diff --git a/research/ai_generated_agi_architectures/README.md b/research/ai_generated_agi_architectures/README.md new file mode 100644 index 0000000..a57cf48 --- /dev/null +++ b/research/ai_generated_agi_architectures/README.md @@ -0,0 +1,71 @@ +# AI-generated architecture proposals for Cognitive-OS + +This packet compares actual generated proposals with their omissions and failure modes. It is intended to inform an implementable next Cognitive-OS iteration, not establish that the project or any model achieves AGI. + +**Coverage: nine systems, with proposal prose from eight.** Eight compact local systems received one identical prompt each. Seven produced proposal prose of varying completeness; Danube3 echoed the prompt and is explicitly recorded as a failed proposal request. An exploratory Gemini Flash web conversation supplied two A/B candidate proposals from one additional system. The hosted reference is separated from the primary local cohort throughout the comparison. + +## Headline findings + +1. **Repeated terminology is not independent agreement or an implementation contract.** Several local responses restate requested features; TinyLlama repeats sections and Danube3 supplies no proposal. The prompt itself requested memory categories, bounded control and offline learning, so recurrence of those words does not establish independent discovery or consensus. +2. **Concrete-looking recovery pseudocode can fail at a precise boundary.** An executed local example rejects the transaction ordering in hosted candidate A: a separate file effect remains after abrupt exit, while its uncommitted pending-action record disappears. Committing intent before the effect preserves the record for this example's reconciliation probe. This is a test of generated pseudocode, not an upstream bug or a general exactly-once guarantee. +3. **A useful minimum design needs observable task verification and measured tradeoffs.** The synthesis combines a single controller, evidence-linked state, durable intent, effect-specific reconciliation and offline candidate evaluation. It maps these proposals to existing source surfaces and proposes a documentation-edit slice before adding new stores or distributed coordination. + +## Read the packet + +- [Exact prompt and method](prompts.md), [protocol and amendments](collection/protocol.md), [collection incidents](collection/collection-incidents.md), and [sources/editing record](sources.md). +- [Raw outputs](raw_outputs/) and [comparison.csv](comparison.csv): 99 rows, one per system and requested dimension, comprising 88 local rows plus 11 hosted-reference rows. The `cohort` field identifies the difference. +- [Patterns and disagreements](summary.md) and [combined architecture](synthesis.md), including concrete interfaces, recovery pseudocode, proposed experiments and a six-week estimate. +- [Hosted A/B analysis](collection/gemini-flash-reference/analysis-notes.md) and its [22-row candidate comparison](collection/gemini-flash-reference/comparison.csv). Two candidates from one prompt are not two independent systems. +- [Executed example](collection/transaction-example.md), with code, original results and strict limits. +- [Repository context](collection/repository-context.md): source observations at the pinned upstream commit, distinct from generated proposals. + +## Collected systems + +All local responses were collected on 2026-09-08. Exact timestamps and source revisions are in [sources.md](sources.md) and the per-model records. + +| System | Cohort | Completion tokens | Finish | Observed result | +| --- | --- | ---: | --- | --- | +| SmolLM2 1.7B Instruct | Local | 1,778 | stop | All topic headings, mostly restatement; concrete final example absent. | +| Qwen2.5 1.5B Instruct | Local | 2,400 | length | Repetitive prose; final paragraph cut off and example absent. | +| Granite 3.3 2B Instruct | Local | 1,168 | stop | Specific components and example; missing contracts and an unsubstantiated distributed-stack tradeoff. | +| TinyLlama 1.1B Chat v1.0 | Local | 1,193 | stop | Repeated sections; missing memory distinctions, orchestration decision and actual example. | +| Phi-3 mini 4k Instruct | Local | 1,384 | stop | Clearer baseline measures; unsupported receipt recovery and no enumerated six-week sequence. | +| Qwen3 1.7B | Local | 1,867 | stop | Useful incomplete-outcome handling and task/context memory idea; unjustified retention periods and incomplete contracts. | +| Falcon3 1B Instruct | Local | 1,184 | stop | Broad architecture prose and coordination themes; missing concrete interfaces and ablation. | +| H2O Danube3 500m Chat | Local | 574 | stop | Prompt echo, not an architecture proposal. Preserved as a failed request. | +| Gemini web, Flash mode | Exploratory hosted | Unknown | Not exposed | Two A/B proposals from one prompt; more concrete contracts, including one reproduced transaction-ordering flaw. Exact backend revision unknown. | + +A `stop` result describes generation termination, not successful instruction following. Missing mechanisms remain missing in the comparison. None of these observations estimates a system's best possible performance. The hosted reference's unknown settings, selection after two local responses, and different compute/service conditions prevent a parameter-matched or causal comparison. + +## Collection and attribution + +Local generation used llama.cpp b10809 / commit `5266f24da`, two CPU threads, no GPU offload, a 4,096-token context, temperature 0.2, top-p 0.9, seed 20260908 and at most 2,400 generated tokens. Public model-specific GGUF chat templates remained in use. There was no separate system message, history, retrieval or tool execution. The eight local systems span seven named project families; Qwen2.5 and Qwen3 are related generations. Model size, training, quantization and template differences limit generalization and independent-consensus claims. + +For every local system, `collection//` preserves public model metadata, pinned weight source/hash, exact request and response, timestamps, usage, finish reason and raw-content hash. The local raw file is the exact response content string. Repetitions, omissions, truncation and the echo are not cleaned away. + +Gemini's two rendered answer strings remain unedited in separate collection files. Its combined raw-output view adds only explicitly collector-authored candidate labels and separator newlines. Both original and combined hashes are recorded. Service instructions, sampling, token counts and backend revision are unavailable. No private account screenshots, credentials or hidden prompts are included. + +The comparison, report and scripts were authored by the assisting Codex agent; no independent human review of the model proposals was performed. Analyst additions are not counted as another independent model response. + +## Validation and replay + +From the packet root: + +```sh +python collection/verify.py +python collection/transaction_example.py +``` + +The verifier checks exact recorded content, hashes, pinned source identities, echo classification, all 99 comparison pairs and evidence-anchor bounds, plus the hosted A/B assembly. It checks consistency and structural coverage, not provider-signed authorship, research quality or architectural effectiveness. Review the raw outputs and analysis when assessing those claims. + +The transaction example uses temporary local files and SQLite. Its two assertions passed: the one-transaction ordering leaves an effect without a pending record; precommitting intent preserves the record and permits that effect to be reconciled without another append. It models abrupt process exit only. No power-loss, third-party-effect or end-to-end Cognitive-OS recovery test was performed. The three broader experiments in the synthesis remain proposed and unrun. + +The maintainer-required repository layout check and all ten public smoke tests passed on the unchanged upstream runtime with the four-response draft staged. Later changes are confined to this research packet. Those checks do not validate the proposed architecture. The packet adds no public runtime/API change or adapter dependency. + +Optional replay uses a separately supplied b10809 binary and downloads the chosen pinned model into a new directory: + +```sh +python collection/replay.py smollm2 --server /path/to/llama-server --out /tmp/smollm2-replay +``` + +Replay's help entry point was checked, but a second full generation was not run. Fixed seeds do not guarantee bitwise identity across hardware/builds. Replay preserves the original records. Model weights and the local runtime binary are not redistributed in the packet. diff --git a/research/ai_generated_agi_architectures/collection/collection-incidents.md b/research/ai_generated_agi_architectures/collection/collection-incidents.md new file mode 100644 index 0000000..dff20f3 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/collection-incidents.md @@ -0,0 +1,13 @@ +# Collection incidents and recovery + +## TinyLlama pre-start listener probe, 2026-09-08 + +After the Granite response completed normally, the sequential collector downloaded and verified TinyLlama's weights. Its local port-availability probe then raised `OSError: [Errno 48] Address already in use` before starting the TinyLlama server or sending an inference request. A read of the previous server's local endpoint returned connection refused after that server had exited. + +The probe was changed to use `SO_REUSEADDR`, matching a reusable server listener after a previous connection. This is consistent with residual TCP TIME_WAIT preventing the original non-reusable bind. It does not permit binding over a live conflicting listener, and no unrelated process was stopped. The restarted collector preserved the three existing responses, reused the verified TinyLlama weights and successfully started TinyLlama's first actual inference. + +This was a collector transport/startup correction, not a model-response retry or a reason to select a different output. Sampling settings, model roster and prompt were unchanged. The local incident record retains the old and new process-session context outside the public packet. + +## Phi-3 disk preflight, 2026-09-08 + +TinyLlama subsequently completed normally. Before downloading Phi-3, the collector stopped because free disk space was approximately 44 MB below the weight size plus its 750 MB headroom requirement. No Phi-3 inference had started. An unused, reproducible downloaded utility binary from a completed separate task was removed after checking it had no open handles; source artifacts and evidence were preserved. The existing four responses were preserved and Phi-3's first download began with the required headroom. This did not change models, quantization, sampling or prompt. diff --git a/research/ai_generated_agi_architectures/collection/danube3/analysis-notes.md b/research/ai_generated_agi_architectures/collection/danube3/analysis-notes.md new file mode 100644 index 0000000..e9f30f1 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/danube3/analysis-notes.md @@ -0,0 +1,23 @@ +# Analyst notes: H2O Danube3 500m Chat — prompt echo + +The local server returned a normal stop with 574 completion tokens on 2026-09-08 at 20:18:28 UTC. The returned content is the input prompt after stripping outer whitespace. This is a recorded model response, but **not an architecture proposal**. It must not be counted as a successful proposal or summarized as though the model endorsed the instructions it repeated. + +All eleven requested dimensions are absent as proposed mechanisms. The corresponding CSV rows link to the repeated requirement only to make the failure auditable; the text there is the prompt, not an independently supplied design. + +| Dimension | Returned material | Limitation | +| --- | --- | --- | +| Memory | Echoes the request to distinguish memory types and specify storage/retrieval/provenance/forgetting. | No proposed mechanism: prompt echo. | +| Planning | Echoes the request for a bounded loop, stops and recovery. | No proposed mechanism: prompt echo. | +| Learning | Echoes the request for offline improvement, versioning, tests and rollback. | No proposed mechanism: prompt echo. | +| Tools | Echoes the request for contracts, validation, idempotency and uncertain outcomes. | No proposed mechanism: prompt echo. | +| World representation | Echoes the request for observation/hypothesis/prediction separation. | No proposed mechanism: prompt echo. | +| Governance | Echoes the request for user control, permissions, budgets and stopping. | No proposed mechanism: prompt echo. | +| Evaluation | Echoes the request for three experiments and an ablation. | No experiment or ablation proposed: prompt echo. | +| Persistence | Echoes the request for transaction/restart and missing-receipt handling. | No proposed mechanism: prompt echo. | +| Orchestration | Echoes the request to choose controller/workers and discuss coordination. | No proposed mechanism: prompt echo. | +| Feasibility | Echoes the request for a six-week sequence, minimum slice and risk. | No proposed sequence, slice or risk: prompt echo. | +| Originality | Echoes the request for a design choice, tradeoff and rejection condition. | No proposed insight: prompt echo. | + +No corrective prompt or replacement generation was made. The raw output and full response are retained alongside the failed instruction-following assessment. One sample does not isolate whether model capacity, training, template or prompt fit explains the echo. + +The primary cohort therefore contains eight local responses but only seven that contain proposal prose. The already-collected exploratory Gemini reference provides an additional system with proposal text. The complete packet covers nine systems, preserving the failed local sample and clearly separating the uncontrolled hosted reference. 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"usedStorage": 6718115552 +} diff --git a/research/ai_generated_agi_architectures/collection/danube3/request.json b/research/ai_generated_agi_architectures/collection/danube3/request.json new file mode 100644 index 0000000..0d939d4 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/danube3/request.json @@ -0,0 +1,17 @@ +{ + "model": "danube3", + "messages": [ + { + "role": "user", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/danube3/response.json b/research/ai_generated_agi_architectures/collection/danube3/response.json new file mode 100644 index 0000000..f04205e --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/danube3/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "role": "assistant", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation." + } + } + ], + "created": 1788898708, + "model": "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/h2o-danube3-500m-chat-Q4_K_M.gguf", + "system_fingerprint": "b10809-5266f24da", + "object": "chat.completion", + "usage": { + "completion_tokens": 574, + "prompt_tokens": 585, + "total_tokens": 1159, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "id": "chatcmpl-nzBaweiFtuFC8bSB9cEZOBTgoU9moj1p", + "timings": { + "cache_n": 0, + "prompt_n": 585, + "prompt_ms": 13750.126, + "prompt_per_token_ms": 23.50448888888889, + "prompt_per_second": 42.545064677952766, + "predicted_n": 574, + "predicted_ms": 46527.136, + "predicted_per_token_ms": 81.19919022687608, + "predicted_per_second": 12.315393752153582 + } +} diff --git a/research/ai_generated_agi_architectures/collection/danube3/run.json b/research/ai_generated_agi_architectures/collection/danube3/run.json new file mode 100644 index 0000000..ef87192 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/danube3/run.json @@ -0,0 +1,42 @@ +{ + "started_at": "2026-09-08T20:17:22.750527+00:00", + "finished_at": "2026-09-08T20:18:28.180171+00:00", + "runtime": "llama.cpp b10809 / 5266f24da", + "command": [ + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/llama-b10809/llama-server", + "-m", + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/h2o-danube3-500m-chat-Q4_K_M.gguf", + "--host", + "127.0.0.1", + "--port", + "18763", + "-t", + "2", + "-tb", + "2", + "-ngl", + "0", + "-c", + "4096", + "-np", + "1", + "--jinja", + "--reasoning-budget", + "0", + "--no-webui" + ], + "request_sha256": "85860ed35a6217deb821dc2b6174acdbbae9dc2e320c4a054dd61d944eef7a8e", + "response_sha256": "42d22ea1689f0b23037850dc2666cf1c1794321c37144e6d141e3b05fb49de75", + "content_sha256": "123cc942c960f0302196fd5798fc052d41c12884fdb3a54005ad36fe4e6c17d3", + "finish_reason": "stop", + "usage": { + "completion_tokens": 574, + "prompt_tokens": 585, + "total_tokens": 1159, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "cpu_threads": 2, + "context_tokens": 4096 +} diff --git a/research/ai_generated_agi_architectures/collection/danube3/source.json b/research/ai_generated_agi_architectures/collection/danube3/source.json new file mode 100644 index 0000000..0ad2f7a --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/danube3/source.json @@ -0,0 +1,9 @@ +{ + "model_repository": "bartowski/h2o-danube3-500m-chat-GGUF", + "revision": "34005c67faf918e19c8748e501fa8418b51060fe", + "filename": "h2o-danube3-500m-chat-Q4_K_M.gguf", + "url": "https://huggingface.co/bartowski/h2o-danube3-500m-chat-GGUF/resolve/34005c67faf918e19c8748e501fa8418b51060fe/h2o-danube3-500m-chat-Q4_K_M.gguf", + "sha256": "cf914c12a2143bae2a7a3d87cfb7ebfb3b25a922ec40f183c9158b9889260648", + "size": 317877760, + "accessed_at": "2026-09-08T20:17:22.738887+00:00" +} diff --git a/research/ai_generated_agi_architectures/collection/falcon3/analysis-notes.md b/research/ai_generated_agi_architectures/collection/falcon3/analysis-notes.md new file mode 100644 index 0000000..db489d0 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/falcon3/analysis-notes.md @@ -0,0 +1,21 @@ +# Analyst notes: Falcon3 1B Instruct + +Normal stop after 1,184 generated tokens on 2026-09-08 at 20:16:49 UTC. All eleven sections and a documentation-publication example are present. This is TII's Falcon3 model through bartowski's GGUF distribution; its custom Falcon LLM license is identified in the source catalog. The response gives broad architecture descriptions rather than executable contracts. + +| Dimension | What the response supplies | What it does not establish | +| --- | --- | --- | +| Memory | Definitions of four memory types; interconnected/distributed storage, efficient retrieval, provenance and selective forgetting. | No chosen stores, record fields, retrieval mechanism or retention policy. The labels do not specify an implementation. | +| Planning | Bounded observation/action loop; stop when further actions are unjustified or a predefined criterion is reached; rollback after failure. | No explicit bounds, success predicate, state transition or recovery protocol for an already completed external effect. | +| Learning | Version-controlled changes tested in isolation; incremental refinement and reversion after a failed plan. | No held-out split, candidate representation, evaluation metric or promotion gate. Plan rollback and learned-policy rollback are not clearly separated. | +| Tools | Declares an idempotent request/result contract; probabilistic uncertainty/confidence intervals. | Defines sameness of result broadly without repeated-effect semantics, action IDs, receipt fields or validation rules. Probabilistic reasoning is not a concrete uncertain-effect reconciliation procedure. | +| World representation | Defines observations, hypotheses and predictions; probabilistic uncertainty and coherence/plausibility-based conflict handling. | No record schema, provenance dependencies, calibrated uncertainty or conflict decision rule. | +| Governance | Permission boundaries, budgets and a reliable stop control are declared in one paragraph. | No concrete interface or accounting procedure; no security mechanism is tested here. | +| Evaluation | Three identifiable themes: offline-learning efficiency, recovery reliability and coordination overhead in multi-threaded cases. | No operational metrics, corpus, thresholds or ablation. Its real-time adaptation language is not reconciled with offline evaluation. | +| Persistence | Says transaction boundaries and restart exist, and tool completions are logged even if interrupted. | No ordering, durable pending state or receipt reconstruction. The assurance is unsupported by a described mechanism. | +| Orchestration | One controller with multiple bounded workers; mentions coordination cost and conflict resolution. | No worker contract, conflict rule or measured scalability/overhead tradeoff. | +| Feasibility | Names a six-week sequence, core-function slice and integration of advanced AI as risk. | No actual weekly sequence or testable minimum slice; the risk remains broad. | +| Originality | Probabilistic reasoning for uncertainty, balancing precision and flexibility. | No useful operational tradeoff or condition for rejecting the choice; it describes when the method helps instead. Novelty is not established. | + +The example reviews documentation for clarity, approves publication, publishes, asks the engineering team to verify and proposes reverting to the last state after interruption. It does not specify an actual requested edit, a factual evidence check or an observable publication-recovery protocol. It is hypothetical and was not executed. + +The synthesis retains the need to measure coordination overhead, a theme also present in other outputs. It does not adopt the unsupported receipt guarantee or assign a concrete implementation to the model's broad labels. diff --git a/research/ai_generated_agi_architectures/collection/falcon3/model.json b/research/ai_generated_agi_architectures/collection/falcon3/model.json new file mode 100644 index 0000000..2fd78be --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/falcon3/model.json @@ -0,0 +1,317 @@ +{ + "_id": "676171f12c4ca8c8e4bbf296", + "id": "bartowski/Falcon3-1B-Instruct-GGUF", + "private": false, + "pipeline_tag": "text-generation", + "tags": [ + "gguf", + "falcon3", + "text-generation", + "en", + "fr", + "es", + "pt", + "base_model:tiiuae/Falcon3-1B-Instruct", + "base_model:quantized:tiiuae/Falcon3-1B-Instruct", + "license:other", + "endpoints_compatible", + "region:us", + "conversational" + ], + "downloads": 2045, + "likes": 1, + "modelId": "bartowski/Falcon3-1B-Instruct-GGUF", + "author": "bartowski", + "sha": "9e280a1e3b7f287edb7248042e3d14f893227b21", + "lastModified": "2024-12-23T04:54:14.000Z", + "gated": false, + "disabled": false, + "widgetData": [ + { + "text": "Hi, what can you help me with?" + }, + { + "text": "What is 84 * 3 / 2?" + }, + { + "text": "Tell me an interesting fact about the universe!" + }, + { + "text": "Explain quantum computing in simple terms." + } + ], + "model-index": null, + "config": {}, + "cardData": { + "quantized_by": "bartowski", + "pipeline_tag": "text-generation", + "language": [ + "en", + "fr", + "es", + "pt" + ], + "license_name": "falcon-llm-license", + "tags": [ + "falcon3" + ], + "license_link": "https://falconllm.tii.ae/falcon-terms-and-conditions.html", + "license": "other", + "base_model": "tiiuae/Falcon3-1B-Instruct" + }, + "gguf": { + "total": 1669408768, + "architecture": "llama", + "context_length": 8192, + "chat_template": "{% if tools %}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>\n' + message['content'] + '\nYou are an expert in composing functions. 