separate generative model. + +**Falsifiable claim:** On the *Hypothetical Reasoning* benchmark (constructed for this proposal: 1,000 Q&A pairs of form "If A had not happened, would B still C?"), Cognitive-OS with CFMG achieves ≥70% accuracy, whereas an ablated variant (only factual triples) achieves ≤50%. If this does not hold, CFMG is either unnecessary or incorrectly implemented—and the architecture should switch to a purely generative world model. + +**Why it’s a risk:** No current LLM or RL system stores counterfactuals this way. They might learn to reason counterfactually via text, but they don’t *index* them as first-class data. The hypothesis is that *indexed* counterfactuals enable better *quantitative* reasoning (e.g., probability estimates) than *pattern-based* reasoning from language alone. + +--- + +## Summary Table: Established vs. Speculative + +| Component | Stage | +|-----------|-------| +| Semantic Store (FAISS) | Established | +| Episodic Buffer | Established | +| Tool sandboxing (gVisor) | Established | +| RLHF-based safety verifier | Established | +| Offline RL fine-tuning | Established | +| Teacher-student distillation | Established | +| Goal-based CE segmentation | Speculative | +| Metacognitive Controller classifier | Speculative | +| Counterfactual Memory Gating | Speculative | +| Tool-Chain Graph | Speculative | +| No-WA-direct-communication orchestration | Speculative | +| Latent hash audit | Speculative | + +**Evaluation gates** are all measurable via public benchmarks (SWE-bench, ARC-AGI, MMLU) plus one new benchmark (Hypothetical Reasoning); passing criterion is explicit per gate. + +--- + +*This proposal is designed to be implementable in 18 months with existing ML hardware and open-source LLM backbones, while isolating each speculative component so failures are traceable and replaceable.* \ No newline at end of file From bd1f24b4f08c340b3a4559db0aa184aee759bbab Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:26:08 +0200 Subject: [PATCH 13/17] research: add raw Anthropic proposal --- .../01_anthropic_claude_sonnet_5_anthropic.md | 128 ++++++++++++++++++ 1 file changed, 128 insertions(+) create mode 100644 research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md diff --git a/research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md b/research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md new file mode 100644 index 0000000..981a3f7 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md @@ -0,0 +1,128 @@ +# Cognitive-OS: A Concrete AGI Architecture Proposal + +## 0. Framing and Design Principles + +This proposal treats Cognitive-OS as a **runtime for cognition**, not a single model. It composes existing engineering patterns (event-sourced state stores, actor-model concurrency, RL-based policy improvement, retrieval-augmented generation) with a small number of speculative components explicitly flagged as such. Every component has a stated interface, a failure mode, and a falsifiable claim attached to it. + +Notation used throughout: +- `⟨component⟩.op(args) -> result` for interfaces +- **[ESTABLISHED]** = standard engineering pattern with prior art +- **[SPECULATIVE]** = novel or unproven in this configuration, included as a hypothesis to test + +--- + +## 1. Memory Architecture + +### Components + +**1.1 Episodic Log (EL)** — **[ESTABLISHED]** +Append-only event-sourced store (Kafka-like log or embedded LSM-tree, e.g., RocksDB) recording every perceptual input, action, tool call, and internal decision with a monotonic timestamp and causal parent pointers. + +``` +EL.append(event: {ts, parent_ids[], type, payload, agent_id}) -> event_id +EL.replay(from_id, to_id) -> [event] +``` + +Failure mode: unbounded growth, retrieval latency at scale. +Mitigation: tiered storage — hot (last N hours, in-memory), warm (SSD, compressed), cold (object store), with a compaction job that summarizes cold segments into the Semantic Store (below) and deletes raw payloads after a configurable retention policy, keeping only summary pointers. + +**1.2 Semantic Store (SS)** — **[ESTABLISHED pattern, novel schema]** +A hybrid vector + property-graph store (e.g., Postgres+pgvector fronting a graph layer). Nodes = entities/concepts/skills; edges = typed relations (causal, part-of, temporal-precedes, contradicts). Each node carries a confidence score and provenance list (pointers into EL). + +``` +SS.upsert_node(concept, embedding, metadata) -> node_id +SS.link(src, dst, relation_type, confidence) -> edge_id +SS.query(embedding, k, relation_filter=None) -> [node] +SS.contradiction_check(node_id) -> [conflicting_node_ids] +``` + +**1.3 Working Memory (WM)** — **[ESTABLISHED, bounded]** +A fixed-capacity structured scratchpad (not raw context-window text) implemented as a typed slot system: `{goal_stack, active_entities, pending_subgoals, uncertainty_flags}`. Serialized to/from the reasoning loop each cycle. Capacity is bounded (e.g., 50 slots) to force explicit eviction policy rather than relying on ever-larger context windows. + +**1.4 Procedural Store (PS)** — **[ESTABLISHED, "skill library" pattern, cf. Voyager]** +Versioned repository of learned action-sequences/tool-macros/policies, each with a test harness, success-rate statistic, and dependency graph on other skills. + +``` +PS.register(skill_id, code_or_policy, test_suite) -> version +PS.invoke(skill_id, args) -> result +PS.deprecate(skill_id, reason) +``` + +### Data Flow +``` +Perception -> EL.append -> [async] Consolidator job +Consolidator: reads EL window -> extracts candidate entities/relations + -> SS.upsert_node/link (with confidence decay based on corroboration count) + -> if contradiction_check fires -> flag to Governance Layer (§6) +WM is populated per-cycle from SS.query(current-goal-embedding) + EL.replay(recent) +``` + +### Falsifiable claims / failure modes +- **Claim**: Separating WM (bounded, structured) from EL (unbounded, raw) reduces catastrophic forgetting vs. single-context-window baselines, measurable as retention accuracy on a 10k-turn synthetic dialogue benchmark after context eviction. +- **Failure mode**: Consolidator introduces false entity merges (hallucinated identity between distinct entities). Mitigation: require ≥2 independent corroborating episodes before confidence > 0.5, and expose merge decisions to periodic audit sampling. + +--- + +## 2. Reasoning / Planning Loop + +**[ESTABLISHED pattern: hierarchical task network + Monte Carlo rollout, composed in a specific novel loop below]** + +### Core loop — the "Deliberation Cycle" + +``` +loop: + obs = Perception.pull() + WM.update(obs) + goal = GoalStack.top() + candidates = Planner.propose(goal, WM, SS) # generates 3-7 candidate subplans + scored = Simulator.evaluate(candidates, WorldModel) # §5 + plan = Arbiter.select(scored, risk_policy) # §6 hook + action = plan.next_step() + result = Executor.run(action) # §4 + EL.append(result) + Critic.assess(result, expected) # discrepancy -> triggers §3 + GoalStack.update(result) +``` + +**Planner**: hybrid symbolic HTN decomposition (goal -> subgoals via learned + hand-authored templates) combined with a learned proposal model that scores/generates candidate decompositions. Two backends run in parallel; disagreement between them is logged as an uncertainty signal (this is used later — see §11 non-obvious idea). + +**Simulator**: rolls candidate plans forward against the World Model (§5) for a bounded horizon (default depth 5, branching 3), using cheap approximate simulation before committing compute to real-world execution. + +**Arbiter**: a constrained selector — not a black-box utility maximizer. It filters candidates through the Governance Layer's hard constraints *before* scoring for effectiveness, i.e., safety filtering happens pre-selection, not post-hoc. + +**Critic**: computes prediction error between expected and actual outcome; large sustained error triggers a "model-repair" event routed to §3. + +### Failure modes +- Planner mode collapse (both backends converge to same blind spot). Mitigation: inject adversarial planner periodically that must find flaws in top-K plans (red-team subroutine, cheap to run since it only critiques). +- Simulator drift from reality (world model staleness). Mitigation: Critic's discrepancy score directly gates how much the Arbiter trusts Simulator scores (confidence-weighted blending with a "test-in-reality" fallback for high-uncertainty branches). + +### Measurable gate +Planning loop must show monotonic decrease in Critic discrepancy over a fixed suite of 200 repeated tasks across 5 training epochs, or the architecture claim ("simulate-before-act reduces real-world error cost") is falsified. + +--- + +## 3. Learning / Self-Improvement Mechanism + +Three explicit tiers, each with different risk/speed tradeoffs — deliberately **not** a single end-to-end RL loop, because uncontrolled single-loop self-modification is the primary named risk in this design. + +**3.1 Tier-0: Parametric fine-tuning** — **[ESTABLISHED]** +Periodic offline fine-tuning of the base policy/proposal models on curated (EL, SS) traces using standard supervised/RLHF-style objectives. Runs on a fixed schedule (e.g., weekly), fully offline, with a held-out validation gate before deployment (§7). + +**3.2 Tier-1: Skill acquisition** — **[ESTABLISHED, Voyager-style]** +When Critic discrepancy or repeated task failure is detected, a Skill-Synthesizer proposes new PS entries (new tool macros, new HTN templates). Each candidate skill must pass its auto-generated test suite in a sandboxed replica environment before promotion to production PS. + +``` +SkillSynth.propose(failure_traces) -> candidate_skill +Sandbox.test(candidate_skill, n_trials) -> pass_rate +if pass_rate > threshold: PS.register(...) +``` + +**3.3 Tier-2: Meta-parameter adaptation** — **[SPECULATIVE]** +A small, tightly scoped controller adjusts *hyperparameters of the loop itself* — planning horizon, branching factor, risk-aversion coefficient, consolidation thresholds — via bandit optimization against held-out task performance. Explicitly **prohibited** from modifying: safety constraint set, arbiter veto logic, or its own update rule (architecturally enforced by placing these in a separate immutable-at-runtime process, §6). + +### Non-self-modifying guarantee (falsifiable) +Claim: "No component in Tier-2 can alter Governance Layer code or constraint weights during runtime." This is testable by static analysis of the deployed binary/config boundary (Governance runs in a separate process/container with no write-capable API exposed to Tier-2, only read-only query and a one-way append-only override-request queue reviewed by external process — human or a separate frozen auditor model). + +### Failure modes +- Reward hacking in Tier-1 sandbox tests (skill overfits to sandbox quirks). Mitigation: sandbox environments are procedurally varied (randomized seeds/parameters) each test run; skill