research: compare 8 AI-generated AGI architecture proposals

research/ai_generated_agi_architectures/README.md

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+# 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

research/ai_generated_agi_architectures/acceptance_report.json

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+{
+  "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"
+  ]
+}

research/ai_generated_agi_architectures/bundle.sha256

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+573f024e740d1c5e16af342225f121d6179488442849831136b0355bcdd770ae

research/ai_generated_agi_architectures/prompts.md

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+# 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`

research/ai_generated_agi_architectures/raw_outputs/01_anthropic_claude_sonnet_5_anthropic.md

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+# 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-