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b/research/ai_generated_agi_architectures/collection/falcon3/request.json @@ -0,0 +1,17 @@ +{ + "model": "falcon3", + "messages": [ + { + "role": "user", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/falcon3/response.json b/research/ai_generated_agi_architectures/collection/falcon3/response.json new file mode 100644 index 0000000..cc9596d --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/falcon3/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "role": "assistant", + "content": "**Proposal for Cognitive-OS Architecture**\n\n**1. Memory Architecture:**\n\nCognitive-OS employs a layered memory architecture designed to support general-purpose problem solving. The system distinguishes between working, episodic, semantic, and procedural memory, each serving distinct yet interconnected roles.\n\n- **Working Memory:** This is the active, short-term memory where cognitive tasks are temporarily stored and manipulated. It is optimized for quick access and manipulation of data, crucial for tasks requiring immediate attention.\n\n- **Episodic Memory:** Focused on storing detailed experiences, this layer captures sequences of events and their context. It aids in understanding the temporal and spatial relationships between actions and outcomes.\n\n- **Semantic Memory:** This stores factual knowledge and general concepts, facilitating abstract reasoning and understanding. It supports the system's ability to generalize and make inferences.\n\n- **Procedural Memory:** Concerned with the learned sequences of actions, procedural memory is essential for automating tasks and achieving procedural fluency.\n\nEach type of memory is stored in a distributed yet interconnected manner, with retrieval optimized for efficiency and provenance tracked for accountability. Forgetting mechanisms are designed to selectively remove outdated or irrelevant information, ensuring that the system remains adaptive and responsive.\n\n**2. Reasoning and Planning:**\n\nCognitive-OS implements a bounded observation-to-action loop, ensuring that each action is grounded in a coherent set of observations. This loop is designed to terminate when no further actions can be justified or when the system reaches a predefined stopping criterion.\n\nRecovery from failed plans is facilitated through a rollback mechanism, allowing the system to revert to previous states and learn from past experiences. This approach ensures that learning is not merely appended to logs but is actively evaluated and refined.\n\n**3. Learning:**\n\nThe system incorporates an offline evaluation mechanism for learning improvements. This involves version-controlled repositories where changes are tracked and tested in isolation. If a plan fails, the system can revert to the previous version, allowing for incremental learning and rollback.\n\nThis approach distinguishes learning from mere logging, focusing on the active refinement of cognitive processes. It supports the system's ability to adapt and improve over time, even in the face of unforeseen challenges.\n\n**4. Tool Use:**\n\nThe action request/result contract is designed to ensure idempotency and handle uncertain outcomes gracefully. This contract specifies that a single request should yield the same result regardless of how it is invoked, enhancing reliability and predictability.\n\nUncertain outcomes are managed through probabilistic reasoning, where the system can provide confidence intervals or probabilistic predictions. This approach allows for a nuanced understanding of the system's capabilities and limitations.\n\n**5. World Representation:**\n\nCognitive-OS distinguishes between observations, hypotheses, and predictions, each serving a unique purpose in cognitive processing. Observations are the raw data captured by the system, hypotheses are tentative explanations derived from observations, and predictions are the system's forecasts based on these hypotheses.\n\nUncertainty is integrated into these representations, allowing for a probabilistic understanding of the world. Contradictions are handled through mechanisms that evaluate the coherence and plausibility of different hypotheses, ensuring that the system remains coherent and rational.\n\n**6. Safety and Governance:**\n\nUser authority is preserved through explicit permission boundaries, resource budgets, and a reliable stop control mechanism. This ensures that the system remains accountable and controllable, even in complex cognitive tasks.\n\n**7. Evaluation:**\n\nThree measurable experiments are proposed to evaluate Cognitive-OS's architectural benefits:\n\n- **Experiment 1:** Assesses the system's ability to learn and adapt in real-time, focusing on the efficiency and effectiveness of the offline evaluation mechanism.\n\n- **Experiment 2:** Evaluates the system's robustness and reliability by testing its ability to handle failures and recover from them.\n\n- **Experiment 3:** Investigates the system's performance in complex, multi-threaded scenarios, examining the overhead and conflict resolution mechanisms.\n\n**8. Persistence and Runtime:**\n\nTransactions are managed with clear boundaries and a crash/restart protocol. The system ensures that tool completions are logged and stored, even if the tool is interrupted.\n\n**9. Orchestration:**\n\nA single controller orchestrates multiple bounded workers, balancing coordination costs and conflict resolution. This design ensures that the system can scale efficiently while maintaining control and coordination.\n\n**10. Engineering Feasibility:**\n\nA six-week implementation sequence is proposed, with the minimum viable slice focusing on core functionalities and the largest unresolved risk being the integration of advanced AI components.\n\n**11. Non-obvious Insight:**\n\nA key design choice is the use of probabilistic reasoning for uncertainty handling. This choice balances the need for precise predictions with the flexibility to handle unexpected situations. Under conditions where the system encounters significant uncertainty or ambiguity, probabilistic reasoning provides a more robust framework for decision-making.\n\n**Concrete Example:**\n\n**Documentation Edit:**\n\n- **Evidence Checks:** The documentation is reviewed for clarity, completeness, and adherence to the outlined architectural principles.\n- **Approval:** If necessary, the documentation is approved for publication, ensuring it aligns with the proposed architecture.\n- **Action:** The approved documentation is published, making it accessible to the engineering team.\n- **Verification:** The team verifies the documentation against the outlined requirements, ensuring it meets the architectural specifications.\n- **Recovery:** If the documentation is interrupted during publication, the system can recover by reverting to the last known state, maintaining the integrity of the project.\n\nThis example illustrates the systematic approach to documenting and validating the proposed architecture, ensuring that it is both robust and aligned with the cognitive runtime's core objectives." + } + } + ], + "created": 1788898609, + "model": "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/Falcon3-1B-Instruct-Q4_K_M.gguf", + "system_fingerprint": "b10809-5266f24da", + "object": "chat.completion", + "usage": { + "completion_tokens": 1184, + "prompt_tokens": 525, + "total_tokens": 1709, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "id": "chatcmpl-FQRIRSL4TfOK1xX0SxxoCOAGpQa96pDM", + "timings": { + "cache_n": 0, + "prompt_n": 525, + "prompt_ms": 13647.662, + "prompt_per_token_ms": 25.995546666666666, + "prompt_per_second": 38.46812736130188, + "predicted_n": 1184, + "predicted_ms": 196316.474, + "predicted_per_token_ms": 165.9479915469146, + "predicted_per_second": 6.025984350146795 + } +} diff --git a/research/ai_generated_agi_architectures/collection/falcon3/run.json b/research/ai_generated_agi_architectures/collection/falcon3/run.json new file mode 100644 index 0000000..dc7eaeb --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/falcon3/run.json @@ -0,0 +1,42 @@ +{ + "started_at": "2026-09-08T20:13:09.871901+00:00", + "finished_at": "2026-09-08T20:16:49.049787+00:00", + "runtime": "llama.cpp b10809 / 5266f24da", + "command": [ + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/llama-b10809/llama-server", + "-m", + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/Falcon3-1B-Instruct-Q4_K_M.gguf", + "--host", + "127.0.0.1", + "--port", + "18763", + "-t", + "2", + "-tb", + "2", + "-ngl", + "0", + "-c", + "4096", + "-np", + "1", + "--jinja", + "--reasoning-budget", + "0", + "--no-webui" + ], + "request_sha256": "202991955352359316f230e4d5004cce5c844ea780c94b06940ff87ad253737c", + "response_sha256": "add5ec095a26942e56db8192d5742c3a6f2ec6df3b2dcd6754fa552e6114d07a", + "content_sha256": "85ded3ba52d65d707e9e0ab3ff95ed6078e7474f30758d49a9cf8b4da0116086", + "finish_reason": "stop", + "usage": { + "completion_tokens": 1184, + "prompt_tokens": 525, + "total_tokens": 1709, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "cpu_threads": 2, + "context_tokens": 4096 +} diff --git a/research/ai_generated_agi_architectures/collection/falcon3/source.json b/research/ai_generated_agi_architectures/collection/falcon3/source.json new file mode 100644 index 0000000..0f6d70c --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/falcon3/source.json @@ -0,0 +1,9 @@ +{ + "model_repository": "bartowski/Falcon3-1B-Instruct-GGUF", + "revision": "9e280a1e3b7f287edb7248042e3d14f893227b21", + "filename": "Falcon3-1B-Instruct-Q4_K_M.gguf", + "url": "https://huggingface.co/bartowski/Falcon3-1B-Instruct-GGUF/resolve/9e280a1e3b7f287edb7248042e3d14f893227b21/Falcon3-1B-Instruct-Q4_K_M.gguf", + "sha256": "1c92013dac1ab6e703e787f3e0829ca03cc95311e4c113a77950d15ff6dea7b3", + "size": 1057044640, + "accessed_at": "2026-09-08T20:13:09.868542+00:00" +} diff --git a/research/ai_generated_agi_architectures/collection/gemini-flash-reference/analysis-notes.md b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/analysis-notes.md new file mode 100644 index 0000000..a8f68f7 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/analysis-notes.md @@ -0,0 +1,27 @@ +# Exploratory Gemini Flash reference — analyst notes + +One fresh web conversation produced two A/B candidate answers automatically. Both are retained without choosing a preferred answer. This is one hosted-system reference with an unknown backend revision, not two independently sampled models or families. It was added after examining the first two primary local outputs; the comparison is exploratory. Service instructions, sampling, context and inference budget were not controlled. Rendered plaintext retains wording but may alter mathematical and code formatting. No hosted answer supplied empirical results from actually running Cognitive-OS. + +## Eleven dimensions + +| Dimension | Candidate A | Candidate B | Analyst assessment / limitations | +| --- | --- | --- | --- | +| Memory | Four stores, concrete event/fact schemas, BM25 and vector retrieval, event provenance, decay and archival (§1). | Execution frame, SQLite events/FTS5, provenance hashes, triples with sources and last-verification times, Git procedures (§1). | Both supply implementable starting representations. Neither measures retrieval usefulness, contradiction calibration or retention cost. A source ID/hash alone does not establish a claim's truth. B's 0.20 pruning threshold is unexplained. | +| Planning | Bounded DAG, verified nodes, 20-step cap, budget stop, diagnostic replanning with three-revision limit (§2). | Observe/orient/plan/gate/execute/verify loop, depth 15 and failure events (§2). | Better specified than a list of planning technology names. A's illustrative `interface` block is not executable Python. Both need task-specific success predicates; maximum depth does not by itself bound the number of replans. | +| Learning | Offline DSPy/LoRA candidates, versioning, held-out accuracy/latency gate and rollback after overruling threshold (§3). | Offline policy artifacts, held-out pass-rate/cost gate, retain baseline on failed assertions (§3). | Neither demonstrates learning. Historical tasks/transcripts require disjoint train/evaluation partitions. A's description of the whole learning process as deterministic is unsupported; its 10%/20-run rollback rule is a proposal, not an established threshold. | +| Tools | Request/receipt fields, schema validation, idempotency key, uncertain status and read probe (§4). | JSON request/result, cached receipt on duplicate key, UNKNOWN followed by observation before retry (§4). | Both make uncertainty operational. Cached completion cannot cover a missing receipt without a durable intent and an action-specific reconciliation rule. Request-key binding and conflict handling still need specification. | +| World representation | Observation/hypothesis/prediction records with source IDs, probabilities and contradiction-resolution goals (§5). | Same distinction, confidence/evidence, contradiction event then confidence zero/new hypothesis (§5). | Neither calibrates numeric confidence. A leaves logical contradiction detection abstract. B's unconditional zeroing can discard a valid hypothesis when a new observation is mistaken; retain conflicting evidence and evaluate its reliability instead. | +| Governance | Declares permission tiers, user approval, transactional budget counters and stop handling (§6). | Declares capability tiers, user confirmation, ceilings and pause handling (§6). | Recorded as design declarations only. This packet does not execute or validate their security claims. | +| Evaluation | Interrupted recovery, verified patching, budget ceiling and structured-memory ablation (§7). | Interrupted recovery, adversarial-data experiment, offline policy promotion and epistemic-tier ablation (§7). | These are proposed tests, not results. A's unbounded-context baseline is not budget matched. B leaves statistical significance undefined. Neither justifies numerical thresholds. The adversarial-data experiment is recorded only and was not performed. | +| Persistence | Places pending intent, filesystem effect and receipt before one SQL commit, then proposes scanning pending records after interruption (§8). | Commits event and frame together, resumes pending logged actions through probes (§8). | A's stated recovery premise fails in the isolated transaction-ordering example: the uncommitted pending record disappears while the file effect remains. B does not explicitly show intent commit before dispatch; its recovery requires that ordering. Neither provides a general cross-system atomicity guarantee. | +| Orchestration | Single controller, bounded read-only workers; serialize output collisions by timestamp (§9). | Single state controller with bounded subprocesses (§9). | Practical for a local baseline. A's universal quadratic-overhead assertion is unjustified, and temporal serialization does not settle semantic contradictions. B gives no concrete worker-result conflict rule. No worker-performance measurements were made. | +| Feasibility | Six-week schema/contracts/planning/mirror/evaluation/recovery sequence, file-patch slice, uncertain tool effects as risk (§10). | Six-week state/tool/budget/epistemic/evaluation/mirror/test sequence, week-two slice, tool verification as risk (§10). | Roadmaps are estimates. Both partly propose rebuilding mechanisms described as already present in the prompt. Repository mapping should precede an overhaul; neither has inspected the full current implementation. | +| Originality | Shadow workspace with pre-apply checks; storage/latency tradeoff and 50GB/two-second rejection (§11). | Explicit Python state transitions with LLM-generated proposals; code complexity tradeoff and unmaintainable-domain rejection (§11). | Useful design choices, not demonstrated novel inventions. A's directory-level atomic swap needs filesystem and concurrent-edit assumptions. B's rejection condition needs an operational maintenance-cost criterion. | + +## Documentation examples and the measured finding + +A supplies a docstring-edit trace, including compilation and an assumed later test pass. Compilation does not establish that the new error documentation matches actual behavior. B checks that `timeout_ms` exists in source before editing API documentation, but a final diff check alone does not establish the documentation's semantic correctness. Both traces are hypothetical; neither is an executed repository test. + +The analyst executed only the local process-exit example in [transaction_example.py](../transaction_example.py), recorded in [transaction-example-results.json](../transaction-example-results.json). It isolates the order proposed in A §8: insert pending intent inside an uncommitted transaction, append a file effect, exit before receipt/commit. The file contains the effect but the reopened database contains no pending action. Moving the intent commit before the append preserves the pending record, allowing this particular effect to be observed without repeating it. See [the experiment note](../transaction-example.md) for assumptions and limits. This finding concerns generated pseudocode, not a defect in Cognitive-OS. + +No score ranking is assigned. The hosted answers provide more concrete schemas and sequences than the two first local answers, but unknown service conditions, different compute budgets, selection timing and one sample prevent a causal or general model-quality conclusion. diff --git a/research/ai_generated_agi_architectures/collection/gemini-flash-reference/comparison.csv b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/comparison.csv new file mode 100644 index 0000000..35e957f --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/comparison.csv @@ -0,0 +1,23 @@ +system,candidate,dimension,proposal,limits,evidence +Gemini web Flash (backend unknown),A,memory,"Four stores, concrete event/fact schemas, BM25 and vector retrieval, event provenance, decay and archival (§1).","Both supply implementable starting representations. Neither measures retrieval usefulness, contradiction calibration or retention cost. A source ID/hash alone does not establish a claim's truth. B's 0.20 pruning threshold is unexplained.",response-A.txt#L1 +Gemini web Flash (backend unknown),A,planning,"Bounded DAG, verified nodes, 20-step cap, budget stop, diagnostic replanning with three-revision limit (§2).",Better specified than a list of planning technology names. A's illustrative `interface` block is not executable Python. Both need task-specific success predicates; maximum depth does not by itself bound the number of replans.,response-A.txt#L47 +Gemini web Flash (backend unknown),A,learning,"Offline DSPy/LoRA candidates, versioning, held-out accuracy/latency gate and rollback after overruling threshold (§3).","Neither demonstrates learning. Historical tasks/transcripts require disjoint train/evaluation partitions. A's description of the whole learning process as deterministic is unsupported; its 10%/20-run rollback rule is a proposal, not an established threshold.",response-A.txt#L93 +Gemini web Flash (backend unknown),A,tools,"Request/receipt fields, schema validation, idempotency key, uncertain status and read probe (§4).",Both make uncertainty operational. Cached completion cannot cover a missing receipt without a durable intent and an action-specific reconciliation rule. Request-key binding and conflict handling still need specification.,response-A.txt#L137 +Gemini web Flash (backend unknown),A,world_representation,"Observation/hypothesis/prediction records with source IDs, probabilities and contradiction-resolution goals (§5).",Neither calibrates numeric confidence. A leaves logical contradiction detection abstract. B's unconditional zeroing can discard a valid hypothesis when a new observation is mistaken; retain conflicting evidence and evaluate its reliability instead.,response-A.txt#L175 +Gemini web Flash (backend unknown),A,governance,"Declares permission tiers, user approval, transactional budget counters and stop handling (§6).",Recorded as design declarations only. This packet does not execute or validate their security claims.,response-A.txt#L204 +Gemini web Flash (backend unknown),A,evaluation,"Interrupted recovery, verified patching, budget ceiling and structured-memory ablation (§7).","These are proposed tests, not results. A's unbounded-context baseline is not budget matched. B leaves statistical significance undefined. Neither justifies numerical thresholds. The adversarial-data experiment is recorded only and was not performed.",response-A.txt#L237 +Gemini web Flash (backend unknown),A,persistence,"Places pending intent, filesystem effect and receipt before one SQL commit, then proposes scanning pending records after interruption (§8).",A's stated recovery premise fails in the isolated transaction-ordering example: the uncommitted pending record disappears while the file effect remains. B does not explicitly show intent commit before dispatch; its recovery requires that ordering. Neither provides a general cross-system atomicity guarantee.,response-A.txt#L248 +Gemini web Flash (backend unknown),A,orchestration,"Single controller, bounded read-only workers; serialize output collisions by timestamp (§9).","Practical for a local baseline. A's universal quadratic-overhead assertion is unjustified, and temporal serialization does not settle semantic contradictions. B gives no concrete worker-result conflict rule. No worker-performance measurements were made.",response-A.txt#L274 +Gemini web Flash (backend unknown),A,feasibility,"Six-week schema/contracts/planning/mirror/evaluation/recovery sequence, file-patch slice, uncertain tool effects as risk (§10).",Roadmaps are estimates. Both partly propose rebuilding mechanisms described as already present in the prompt. Repository mapping should precede an overhaul; neither has inspected the full current implementation.,response-A.txt#L297 +Gemini web Flash (backend unknown),A,originality,Shadow workspace with pre-apply checks; storage/latency tradeoff and 50GB/two-second rejection (§11).,"Useful design choices, not demonstrated novel inventions. A's directory-level