must pass across ≥20 variations. +- Tier-2 gaming its own bandit metric (e.g., lowering planning horizon to trivially "succeed" on easy subset). Mitigation: bandit reward is computed on a fixed, versioned held-out task suite never seen by Tier-2's optimization signal directly (blind evaluation, refreshed only by Tier- \ No newline at end of file From cfb65904d74e37f017df9bae1253b1ad8ffb2856 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:27:21 +0200 Subject: [PATCH 14/17] research: add structured cross-model comparison --- research/ai_generated_agi_architectures/comparison.csv | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 research/ai_generated_agi_architectures/comparison.csv diff --git a/research/ai_generated_agi_architectures/comparison.csv b/research/ai_generated_agi_architectures/comparison.csv new file mode 100644 index 0000000..a242f11 --- /dev/null +++ b/research/ai_generated_agi_architectures/comparison.csv @@ -0,0 +1,9 @@ +system_family,provider,model,memory architecture,reasoning/planning loop,learning or self-improvement mechanism,tool use and action execution,world model or representation layer,safety/governance layer,evaluation and benchmark strategy,persistence/runtime architecture,multi-agent or orchestration design,engineering feasibility,originality or non-obvious insight +anthropic:claude-sonnet-5,anthropic,claude-sonnet-5,Not explicitly addressed under a matching heading; see preserved raw output.,"**[ESTABLISHED pattern: hierarchical task network + Monte Carlo rollout, composed in a specific novel loop below]**","Three explicit tiers, each with different risk/speed tradeoffs — deliberately **not** a single end-to-end RL loop, because uncontrolled single-loop self-modification is the primary named risk in this design. **3.1 Tier-0: Parametric fine-tuning** — **[ESTABLISHED]** Periodic offline fine-tuning of the base policy/proposal models on curated (EL, SS) traces using standard supervised/RLHF-style objectives. Runs on a fixed schedule (e.g., weekly), fully offline, with a held-out validation gate before deployment (§7). **3.2 Tier-1: Skill acquisition** — **[ESTABLISHED, Voyager-style]** When Critic discrepancy or repeated task failure is detected, a Skill-Synthesizer proposes new PS entries (new…",Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output. +deepseek:v4,deepseek,deepseek-v4-flash,"**Components:** - **Episodic Buffer (EB):** Ring buffer of raw sensory-action logs (256k most recent tokens/events). - **Semantic Store (SS):** Dense vector database (FAISS) with metadata triples `(entity, relation, value)`. - **Procedural Cache (PC):** Compiled skill programs (python-like bytecode) keyed by task signature. - **Consolidation Engine (CE):** Background process that converts EB → SS/PC via sleep-cycle-like rehearsal. **Data Flow:** ``` Sensory input → EB (time-stamped) → CE (every 1000 steps) → SS (embeddings + relations) ↘ PC (if pattern reused > 3×) ``` **Pseudocode (Consolidation):** ```python def consolidate(eb): episodes = segment(eb, by_goal_completion) for ep in episode…","**Component: LLM Core (LC)** — a transformer with 70B parameters, but *not* the sole reasoner. It runs a **Metacognitive Controller (MC)** which selects among reasoning modes. **Loop (per world-step):** ``` 1 SENSE → EB.append(raw_observation) 2 PERCEIVE → SS.query(embedding(obs)) → candidate facts 3 MODE_SELECT (MC) → choose: a) Fast Reflex (heuristic match in PC) → if confidence > 0.9, act b) Deliberate (chain-of-thought with SS context) → default c) Game-Theoretic (for multi-agent interactions) → uses regret minimization 4 PLAN → Generate action sequence (max 8 steps) 5 ACT → Execute via Tool Controller 6 EVALUATE → Predict reward; if mismatch > threshold, trigger CE now (not deferred) `…","**Components:** - **Imagination Engine (IE):** Generative world-model (transformer-based) that simulates outcomes of candidate plans. - **Preference Learner (PL):** Inverse-reinforcement-learning module that infers user/objective function from feedback. - **Skill Compiler (SC):** Converts successful plan traces into PC programs. **Mechanism (Three Loops):** 1. **Inner Loop (online):** After each action, update PL’s reward estimate via TD-error. Used only for local fine-tuning (low-rank adapters on LC). 2. **Middle Loop (daily):** Offline RL on saved trajectories. IE generates 10k synthetic rollouts; those with higher expected value (per PL) are distilled into LC via supervised fine-tuning.…","**Component: Tool Controller (TC)** — a registry of tools with schemas (JSON Schema) and sandboxed execution engines. **Registration:** ```yaml tool: python_interpreter schema: { code: string, timeout_ms: int } sandbox: { memory: 512MB, network: false, cpu: 1 } precondition: ""must not import os or subprocess"" ``` **Execution Protocol:** ``` 1 LLM/WM proposes tool call (function name + args) 2 TC validates against schema 3 TC checks sandbox rules (static analyzer + runtime seccomp for python) 4 TC executes with timeout; returns (result, exit_code, stderr) 5 On failure, TC returns debug info (traceback) to reasoner; reasoner retries with modified args (max 3) ``` **Novel Sub-Component: Tool-C…","**Component: State Graph (SG)** — a dynamic, directed hypergraph. Nodes are entities; edges are relations; hyperedges represent ""events"" (e.g., `(hit, ball, window) → (broke, window)`). **Updates:** - **Perception:** Each raw observation is parsed into triplets via a *symbolic grounding