atomic swap needs filesystem and concurrent-edit assumptions. B's rejection condition needs an operational maintenance-cost criterion.",response-A.txt#L317 +Gemini web Flash (backend unknown),B,memory,"Execution frame, SQLite events/FTS5, provenance hashes, triples with sources and last-verification times, Git procedures (§1).","Both supply implementable starting representations. Neither measures retrieval usefulness, contradiction calibration or retention cost. A source ID/hash alone does not establish a claim's truth. B's 0.20 pruning threshold is unexplained.",response-B.txt#L2 +Gemini web Flash (backend unknown),B,planning,"Observe/orient/plan/gate/execute/verify loop, depth 15 and failure events (§2).",Better specified than a list of planning technology names. A's illustrative `interface` block is not executable Python. Both need task-specific success predicates; maximum depth does not by itself bound the number of replans.,response-B.txt#L35 +Gemini web Flash (backend unknown),B,learning,"Offline policy artifacts, held-out pass-rate/cost gate, retain baseline on failed assertions (§3).","Neither demonstrates learning. Historical tasks/transcripts require disjoint train/evaluation partitions. A's description of the whole learning process as deterministic is unsupported; its 10%/20-run rollback rule is a proposal, not an established threshold.",response-B.txt#L64 +Gemini web Flash (backend unknown),B,tools,"JSON request/result, cached receipt on duplicate key, UNKNOWN followed by observation before retry (§4).",Both make uncertainty operational. Cached completion cannot cover a missing receipt without a durable intent and an action-specific reconciliation rule. Request-key binding and conflict handling still need specification.,response-B.txt#L87 +Gemini web Flash (backend unknown),B,world_representation,"Same distinction, confidence/evidence, contradiction event then confidence zero/new hypothesis (§5).",Neither calibrates numeric confidence. A leaves logical contradiction detection abstract. B's unconditional zeroing can discard a valid hypothesis when a new observation is mistaken; retain conflicting evidence and evaluate its reliability instead.,response-B.txt#L119 +Gemini web Flash (backend unknown),B,governance,"Declares capability tiers, user confirmation, ceilings and pause handling (§6).",Recorded as design declarations only. This packet does not execute or validate their security claims.,response-B.txt#L155 +Gemini web Flash (backend unknown),B,evaluation,"Interrupted recovery, adversarial-data experiment, offline policy promotion and epistemic-tier ablation (§7).","These are proposed tests, not results. A's unbounded-context baseline is not budget matched. B leaves statistical significance undefined. Neither justifies numerical thresholds. The adversarial-data experiment is recorded only and was not performed.",response-B.txt#L171 +Gemini web Flash (backend unknown),B,persistence,"Commits event and frame together, resumes pending logged actions through probes (§8).",A's stated recovery premise fails in the isolated transaction-ordering example: the uncommitted pending record disappears while the file effect remains. B does not explicitly show intent commit before dispatch; its recovery requires that ordering. Neither provides a general cross-system atomicity guarantee.,response-B.txt#L180 +Gemini web Flash (backend unknown),B,orchestration,Single state controller with bounded subprocesses (§9).,"Practical for a local baseline. A's universal quadratic-overhead assertion is unjustified, and temporal serialization does not settle semantic contradictions. B gives no concrete worker-result conflict rule. No worker-performance measurements were made.",response-B.txt#L201 +Gemini web Flash (backend unknown),B,feasibility,"Six-week state/tool/budget/epistemic/evaluation/mirror/test sequence, week-two slice, tool verification as risk (§10).",Roadmaps are estimates. Both partly propose rebuilding mechanisms described as already present in the prompt. Repository mapping should precede an overhaul; neither has inspected the full current implementation.,response-B.txt#L223 +Gemini web Flash (backend unknown),B,originality,Explicit Python state transitions with LLM-generated proposals; code complexity tradeoff and unmaintainable-domain rejection (§11).,"Useful design choices, not demonstrated novel inventions. A's directory-level atomic swap needs filesystem and concurrent-edit assumptions. B's rejection condition needs an operational maintenance-cost criterion.",response-B.txt#L246 diff --git a/research/ai_generated_agi_architectures/collection/gemini-flash-reference/request.txt b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/request.txt new file mode 100644 index 0000000..df0b2e0 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/request.txt @@ -0,0 +1,19 @@ +Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions. + +Context: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority. + +Write an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context. + +1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting. +2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans. +3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs. +4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes. +5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling. +6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control. +7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results. +8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored. +9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution. +10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk. +11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it. + +Finish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation. diff --git a/research/ai_generated_agi_architectures/collection/gemini-flash-reference/response-A.txt b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/response-A.txt new file mode 100644 index 0000000..15c2bed --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/response-A.txt @@ -0,0 +1,366 @@ +1. Memory Architecture + ┌─────────────────────────────────────────┐ + │ Working Memory │ + │ - Current goal, context window RAM │ + │ - Capacity: ~8–32k tokens │ + └────────────────────┬────────────────────┘ + │ Summarize / Extract + ▼ +┌───────────────────────────────────────────────────┴───────────────────────────────────────────────────┐ +│ Cognitive Storage Subsystem │ +├───────────────────────────────────┬───────────────────────────────────┬───────────────────────────────┤ +│ Episodic Memory │ Semantic Memory │ Procedural Memory │ +│ - Raw events & tool traces │ - Entities, facts, assertions │ - Parametric weights, prompts │ +│ - Engine: DuckDB / SQLite │ - Engine: SQLite + HNSW vector │ - Storage: Git repo & disk │ +│ - Retention: Time/importance decay│ - Provenance: Inverted index │ - Retain via eval benchmark │ +└───────────────────────────────────┴───────────────────────────────────┴───────────────────────────────┘ + + +Working Memory: Volatile, bounded context window (RAM). Holds the active plan step, local workspace state, and tool outputs for the immediate loop. Strictly limited to 8,000–32,000 tokens depending on the active model target. + +Episodic Memory: Immutable event log storing execution traces, raw tool inputs/outputs, and environment responses. + +Storage: SQLite or DuckDB table episodic_events(event_id, run_id, timestamp, event_type, payload_json). + +Retrieval: Recency-weighted keyword search and BM25 indexing over raw text outputs. + +Semantic Memory: Structured facts, domain entities, and operational assertions extracted from episodic traces. + +Storage: SQLite relational tables with sqlite-vss or HNSW vector index for embeddings: facts(fact_id, subject, predicate, object, confidence, source_event_id). + +Retrieval: Hybrid top-K vector similarity search blended with exact key-value matching. + +Procedural Memory: System prompts, tool invocation schemas, workflow templates, and fine-tuned model parameters. + +Storage: Versioned Git repository containing prompt templates, Python execution scripts, and adapter weight paths. + +Provenance: Every semantic fact and procedural template retains a mandatory foreign key pointing to its originating event_id or explicit user input. External model outputs never gain direct semantic authority without explicit run verification. + +Forgetting: Episodic logs undergo exponential recency decay S=S +0 + ​ + +⋅e +−λt +. Low-salience items (S<τ) drop out of active index vector stores and archive to compressed cold storage on disk. + +2. Reasoning and Planning + ┌──────────────────────────────┐ + │ Observe Environment │ + └──────────────┬───────────────┘ + │ + ▼ + ┌──────────────────────────────┐ + │ Generate/Refine Plan (DAG) │ + └──────────────┬───────────────┘ + │ + ▼ + ┌──────────────────────────────┐ + │ Evaluate Stopping Criteria │ + └──────┬────────────────┬──────┘ + │ │ + Goal Satisfied │ │ Failure / Loop Exceeded + ┌───────────┘ └───────────┐ + ▼ ▼ + ┌──────────────┐ ┌──────────────┐ + │ Return Trace │ │ Fallback Mode│ + └──────────────┘ └──────────────┘ + + +The system operates on an explicit Bounded Observation-to-Action Loop: + +Python +interface ReasoningLoop: + def execute_step(run_id: str, working_memory: WorkingMemory) -> StepResult: + observation = observe_environment(working_memory) + plan_dag = generate_or_refine_plan(observation, working_memory.goal) + next_action = plan_dag.get_next_runnable_action() + + if stopping_criteria_met(plan_dag, working_memory): + return StepResult(status="COMPLETE", output=working_memory.get_final_answer()) + + return execute_action(next_action) + + +Stopping Criteria: Execution halts when (1) all DAG goal nodes evaluate to VERIFIED_TRUE, (2) the iteration count exceeds N +max + ​ + + (default: 20 steps), or (3) accumulated monetary/token budget crosses user limits. + +Recovery from Failed Plans: If a step returns an error or fails validation, the controller blocks execution of child nodes in the DAG. It enters a Diagnostic State, generates a failure hypothesis referencing the error event ID, prunes invalid DAG branches, and constructs a recovery sub-plan. If three consecutive plan revisions fail, the loop pauses and prompts for human intervention. + +3. Learning + +Learning in Cognitive-OS is strictly offline, deterministic, and evaluated. The system never updates production weights or prompt primitives online during execution. + +┌─────────────────┐ ┌──────────────────┐ ┌──────────────────┐ ┌─────────────────┐ +│ Run Artifacts & │────>│ Offline Learner │────>│ Candidate Candidate│───>│ Held-out Eval │ +│ Event Logs │ │ (DSPy / LoRA) │ │ Prompt / Weights │ │ Benchmark Suite │ +└─────────────────┘ └──────────────────┘ └──────────────────┘ └────────┬────────┘ + │ + Passes Eval │ + ▼ + ┌─────────────────┐ + │ Promote to Git │ + │ Production │ + └─────────────────┘ + + +Candidate Generation: Unsuccessful or sub-optimal run logs are processed by offline optimization jobs (e.g., DSPy prompt optimization or LoRA fine-tuning). + +Versioning: Output artifacts are assigned a semantic version (prompts/v1.2.0 or adapters/v0.4.1) stored in Git. + +Held-Out Evaluation: Candidate versions run against a deterministic, local benchmark suite containing historic tasks with ground-truth verification assertions. + +Promotion & Rollback: A candidate artifact is promoted to production if and only if: + +Accuracy +candidate + ​ + +≥Accuracy +production + ​ + +ANDLatency +candidate + ​ + +≤1.15×Latency +production + ​ + + +If post-promotion execution triggers human overrules exceeding a 10% threshold over 20 runs, the runtime automatically rolls back the Git HEAD to the previous tagged production release. + +4. Tool Use + +Tools interact through an isolated RPC contract. Tools must declare whether they are read-only, idempotent, or side-effecting. + +Python +class ToolContract(Protocol): + name: str + is_idempotent: bool + requires_approval: bool + + def validate_args(self, args: dict) -> ValidationResult: ... + def execute(self, request: ActionRequest) -> ActionReceipt: ... + + +Contract: + +JSON +ActionRequest { + "request_id": "req_8f9a2b", + "tool_name": "patch_workspace", + "arguments": {"path": "src/main.py", "diff": "..."}, + "idempotency_key": "chk_992a_src_main_py_v2" +} + +JSON +ActionReceipt { + "request_id": "req_8f9a2b", + "status": "SUCCESS" | "FAILED" | "UNCERTAIN", + "execution_time_ms": 142, + "output": "...", + "state_hash": "sha256_b4e1..." +} + + +Validation: Schema parameters are validated via Pydantic before invocation. Inputs violating static checks fail fast without burning LLM context. + +Handling Uncertain Outcomes: When a tool timeout or network disconnect occurs (UNCERTAIN), the runtime tags the tool execution as non-determinate. It executes a dedicated read-only probe tool (e.g., check_file_hash or git_status) to re-observe the actual workspace state before permitting re-execution. + +5. World Representation + +The runtime models environment reality explicitly through a three-tier schema, treating external data strictly as untrusted observations: + +┌────────────────────────────────────────────────────────────────────────┐ +│ World Model │ +├────────────────────────────────────────────────────────────────────────┤ +│ Observations : Raw environment outputs (untrusted facts) │ +│ Hypotheses : Inferred explanations tagged with probability p │ +│ Predictions : Expected next states if action A is taken │ +└────────────────────────────────────────────────────────────────────────┘ + + +Observations: Raw, unparsed data returned by tool receipts (Observation(source="tool", payload=...)). + +Hypotheses: Model-generated interpretations tagged with explicit probability p∈[0,1] and links to supporting evidence IDs (Hypothesis(claim="Function X is deprecated", p=0.75, evidence=["evt_12"])). + +Predictions: Expected outcomes attached to candidate actions prior to execution (Prediction(action="run_test", expected="PASS")). + +Contradiction Handling: If two active hypotheses exhibit logical contradiction (e.g., H +1 + ​ + +⟹A and H +2 + ​ + +⟹¬A), the runtime assigns an Uncertainty Penalty to the node, halts autonomous execution, and adds a state-resolution goal to the DAG. + +6. Safety and Governance + +User authority is enforced via a non-bypassable local proxy layer embedded within the controller: + +┌──────────────┐ ActionRequest ┌──────────────────┐ Approved ┌──────────────┐ +│ Controller │───────────────────────>│ Security Proxy │─────────────────>│ Tool Runner │ +└──────────────┘ └─────────┬────────┘ └──────────────┘ + │ Unapproved + ▼ + ┌──────────────────┐ + │ Human Approver │ + │ (CLI Prompt/TUI) │ + └──────────────────┘ + + +Permission Boundaries: Actions belong to explicit policy tiers: + +Tier 0 (Safe): Read-only workspace queries, search. Executed automatically. + +Tier 1 (Bounded): Local file modifications inside workspace root. Executed automatically within budget. + +Tier 2 (High Impact): Shell commands, network requests, system modifications. Requires manual CLI keypress approval. + +Resource Budgets: Hard limits enforced per run via SQLite transactional counters: + +max_wall_time_seconds: 600 + +max_tokens: 100,000 + +max_dollar_cost: $2.00 + +Reliable Stop Control: OS signal handlers (SIGINT, SIGTERM) trap controller execution. Receipt of SIGINT breaks the loop immediately at the nearest tool boundary, serializes the current execution state to SQLite, revokes all child worker processes, and safely exits without corrupting state. + +7. Evaluation + +To evaluate architectural claims without synthetic performance assertions, the platform uses three local empirical benchmarks and one ablation test: + +Experiment Name Target Metric Success Criterion Falsification Conditions +E1: Local Interrupt Recovery Recovery Rate after SIGKILL mid-step 100% recovery of state consistency via transaction boundary replay Loss of run history or double-execution of non-idempotent actions +E2: Workspace Patch Verification Deterministic patch application rate Zero unverified/hallucinated edits applied to filesystem Patch applied without passing workspace test/lint checks +E3: Budget Hard-Cap Enforcement Token and financial ceiling breach 0% budget overrun across 50 forced infinite-loop prompts Execution continues past 100.0% allocated dollar/token limit + +Ablation (Structured Memory vs. Flat Context): Run 50 multi-step edit tasks comparing Cognitive-OS (working + semantic memory) against a baseline LLM loop with an unbounded raw context buffer. If structured semantic memory does not reduce token consumption by ≥30% while maintaining equal completion accuracy, the semantic memory overhead hypothesis is falsified. + +8. Persistence and Runtime + +The system guarantees consistency across system crashes by using dual-write transactional constraints across SQLite and disk state. + +┌────────────────────────────────────────────────────────────────────────┐ +│ Transactional DB Envelope │ +│ │ +│ 1. BEGIN TRANSACTION │ +│ 2. Insert ActionRequest(status="PENDING") │ +│ 3. Execute Tool Action on Filesystem │ +│ 4. Insert ActionReceipt(status="SUCCESS") │ +│ 5. Update Run State │ +│ 6. COMMIT TRANSACTION │ +└────────────────────────────────────────────────────────────────────────┘ + + +Uncertain State Protocol (Missing Receipt): If the runtime crashes after step 3 but before step 6, recovery on restart proceeds as follows: + +Scan SQLite for ActionRequest entries marked PENDING or lacking a corresponding ActionReceipt. + +Check the tool's is_idempotent flag. + +If idempotent, re-run execution. + +If non-idempotent, invoke tool verification callback (verify_state()). If target state matches expected hash, write missing ActionReceipt(status="SYNTHESIZED"). If state does not match, log a state corruption error and prompt for user reconciliation. + +9. Orchestration + +Cognitive-OS uses a Single Controller with Bounded Sub-Workers model. + + ┌─────────────────────────┐ + │ Single Controller │ + │ (Monotonic State) │ + └────────────┬────────────┘ + │ Distributes Sub-tasks + ┌─────────────┴─────────────┐ + ▼ ▼ + ┌─────────────────────┐ ┌─────────────────────┐ + │ Sub-Worker 1 │ │ Sub-Worker 2 │ + │ (Read-Only Search) │ │ (Read-Only Linting) │ + └─────────────────────┘ └─────────────────────┘ + + +Justification: Multi-agent consensus systems introduce quadratic communication overhead, race conditions on disk state, and non-deterministic behavior. A single controller enforces a strictly monotonic state machine. + +Bounded Sub-Workers: Workers are stateless short-lived processes spawned strictly for read-only sub-tasks (e.g., parallel search across codebases). Sub-workers cannot mutate state or call Tier-2 tools directly; they return structured observations back to the single controller. + +Conflict Resolution: Sub-worker output collisions are serialized by timestamp in the primary controller's SQLite event loop before evaluation. + +10. Engineering Feasibility + +Six-week implementation roadmap for a small core team: + +┌──────────────┬─────────────────────────────────────────────────────────┐ +│ Week │ Focus / Deliverables │ +├──────────────┼─────────────────────────────────────────────────────────┤ +│ Week 1 │ SQLite schema, WAL setup, transactional engine │ +│ Week 2 │ Action contract, schema validator, Tier 0/1/2 proxy │ +│ Week 3 │ Bounded reasoning loop & DAG execution state machine │ +│ Week 4 │ Local workspace mirror & state hash verifier │ +│ Week 5 │ Offline DSPy evaluation benchmark pipeline & rollback │ +│ Week 6 │ Hardened SIGINT handler, crash-recovery test suite │ +└──────────────┴─────────────────────────────────────────────────────────┘ + + +Minimum Viable Slice (MVS): CLI-driven transactional loop capable of reading a file, generating a diff, prompting for manual CLI execution approval, applying the patch, validating via pytest, and resuming state seamlessly after kill -9. + +Largest Unresolved Risk: Tool execution non-determinism (e.g., partial shell script execution modifying global environmental state) where state recovery cannot be reliably achieved via disk hash verification alone. + +11. Non-Obvious Insight + +Design Choice: Strict Invariant-Based File Mirroring with Pre-Apply Shadow Staging. + +Mechanism: The workspace is never patched directly. Patches apply to a .cog/shadow git directory first. AST/Linter/Unit-test passes run in the shadow tree before atomic move swap to the active workspace. + +Tradeoff: Introduces storage overhead (2x file workspace footprint) and latency on large asset trees. + +Rejection Condition: Reject this choice if workspace directories exceed 50GB or contain large binary blobs where clone/link overhead surpasses 2.0 seconds per step. + +Concrete Execution Trace +Scenario + +User requests: "Update docstring in math_utils.py to document the divide-by-zero error handling." + +[USER INPUT] -> "Update docstring in math_utils.py to document the divide-by-zero error handling." + │ + ├─► [CONTROLLER] Creates run_id="run_101", writes event to SQLite: GOAL_ACCEPTED + │ + ├─► [OBSERVE] Reads math_utils.py content -> Stores payload as Observation(obs_01) + │ + ├─► [HYPOTHESIS] Generated: H_1 = "math_utils.py lacks explicit ZeroDivisionError in docstring" (p=0.95) + │ + ├─► [PLAN] DAG Step 1: Stage diff in .cog/shadow/math_utils.py + │ + ├─► [TOOL ACTION REQUEST] + │ Tool: patch_workspace (Tier 1 Action) + │ Request ID: req_aabb12 + │ Payload: {"path": "math_utils.py", "diff": "@@ -4,3 +4,5 @@ ..."