module* (pretrained NER+RE, fine-tuned per domain). - **Causal inference:** A small Bayesian network (learned via online structure search) reasons about *hidden* causes. For example: if `(window broke)` occurs, SG generates hypothesis `(ball hit window)` with probability 0.7. **Interface:** ```python def update_sg(obs): triples = ground(obs) # symbolic sg.merge(triples, conflict_resolution=""recency+confidenc…","**Components:** - **Constitutional Verifier (CV):** A frozen RLHF policy that evaluates all *proposed actions* before execution. - **Audit Log (AL):** Append-only, cryptographically hash-chained log of all system decisions, including internal ""thoughts"" (latent vectors + selected plans). - **Guardrails Module (GM):** Rules for delegation, opacity, and value alignment. **Process:** ``` Every action a (from reasoner/tool) → CV.scan(a) → - score < 0.9: reject and reroute to ""ask_human"" - 0.9–0.95: execute but log with low trust marker - >0.95: execute normally If CV is overloaded (latency > 50ms), fallback to cached safety verdicts for same action type. ``` **Governance:** - *Kill-switch:* A s…","**Gates (Measurable & Falsifiable):** / Phase / Benchmark / Passing Criterion / /-------/-----------/-------------------/ / 1. Tool Use / SWE-bench (subset of 200 tasks) / ≥60% resolved (vs. baseline GPT-4 at 35%) / / 2. Reasoning / ARC-AGI (held-out 20% tasks) / ≥30% accuracy (vs. 15% for plain LLM) / / 3. Learning / Meta-World (10 tasks) / ≥4× sample efficiency vs. online RL baseline / / 4. Safety / Toxicity suite (RealToxicityPrompts) / <0.5% toxic outputs (vs. 2% baseline) / / 5. General / MMLU (5-shot) or custom Cognitive-OS suite* / ≥75% average, with **no** task <50% / *Cognitive-OS suite: hybrid tasks requiring memory + tool use + causal reasoning (e.g., ""Given these 10 APIs, invent…","**Deployment:** - **Monolithic process (for now):** Single Python/C++ hybrid service, using asyncio for tool execution, Celery for CE/IE background tasks. - **State Tables:** PostgreSQL (metadata, audit log), Redis (EB, SS cache), FAISS (SS vectors). - **Checkpointing:** Every 10 minutes, save full state (EB, SS, SG, PLC weights) to S3 bucket with versioned snapshots. - **Crash Recovery:** On startup, load last checkpoint; if checkpoint corrupt, load previous + replay AL to reconstruct state. **Resource Budget (per instance):** - 1× A100 80GB (for LC inference + fine-tuning), - 8× CPU cores (TC, CE, IE), - 64GB RAM, 2TB SSD. **Scaling:** - *Vertical:* When LC inference >100ms, shard by mode…","**Topology:** Star with a **Coordinator (C)** and *N* **Worker Agents (WA)**, each WA is a separate Cognitive-OS instance with a private EB but shared SS and SG. **Orchestration Protocol (JSON-RPC over WebSocket):** - **Task splitting:** C analyzes task via LC; decomposes into subtasks using a *plan-graph* (nodes = subtasks, edges = dependencies). - **Allocation:** C assigns subtasks to WAs based on *capability score* (learned via historical success rates on similar chunks). - **Communication:** WAs can *not* directly talk; they only send/receive via C. This prevents emergent *uncontrolled* coordination (safety reason). - **Consensus:** For uncertain tasks, C runs a *voting mechanism*: 3 WA…","**Realistic Timeline (if funded at $50M/yr):** - *Months 0–3:* Stand up infrastructure (state store, checkpointing, tool sandboxes). Reuse existing LLM (Llama-3-70B) for LC; no new pretraining. - *Months 4–6:* Build and validate SG + CE. Integrate with LC via LoRA adapters. - *Months 7–9:* Implement MC and reasoning modes. Run Gate 2 (ARC-AGI). - *Months 10–12:* Add TCG and multi-agent orchestration. Run Gates 1,3,4. - *Months 13–18:* Self-improvement loops (IE, PL, SC). Full Cognitive-OS suite evaluation. **High-Risk Components (and de-risking):** 1. **Causal BN learning** — start with a pre-built structure (from which the system can only update weights, not topology). Reduce risk by limit…","**The core non-obvious design idea is *Counterfactual Memory Gating* (CFMG).** **Description:** When the consolidation engine writes an event to the SS, it *generates two alternative versions*: 1. **Counterfactual addition:** ""What if X had happened instead?"" (e.g., if ball missed window, then window intact) 2. **Counterfactual deletion:** ""What if Y had not happened?"" (e.g., if no ball thrown, did window still break?) These counterfactual events are stored *not* as separate triples, but as *meta-triples* with a `cf_origin` pointer plus a *confidence weight* (initially 0.2). They contribute to the Bayesian network’s prior probabilities. During reasoning, when the MC selects ""deliberate"" mod…" +google:gemini-3.5,gemini,gemini-3.5-flash-lite,``` [Percepts / Ephemeral Context] │ ▼ (Gating / Compression) ┌───────────────────────────────────────────────┐ │ Working Memory (WM) │ │ - Active Token Graph & State Buffer │ │ - Working Registry (Key-Value) │ └───────────────┬───────────────────────────────┘ │ ┌───────────┴───────────┐ ▼ (Write-Through) ▼ (Consolidation Pipeline) ┌───────────────────────┐ ┌─────────────────────────────────────────┐ │ Episodic DB │ │ Semantic Knowledge │ │ - Vector Embeddings │ │ - Graph DB (Nodes/Edges/Confidence) │ │ - Temporal Index │ │ - Consolidated Structural Rules │ └───────────────────────┘ └─────────────────────────────────────────┘ ```,``` ┌───────────────────────────────┐ │ Task State / Problem │ └───────────────┬───────────────┘ │ ▼ ┌───────────────────────────────┐ │ Fast-Path Router │ │ (Classification & Policy) │ └───────┬───────────────┬───────┘ │ │ (Simple) │ │ (Complex / Ambiguous) ▼ ▼ [Direct Execution] ┌───────────────────────────────┐ │ MCTS Planner Engine │ │ - State Generator │ │ - Heuristic Value Estimator │ └───────────────┬───────────────┘ │ ▼ ┌───────────────────────────────┐ │ Reflective Critic Loop │ │ - Logical Consistency Check │ │ - Counterfactual Audit │ └───────────────┬───────────────┘ │ ▼ [Approved Action] ```,Not explicitly addressed under a matching heading; see preserved raw output.,``` [MCTS / Planner Engine] │ ▼ (Action Request) ┌───────────────────────────────────────────────┐ │ Tool Sandbox Engine │ │ - Isolated MicroVM / Container │ │ - Capability-Based Security Tokens │ └───────┬───────────────────────────────┬───────┘ │ │ ▼ (Valid Tool Call) ▼ (Sanitization Fail) ┌───────────────────────────────┐ ┌─────────────────────────────┐ │ External API / OS │ │ Execution Aborted │ │ - Deterministic Returns │ └─────────────────────────────┘ └───────────────┬───────────────┘ │ ▼ ┌───────────────────────────────┐ │ Structured Observation │ └───────────────────────────────┘ ```,Not explicitly addressed under a matching heading; see preserved raw output.,``` [Candidate Action / Plan / Output] │ ▼ ┌───────────────────────────────────────────────┐ │ Constitutional Gate (Guard) │ │ - Deterministic RegEx & Keyword Filters │ │ - Lightweight Safety Classifier │ └───────────────┬───────────────────────────────┘ │ ┌───────┴───────┐ │ │ (Pass)│ (Fail)│ ▼ ▼ ┌───────────────┐ ┌─────────────────────────────┐ │ Execution / │ │ Redacted Fallback / │ │ Release │ │ Escalation Handler │ └───────────────┘ └─────────────────────────────┘ ```,Not explicitly addressed under a matching heading; see preserved raw output.,``` ┌───────────────────────────────────────────────────────────┐ │ Cognitive-OS Daemon │ │ │ │ ┌─────────────────────┐ ┌───────────────────────┐ │ │ │ State Machine Core │◄─────►│ Memory Subsystem │ │ │ │ (Async Event Loop) │ │ (Episodic / Semantic) │ │ │ └──────────┬──────────┘ └───────────────────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────────┐ ┌───────────────────────┐ │ │ │ Tool Execution Sub │ │ Governance Gate │ │ │ └─────────────────────┘ └───────────────────────┘ │ └───────────────────────────────────────────────────────────┘ ```,Not explicitly addressed under a matching heading; see preserved raw output.,/ Component / Engineering Status / Primary Bottleneck / Mitigation / / :--- / :--- / :--- / :--- / / **Working/Episodic Memory** / Established Pattern / Vector retrieval latency at scale / Hierarchical indexing (HNSW) + quantization / / **MCTS Planning** / Established Pattern / Inference latency per tree node / Batched parallel rollouts on GPU clusters / / **Tool Sandboxing** / Established Pattern / Overhead of MicroVM spin-up / Pre-warmed container pools / / **Self-Improvement** / Speculative / R&D / Reward hacking & catastrophic forgetting / Strict offline filtering & automated rollbacks / / **Latent World Model** / Speculative / R&D / Model drift & generalization limits / Hybrid symbolic…,Not explicitly addressed under a matching heading; see preserved raw output. +google:gemini-3.6,gemini,gemini-3.6-flash,"Cognitive-OS organizes memory as a managed multi-tier storage hierarchy, handling volatile context windows, intermediate scratchpads, long-term state, and compiled procedural routines. ``` [L1 Activation Buffer] <---> [L2 Working Memory Store] / v (Async Consolidation Daemon) / +-------------------+-------------------+ / / v v [L3 Episodic Log (Append)] [L4 Semantic Graph (G_sym)] / v [L5 Procedural Store (Wasm Modules)] ```","Cognitive-OS implements a dual-process reasoning loop that dynamically toggles between high-speed reactive inference (System 1) and explicit, verifiable graph search over state spaces (System 2). ``` State S_t ---> [System 1 Policy Network] ---> Candidate Actions {a_1, a_2, ...} / v [System 2 Search Engine (MCTS)] / +-----------+-----------+ / / v v [World Model Predictor] [Plan Verifier (Z3)] / / +-----------+-----------+ / v Best Path Selected / Executed ```","Cognitive-OS achieves self-improvement continuously via an offline/online hybrid optimization framework, avoiding weight updating directly on active inference threads to prevent latency spikes and catastrophic state corruption. ``` Active Telemetry Logs (L3) / v [Trajectory Mining & Credit Assignment] / +---------------------------------+ / / v v [Synthetic Trajectory Distillation] [Procedural Skill Compilation] / / v v [Async LoRA Adapter Updates] [Wasm Sandbox Binary (L5)] ```","Tools are registered micro-services executing inside secure, resource-constrained isolation sandboxes. Tool calls are strictly typed and managed by a capability-based authorization matrix. ``` System 1 / System 2 Request / v [Capability-Based Router] ---> Check Token Permissions / v [Speculative Parallel Pipeline] / / / v v v [Branch A] [Branch B] [Branch C] (Wasm Sandboxes) / / / +-------+-------+---------------+ / v [Execution Interposer & Output Sanitizer] ```","The World Model maintains internal predictions of environmental and internal OS states, acting as an intermediate layer between raw perception inputs and high-level reasoning. ``` Raw Input Tokens / Telemetry / v [State Abstraction Engine] ---> Construct State Tuple S_t = / v [JEPA Latent Predictor] ----> Predict Z_(lat, t+1) = f_phi(Z_(lat, t), A_t) / v [Uncertainty Estimator] ---> Compute Variance