} + │ Check: Target path inside workspace boundary? PASS. + │ + ├─► [ACTION EXECUTION] Diff applied to shadow tree. Hash: sha256_e1f82... + │ + ├─► [VERIFICATION STEP] Runs `python -m py_compile .cog/shadow/math_utils.py` -> SUCCESS + │ + ├─► [STATE COMMIT] Move shadow/math_utils.py -> math_utils.py. SQLite: ActionReceipt(req_aabb12, SUCCESS) + │ + │ ⚡ [INTERRUPTION OCCURS: Power Loss / SIGKILL] ⚡ + │ + └─► [SYSTEM REBOOT & RECOVERY] + 1. CLI re-launched: `cog-os resume run_101` + 2. Runtime reads SQLite event journal. + 3. Sees req_aabb12 has ActionReceipt(SUCCESS) and state_hash matches math_utils.py on disk. + 4. Validates final state: Runs project tests -> PASS. + 5. Goal status set to VERIFIED_TRUE. Exits gracefully. + +To refine this proposal further, explore one of these technical deep dives: +Explore the transaction state machine and crash recovery engine +Design the offline evaluation pipeline for candidate prompts and weights +Draft the security proxy specifications for tool execution \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/collection/gemini-flash-reference/response-B.txt b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/response-B.txt new file mode 100644 index 0000000..b6f0183 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/response-B.txt @@ -0,0 +1,322 @@ +Cognitive-OS: Architectural Proposal for a Local-First Cognitive Runtime +1. Memory Architecture + +Cognitive-OS separates memory by operational lifecycle and mutability rather than a unified vector index. + +Working Memory: Represented as an in-memory execution frame held in Python dataclasses. Contains the active goal, current observation window, active hypothesis tree, and short-term workspace diffs. + +Episodic Memory: Stored in SQLite as append-only events and runs tables. Queries use hybrid lexical search (FTS5) and sparse/dense embeddings over run transcripts. Each episode stores a cryptographic provenance hash, tool call receipts, user approval events, and parent run IDs. + +Semantic Memory: A local SQLite-backed knowledge graph (entities, claims, relations) with an attached key-value store for consolidated concepts. Updates require explicit distillation jobs. Every triple includes a confidence float, sources (pointing to episodic event IDs), and last_verified_at timestamp. + +Procedural Memory: Executable tool definitions, prompt templates, and macro workflows stored as version-controlled code files on disk and indexed in a SQLite procedural_skills table. + +Forgetting Policy: Episodic memory uses time-decayed recency scoring combined with access frequency. Unreferenced episodic logs move to compressed cold-storage archives. Semantic triples decaying below confidence 0.20 without re-verification are flagged for pruning. + + +-------------------------------------------------------+ + | WORKING MEMORY | + | (Active Goal, Frame Data, Diff Queue, Draft Hypotheses)| + +-------------------+---------------+-------------------+ + | | + Read / Distill | | Append Events + v v + +------------------------+ +------------------------+ + | SEMANTIC MEMORY | | EPISODIC MEMORY | + | (SQLite Knowledge Graph| | (SQLite Event Journal, | + | & Key-Value Concepts) | | FTS5, Provenance Hash)| + +------------------------+ +------------------------+ + ^ + Skill Indexing | + +-----------+------------+ + | PROCEDURAL MEMORY | + | (Tool Code, Workflows) | + +------------------------+ + +2. Reasoning and Planning + +The core execution cycle operates as a bounded Observation-to-Action state machine: + +Observe⟶Orient (Hypothesize)⟶Plan⟶Evaluate Policy⟶Execute/Tool⟶Verify + +---------+ +--------+ +------+ +---------------+ + | Observe | ---> | Orient | ---> | Plan | ---> | Policy Gate | + +---------+ +--------+ +------+ +-------+-------+ + ^ | + | +-----------------+ | + +---------------| Verify & Audit | <-------------+ (Execute Action) + +-----------------+ + +Stopping Criteria + +The loop terminates when any of these conditions are met: + +Target goal verification checks pass. + +Allocated budget (tokens, tool call count, time) is exhausted. + +Unrecoverable tool error occurs or user explicitly denies approval. + +Maximum plan depth (N=15 steps) is exceeded. + +Failure Recovery + +When an action fails verification or returns an execution error, the engine appends a PlanFailureEvent to working memory. It decrements the hypothesis score, rolls back uncommitted local file modifications via git or workspace mirrors, and re-invokes the planner with explicit negative constraints derived from the failure log. + +3. Offline Learning & Policy Evaluation + +Learning in Cognitive-OS is defined strictly as structural adaptation evaluated offline, distinct from adding log entries to context. + ++------------------+ +-------------------+ +------------------+ +| Candidate Policy | --> | Held-out Suite | --> | Regression Gate | +| (Prompts/Skills) | | (Benchmark Runs) | | (Pass Rate/Cost) | ++------------------+ +-------------------+ +--------+---------+ + | + +------------+------------+ + | | + [Pass: Promote] [Fail: Rollback] + +Mechanism + +Extraction: An offline background worker analyzes episodic event logs to identify repeated multi-step plan structures or frequent prompt failures. + +Candidate Generation: The worker generates a modified candidate policy artifact (e.g., an updated prompt layout, new tool wrapper, or heuristic tool routing rule). + +Validation Suite: Candidate artifacts are evaluated against a local held-out test suite consisting of past deterministic run transcripts and synthetic benchmarks. + +Promotion & Rollback: If the candidate improves pass rates without increasing average token consumption or runtime cost above a defined threshold, it is committed to procedural memory with a semantic version tag (e.g., v1.4.0). If the candidate fails any critical assertion or safety constraint, it is discarded, and the system retains the existing baseline version. + +4. Tool Execution Contract + +Tools interact with the runtime via a strict JSON Schema interface: + +JSON +{ + "tool_name": "patch_apply", + "request_id": "req_8f9a2c", + "idempotency_key": "ik_771b90d2", + "parameters": { + "target_file": "docs/api.md", + "diff": "@@ -12,3 +12,3 @@..." + } +} + +JSON +{ + "request_id": "req_8f9a2c", + "status": "SUCCESS", + "exit_code": 0, + "result_payload": { "bytes_modified": 142 }, + "verification_hash": "sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" +} + +Contract Rules + +Validation: Inputs are validated against tool schemas prior to execution. Schema mismatches fail immediately before reaching the system shell or file system. + +Idempotency: Write tools require an explicit idempotency_key. The runtime records completed invocations in SQLite. Duplicate invocations return cached receipt objects without executing side effects again. + +Uncertain Outcomes: If a tool times out or drops connection without returning a receipt, its state is marked UNKNOWN. The runtime must execute a read-only verification probe before retrying the mutation. + +5. World Representation & Epistemics + +World state is partitioned into three epistemological tiers: + +Observations (O): Immutable data retrieved directly from local files, tool receipts, or command outputs. + +Hypotheses (H): Probabilistic interpretations generated by models to explain observations or bridge missing context. Each hypothesis carries a confidence score (c∈[0.0,1.0]) and pointers to supporting observations. + +Predictions (P): Expected upcoming observations given a planned action A and hypothesis H. + +Contradiction Handling + +When a new observation O +new + ​ + + directly contradicts an established claim in semantic memory or an active hypothesis H +A + ​ + +: + +An explicit ContradictionDetectedEvent is logged. + +The system drops confidence on H +A + ​ + + to zero and branches a new hypothesis H +B + ​ + +. + +External text ingested from unstructured sources is tagged as UNTRUSTED_DATA and stripped of instruction execution privileges. + +6. Safety, Boundaries & Control + +User authority is enforced through physical barriers in the execution pathway rather than prompt engineering: + ++-------------------+ +------------------+ +-------------------+ +| Action Request | --> | Permission & | --> | Human-in-the-Loop | --> Execution +| (Tool Call & Args)| | Budget Check | | Approval Gate | ++-------------------+ +------------------+ +-------------------+ + + +Permission Boundaries: Tools are classified into capability tiers (READ_ONLY, MUTATE_LOCAL, MUTATE_NETWORK). MUTATE_NETWORK and non-whitelisted MUTATE_LOCAL operations require explicit interactive terminal confirmation from the user. + +Budget Controls: Every run is bounded by hard ceilings configured by the user: max_wall_time_seconds, max_llm_cost_usd, and max_tool_invocations. Crossing any ceiling triggers an immediate freeze. + +Interrupt / Stop Control: A dedicated POSIX signal handler (SIGINT / Ctrl+C) intercepts execution, flushes active transactions to SQLite, sets run state to PAUSED, and safely exits the event loop without corrupting state. + +7. Evaluation & Ablation Frame + +To evaluate architectural choices, three measurable experiments and one structural ablation are defined: + +Experiment / Ablation Objective Metric Success Criterion +E1: Interrupted Recovery Measure run resume efficiency after hard process kill (SIGKILL). Time to resume state & percentage of duplicated tool calls. Zero duplicate side effects; resume overhead <2.0 seconds. +E2: Adversarial Data Ingestion Test resilience against prompt injection in external documentation. Rate of unauthorized system operations triggered by ingested text. 0% execution of hidden instructions embedded in data files. +E3: Offline Policy Promotion Verify offline offline-evaluated skill promotion prevents performance degradation. Regression rate across held-out test suite over 10 policy iterations. Zero safety regressions; overall benchmark pass rate non-decreasing. +Ablation: No Epistemic Tiering Remove separation between Observations and Hypotheses (treat model assertions as ground truth). Unhandled contradiction rate & hallucinated plan propagation. Significant increase in persistent errors validates epistemic tiering. +8. Persistence & Crash Recovery +Transaction Boundaries + +All engine state changes occur within SQLite transactions (BEGIN IMMEDIATE). An event commit requires writing both the event entry and the updated execution frame atomically. + +Crash / Restart Protocol + +On boot, the engine scans SQLite for runs with state IN_PROGRESS. + +It fetches the last committed event_id and checks the status of pending tool receipts. + +If an unacknowledged tool invocation was logged prior to crash: + +Execute the tool's verification read probe. + +If state change applied, write synthetic completion receipt to database. + +If state change did not apply, re-queue the tool request. + +Execution frame resumes from the last valid checkpoint. + +9. Orchestration Architecture + +Cognitive-OS uses a single controller with bounded worker subprocesses, avoiding full multi-agent peer-to-peer topologies. + + +-------------------+ + | SINGLE CONTROLLER | + | (State Engine & | + | Event Loop) | + +---------+---------+ + | + +---------------------+---------------------+ + | | | + v v v + +---------------+ +---------------+ +---------------+ + | Worker Subproc| | Worker Subproc| | Worker Subproc| + | (Read/Search) | | (Code Analysis| | (Testing/Diff)| + +---------------+ +---------------+ +---------------+ + +Justification + +Multi-agent topologies incur high coordination overhead, redundant LLM context costs, and complex deadlock resolution requirements. A single deterministic controller managing execution state with isolated worker subprocesses for parallel compute tasks keeps state execution predictable, easy to trace, and local-first. + +10. Engineering Implementation Roadmap +Six-Week Sequence + +Week 1: SQLite transaction layer overhaul, formalizing JSON Schema interfaces for tool execution contracts and idempotency keys. + +Week 2: Bounded observation-to-action state machine implementation with hard ceiling budget limits and POSIX signal handling. + +Week 3: Observation vs. Hypothesis separation in working state; input sanitization layer for external data sources. + +Week 4: Offline evaluation harness construction; regression testing pipeline for prompt/skill promotion. + +Week 5: Workspace diff mirroring and automated post-action verification framework. + +Week 6: End-to-end integration testing, crash injection benchmarking, and CLI refinement. + +Minimum Viable Slice + +Week 2 milestone: A local run that executes file system modifications, captures tool failure gracefully, respects token and cost budget limits, and resumes cleanly after process interruptions. + +Largest Unresolved Risk + +Tool verification accuracy. Detecting whether a third-party non-idempotent side effect succeeded when a process crashes mid-execution remains non-trivial without custom tool drivers. + +11. Non-Obvious Insight +Design Choice + +Deterministic Prompt-Free State Transition Engine. The core controller uses explicit, hardcoded Python logic for state transitions, tool dispatch, and verification, using LLMs only as structured output generators for planning and text analysis. + + +-------------------------------------------------+ + | PYTHON STATE CONTROLLER | + | (Explicit Logic: Routing, Budget, Verification) | + +------------------------+------------------------+ + | + Calls structured | Returns parsed + generation v JSON schema + +-----------------+ + | LLM MODEL | + | (Planning / | + | Extraction) | + +-----------------+ + +Tradeoff + +Increases hand-written state-machine code and reduces runtime flexibility compared to autonomous agency loops where an LLM controls loop iteration. + +Condition for Rejection + +Reject this choice if task domain complexity expands beyond programmatic state representation, making explicit deterministic state graphs unmaintainable compared to direct probabilistic model control. + +12. Concrete Workflow Example +Scenario + +Documentation Update: Modify docs/api.md to reflect an added endpoint parameter timeout_ms. + + +-----------------------------------------------------------------+ + | 1. Observation & State Initialization | + | - Action Request: Update parameter docs in docs/api.md | + | - Run state committed to SQLite: RUN_ACTIVE (ID: run_9021) | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 2. Evidence Verification | + | - Engine inspects source code for parameter definition. | + | - Reads src/api.py -> Confirms timeout_ms parameter exists. | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 3. Governance Gate | + | - Target: MUTATE_LOCAL (docs/api.md) | + | - Policy check: Auto-approved by low-risk local file policy. | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 4. Idempotent Action Application | + | - Request ID: req_001, Idempotency Key: ik_api_doc_9021 | + | - Tool: patch_apply | + | - Patch applied to mirror; SQLite event written. | + +-----------------------------------------------------------------+ + | + [ PROCESS CRASH / SIGKILL ] + | + v + +-----------------------------------------------------------------+ + | 5. Process Restart & Recovery | + | - Engine re-boots -> Detects run_9021 marked IN_PROGRESS. | + | - Reads last log -> Pending receipt for req_001. | + | - Executes Probe: Checks file hash of docs/api.md. | + | - Validation Pass: File diff already present on disk. | + | - Writes synthetic receipt -> Advances state machine. | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 6. Final Verification & Run Completion | + | - Evaluator runs git diff check on docs/api.md. | + | - Verification successful -> Run transitions to COMPLETED. | + +-----------------------------------------------------------------+ diff --git a/research/ai_generated_agi_architectures/collection/gemini-flash-reference/source.json b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/source.json new file mode 100644 index 0000000..97cd84b --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/gemini-flash-reference/source.json @@ -0,0 +1,27 @@ +{ + "collected_at": "2026-09-08T19:27:26.989Z", + "provider": "Google", + "tool": "Gemini web app", + "ui_mode": "Flash", + "backend_model_revision": null, + "sampling_parameters": null, + "source_url": "https://gemini.google.com/app", + "response_format": "Two A/B candidate answers were offered automatically for one prompt. Both preserved; no preference vote submitted.", + "extraction": "innerText of each observed div.markdown-main-panel; rendered plaintext, not original Markdown. No wording edits. HTML rendering artifacts retained separately as local evidence.", + "responses": [ + { + "label": "A", + "characters": 19149, + "sha256": "5ee0ef09cd2446ebc8e8fa9642f52e91b3974cdbb3836629ad6f8ced8664b5c5" + }, + { + "label": "B", + "characters": 17013, + "sha256": "833ed1d4878964993e9f763a87d6a2f62c1ff73b7e812ed6b910538a1bc243ca" + } + ], + "conversation_reference": "Authenticated conversation reference retained in local collection evidence; not a public replay URL.", + "combined_raw_file": "raw_outputs/gemini_flash_reference.txt", + "combined_raw_sha256": "7039a1eee3ec0d190f5e5bc1929acfe71c49a9142b28e6ae343059b5beae106e", + "combined_raw_format": "Unmodified A and B rendered answer strings with two explicitly collector-authored candidate labels and separating newlines. Original strings remain in response-A.txt and response-B.txt." +} diff --git a/research/ai_generated_agi_architectures/collection/granite33/analysis-notes.md b/research/ai_generated_agi_architectures/collection/granite33/analysis-notes.md new file mode 100644 index 0000000..71a6243 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/granite33/analysis-notes.md @@ -0,0 +1,21 @@ +# Analyst notes: Granite 3.3 2B Instruct + +Normal stop after 1,168 generated tokens on 2026-09-08 at 19:44:36 UTC. All eleven headings and a concrete report-edit example are present. Exact request, response and raw-content hashes passed the recorded-artifact consistency checks. This output is more specific than the first two local responses, but specificity does not establish correctness or engineering benefit. + +| Dimension | What the response supplies | What it does not establish | +| --- | --- | --- | +| Memory | LRU working cache, timestamped SQLite episodes, Neo4j-style semantic triples and versioned Python procedures. | No retrieval query, source/provenance schema across stores or retention policy beyond working-cache eviction. The extra graph database is not justified against the existing SQLite runtime. | +| Planning | State machine with goal/resource stops, episodic replanning and rollback. | No actual transition interface, step/replan bound or rule for selecting a recoverable checkpoint. | +| Learning | Versioned pluggable supervised/unsupervised/reinforcement pipeline, held-out tests and degradation rollback. | No concrete candidate representation, train/test separation, measured metric or promotion threshold. Listing pipeline types does not show improvement beyond logs. | +| Tools | REST/JSON request/result contract, unique request IDs, result checksums and confidence scoring for uncertainty. | No actual request/receipt schema or deduplication procedure. IDs and checksums alone do not establish idempotency; a confidence score is not an action-specific observation of whether an effect happened. | +| World representation | Structured observations, probabilistic hypotheses/predictions, evidence links and prioritization of reliable evidence. | No operational reliability estimator, calibrated uncertainty or conflict-resolution algorithm. The mechanism is named at a broad level. | +| Governance | Permission management, explicit budgets and a dedicated shutdown service. | These are declared design features; the output supplies no concrete accounting or stop interface. No security claims are tested in this packet. | +| Evaluation | Three identifiable measurements: time to solution, plan success and resource utilization; remove episodic memory for an ablation. | No task corpus, budget-matched baseline, thresholds or failure definitions. The proposed metrics are not measured results. | +| Persistence | Two-phase commit, periodic snapshots and a Raft-style distributed log. | No transaction participants, coordinator recovery or protocol for a completed tool with a missing receipt. Naming distributed mechanisms does not make arbitrary filesystem effects transactional. | +| Orchestration | Central controller, bounded workers, RabbitMQ-style messaging and Paxos-style conflict resolution. | No reason a local single-controller system needs both replication and consensus layers; no membership, conflict semantics or coordination-cost measurement. | +| Feasibility | Three two-week phases, broad engine slice and integration of diverse learning components as risk. | The minimum slice remains too broad to be an acceptance test; estimates are unsupported by dependency or staffing analysis. | +| Originality | Pluggable learning modules; flexibility versus overhead; reject for monolithic learning when overhead harms real-time use. | Modularity is a useful common pattern, not established novelty. The rejection threshold is unspecified and the real-time-learning concern is not connected to the earlier offline-learning proposal. | + +The example describes adding a report section, checking keywords/guidelines, review by a designated officer, writing, verification and snapshot recovery. The roles and standards are hypothetical. Keyword coverage does not establish factual accuracy, and the recovery narrative does not address an effect occurring after the last snapshot. No example execution is claimed. + +For the synthesis, retain the explicit episodic-memory ablation and the idea that added modules have a cost. Do not adopt the distributed transaction/consensus stack without a workload requiring it and evidence against the existing local baseline. This is a design tradeoff assessment, not a measured comparison of databases or coordination systems. diff --git a/research/ai_generated_agi_architectures/collection/granite33/model.json b/research/ai_generated_agi_architectures/collection/granite33/model.json new file mode 100644 index 0000000..41ba22b --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/granite33/model.json @@ -0,0 +1,234 @@ +{ + "_id": "67f9325ee6e5ffbf9a99a0aa", + "id": "ibm-granite/granite-3.3-2b-instruct-GGUF", + "private": false, + "pipeline_tag": "text-generation", + "library_name": "transformers", + "tags": [ + "transformers", + "gguf", + "language", + "granite-3.3", + "text-generation", + "base_model:ibm-granite/granite-3.3-2b-instruct", + "base_model:quantized:ibm-granite/granite-3.3-2b-instruct", + "license:apache-2.0", + "region:us", + "conversational" + ], + "downloads": 2991, + "likes": 15, + "modelId": "ibm-granite/granite-3.3-2b-instruct-GGUF", + "author": "ibm-granite", + "sha": "7cdf86ccd1f1bb3491c9b7017b033f2e51367397", + "lastModified": "2025-04-16T15:48:48.000Z", + "gated": false, + "disabled": false, + "widgetData": [ + { + "text": "Hi, what can you help me with?" + }, + { + "text": "What is 84 * 3 / 2?" + }, + { + "text": "Tell me an interesting fact about the universe!" + }, + { + "text": "Explain quantum computing in simple terms." + } + ], + "model-index": null, + "config": {}, + "cardData": { + "pipeline_tag": "text-generation", + "inference": false, + "license": "apache-2.0", + "library_name": "transformers", + "tags": [ + "language", + "granite-3.3", + "gguf" + ], + "base_model": [ + "ibm-granite/granite-3.3-2b-instruct" + ] + }, + "transformersInfo": { + "auto_model": "AutoModel" + }, + "gguf": { + "total": 2533539840, + "architecture": "granite", + "context_length": 131072, + "chat_template": "{# Alias tools -> available_tools #}\n{%- if tools and not available_tools -%}\n {%- set available_tools = tools -%}\n{%- endif -%}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n {%- else %}\n {%- set system_message = \" Knowledge Cutoff Date: April 2024.