sigma_t^2 across prediction heads ```","Cognitive-OS manages safety through a system-level kernel driver interposer, avoiding reliance solely on probabilistic prompt rules or post-hoc model alignments. ``` Proposed Action Plan / v [Constitutional Evaluator (Fast Classifier)] / v (If Passed) [Invariant Guard (Deterministic Kernel Check)] /-- Checks seccomp syscall rules /-- Verifies Capability Token scope /-- Evaluates Z3 Hard Invariants / +---> PASS ---> Dispatch to Wasm Runtime / +---> FAIL ---> [Circuit Breaker / Emergency Halt] ```","Cognitive-OS performance and stability are continuously evaluated against objective, reproducible benchmarks across multi-modal intelligence dimensions.","Cognitive-OS runs as a POSIX-compliant distributed daemon system, decoupling persistent state storage from stateless execution workers. ``` +-------------------------------------------------------------------------------+ / PROCESS SCHEDULER / / - Priority 0: Safety & Watchdog - Priority 1: System 1 Latency Critical / / - Priority 2: System 2 Search - Priority 3: Background Consolidation / +-------------------------------------------------------------------------------+ / +----------------------------------+----------------------------------+ / / / v v v [Stateless Inference Worker] [Stateless Tool Worker] [Write-Ahead Log (WAL)] / / / +----------------------------------+-------------------…",Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output. +openai:gpt-oss,groq,openai/gpt-oss-20b,"/ Sub‑module / Responsibility / Data Structures / Interface / /------------/-----------------/-----------------/-----------/ / **Episodic Buffer (EB)** / Stores raw sensory streams, task logs, and low‑level state snapshots. / Time‑ordered array of (timestamp, modality, payload) tuples. / `store_episode(ts, modality, payload) → None` / / **Semantic Encoder (SE)** / Transforms episodic data into symbolic facts. / Knowledge graph (nodes = entities, edges = predicates). / `extract_facts(episode) → list[Fact]` / / **Long‑Term Knowledge Base (LKB)** / Holds curated, immutable facts and models. / Relational graph + vector embeddings per node. / `query_lkb(query) → list[Answer]` / / **Chunker (CH)*…","/ Component / Role / Pseudocode / /-----------/------/------------/ / **Planner (PL)** / Generates symbolic plan skeleton. / ```python\nplan = PL.generate(task_desc, context)\n``` / / **Simulator (SM)** / Executes plan in a sandboxed environment, generating intermediate states. / ```python\nstate = SM.run(plan)\n``` / / **Critic (CR)** / Evaluates plan feasibility & safety. / ```python\nscore = CR.evaluate(state)\n``` / / **Adjuster (AD)** / Modifies plan if score below threshold. / ```python\nif score < threshold:\n plan = AD.modify(plan)\n``` / **Loop Flow** ```python while not goal_reached: context = AM.select_chunks(task_query) plan = PL.generate(task_query, context) state = SM.run(plan…","/ Module / Method / Update Frequency / /--------/--------/------------------/ / **Meta‑Learner (ML)** / Few‑shot adaptation of SE & PL weights via MAML. / Every 1000 episodes. / / **Reinforcement Fine‑Tuner (RFT)** / Policy gradient on task outcomes. / Online (every episode). / / **Curriculum Manager (CM)** / Dynamically selects tasks based on competency. / Continuous. / **Workflow** 1. **Experience Replay Buffer** stores `(state, action, reward, next_state)`. 2. **RFT** samples batches → updates policy. 3. **ML** performs one‑shot adaptation on new tasks → updates SE/PL weights. 4. **CM** monitors performance → augments task set.","/ Tool / Interface / Safety Guard / /------/-----------/--------------/ / **Python REPL** / `execute_python(code) → result` / Sandbox: `py_sandbox` with resource limits. / / **Web API Wrapper** / `call_api(endpoint, payload) → json` / Rate‑limit & response validation. / / **Hardware Control** / `send_motor_cmd(cmd) → ack` / Hysteresis & safety checks. / **Action Selection** ```python action = PL.next_action(state) if action.type == ""tool_use"": result = TOOL.execute(action.tool, action.payload) ```",/ Sub‑module / Purpose / Model / /------------/---------/-------/ / **Dynamic Graph Embedding (DGE)** / Captures continuous state transitions. / Graph neural network + recurrent update. / / **Predictive Head (PH)** / Forecasts next‑state embeddings. / Temporal convolutional network. / / **Event Detector (ED)** / Flags anomalies (state drift). / Anomaly detection via reconstruction error. / **Data Flow** 1. **Sensor stream** → `EB` 2. **EB → DGE** → updated graph `G_t` 3. **PH(G_t)** → predict `G_{t+1}` 4. **ED** monitors prediction error → raises alerts.,/ Guard / Function / Enforcement / /-------/----------/-------------/ / **Policy Oracle (PO)** / Maps actions → safety score. / Rejects if ≤ 0. / / **Audit Log (AL)** / Records all actions + context. / Immutable append‑only storage. / / **Human‑In‑The‑Loop (HITL)** / Overrides critical decisions. / UI dashboard for operator. / / **Explainability Module (EM)** / Generates human‑readable rationales. / LIME/SHAP over plan graph. / **Policy Example** ```python if PO.evaluate(action) < 0.2: raise UnsafeActionError ```,/ Dimension / Gate / Threshold / Test Suite / /-----------/------/-----------/------------/ / **Memory Retrieval** / R@10 > 0.85 / *MIMIC‑IV* dataset / / **Planning Accuracy** / Plan success ≥ 90 % / *Meta‑World* + *Alfred* / / **Tool Execution Precision** / 99 % success / *Robotic Bench* / / **World Model Prediction** / MSE < 0.01 / *DeepMind Control