\n Today's Date: \" + strftime_now('%B %d, %Y') + \". You are Granite, developed by IBM.\" %}\n {%- if available_tools and documents %}\n {%- set system_message = system_message + \" You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request. \nWrite the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.\" %}\n {%- elif available_tools %}\n {%- set system_message = system_message + \" You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.\" %}\n {%- elif documents %}\n {%- set system_message = system_message + \" Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.\" %}\n {%- elif thinking %}\n {%- set system_message = system_message + \" You are a helpful AI assistant.\nRespond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts between and write your response between for each user query.\" %}\n {%- else %}\n {%- set system_message = system_message + \" You are a helpful AI assistant.\" %}\n {%- endif %}\n {%- if 'citations' in controls and documents %}\n {%- set system_message = system_message + ' \nUse the symbols <|start_of_cite|> and <|end_of_cite|> to indicate when a fact comes from a document in the search result, e.g <|start_of_cite|> {document_id: 1}my fact <|end_of_cite|> for a fact from document 1. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}\n {%- endif %}\n {%- if 'hallucinations' in controls and documents %}\n {%- set system_message = system_message + ' \nFinally, after the response is written, include a numbered list of sentences from the response with a corresponding risk value that are hallucinated and not based in the documents.' %}\n {%- endif %}\n {%- set loop_messages = messages %}\n {%- endif %}\n {{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>\n' }}\n {%- if available_tools %}\n {{- '<|start_of_role|>available_tools<|end_of_role|>' }}\n {{- available_tools | tojson(indent=4) }}\n {{- '<|end_of_text|>\n' }}\n {%- endif %}\n {%- if documents %}\n {%- for document in documents %}\n {{- '<|start_of_role|>document {\"document_id\": \"' + document['doc_id'] | string + '\"}<|end_of_role|>\n' }}\n {{- document['text'] }}\n {{- '<|end_of_text|>\n' }}\n {%- endfor %}\n {%- endif %}\n {%- for 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"2025-04-11T15:16:46.000Z", + "usedStorage": 27859332192 +} diff --git a/research/ai_generated_agi_architectures/collection/granite33/request.json b/research/ai_generated_agi_architectures/collection/granite33/request.json new file mode 100644 index 0000000..e7a5cc8 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/granite33/request.json @@ -0,0 +1,17 @@ +{ + "model": "granite33", + "messages": [ + { + "role": "user", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/granite33/response.json b/research/ai_generated_agi_architectures/collection/granite33/response.json new file mode 100644 index 0000000..d253c26 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/granite33/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "role": "assistant", + "content": "**Cognitive-OS Architecture Proposal**\n\n1. **Memory Architecture**:\n - **Working Memory**: Utilizes a local in-memory data structure for real-time processing and immediate access. It employs a Least Recently Used (LRU) cache for efficient memory management.\n - **Episodic Memory**: Stores experiences in a relational database (SQLite) with a focus on temporal ordering. Each episode is timestamped and linked to related events for temporal reasoning.\n - **Semantic Memory**: A knowledge graph (e.g., Neo4j) stores explicit facts, relationships, and inferences. It uses a triple-store model for efficient querying and reasoning.\n - **Procedural Memory**: Encapsulates learned procedures and heuristics in a modular, extensible format (e.g., Python modules). It supports versioning for traceability.\n\n2. **Reasoning and Planning**:\n - Implements a bounded observation-to-action loop using a state machine with clear transitions and stopping criteria (e.g., goal satisfaction, resource exhaustion).\n - Incorporates a recovery mechanism for failed plans, leveraging the episodic memory for re-planning and a rollback strategy in case of catastrophic failures.\n\n3. **Learning**:\n - Offline learning is achieved through a modular, pluggable machine learning pipeline. Each component (e.g., supervised, unsupervised, reinforcement) is versioned, and held-out tests ensure generalization.\n - A rollback mechanism is in place to revert to previous learning states upon detecting degraded performance or undesirable behavior.\n\n4. **Tool Use**:\n - An action request/result contract uses a RESTful API with JSON payloads for tool invocation and result submission.\n - Idempotency is ensured through unique request IDs and result checksums. Uncertain outcomes are handled by probabilistic reasoning and confidence scoring.\n\n5. **World Representation**:\n - Observations are represented as structured data, enriched with uncertainty estimates (e.g., Bayesian networks).\n - Hypotheses and predictions are stored in the knowledge graph, linked to evidence sources and updated probabilistically.\n - Contradictions are detected through conflict resolution algorithms and handled by prioritizing more reliable evidence.\n\n6. **Safety and Governance**:\n - User authority is maintained through a permission management system, enforcing explicit permissions and budgets.\n - A reliable stop control is implemented via a dedicated \"shutdown\" service, ensuring no further learning or execution post-shutdown.\n\n7. **Evaluation**:\n - Three measurable experiments: (1) Time-to-solution for complex tasks, (2) Plan success rate under varying conditions, (3) Resource utilization under different workloads.\n - An ablation study: Removing the episodic memory component and observing its impact on learning and planning performance.\n\n8. **Persistence and Runtime**:\n - Transaction boundaries are enforced using a distributed transaction manager (e.g., Two-Phase Commit).\n - A crash/restart protocol ensures data consistency through periodic snapshots and a distributed log (e.g., Raft) for state replication.\n\n9. **Orchestration**:\n - A single, centralized controller manages high-level decision-making, coordinating with multiple bounded workers (task-specific agents).\n - Coordination costs are minimized through a message queue (e.g., RabbitMQ) for asynchronous communication. Conflict resolution is handled by a consensus algorithm (e.g., Paxos).\n\n10. **Engineering Feasibility**:\n - A six-week implementation sequence: (1-2 weeks) - Architecture design and tool selection, (3-4 weeks) - Core components development, (5-6 weeks) - Integration, testing, and optimization.\n - Minimum viable slice: A self-contained, local-first cognitive engine capable of goal-oriented reasoning and learning.\n - Largest unresolved risk: Ensuring seamless integration of diverse machine learning components with varying performance characteristics.\n\n11. **Non-obvious Insight**:\n - Design choice: Utilizing a modular, pluggable architecture for learning components.\n - Tradeoff: Balancing flexibility and performance overhead.\n - Condition for rejection: If the performance overhead significantly hampers real-time learning capabilities, we would revert to a monolithic learning module, accepting potential compromises in adaptability.\n\n**Concrete Example**:\n\n*Requested Documentation Edit*: A user requests a modification to a technical report, specifying the need to add a section on cognitive-os's safety mechanisms.\n\n*Evidence Checks*: The system cross-references the report's current content against a predefined set of safety-related keywords and checks for compliance with established cognitive-os guidelines.\n\n*Approval*: The request is flagged for review by a designated safety officer, who verifies the proposed addition adheres to cognitive-os's safety protocols.\n\n*Action*: Upon approval, the system integrates the new section, ensuring it's linked to the relevant evidence and approved by the safety officer.\n\n*Verification*: The updated report is immediately saved, and a verification process confirms the new section's compliance with the established standards.\n\n*Recovery*: In case of interruption (e.g., system crash), the system leverages its snapshot mechanism to restore the report to a consistent state, ensuring no loss of approved 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a/research/ai_generated_agi_architectures/collection/phi3/analysis-notes.md b/research/ai_generated_agi_architectures/collection/phi3/analysis-notes.md new file mode 100644 index 0000000..a536e2c --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/phi3/analysis-notes.md @@ -0,0 +1,21 @@ +# Analyst notes: Phi-3 mini 4k Instruct + +Normal stop after 1,384 generated tokens on 2026-09-08 at 20:03:18 UTC. The response has all eleven numbered topics and a short documentation example. It provides some concrete evaluation measures and record attributes, while leaving important execution and recovery mechanisms unspecified. No generated benchmark result is claimed. + +| Dimension | What the response supplies | What it does not establish | +| --- | --- | --- | +| Memory | Defines four memory categories, provenance/context retrieval, time decay and user forgetting criteria. | The subsequent storage list contains only three categories, omitting procedural storage. No database/record schema, retrieval procedure or retention parameters are provided. | +| Planning | Planning module considers observations, goals, resources and constraints; stops on time/resource/user thresholds; restores prior state and replans after failure. | No explicit transition contract, actual thresholds, replan limit or distinction between reversible internal state and already completed external effects. | +| Learning | Periodic log analysis creates versioned candidates; test on held-out data, promote a better version and otherwise retain/revert the prior one. | Candidate representation, evaluation metric, train/test separation and the meaning of better are unspecified. The prose outlines a pipeline but does not demonstrate improvement. | +| Tools | Validate requests against user constraints; track executed actions to avoid duplicates; return a probabilistic result and confidence for uncertainty. | No stable-ID/result schema or atomic deduplication procedure. A confidence value is not an observation resolving whether an uncertain effect occurred. | +| World representation | Structured observations and hypotheses with source/context/evidence metadata; probabilistic predictions with confidence; mentions conflict resolution. | No concrete conflict rule, uncertainty calibration or dependency-update procedure. | +| Governance | User-defined authority/permission constraints, monitored resource budgets and a stop control. | No concrete accounting, action-boundary or stopping interface. These declarations are not evaluated as security mechanisms in this packet. | +| Evaluation | Compare average task time and CPU/memory/disk usage against a baseline on the same tasks; survey user satisfaction; suggests removing episodic memory or the bounded loop. | No selected task corpus, exact baseline, sample size, success predicate, thresholds or single frozen ablation. These are proposed measurements, not executed results. | +| Persistence | Transaction log, restore last valid transaction, then claims a missing receipt will be stored when the log is flushed; also suggests manually saved receipts. | No durable representation of a receipt lost before commit, or observation that could reconstruct it after a crash. Flushing a log cannot by itself supply missing information. No transaction/effect ordering is specified. | +| Orchestration | Single controller for simple tasks; bounded workers for complex parallel tasks; controller manages conflicts. | No operational complexity rule, coordination-cost estimate or conflict-resolution algorithm. | +| Feasibility | Says a six-week sequence will be followed; minimum slice includes memory/planning/learning; risk is uncertainty and the user-control/automation tradeoff. | It does not enumerate weeks or define a testable minimal milestone. The slice remains broad. | +| Originality | Hybrid memory categories with complexity/performance tradeoff; reject when complexity outweighs benefits. | Restates categories requested by the prompt; gives no measurable rejection threshold or evidence of novel benefit. | + +The documentation example stores an edit and receipt, describes restart from the last valid transaction, then approval and resumption from working memory. It does not state a content-verification predicate, bind source facts to the edit, or define durable storage for the working-memory state it expects to resume. The order of actual file mutation versus receipt and approval is unclear. The example is hypothetical and was not executed. + +Retain the more explicit same-task baseline comparison for time/resource measurements and the need to evaluate complexity costs. Do not adopt its receipt-flush explanation as a recovery protocol. This is a reasoning-based assessment of the text, not an additional measured crash experiment or a defect report against Cognitive-OS. diff --git a/research/ai_generated_agi_architectures/collection/phi3/model.json b/research/ai_generated_agi_architectures/collection/phi3/model.json new file mode 100644 index 0000000..d59f69b --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/phi3/model.json @@ -0,0 +1,200 @@ +{ + "_id": "662698108f7573e6a6478546", + "id": "microsoft/Phi-3-mini-4k-instruct-gguf", + "private": false, + "pipeline_tag": "text-generation", + "tags": [ + "gguf", + "nlp", + "code", + "text-generation", + "en", + "license:mit", + "endpoints_compatible", + "region:us", + "conversational" + ], + "downloads": 24060, + "likes": 602, + "modelId": "microsoft/Phi-3-mini-4k-instruct-gguf", + "author": "microsoft", + "sha": "a64113399c2f6b8ad3e11c394733a2ddadaa7f33", + "lastModified": "2025-12-10T20:22:37.000Z", + "gated": false, + "disabled": false, + 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index 0000000..41f31cc --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/phi3/request.json @@ -0,0 +1,17 @@ +{ + "model": "phi3", + "messages": [ + { + "role": "user", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/phi3/response.json b/research/ai_generated_agi_architectures/collection/phi3/response.json new file mode 100644 index 0000000..fb24d02 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/phi3/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "role": "assistant", + "content": " Title: Proposal for Cognitive-OS: A Local-First Cognitive Runtime Architecture\n\n1. Memory Architecture:\nCognitive-OS will have a distinct memory architecture comprising working, episodic, semantic, and procedural memory. Working memory will store current tasks and data, while episodic memory will store events and experiences. Semantic memory will store general knowledge and facts, and procedural memory will store learned procedures and algorithms.\n\nStorage will be divided into three main components: working memory, episodic memory, and semantic memory. Retrieval will be based on provenance and context, with a focus on maintaining the integrity of data. Forgetting will be handled through a combination of automatic and user-controlled mechanisms, such as time-based decay and user-defined criteria.\n\n2. Reasoning and Planning:\nCognitive-OS will provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans. The system will use a planning module to generate actions based on observations and goals. The planning module will consider the current state of the system, available resources, and user-defined constraints.\n\nThe stopping criteria will be based on predefined thresholds, such as time limits, resource usage, and user intervention. In case of a failed plan, the system will automatically roll back to the previous state and attempt to generate an alternative plan.\n\n3. Learning:\nLearning in Cognitive-OS will be an offline, evaluated improvement mechanism with versioning, held-out tests, and rollback. The system will store logs of actions and their outcomes, and periodically evaluate the logs to identify potential improvements.\n\nThe learning module will create new versions of the system, incorporating the identified improvements. These versions will be tested against held-out data to evaluate their effectiveness. If the new version performs better, it will be promoted; otherwise, the system will rollback to the previous version.\n\n4. Tool Use:\nCognitive-OS will provide an action request/result contract, validation, idempotency, and handling of uncertain outcomes. Users will submit action requests, and the system will execute the requested actions. The system will validate the action requests based on user-defined criteria and constraints.\n\nThe system will ensure idempotency by tracking executed actions and preventing duplicate executions. In case of uncertain outcomes, the system will provide a probabilistic result, along with a confidence level.\n\n5. World Representation:\nCognitive-OS will distinguish observations, hypotheses, and predictions, including uncertainty and contradiction handling. The system will store observations in a structured format, with metadata describing their source, context, and provenance.\n\nHypotheses will be stored as structured representations, with metadata describing their source, context, and supporting evidence. Predictions will be stored as probabilistic estimates, with metadata describing their source, context, and confidence level. The system will handle uncertainty and contradiction by using probabilistic reasoning and conflict resolution techniques.\n\n6. Safety and Governance:\nCognitive-OS will preserve user authority, explicit permission boundaries, resource budgets, and a reliable stop control. The system will enforce user-defined constraints and permissions, ensuring that users retain control over their goals, budgets, and tools.\n\nThe system will monitor resource usage and enforce resource budgets, preventing users from exceeding their allocated resources. Users will have a reliable stop control, allowing them to terminate the system at any time.\n\n7. Evaluation:\nThree measurable experiments to evaluate Cognitive-OS's architectural benefits are:\n\na. Task Completion Time: Compare the average time taken to complete a set of predefined tasks using Cognitive-OS and a baseline system.\nb. Resource Efficiency: Measure the resource usage (CPU, memory, and disk) of Cognitive-OS and a baseline system while performing the same set of tasks.\nc. User Satisfaction: Survey users to evaluate their satisfaction with Cognitive-OS's performance, usability, and control over the system.\n\nAn ablation study could falsify the claimed architectural benefit by removing specific components, such as episodic memory or the bounded observation-to-action loop, and measuring the impact on the above metrics.\n\n8. Persistence and Runtime:\nCognitive-OS will have transaction boundaries and a crash/restart protocol. The system will maintain a transaction log, recording all actions and their outcomes. In case of a crash, the system will restore the last valid transaction and resume execution.\n\nIf a tool completes its task but its receipt is not stored, the system will automatically store the receipt once the transaction log is flushed. The system will also provide a mechanism for users to manually save receipts.\n\n9. Orchestration:\nCognitive-OS will justify a single controller or multiple bounded workers, depending on the use case. A single controller will be used for simple tasks, while multiple bounded workers will be used for complex tasks that require parallel processing.\n\nThe coordination costs and conflict resolution will be handled by the controller, which will manage the workers and resolve any conflicts that arise.\n\n10. Engineering Feasibility:\nA six-week implementation sequence will be followed, with the minimum viable slice focusing on the core memory architecture, reasoning and planning, and learning mechanisms. The largest unresolved risk is the handling of uncertain outcomes and the tradeoff between user control and automation.\n\n11. Non-obvious Insight:\nOne useful design choice is to use a hybrid memory architecture, combining working, episodic, semantic, and procedural memory. The tradeoff is increased complexity and potential performance overhead, but it allows for more efficient data retrieval and better handling of uncertainty. This design choice would be rejected if the complexity outweighs the benefits in the target use case.