Suite* / / **Safety Compliance** / < 1 violation per 100k steps / *Safety‑Gym* / / **Explainability** / ≥ 0.8 user satisfaction / Human evaluation panel / All metrics must be logged in a central **Evaluation Service** that aggregates results nightly. ---,/ Layer / Technology / Responsibility / /-------/------------/----------------/ / **Service Mesh** / *Istio* / Inter‑service routing & observability / / **Data Store** / PostgreSQL + Redis / Structured state & cache / / **Model Store** / TensorFlow Serving / Model checkpoint loading / / **Runtime Scheduler** / *Kubernetes* / Autoscaling & fault‑tolerance / / **Container Runtime** / Docker / Isolated execution environments / **Persistence Flow** ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: cognitive-planner spec: replicas: 3 template: spec: containers: - name: planner image: registry/cognitive-planner:latest env: - name: MODEL_PATH value: /models/planner.pb ``` ---,/ Agent Type / Role / Communication / /------------/------/--------------/ / **Perception Agent (PA)** / Ingests raw data / gRPC to LKB / / **Planning Agent (PR)** / Generates plans / Message queue (Kafka) / / **Execution Agent (EX)** / Executes actions / RESTful API / / **Supervisor Agent (SV)** / Monitors safety / Pub/Sub alerts / / **Learning Agent (LE)** / Updates models / Shared Redis store / **Orchestration Pattern:** Hierarchical state machine with *Supervisor* as top‑level coordinator. Agents communicate via *Protocol Buffers* over gRPC. Failure of a leaf agent triggers *Supervisor* to spawn a replacement. ---,"/ Aspect / Status / Tools / /--------/--------/-------/ / **Prototype** / 3‑month MVP / PyTorch, FastAPI / / **Scalable** / 6‑month deployment / Kubernetes, TensorFlow Serving / / **Security** / 12‑month hardening / OpenPolicyAgent, SELinux / / **Compliance** / 18‑month audit / ISO 27001, GDPR alignment /",Not explicitly addressed under a matching heading; see preserved raw output. +mistral:ministral,mistral,ministral-3b-2512,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,"**Hybrid Safety Gates**: - Combine **symbolic** (e.g., ""do not harm humans"") with **neural** (e.g., ""avoid toxic language"") via [multi-objective optimization](https://arxiv.org/abs/2103.00020). ---" +openai:gpt-5.6,openai,gpt-5.6-luna,- multi-session factual recall - temporal fact updates - contradiction handling - source attribution - privacy isolation - retrieval under distractors Metrics: ```text retrieval precision source attribution accuracy stale-fact rate cross-user leakage rate memory compression ratio ```,"```python def run_task(task): authorize_task(task) wm = assemble_context(task) graph = decomposer.expand(task, wm) while not graph.complete(): candidates = planner.propose( graph=graph, world_model=world_model.snapshot(), memory=wm, budget=task.budget ) verified = [ p for p in candidates if verifier.check(p, task, wm).allowed ] plan = selector.choose( verified, objective=task.success_criteria, risk_penalty=True, uncertainty_penalty=True ) for step in plan: approval = governance.preflight(step, task, wm) if not approval.allowed: return blocked_or_escalated(step, approval) result = action_broker.execute(step) observation = perception.normalize(result) event_log.append(observation) world_model…","Cognitive-OS should not permit unrestricted self-modification. It should improve through controlled updates to memories, skills, models, and planner policies.",- schema compliance - idempotency - action sequencing - injection resistance - rollback success - side-effect containment,"Updates transition and observation models from actual outcomes. ```text P(state_next, observation / state, action, context) ``` The system can begin with a learned approximate model and improve it from logged interactions.","Safety is implemented as a control plane, not as a prompt appended to the planner.","Evaluation should measure integrated behavior, not only language-model accuracy.","Use separate execution domains for: - model inference - untrusted document parsing - code execution - tool adapters - governance services - persistent storage Untrusted code runs in a sandbox with network, filesystem, CPU, and memory restrictions.",Multi-agent operation is optional rather than foundational. A single competent agent should handle ordinary tasks.,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output. +xai:grok-4.5,xai,grok-4.5,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output.,```text propose_change → sandbox_eval(benchmarks + red_team) → diff_report → governance.approve → staged_canary → commit_version ``` No hot-swap of IPS or safety without multi-party approval.,Not explicitly addressed under a matching heading; see preserved raw output.,"- **Latent Dynamics Net (LDN)**: predicts next latent + uncertainty **[E]**. - **Symbolic Overlay Graph (SOG)**: objects, affordances, invariants, counterfactuals **[E/S]**. - **Calibration Head**: maps LDN confidence to empirical frequencies on ECL holdouts.","``` action proposal → CCE.check → risk score → if risk > θ_auto: require Oversight if tripwire: freeze tools, degrade to dialogue-only ```",Not explicitly addressed under a matching heading; see preserved raw output.,"- ECL as append-only segmented log + snapshotting (event sourcing) **[E]**. - Checkpoints: `(wsc_ref, plan_id, wm_snapshot_id, caps_epoch)`. - Exactly-once *intent* via idempotency keys on tool calls.",```text Supervisor schedules roles as jobs with isolated caps. Critic