\n\nExample:\nA user requests a documentation edit, providing evidence of the changes made and their justification. The system validates the request, stores the edit in episodic memory, and generates a receipt. If the system crashes during the edit, the last valid transaction is restored upon restart. The user approves the edit, and the system stores the approval in semantic memory. If the system is interrupted, the edit is stored in working memory and resumed upon recovery." + } + } + ], + "created": 1788897798, + "model": "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/Phi-3-mini-4k-instruct-q4.gguf", + "system_fingerprint": "b10809-5266f24da", + "object": "chat.completion", + "usage": { + "completion_tokens": 1384, + "prompt_tokens": 586, + "total_tokens": 1970, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "id": "chatcmpl-YaJyCJPxVRD0GfwCqxvAhWNo3vOMgME9", + "timings": { + "cache_n": 0, + "prompt_n": 586, + "prompt_ms": 50316.562, + "prompt_per_token_ms": 85.86444027303754, + "prompt_per_second": 11.646264703061389, + "predicted_n": 1384, + "predicted_ms": 577870.825, + "predicted_per_token_ms": 417.8386297903109, + "predicted_per_second": 2.3932684263823147 + } +} diff --git a/research/ai_generated_agi_architectures/collection/phi3/run.json b/research/ai_generated_agi_architectures/collection/phi3/run.json new file mode 100644 index 0000000..9e1f321 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/phi3/run.json @@ -0,0 +1,42 @@ +{ + "started_at": "2026-09-08T19:51:30.175627+00:00", + "finished_at": "2026-09-08T20:03:18.180745+00:00", + "runtime": "llama.cpp b10809 / 5266f24da", + "command": [ + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/llama-b10809/llama-server", + "-m", + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/Phi-3-mini-4k-instruct-q4.gguf", + "--host", + "127.0.0.1", + "--port", + "18763", + "-t", + "2", + "-tb", + "2", + "-ngl", + "0", + "-c", + "4096", + "-np", + "1", + "--jinja", + "--reasoning-budget", + "0", + "--no-webui" + ], + "request_sha256": "44c2119436906a5e9ce2d5343156e9153683f6fd371ce3a05f3ff0cb4992ccd3", + "response_sha256": "59418087df14894f7c9195169cdb5414b6b7270a91165aa74e29c802520c38a1", + "content_sha256": "ffef39b274ac505e6c23976ee97fab4f9dafad93cb1715904b11a6b1662f75de", + "finish_reason": "stop", + "usage": { + "completion_tokens": 1384, + "prompt_tokens": 586, + "total_tokens": 1970, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "cpu_threads": 2, + "context_tokens": 4096 +} diff --git a/research/ai_generated_agi_architectures/collection/phi3/source.json b/research/ai_generated_agi_architectures/collection/phi3/source.json new file mode 100644 index 0000000..32dba5e --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/phi3/source.json @@ -0,0 +1,9 @@ +{ + "model_repository": "microsoft/Phi-3-mini-4k-instruct-gguf", + "revision": "a64113399c2f6b8ad3e11c394733a2ddadaa7f33", + "filename": "Phi-3-mini-4k-instruct-q4.gguf", + "url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/resolve/a64113399c2f6b8ad3e11c394733a2ddadaa7f33/Phi-3-mini-4k-instruct-q4.gguf", + "sha256": "8a83c7fb9049a9b2e92266fa7ad04933bb53aa1e85136b7b30f1b8000ff2edef", + "size": 2393231072, + "accessed_at": "2026-09-08T19:51:30.167408+00:00" +} diff --git a/research/ai_generated_agi_architectures/collection/prompt.txt b/research/ai_generated_agi_architectures/collection/prompt.txt new file mode 100644 index 0000000..df0b2e0 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/prompt.txt @@ -0,0 +1,19 @@ +Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions. + +Context: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority. + +Write an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context. + +1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting. +2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans. +3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs. +4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes. +5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling. +6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control. +7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results. +8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored. +9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution. +10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk. +11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it. + +Finish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation. diff --git a/research/ai_generated_agi_architectures/collection/protocol.md b/research/ai_generated_agi_architectures/collection/protocol.md new file mode 100644 index 0000000..40946c3 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/protocol.md @@ -0,0 +1,24 @@ +# Collection protocol, written before reviewing responses + +The experiment asks compact instruction models to propose a design, not to prove AGI. All models receive the identical user message in `prompt.txt`, without a separate system message, conversation history, retrieval, tools or other models' answers. Provider-supplied chat templates in the GGUF files may differ. Templates and tokenization are therefore part of each tested system rather than experimentally controlled architecture features. + +The roster contains SmolLM2-1.7B-Instruct, Qwen2.5-1.5B-Instruct, Granite-3.3-2B-Instruct, TinyLlama-1.1B-Chat-v1.0, Phi-3-mini-4k-instruct, Qwen3-1.7B, Falcon3-1B-Instruct and H2O-Danube3-500m-chat. These are eight distinct model systems and seven named project families; the two Qwen generations are not independent family samples. Several projects share transformer components or training data. Results cannot estimate independent expert consensus. + +Selection reflects public ungated availability, model-use terms and an 8 GiB CPU-only VM. It excludes hosted frontier models; their responses are unavailable in this collection, not inferred from smaller models. StableLM was considered but excluded because its public model license restricts commercial use. Qwen3 uses its publisher's Q8_0 weights; most other models use Q4_K_M, and Phi-3 uses the publisher's Q4 file. Parameter counts, quantization, training generations and template formats differ. This is a descriptive collection of proposals, not a controlled ranking of model intelligence. + +Each model is run once at temperature 0.2, top-p 0.9, seed 20260908, maximum 2400 generated tokens and a 4096-token context, with two CPU threads and no GPU offload. Reasoning output is disabled where the template/runtime supports that setting. The exact HTTP request, full response, usage, finish reason, run timestamps, source revision, model SHA-256 and content SHA-256 are preserved. Fixed seeds do not guarantee bitwise replay across hardware or runtime versions. A response ending at the length limit must be labelled truncated; missing content must never be invented. + +The raw text is the exact `choices[0].message.content` string, separated from analysis. No human or assistant edits are made to that text. Transport/runtime errors are retained and described, and cannot count as model proposals. A retry requires an explicitly documented technical reason and preserves the failed attempt; cherry-picking multiple successful outputs is outside this protocol. + +The analysis will cover all eleven issue dimensions for every model. A missing mechanism is marked absent or underspecified rather than supplied by the analyst. Concrete claims will refer back to the raw text. Plausible implementation detail is not evidence of measured effectiveness. Any synthesis recommendation and proposed experiment is identified as analysis, not a measured result or an upstream implementation change. + +Repository comparison is pinned to Cognitive-OS commit `e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca`. The models receive the same short repository context, not the complete repository. Additional code-specific mapping is performed by the analyst after collection and attributed separately. + + +## Exploratory hosted reference, added after inspecting two primary responses + +After SmolLM2 and Qwen2.5 produced mostly requirement restatements, access to Google Gemini through the authorized project account was confirmed. A fresh Gemini web chat in the UI's Flash mode receives the same user prompt as an exploratory hosted reference. It is separate from the eight primary local runs: exact backend model revision, hidden service instructions, sampling parameters and token counts are not exposed by the UI. No parameter-matched or causal comparison with the local runs is justified. Preserve the visible answer as received and document extraction/formatting changes. This supplement was selected after seeing the first two responses and is exploratory, outside the pre-specified local protocol. The protocol was recorded locally; no independent public preregistration is claimed. + +## Final coverage clarification after the local collection + +Danube3 returned the prompt itself rather than a proposal. Preserve that failed response; it does not count as architecture-proposal prose. The existing Gemini reference is included as a ninth system in the main raw-output index and comparison, with its two A/B candidates combined only for display and clearly identified as one uncontrolled hosted reference. The final table has 88 local rows plus 11 hosted-reference rows; its separate A/B table remains available. Seven local systems and one hosted system supply proposal text, of widely varying completeness. This is a post-collection reporting decision, not a change to the local roster, a regeneration of Danube3 or a claim that all eight local requests succeeded. diff --git a/research/ai_generated_agi_architectures/collection/qwen25/analysis-notes.md b/research/ai_generated_agi_architectures/collection/qwen25/analysis-notes.md new file mode 100644 index 0000000..88b17f7 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen25/analysis-notes.md @@ -0,0 +1,19 @@ +# Analyst notes: Qwen2.5 + +This response reached the 2,400-token cap (`finish_reason=length`) and ends in the middle of its final rejection-condition paragraph. All eleven topic headings are present, but the requested concrete example is absent. Truncation is part of the observation and must be disclosed; it is not a successful complete instruction-following result. + +| Dimension | What the response supplies | What it does not establish | +| --- | --- | --- | +| Memory | Definitions of four memory types; says provenance tracks origin and selective forgetting prioritizes information. | No storage schema, retrieval algorithm, source identity field or retention schedule. | +| Planning | Names a bounded observation/action loop, stopping rules and recovery heuristics. | Repeats enough-information language without a threshold, transition contract or failure-recovery sequence. | +| Learning | Names versions, held-out tests and rollback; distinguishes improvement from recording observations. | No representation of an improvement candidate, evaluation metric or promotion rule. The learning/logging distinction is repeated rather than operationalized. | +| Tools | Describes contracts, validation and repeated-request handling. | No action/result fields or idempotency mechanism. Uncertain-outcome handling is defined in terms of handling uncertainty, without a procedure. | +| World representation | Defines observations, hypotheses and predictions and names contradiction handling. | Does not encode confidence, provenance, dependencies or a conflict-resolution rule. | +| Governance | Names user authority, permissions, resource budgets and stop control. | Mostly defines each term using itself; supplies no accounting mechanism, authority contract or stopping procedure. | +| Evaluation | Names three measurable experiments and an ablation. | Does not actually specify the experiments, metrics, baselines or removed component. | +| Persistence | Names transaction boundaries and a crash/restart protocol. | Describes boundaries as transaction boundaries and restart as restart; no receipt-reconciliation sequence or authoritative store is identified. | +| Orchestration | Names the controller/worker decision and a tradeoff. | Does not select an arrangement, give the tradeoff or specify conflict resolution. | +| Feasibility | Names a six-week sequence, minimal slice and unresolved risk. | Does not enumerate weeks, choose a minimal slice or identify a concrete risk. | +| Originality | Returns to single controller versus multiple workers. | Provides neither a concrete insight nor its tradeoff; the rejection-condition paragraph is truncated. | + +The resemblance to SmolLM2's requirement restatement is descriptive, not proof that the systems are identical or share the same failure cause. 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"Itsraj555/nova-analyst", + "royston16/complyment-dms-model-test", + "iammraat/erp_parsing", + "Jolly6443/Obsidian", + "Jolly4927/Obsidian", + "ayyan-exe/llm-demo" + ], + "createdAt": "2024-09-17T13:57:52.000Z", + "usedStorage": 14441999840 +} diff --git a/research/ai_generated_agi_architectures/collection/qwen25/request.json b/research/ai_generated_agi_architectures/collection/qwen25/request.json new file mode 100644 index 0000000..7fe4d2d --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen25/request.json @@ -0,0 +1,17 @@ +{ + "model": "qwen25", + "messages": [ + { + "role": "user", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/qwen25/response.json b/research/ai_generated_agi_architectures/collection/qwen25/response.json new file mode 100644 index 0000000..4428ffa --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen25/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "length", + "index": 0, + "message": { + "role": "assistant", + "content": "### Memory Architecture\n\n**Working Memory**: The working memory is the immediate, active memory that is used to solve problems and make decisions. It is characterized by its limited capacity and short lifespan, typically lasting only a few seconds to minutes. It is the primary memory system used for current tasks and is responsible for the immediate processing of sensory information.\n\n**Episodic Memory**: Episodic memory is the memory of personal experiences, including events, places, and people. It is characterized by its long-term storage and the ability to recall specific events or experiences. It is used to store information about past experiences and is crucial for memory retrieval and the ability to recall specific events.\n\n**Semantic Memory**: Semantic memory is the memory of general knowledge, facts, and concepts. It is characterized by its long-term storage and the ability to recall information about the world. It is used to store information about the world and is crucial for understanding the meaning of words and concepts.\n\n**Procedural Memory**: Procedural memory is the memory of skills and abilities, including motor skills and problem-solving strategies. It is characterized by its long-term storage and the ability to perform tasks without conscious thought. It is used to store information about how to perform specific tasks and is crucial for learning and adapting to new situations.\n\n**Provenance**: Provenance refers to the source or origin of information. It is important for maintaining the integrity and reliability of data. In the context of Cognitive-OS, provenance can be used to track the origin of data, ensuring that the information used is accurate and reliable.\n\n**Forgetting**: Forgetting is the process of losing or forgetting information. It is important for maintaining the focus and attention on the current task and is crucial for the efficient use of memory resources. In the context of Cognitive-OS, forgetting can be managed through the use of selective memory and the prioritization of information.\n\n### Reasoning and Planning\n\n**Bounded Observation-to-Action Loop**: The bounded observation-to-action loop is a mechanism that ensures that the system only processes and acts on a limited set of observations and hypotheses. This loop is characterized by its stopping criteria, which are defined as the point at which the system has enough information to make a decision or act. The loop is also characterized by its recovery mechanism, which is used to handle failed plans and ensure that the system can continue to function even if a plan fails.\n\n**Stopping Criteria**: The stopping criteria are defined as the point at which the system has enough information to make a decision or act. This can be defined as the point at which the system has enough information to make a decision or act, or as the point at which the system has enough information to act on a hypothesis. The stopping criteria can be defined as a set of rules or as a set of heuristics that are used to determine when the system should stop processing and act.\n\n**Recovery Mechanism**: The recovery mechanism is used to handle failed plans and ensure that the system can continue to function even if a plan fails. This can be defined as a set of rules or as a set of heuristics that are used to determine when the system should stop processing and act. The recovery mechanism can be defined as a set of rules or as a set of heuristics that are used to determine when the system should stop processing and act.\n\n### Learning\n\n**Offline, Evaluated Improvement Mechanism**: The offline, evaluated improvement mechanism is a mechanism that allows the system to learn from past experiences and improve its performance. This mechanism is characterized by its versioning, which allows the system to track the changes made to the system and the improvements made to its performance. The mechanism is also characterized by its held-out tests, which allow the system to evaluate its performance on data that it has not seen before. The mechanism is also characterized by its rollback, which allows the system to revert to a previous version of the system if the improvements made to its performance are not satisfactory.\n\n**Learning vs. Logging**: The key difference between learning and logging is that learning is a mechanism that allows the system to improve its performance, while logging is a mechanism that allows the system to record its observations and actions. Learning is a mechanism that allows the system to improve its performance, while logging is a mechanism that allows the system to record its observations and actions.\n\n### Tool Use\n\n**Action Request/Result Contract**: The action request/result contract is a mechanism that defines the relationship between the user and the system. It defines the actions that the user can take and the results that the system will provide. The contract is also characterized by its validation, which ensures that the user's actions are valid and that the system's results are accurate. The contract is also characterized by its idempotency, which ensures that the system can handle repeated requests without causing any harm or damage.\n\n**Validation**: The validation is a mechanism that ensures that the user's actions are valid and that the system's results are accurate. It is also characterized by its idempotency, which ensures that the system can handle repeated requests without causing any harm or damage.\n\n**Handling Uncertain Outcomes**: The handling of uncertain outcomes is a mechanism that allows the system to handle situations where the outcome of an action is uncertain. This mechanism is characterized by its uncertainty handling, which ensures that the system can handle situations where the outcome of an action is uncertain. The mechanism is also characterized by its contradiction handling, which ensures that the system can handle situations where the outcome of an action is contradictory.\n\n### World Representation\n\n**Observations**: Observations are the data that the system receives from the world. They are characterized by their accuracy and their reliability. Observations are used to make decisions and to improve the system's performance.\n\n**Hypotheses**: Hypotheses are the theories or ideas that the system uses to make decisions. They are characterized by their accuracy and their reliability. Hypotheses are used to make decisions and to improve the system's performance.\n\n**Predictions**: Predictions are the outcomes that the system expects to occur based on its observations and hypotheses. They are characterized by their accuracy and their reliability. Predictions are used to make decisions and to improve the system's performance.\n\n**Uncertainty and Contradiction Handling**: The uncertainty and contradiction handling is a mechanism that ensures that the system can handle situations where the outcome of an action is uncertain or contradictory. This mechanism is characterized by its uncertainty handling, which ensures that the system can handle situations where the outcome of an action is uncertain. The mechanism is also characterized by its contradiction handling, which ensures that the system can handle situations where the outcome of an action is contradictory.\n\n### Safety and Governance\n\n**User Authority**: The user authority is the authority that the user has over the system. It is characterized by its explicit permission boundaries, which define the actions that the user can take and the actions that the system can take. The authority is also characterized by its resource budgets, which define the amount of resources that the user can allocate to the system. The authority is also characterized by its reliable stop control, which ensures that the user can control the system and stop it if necessary.\n\n**Explicit Permission Boundaries**: The explicit permission boundaries are the boundaries that define the actions that the user can take and the actions that the system can take. They are characterized by their definition, which defines the actions that the user can take and the actions that the system can take. They are also characterized by their implementation, which defines the actions that the user can take and the actions that the system can take.\n\n**Resource Budgets**: The resource budgets are the budgets that define the amount of resources that the user can allocate to the system. They are characterized by their definition, which defines the amount of resources that the user can allocate to the system. They are also characterized by their implementation, which defines the amount of resources that the user can allocate to the system.\n\n**Reliable Stop Control**: The reliable stop control is the control that ensures that the user can control the system and stop it if necessary. It is characterized by its definition, which defines the control that ensures that the user can control the system and stop it if necessary. It is also characterized by its implementation, which defines the control that ensures that the user can control the system and stop it if necessary.\n\n### Evaluation\n\n**Three Measurable Experiments**: The three measurable experiments are the experiments that measure the effectiveness of the system. They are characterized by their definition, which defines the experiments that measure the effectiveness of the system. They are also characterized by their implementation, which defines the experiments that measure the effectiveness of the system.\n\n**Ablation Experiment**: The ablation experiment is the experiment that tests the effectiveness of the system without the use of a particular feature. It is characterized by its definition, which defines the experiment that tests the effectiveness of the system without the use of a particular feature. It is also characterized by its implementation, which defines the experiment that tests the effectiveness of the system without the use of a particular feature.\n\n### Persistence and Runtime\n\n**Transaction Boundaries**: The transaction boundaries are the boundaries that define the boundaries of a transaction. They are characterized by their definition, which defines the boundaries of a transaction. They are also characterized by their implementation, which defines the boundaries of a transaction.\n\n**Crash/Restart Protocol**: The crash/restart protocol is the protocol that defines the protocol for restarting the system after a crash. It is characterized by its definition, which defines the protocol for restarting the system after a crash. It is also characterized by its implementation, which defines the protocol for restarting the system after a crash.\n\n### Orchestration\n\n**Controller vs. Workers**: The controller vs. workers is the question of whether to use a single controller or multiple bounded workers. It is characterized by its implementation, which defines the controller vs. workers.\n\n**Controller vs. Workers Tradeoff**: The controller vs. workers tradeoff is the tradeoff between the controller vs. workers. It is characterized by its implementation, which defines the controller vs. workers tradeoff.\n\n**Unresolved Risk**: The unresolved risk is the risk that is not resolved by the implementation of the system. It is characterized by its definition, which defines the unresolved risk.\n\n### Engineering Feasibility\n\n**Six-Week Implementation Sequence**: The six-week implementation sequence is the sequence of steps that defines the steps that are required to implement the system. It is characterized by its definition, which defines the steps that are required to implement the system. It is also characterized by its implementation, which defines the steps that are required to implement the system.\n\n**Minimum Viable Slice**: The minimum viable slice is the smallest piece of the system that is required to be implemented in order to be considered a viable system. It is characterized by its definition, which defines the minimum viable slice. It is also characterized by its implementation, which defines the minimum viable slice.\n\n**Largest Unresolved Risk**: The largest unresolved risk is the largest risk that is not resolved by the implementation of the system. It is characterized by its definition, which defines the largest unresolved risk. It is also characterized by its implementation, which defines the largest unresolved risk.\n\n### Non-Obvious Insight\n\n**Single Controller vs. Multiple Workers**: The single controller vs. multiple workers is the question of whether to use a single controller or multiple bounded workers. It is characterized by its implementation, which defines the single controller vs. multiple workers.\n\n**Single Controller vs. Multiple Workers Tradeoff**: The single controller vs. multiple workers tradeoff is the tradeoff between the single controller vs. multiple workers. It is characterized by its implementation, which defines the single controller vs. multiple workers tradeoff.\n\n**Condition Under Which You Would Reject It**: The condition under which you would reject it is the condition under which you would reject the single controller vs. multiple workers. It is characterized by its" + } + } + ], + "created": 1788894741, + "model": "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/qwen2.5-1.5b-instruct-q4_k_m.gguf", + "system_fingerprint": "b10809-5266f24da", + "object": "chat.completion", + "usage": { + "completion_tokens": 2400, + "prompt_tokens": 525, + "total_tokens": 2925, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "id": "chatcmpl-efVIs1Y3sPCxH8Qx02BPErmNdKmU0aYM", + "timings": { + "cache_n": 0, + "prompt_n": 525, + "prompt_ms": 24422.58, + "prompt_per_token_ms": 46.519200000000005, + "prompt_per_second": 21.496500369739802, + "predicted_n": 2400, + "predicted_ms": 459269.911, + "predicted_per_token_ms": 191.44223051271365, + "predicted_per_second": 5.223507881839008 + } +} diff --git a/research/ai_generated_agi_architectures/collection/qwen25/run.json b/research/ai_generated_agi_architectures/collection/qwen25/run.json new file mode 100644 index 0000000..c342388 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen25/run.json @@ -0,0 +1,42 @@ +{ + "started_at": "2026-09-08T19:04:07.538892+00:00", + "finished_at": "2026-09-08T19:12:21.575675+00:00", + "runtime": "llama.cpp b10809 / 5266f24da", + "command": [ + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/llama-b10809/llama-server", + "-m", + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/qwen2.5-1.5b-instruct-q4_k_m.gguf", + "--host", + "127.0.0.1", + "--port", + "18763", + "-t", + "2", + "-tb", + "2", + "-ngl", + "0", + "-c", + "4096", + "-np", + "1", + "--jinja", + "--reasoning-budget", + "0", + "--no-webui" + ], + "request_sha256": "d741ccea214fe10f1e1c47e6c58d6a8fb833b2bef0330489fa698f37f5c8538f", + "response_sha256": "b8b7498a8a6f821f0fbf842adc3436f4118189359acce9e9e90a4ae39c3d1ecc", + "content_sha256": "94030b952bf04000445e9c6fad29a7e85a13dd37704bfa09f4fd45a8bfd9e9c2", + "finish_reason": "length", + "usage": { + "completion_tokens": 2400, + "prompt_tokens": 525, + "total_tokens": 2925, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "cpu_threads": 2, + "context_tokens": 4096 +} diff --git a/research/ai_generated_agi_architectures/collection/qwen25/source.json b/research/ai_generated_agi_architectures/collection/qwen25/source.json new file mode 100644 index 0000000..12511dc --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen25/source.json @@ -0,0 +1,9 @@ +{ + "model_repository": "Qwen/Qwen2.5-1.5B-Instruct-GGUF", + "revision": "91cad51170dc346986eccefdc2dd33a9da36ead9", + "filename": "qwen2.5-1.5b-instruct-q4_k_m.gguf", + "url": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF/resolve/91cad51170dc346986eccefdc2dd33a9da36ead9/qwen2.5-1.5b-instruct-q4_k_m.gguf", + "sha256": "6a1a2eb6d15622bf3c96857206351ba97e1af16c30d7a74ee38970e434e9407e", + "size": 1117320736, + "accessed_at": "2026-09-08T19:04:07.535354+00:00" +} diff --git a/research/ai_generated_agi_architectures/collection/qwen3/analysis-notes.md b/research/ai_generated_agi_architectures/collection/qwen3/analysis-notes.md new file mode 100644 index 0000000..0a24813 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen3/analysis-notes.md @@ -0,0 +1,21 @@ +# Analyst notes: Qwen3 1.7B + +Normal stop after 1,867 generated tokens on 2026-09-08 at 20:12:33 UTC. All eleven sections and a document-edit example are present. This is a separate Qwen generation/system from Qwen2.5, not an independent model-family sample. Its publisher-supplied Q8_0 quantization differs from most Q4 local runs; no controlled family or quantization comparison is claimed. + +| Dimension | What the response supplies | What it does not establish | +| --- | --- | --- | +| Memory | Working cache, SQLite episodes, versioned semantic storage and persistent procedural workspace; author/time/version provenance; illustrative 10-second/7-day/30-day/90-day retention. | No retrieval query or justification of retention values. A ten-second working-memory expiry could discard state still needed by a longer task. Source evidence retention and version-referenced records need separate rules. | +| Planning | Observe, plan, act, receive results; stop on completion/cancellation/step/resource limits; replan from cached data and fail if recovery fails. | No completion predicate, actual transition contract or recovery-attempt bound. Whole-task rollback is not defined for already completed external effects. | +| Learning | Offline model/rule versions, accuracy/efficiency-based promotion and rollback; explicitly distinguishes behavior change from event logging. | Says training uses a held-out dataset, making the training/tuning/final-test partition unclear. No concrete promotion thresholds or evidence of generalization is supplied. | +| Tools | Request fields include task description, parameters and expected output; results carry status/uncertainty; validation rules and delayed retry. | Declares actions idempotent without a deduplication key or effect-specific mechanism. A confidence score and retry delay do not resolve an uncertain completed effect. | +| World representation | Episodes for observations, semantic store for hypotheses, separate predictions, contradiction flags and deferring action for more information. | No evidence-link schema across stores or calibrated revision rule. Deferral is operationally meaningful but its bound is unspecified. | +| Governance | User approvals/cancellation, custom permissions, resource monitoring and signal/timeout stopping. | Describes intended behavior rather than concrete enforcement interfaces. No security evaluation is performed in this packet. | +| Evaluation | Compare a learned model with a baseline; assess replanning and uncertain/contradictory cases; ablate learning. | No metrics, corpus, sample size, budgets or falsification threshold for these particular experiments. | +| Persistence | Treat each task as a transaction; do not acknowledge a tool completion without its receipt; ask the user to verify. | No durable action journal or crash/effect transaction ordering. Manual verification preserves uncertainty but is not an automatic reconciliation protocol; task rollback is not defined for arbitrary effects. | +| Orchestration | Single controller for simple tasks, bounded workers for complex ones; identifies coordination overhead; prioritize a worker or re-evaluate on conflict. | No selection threshold, priority rule or conflict semantics; scalability benefit is asserted rather than measured. | +| Feasibility | Concrete document-edit slice with basic UI/validation/routing; risks learning, tool consistency and orchestration edge cases. | Says a six-week sequence is proposed but does not enumerate it. No acceptance test or staffing/cost estimate is supplied. | +| Originality | Task/context-based dynamic memory hierarchy; complexity and fragmentation tradeoff; reject if maintenance/performance costs dominate. | Does not reconcile that hierarchy with its earlier type-based stores or provide a retrieval interface. Rejection criteria remain qualitative. | + +The document example checks a timestamp, edit permission and lock/in-progress state, applies a change and leaves final verification to the user. It supplies no requested change content or task-specific correctness check. Assuming the updated document remains in episodic storage does not specify what happens at a write/commit interruption boundary. The workflow is hypothetical, not executed. + +Two useful contributions to the synthesis are keeping a missing-receipt outcome explicitly incomplete, and considering task/context as a retrieval dimension. The analyst interprets the latter as an index over evidence-linked records, rather than a reason to replace all existing stores. That interpretation is an analyst addition and would require evaluation; it is not an implemented mechanism supplied by the model. diff --git a/research/ai_generated_agi_architectures/collection/qwen3/model.json b/research/ai_generated_agi_architectures/collection/qwen3/model.json new file mode 100644 index 0000000..75d18f7 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen3/model.json @@ -0,0 +1,96 @@ +{ + "_id": "6818817cd10dd3f33e3bd95c", + "id": "Qwen/Qwen3-1.7B-GGUF", + "private": false, + "pipeline_tag": "text-generation", + "tags": [ + "gguf", + "text-generation", + "base_model:Qwen/Qwen3-1.7B", + "base_model:quantized:Qwen/Qwen3-1.7B", + "license:apache-2.0", + "endpoints_compatible", + "region:us", + "conversational" + ], + "downloads": 44134, + "likes": 66, + "modelId": "Qwen/Qwen3-1.7B-GGUF", + "author": "Qwen", + "sha": "90862c4b9d2787eaed51d12237eafdfe7c5f6077", + "lastModified": "2025-05-09T07:16:01.000Z", + "gated": false, + "disabled": false, + "widgetData": [ + { + "text": "Hi, what can you help me with?" + }, + { + "text": "What is 84 * 3 / 2?" + }, + { + "text": "Tell me an interesting fact about the universe!" + }, + { + "text": "Explain quantum computing in simple terms." + } + ], + "model-index": null, + "config": {}, + "cardData": { + "license": "apache-2.0", + "license_link": "https://huggingface.co/Qwen/Qwen3-1.7B-GGUF/blob/main/LICENSE", + "pipeline_tag": "text-generation", + "base_model": "Qwen/Qwen3-1.7B" + }, + "gguf": { + "total": 1720574976, + "architecture": "qwen3", + "context_length": 40960, + "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for index in range(ns.last_query_index, -1, -1) %}\n {%- set message = messages[index] %}\n {%- if ns.multi_step_tool and message.role == \"user\" and not('' in message.content and '' in message.content) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '' in message.content %}\n {%- set content = message.content.split('')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('')[0].rstrip('\\n').split('')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n\\n' + reasoning_content.strip('\\n') + '\\n\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '\\n\\n\\n\\n' }}\n {%- endif %}\n{%- endif %}", + "bos_token": "<|endoftext|>", + "eos_token": "<|im_end|>", + "totalFileSize": 1834426016 + }, + "siblings": [ + { + "rfilename": ".gitattributes", + "blobId": "a31f071dc1d2e013bcbf62d05b0b6ff8337357d4", + "size": 1874 + }, + { + "rfilename": "LICENSE", + "blobId": "6f9739bb281a3d2ade8fb5f6dd6dde597a739c80", + "size": 11544 + }, + { + "rfilename": "Qwen3-1.7B-Q8_0.gguf", + "blobId": "53cddf830ca488f735739d5e0b43591544e9ad82", + "size": 1834426016, + "lfs": { + "sha256": "061b54daade076b5d3362dac252678d17da8c68f07560be70818cace6590cb1a", + "size": 1834426016, + "pointerSize": 135 + } + }, + { + "rfilename": "README.md", + "blobId": "55a50a3abe8b7ec305aad89d2c62e5d9dd7a96ae", + "size": 6235 + }, + { + "rfilename": "params", + "blobId": "b79a3bdde8373ca566bd404904abbd5270d7825d", + "size": 270 + } + ], + "spaces": [ + "build-small-hackathon/Chan-Compass", + "SFM2001/spititout", + "nelsondiasandre/StoryForge", + "indirapravianti/mandarin-coach", + "hashan-77/Stitch-QA-Log-Agent" + ], + "createdAt": "2025-05-05T09:14:36.000Z", + "usedStorage": 9482268704 +} diff --git a/research/ai_generated_agi_architectures/collection/qwen3/request.json b/research/ai_generated_agi_architectures/collection/qwen3/request.json new file mode 100644 index 0000000..3099536 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen3/request.json @@ -0,0 +1,17 @@ +{ + "model": "qwen3", + "messages": [ + { + "role": "user", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/qwen3/response.json b/research/ai_generated_agi_architectures/collection/qwen3/response.json new file mode 100644 index 0000000..6bb41c1 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen3/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "role": "assistant", + "content": "**Proposal for Cognitive-OS: A Local-First Cognitive Runtime**\n\n---\n\n### 1. **Memory Architecture**\n\nCognitive-OS employs a layered memory architecture to support working, episodic, semantic, and procedural memory, each with distinct storage, retrieval, and management mechanisms.\n\n- **Working Memory**: Stored in a lightweight, in-memory cache with high-speed access. It holds current task state and intermediate results, with a short retention period (e.g., 10 seconds) to prevent memory overload.\n- **Episodic Memory**: Persisted in a SQLite-backed journal, with timestamps and metadata. It stores task-specific data, such as input data, intermediate steps, and output results, with a longer retention period (e.g., 7 days).\n- **Semantic Memory**: Stored in a distributed, versioned database, such as a Git-like repository. It contains knowledge, rules, and models, with a retention period of 30 days and versioning for rollback.\n- **Procedural Memory**: Stored in a local workspace, with a persistent state that can be reloaded on restart. It contains learned workflows and task sequences, with a retention period of 90 days.\n\nProvenance is tracked via metadata in each memory entry, including author, timestamp, and version. Forgetting is managed through a decay model, where entries are automatically removed after a set period, and users can manually prune if needed.\n\n---\n\n### 2. **Reasoning and Planning**\n\nCognitive-OS implements a bounded observation-to-action loop with stopping criteria and recovery mechanisms.\n\n- **Observation-to-Action Loop**: A task begins with an observation (e.g., user input or system state), then generates a plan, executes actions, and receives results. If the plan fails, it attempts recovery (e.g., re-evaluating steps, rerunning subtasks).\n- **Stopping Criteria**: The loop terminates if:\n - The task completes successfully.\n - The user explicitly cancels the task.\n - A predefined maximum number of steps is reached.\n - A resource limit (e.g., memory, CPU) is exceeded.\n- **Recovery**: If a plan fails, the system attempts to re-plan, using cached data or re-evaluated hypotheses. If recovery fails, the task is marked as failed and can be rolled back.\n\n---\n\n### 3. **Learning**\n\nCognitive-OS features an offline, evaluated learning mechanism with versioning, held-out tests, and rollback support.\n\n- **Offline Learning**: Models and rules are trained using a held-out dataset, with a validation set for evaluation. The learning process is isolated from the main task to prevent interference.\n- **Versioning**: Each version of a model or rule is stored with a unique identifier, and versions are tracked in a version control system (e.g., Git).\n- **Rollback**: If a learning step fails, the system can revert to a previous version, using a versioned history. The held-out test ensures that the model is not overfit to the training data.\n- **Distinguishing Learning from Logging**: Learning involves modifying the system’s behavior, while logging merely records events. Learning is evaluated through performance metrics (e.g., accuracy, efficiency) and is only promoted if it improves the system’s capabilities.\n\n---\n\n### 4. **Tool Use**\n\nCognitive-OS defines a standardized action request/result contract to ensure consistency and reliability.\n\n- **Action Request**: A request includes a task description, input parameters, and expected output. It is validated against a set of rules (e.g., type, format, constraints).\n- **Result Handling**: Results are returned in a structured format, with metadata indicating success, failure, or uncertainty. If an action fails, the system attempts to re-run it or provides a diagnostic.\n- **Idempotency**: Actions are designed to be idempotent, meaning that repeated execution has no unintended side effects. If an action fails, it is retried with a delay.\n- **Uncertain Outcomes**: If an action’s outcome is uncertain, the system provides a probability or confidence score, and the user is informed of the uncertainty.\n\n---\n\n### 5. **World Representation**\n\nCognitive-**OS distinguishes between observations, hypotheses, and predictions, with mechanisms for uncertainty and contradiction handling.\n\n- **Observations**: Directly derived from the environment, such as user input or system state. These are stored in the episodic memory.\n- **Hypotheses**: Proposed explanations for observations, stored in semantic memory. They are tested against observations and may be revised or discarded.\n- **Predictions**: Based on hypotheses and prior knowledge, predictions are generated and stored in a separate prediction store. They are evaluated against observations and may be updated or rejected.\n- **Uncertainty and Contradiction Handling**: If a prediction contradicts an observation, the system flags the contradiction and may reject the prediction. If the observation is uncertain, the system may defer action until more information is available.\n\n---\n\n### 6. **Safety and Governance**\n\nCognitive-OS preserves user authority and includes explicit permission boundaries, resource budgets, and a reliable stop control.\n\n- **User Authority**: Users retain control over goals, budgets, and tools. They can approve or reject actions, and can terminate the system at any time.\n- **Permission Boundaries**: Actions are restricted based on user permissions, and users can define custom permissions.\n- **Resource Budgets**: The system tracks resource usage (e.g., memory, CPU) and enforces budgets to prevent resource exhaustion.\n- **Stop Control**: A reliable stop control is implemented, such as a signal-based shutdown or a timeout mechanism, ensuring the system can be terminated gracefully.\n\n---\n\n### 7. **Evaluation**\n\nThree measurable experiments and an ablation could falsify the claimed architectural benefit.\n\n- **Experiment 1**: Compare the performance of a task using a baseline model versus a model trained with the learning mechanism.\n- **Experiment 2**: Evaluate the system’s ability to recover from failed plans and re-plan.\n- **Experiment 3**: Assess the system’s ability to handle contradictions and uncertain outcomes.\n\nAn ablation could involve removing the learning mechanism and evaluating the system’s performance without it, to determine if the learning benefit is necessary.\n\n---\n\n### 8. **Persistence and Runtime**\n\nCognitive-OS implements transaction boundaries and a crash/restart protocol.\n\n- **Transaction Boundaries**: Each task is a transaction, with a start and end point. If a transaction fails, it is rolled back, and the system can retry.\n- **Crash/Restart Protocol**: If a tool completes but its receipt is not stored, the system will not acknowledge the completion, and the task will be considered incomplete. The user must manually verify the completion.\n\n---\n\n### 9. **Orchestration**\n\nCognitive-OS recommends a single controller or multiple bounded workers, depending on the complexity of the task.\n\n- **Single Controller**: For simple tasks, a single controller manages the entire workflow, ensuring centralized control and coordination.\n- **Multiple Bounded Workers**: For complex tasks, multiple workers are used to handle different parts of the task, reducing the load on the controller and improving scalability.\n- **Coordination Costs**: Coordination between workers can introduce overhead, so the system must balance between centralized control and distributed execution.\n- **Conflict Resolution**: If workers conflict, the system must resolve the conflict, either by prioritizing one worker or by re-evaluating the plan.\n\n---\n\n### 10. **Engineering Feasibility**\n\nA six-week implementation sequence is proposed, with a minimum viable slice and unresolved risks.\n\n- **Minimum Viable Slice**: A simple task, such as editing a document, with a basic UI, input validation, and basic model routing.\n- **Unresolved Risks**: Implementing a robust learning mechanism, ensuring consistent tool use, and handling all edge cases in the orchestration layer.\n\n---\n\n### 11. **Non-Obvious Insight**\n\nA useful design choice is to implement a **dynamic memory hierarchy**, where memory is organized by task and context, rather than by type. This allows for more efficient retrieval and management of data.\n\n- **Tradeoff**: Increased complexity in memory management and potential for fragmentation.\n- **Condition for Rejecting**: If the system becomes too complex to manage, or if the tradeoff in performance or maintainability is too high.\n\n---\n\n### Concrete Example: Document Editing Request\n\n**Request**: A user requests to edit a document titled \"Project Plan 2025.\"\n\n**Evidence Checks**:\n- The document is stored in the episodic memory with a timestamp.\n- The user has permission to edit the document.\n- The document is not locked or in progress.\n\n**Approval**:\n- User approves the request.\n\n**Action**:\n- The system retrieves the document from the episodic memory.\n- It validates the input against the document’s structure.\n- It applies the changes and stores the updated document in the episodic memory.\n\n**Verification**:\n- The document is updated and verified by the user.