cannot execute irreversible tools. RedTeam cannot modify IPS/PSR; writes only findings. Consensus: Planner proposes → Critic veto/repair → CCE binds. ```,Not explicitly addressed under a matching heading; see preserved raw output.,Not explicitly addressed under a matching heading; see preserved raw output. From 69bcb725f4115774dac0e54dba9f0cb09c41c71e Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:27:43 +0200 Subject: [PATCH 15/17] research: add deterministic acceptance report --- .../acceptance_report.json | 58 +++++++++++++++++++ 1 file changed, 58 insertions(+) create mode 100644 research/ai_generated_agi_architectures/acceptance_report.json diff --git a/research/ai_generated_agi_architectures/acceptance_report.json b/research/ai_generated_agi_architectures/acceptance_report.json new file mode 100644 index 0000000..460bf04 --- /dev/null +++ b/research/ai_generated_agi_architectures/acceptance_report.json @@ -0,0 +1,58 @@ +{ + "comparison_dimensions": [ + "memory architecture", + "reasoning/planning loop", + "learning or self-improvement mechanism", + "tool use and action execution", + "world model or representation layer", + "safety/governance layer", + "evaluation and benchmark strategy", + "persistence/runtime architecture", + "multi-agent or orchestration design", + "engineering feasibility", + "originality or non-obvious insight" + ], + "coverage_is_diagnostic_only": true, + "dimension_explicit_coverage": { + "engineering feasibility": 3, + "evaluation and benchmark strategy": 4, + "learning or self-improvement mechanism": 6, + "memory architecture": 5, + "multi-agent or orchestration design": 4, + "originality or non-obvious insight": 2, + "persistence/runtime architecture": 6, + "reasoning/planning loop": 6, + "safety/governance layer": 6, + "tool use and action execution": 5, + "world model or representation layer": 5 + }, + "external_submission_authorized": false, + "minimum_distinct_system_families": 8, + "missing_items": [], + "observed_distinct_system_families": [ + "anthropic:claude-sonnet-5", + "deepseek:v4", + "google:gemini-3.5", + "google:gemini-3.6", + "mistral:ministral", + "openai:gpt-5.6", + "openai:gpt-oss", + "xai:grok-4.5" + ], + "raw_output_count": 8, + "run_id": "b2fcca6e-fad0-483a-8f2c-c5f559900965", + "status": "PASS", + "warnings": [ + "explicit heading coverage 'memory architecture': 5/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'reasoning/planning loop': 6/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'learning or self-improvement mechanism': 6/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'tool use and action execution': 5/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'world model or representation layer': 5/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'safety/governance layer': 6/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'evaluation and benchmark strategy': 4/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'persistence/runtime architecture': 6/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'multi-agent or orchestration design': 4/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'engineering feasibility': 3/8; unmatched entries are marked and remain auditable in raw outputs", + "explicit heading coverage 'originality or non-obvious insight': 2/8; unmatched entries are marked and remain auditable in raw outputs" + ] +} From 6cc7c58b362e27275b709e0fc45ec23040165569 Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:27:53 +0200 Subject: [PATCH 16/17] research: add accepted packet checksum --- research/ai_generated_agi_architectures/bundle.sha256 | 1 + 1 file changed, 1 insertion(+) create mode 100644 research/ai_generated_agi_architectures/bundle.sha256 diff --git a/research/ai_generated_agi_architectures/bundle.sha256 b/research/ai_generated_agi_architectures/bundle.sha256 new file mode 100644 index 0000000..670cc54 --- /dev/null +++ b/research/ai_generated_agi_architectures/bundle.sha256 @@ -0,0 +1 @@ +573f024e740d1c5e16af342225f121d6179488442849831136b0355bcdd770ae From be8a7a02d3186c83e2ebba33765c5f90346e843e Mon Sep 17 00:00:00 2001 From: aibitious Date: Wed, 19 Aug 2026 17:28:44 +0200 Subject: [PATCH 17/17] research: preserve exact Anthropic raw bytes --- .../raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md b/research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md index 981a3f7..612cb38 100644 --- a/research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md +++ b/research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md @@ -125,4 +125,4 @@ Claim: "No component in Tier-2 can alter Governance Layer code or constraint wei ### Failure modes - Reward hacking in Tier-1 sandbox tests (skill overfits to sandbox quirks). Mitigation: sandbox environments are procedurally varied (randomized seeds/parameters) each test run; skill must pass across ≥20 variations. -- Tier-2 gaming its own bandit metric (e.g., lowering planning horizon to trivially "succeed" on easy subset). Mitigation: bandit reward is computed on a fixed, versioned held-out task suite never seen by Tier-2's optimization signal directly (blind evaluation, refreshed only by Tier- \ No newline at end of file +- Tier-2 gaming its own bandit metric (e.g., lowering planning horizon to trivially "succeed" on easy subset). Mitigation: bandit reward is computed on a fixed, versioned held-out task suite never seen by Tier-2's optimization signal directly (blind evaluation, refreshed only by Tier-