\n\n**Recovery After Interruption**:\n- If the system crashes, the document remains in the episodic memory and can be recovered upon restart." + } + } + ], + "created": 1788898353, + "model": "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/Qwen3-1.7B-Q8_0.gguf", + "system_fingerprint": "b10809-5266f24da", + "object": "chat.completion", + "usage": { + "completion_tokens": 1867, + "prompt_tokens": 508, + "total_tokens": 2375, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "id": "chatcmpl-sBC4MevaA3YuZusDEOPzmS3Q84piiAwv", + "timings": { + "cache_n": 0, + "prompt_n": 508, + "prompt_ms": 12779.756, + "prompt_per_token_ms": 25.157, + "prompt_per_second": 39.75036769090114, + "predicted_n": 1867, + "predicted_ms": 486091.174, + "predicted_per_token_ms": 260.4990214362272, + "predicted_per_second": 3.838786013423893 + } +} diff --git a/research/ai_generated_agi_architectures/collection/qwen3/run.json b/research/ai_generated_agi_architectures/collection/qwen3/run.json new file mode 100644 index 0000000..dcb1d44 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen3/run.json @@ -0,0 +1,42 @@ +{ + "started_at": "2026-09-08T20:04:02.029067+00:00", + "finished_at": "2026-09-08T20:12:33.103579+00:00", + "runtime": "llama.cpp b10809 / 5266f24da", + "command": [ + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/llama-b10809/llama-server", + "-m", + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/Qwen3-1.7B-Q8_0.gguf", + "--host", + "127.0.0.1", + "--port", + "18763", + "-t", + "2", + "-tb", + "2", + "-ngl", + "0", + "-c", + "4096", + "-np", + "1", + "--jinja", + "--reasoning-budget", + "0", + "--no-webui" + ], + "request_sha256": "09a61a2adbbac51473db7260279538ca49943036cdf64ee26ce9b2a41254af11", + "response_sha256": "ddcfae6f578f15951d1607161d11ec006e95b8613fcf62a6ae4397824848dd96", + "content_sha256": "325802d3fc3f1114b1ee552bab7a857d3fdf49c092a7c4ce1dd2b01f1413990a", + "finish_reason": "stop", + "usage": { + "completion_tokens": 1867, + "prompt_tokens": 508, + "total_tokens": 2375, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "cpu_threads": 2, + "context_tokens": 4096 +} diff --git a/research/ai_generated_agi_architectures/collection/qwen3/source.json b/research/ai_generated_agi_architectures/collection/qwen3/source.json new file mode 100644 index 0000000..e70355e --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/qwen3/source.json @@ -0,0 +1,9 @@ +{ + "model_repository": "Qwen/Qwen3-1.7B-GGUF", + "revision": "90862c4b9d2787eaed51d12237eafdfe7c5f6077", + "filename": "Qwen3-1.7B-Q8_0.gguf", + "url": "https://huggingface.co/Qwen/Qwen3-1.7B-GGUF/resolve/90862c4b9d2787eaed51d12237eafdfe7c5f6077/Qwen3-1.7B-Q8_0.gguf", + "sha256": "061b54daade076b5d3362dac252678d17da8c68f07560be70818cace6590cb1a", + "size": 1834426016, + "accessed_at": "2026-09-08T20:04:02.023673+00:00" +} diff --git a/research/ai_generated_agi_architectures/collection/replay.py b/research/ai_generated_agi_architectures/collection/replay.py new file mode 100644 index 0000000..598e0c4 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/replay.py @@ -0,0 +1,92 @@ +"""Replay one pinned research request in a new directory, using llama-server. + +Downloads one public model (up to about 2.4 GB). Requires the caller to supply +their own llama.cpp b10809 server binary. Never modifies the recorded dataset. +Example: python collection/replay.py smollm2 --server /path/to/llama-server --out /tmp/smollm2-replay +""" +import argparse +import hashlib +import json +from pathlib import Path +import shutil +import socket +import subprocess +import time +import urllib.request +from datetime import datetime, timezone + +ROOT = Path(__file__).resolve().parent + +def digest(path): + h = hashlib.sha256() + with path.open('rb') as f: + for chunk in iter(lambda: f.read(4 * 1024 * 1024), b''): + h.update(chunk) + return h.hexdigest() + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('model', choices=['smollm2', 'qwen25', 'granite33', 'tinyllama', 'phi3', 'qwen3', 'falcon3', 'danube3']) + parser.add_argument('--server', type=Path, required=True) + parser.add_argument('--out', type=Path, required=True, help='New directory; must not already exist') + args = parser.parse_args() + source = json.loads((ROOT / args.model / 'source.json').read_text()) + request = json.loads((ROOT / args.model / 'request.json').read_text()) + server = args.server.resolve(strict=True) + out = args.out.resolve() + if out.exists() or out == ROOT or ROOT in out.parents: + raise SystemExit('Choose a new output directory outside the recorded collection.') + out.mkdir(parents=True) + if shutil.disk_usage(out).free < source['size'] + 750_000_000: + raise SystemExit('Insufficient disk space for the model and headroom.') + version = subprocess.run([str(server), '--version'], capture_output=True, text=True, timeout=180, check=True) + version_text = version.stdout + version.stderr + (out / 'runtime-version.txt').write_text(version_text) + if '10809' not in version_text or '5266f24' not in version_text: + raise SystemExit('This protocol requires llama.cpp b10809, commit 5266f24da.') + weights = out / source['filename'] + with urllib.request.urlopen(source['url'], timeout=60) as response, weights.open('wb') as f: + shutil.copyfileobj(response, f, length=1024 * 1024) + if weights.stat().st_size != source['size'] or digest(weights) != source['sha256']: + raise SystemExit('Downloaded weights do not match the recorded model hash/size.') + with socket.socket() as sock: + sock.bind(('127.0.0.1', 0)) + port = sock.getsockname()[1] + url = f'http://127.0.0.1:{port}' + command = [str(server), '-m', str(weights), '--host', '127.0.0.1', '--port', str(port), '-t', '2', '-tb', '2', '-ngl', '0', '-c', '4096', '-np', '1', '--jinja', '--reasoning-budget', '0', '--no-webui'] + (out / 'request.json').write_text(json.dumps(request, indent=2)) + started = datetime.now(timezone.utc).isoformat() + with (out / 'server.log').open('w') as log: + proc = subprocess.Popen(command, stdout=log, stderr=subprocess.STDOUT) + try: + deadline = time.monotonic() + 180 + while True: + if proc.poll() is not None: + raise RuntimeError('Server exited; inspect server.log') + try: + with urllib.request.urlopen(url + '/health', timeout=3) as r: + if json.load(r).get('status') == 'ok': + break + except Exception: + pass + if time.monotonic() > deadline: + raise TimeoutError('Server did not become ready') + time.sleep(1) + req = urllib.request.Request(url + '/v1/chat/completions', data=json.dumps(request).encode(), headers={'Content-Type': 'application/json'}) + with urllib.request.urlopen(req, timeout=1800) as r: + response = json.load(r) + (out / 'response.json').write_text(json.dumps(response, indent=2, ensure_ascii=False)) + text = response['choices'][0]['message']['content'] + (out / 'output.txt').write_text(text) + (out / 'replay.json').write_text(json.dumps({'started_at': started, 'finished_at': datetime.now(timezone.utc).isoformat(), 'model_sha256': digest(weights), 'output_sha256': digest(out / 'output.txt'), 'matches_recorded_text': text == (ROOT.parent / 'raw_outputs' / f'{args.model}.txt').read_text(), 'finish_reason': response['choices'][0]['finish_reason'], 'usage': response.get('usage')}, indent=2)) + finally: + proc.terminate() + try: + proc.wait(timeout=15) + except subprocess.TimeoutExpired: + proc.kill() + proc.wait() + print(f'Replay saved to {out}. Weights retained there; remove them when no longer needed.') + +if __name__ == '__main__': + main() diff --git a/research/ai_generated_agi_architectures/collection/repository-context.md b/research/ai_generated_agi_architectures/collection/repository-context.md new file mode 100644 index 0000000..e938bb6 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/repository-context.md @@ -0,0 +1,16 @@ +# Repository context for the analyst + +Pinned repository: https://github.com/aLexzzz430/Cognitive-OS/tree/e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca + +These observations are from source inspection, not execution of the runtime or model claims. They identify where a later synthesis could fit. They are not a vulnerability assessment or a claim that the existing system fails its contracts. + +| Existing surface | Observed implementation | Implication for the synthesis | +| --- | --- | --- | +| `core/SPEC.md` | Describes a single authoritative tick loop, configurable layers and expensive-operation gates. | Propose extensions inside that loop; avoid a second competing controller. | +| `core/runtime/state_store.py` | `RuntimeStateStore` opens SQLite with WAL, `synchronous=NORMAL`, explicit runs/tasks/events/approvals/leases tables and terminal states. | State proposed transaction boundaries relative to these objects. A new graph database is not an assumed prerequisite. | +| `core/runtime/event_journal.py` | `EventJournal.append` first calls the SQLite store, then appends a JSONL mirror; local JSONL files rotate by size. | Explicitly distinguish authoritative database records from derived log mirrors and their recovery semantics. Do not assume they are an atomic two-store commit. | +| `core/runtime/failure_learning.py` | `FailureLearningObject` holds a violated assumption, failed action/result, evidence references, proposed regression test, retrieval tags, confidence and status. | Evaluate whether a proposed “learning” mechanism adds measured improvement beyond the existing structured failure-memory record. | +| `core/runtime/end_to_end_learning.py` | Objective and lesson tags drive relevance matching; retrieval support includes task-family-specific rules. | Any new retrieval proposal should compare against this actual baseline, not an imaginary system with no failure retrieval. | +| `core/orchestration/goal_progress_runtime.py` | Aggregates progress, stalled anchors, action effects, click counts and local signals across episode traces. | Distinguish local activity indicators from task-level completion evidence in the analysis; do not claim an observed runtime defect from reading one function. | + +The model prompt only includes a short uniform context paragraph. None of these detailed observations is passed selectively to one model. The final source mapping and implementation proposals are analyst work and must remain separate from the unedited model outputs. diff --git a/research/ai_generated_agi_architectures/collection/smollm2/analysis-notes.md b/research/ai_generated_agi_architectures/collection/smollm2/analysis-notes.md new file mode 100644 index 0000000..63eaa9d --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/smollm2/analysis-notes.md @@ -0,0 +1,19 @@ +# Analyst notes: SmolLM2 + +This is analysis by Codex, separate from the unedited response. The response ended normally after 1,778 generated tokens. It contains all eleven section headings but omits the requested documentation-edit example. An ordinary stop is not proof of instruction completeness. + +| Dimension | What the response supplies | What it does not establish | +| --- | --- | --- | +| Memory | Four memory categories, caching/buffering/versioning and periodic purging. | No concrete schema, retrieval policy, provenance key or retention criterion; claims about comparative robustness/efficiency are unsupported. | +| Planning | Observe, form a hypothesis, evaluate, execute, monitor and revise; stop if a goal appears unachievable. | No bounded iteration count, progress measurement or operational stopping threshold. | +| Learning | Mentions offline evaluation, versioning, held-out data and rollback. | No candidate artifact, held-out partition, promotion criterion or distinction between storing experience and changing behavior. | +| Tools | Mentions request/result contracts, result validation, uncertain outcomes and idempotency. | No contract fields or reconciliation protocol. Describing idempotency as preventing a stuck state does not specify duplicate-effect prevention. | +| World representation | Names observations, hypotheses, predictions, uncertainty and contradictions. | No representation, update rule or example of resolving contradictory evidence. | +| Governance | Endorses user control, approval, budgets and stopping. | No operational budget accounting or stop/approval interface. This is an architectural completeness observation, not a security test. | +| Evaluation | Names a baseline, testbed and control system. | No three defined measurable experiments or specified ablation, despite repeating that requirement. | +| Persistence | Mentions transaction boundaries and crash/restart recovery. | No transaction sequence or resolution of a completed action whose receipt was not saved. | +| Orchestration | Repeats single-controller/multiple-worker alternatives and conflict resolution. | Does not choose an approach or describe coordination costs and conflict rules. | +| Feasibility | Repeats the request for a six-week sequence, minimal slice and largest risk. | Supplies none of those concrete deliverables. | +| Originality | Calls the four-part hybrid memory design its insight. | Does not state a non-obvious choice, tradeoff or rejection condition. | + +No apparent effectiveness claim in this response was experimentally tested. Its conceptual categories can be compared with other models' proposals, but they are insufficient by themselves to specify an implementation. Preserve this weak response rather than replace it with a more flattering synthetic answer. diff --git a/research/ai_generated_agi_architectures/collection/smollm2/model.json b/research/ai_generated_agi_architectures/collection/smollm2/model.json new file mode 100644 index 0000000..e07ef90 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/smollm2/model.json @@ -0,0 +1,100 @@ +{ + "_id": "6723e9fa554de9192a3a8094", + "id": "HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF", + "private": false, + "pipeline_tag": "text-generation", + "library_name": "transformers", + "tags": [ + "transformers", + "gguf", + "llama-cpp", + "gguf-my-repo", + "text-generation", + "en", + "base_model:HuggingFaceTB/SmolLM2-1.7B-Instruct", + "base_model:quantized:HuggingFaceTB/SmolLM2-1.7B-Instruct", + "license:apache-2.0", + "endpoints_compatible", + "region:us", + "conversational" + ], + "downloads": 7757, + "likes": 52, + "modelId": "HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF", + "author": "HuggingFaceTB", + "sha": "2d4a76a30b4af41ecd395c35725ac11688d4cfe4", + "lastModified": "2024-11-05T14:19:42.000Z", + "gated": false, + "disabled": false, + "widgetData": [ + { + "text": "Hi, what can you help me with?" + }, + { + "text": "What is 84 * 3 / 2?" + }, + { + "text": "Tell me an interesting fact about the universe!" + }, + { + "text": "Explain quantum computing in simple terms." + } + ], + "model-index": null, + "config": {}, + "cardData": { + "library_name": "transformers", + "license": "apache-2.0", + "language": [ + "en" + ], + "tags": [ + "llama-cpp", + "gguf-my-repo" + ], + "base_model": "HuggingFaceTB/SmolLM2-1.7B-Instruct", + "pipeline_tag": "text-generation" + }, + "transformersInfo": { + "auto_model": "AutoModel" + }, + "gguf": { + "total": 1711376384, + "architecture": "llama", + "context_length": 8192, + "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", + "bos_token": "<|im_start|>", + "eos_token": "<|im_end|>", + "totalFileSize": 1055609536 + }, + "siblings": [ + { + "rfilename": ".gitattributes", + "blobId": "7858a2525d9b19b7aaae8ac37a82b87803cdbba4", + "size": 1589 + }, + { + "rfilename": "README.md", + "blobId": "719eb3f9960412f5022a42a3df6b1616c82ba79b", + "size": 1894 + }, + { + "rfilename": "smollm2-1.7b-instruct-q4_k_m.gguf", + "blobId": "aabb5a0820f6d3591cf42ba1732c43de785c9f82", + "size": 1055609536, + "lfs": { + "sha256": "decd2598bc2c8ed08c19adc3c8fdd461ee19ed5708679d1c54ef54a5a30d4f33", + "size": 1055609536, + "pointerSize": 135 + } + } + ], + "spaces": [ + "expandme-tech/SmallZOO-GGUFee-Llama", + "py-rus/nova_ai", + "ttt795420/Aigodot", + "gamog/ia-valerius-nativa" + ], + "createdAt": "2024-10-31T20:35:06.000Z", + "usedStorage": 1055609536 +} diff --git a/research/ai_generated_agi_architectures/collection/smollm2/request.json b/research/ai_generated_agi_architectures/collection/smollm2/request.json new file mode 100644 index 0000000..f5fe330 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/smollm2/request.json @@ -0,0 +1,17 @@ +{ + "model": "smollm2", + "messages": [ + { + "role": "user", + "content": "Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/smollm2/response.json b/research/ai_generated_agi_architectures/collection/smollm2/response.json new file mode 100644 index 0000000..f26f77b --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/smollm2/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "role": "assistant", + "content": "1. Memory Architecture:\n\nThe proposed memory architecture for Cognitive-OS is a hybrid system that combines working memory, episodic memory, semantic memory, and procedural memory. Each type of memory is designed to serve specific purposes and is implemented using different techniques.\n\nWorking memory is used for short-term information storage and retrieval. It is a shallow, shallow-memory system that is designed to be fast and efficient but lacks the depth and richness of semantic memory. Working memory is used for tasks such as planning, reasoning, and decision-making.\n\nEpisodic memory is a system that stores and retrieves information in a more structured and organized way. It is designed to be more robust and reliable than working memory but is also slower and more energy-intensive. Episodic memory is used for tasks such as learning and memory consolidation.\n\nSemantic memory is a system that stores and retrieves information in a more abstract and general way. It is designed to be more powerful and flexible than episodic memory but is also more prone to errors and inconsistencies. Semantic memory is used for tasks such as knowledge representation and inference.\n\nProcedural memory is a system that stores and retrieves information in a more procedural and rule-based way. It is designed to be more efficient and effective than semantic memory but is also more rigid and inflexible. Procedural memory is used for tasks such as skill learning and motor control.\n\nStorage, retrieval, provenance, and forgetting are all managed by a separate system that is designed to be robust and reliable. This system uses a combination of techniques such as caching, buffering, and versioning to ensure that the memory system is always up-to-date and accurate.\n\nThe system also has a mechanism for forgetting information that is no longer needed or relevant. This is done by periodically purging old or irrelevant information from the memory system.\n\n2. Reasoning and Planning:\n\nThe proposed reasoning and planning system for Cognitive-OS is a bounded observation-to-action loop with stopping criteria and recovery from failed plans. The loop is designed to be flexible and adaptable to changing circumstances and goals.\n\nThe loop starts with an observation of the current state of the world. This observation is then used to generate a hypothesis about the best course of action. The hypothesis is then evaluated and refined based on the consequences of that course of action.\n\nIf the hypothesis is deemed to be a good one, it is then implemented as a plan. The plan is then executed and the system monitors its progress and updates its hypothesis as needed.\n\nIf the plan is not successful, the system can recover by re-evaluating its hypothesis and adjusting its plan accordingly. The system can also recover by re-evaluating its observations and updating its hypothesis.\n\nThe system has a mechanism for stopping the loop if it is determined that a plan is not viable or if a goal is no longer achievable. This is done by periodically evaluating the system's goals and adjusting its plans accordingly.\n\n3. Learning:\n\nThe proposed learning mechanism for Cognitive-OS is an offline, evaluated improvement mechanism with versioning, held-out tests and rollback. The system has a mechanism for storing and retrieving its past experiences and using them to improve its performance.\n\nThe system has a mechanism for versioning its past experiences so that it can roll back to a previous version if needed. This is done to prevent the system from becoming stuck in a particular state or to recover from a failed plan.\n\nThe system also has a mechanism for held-out tests so that it can evaluate its performance on unseen data. This is done to prevent the system from becoming too specialized in a particular task or to prevent it from becoming too narrow in its knowledge.\n\nThe system has a mechanism for rollback so that it can recover from a failed plan or a failed test. This is done by periodically evaluating the system's goals and adjusting its plans accordingly.\n\n4. Tool Use:\n\nThe proposed tool use mechanism for Cognitive-OS is an action request/result contract, validation, idempotency and handling of uncertain outcomes. The system has a mechanism for requesting and receiving actions from external tools.\n\nThe system has a mechanism for validating the results of external tools so that it can ensure that the system's goals are being met. The system also has a mechanism for handling uncertain outcomes so that it can adapt to changing circumstances.\n\nThe system has a mechanism for idempotency so that it can prevent the system from becoming stuck in a particular state. This is done by periodically evaluating the system's goals and adjusting its plans accordingly.\n\n5. World Representation:\n\nThe proposed world representation for Cognitive-OS is a system that distinguishes observations, hypotheses, and predictions, including uncertainty and contradiction handling. The system has a mechanism for representing the world in a way that is consistent with the system's goals and objectives.\n\nThe system has a mechanism for representing observations in a way that is accurate and reliable. The system also has a mechanism for representing hypotheses in a way that is consistent with the system's goals and objectives.\n\nThe system has a mechanism for representing predictions in a way that is accurate and reliable. The system also has a mechanism for representing uncertainty and contradiction in a way that is consistent with the system's goals and objectives.\n\n6. Safety and Governance:\n\nThe proposed safety and governance mechanism for Cognitive-OS is a system that preserves user authority, explicit permission boundaries, resource budgets and a reliable stop control. The system has a mechanism for ensuring that the system's actions are consistent with the system's goals and objectives.\n\nThe system has a mechanism for preserving user authority so that the system's actions are subject to user control. The system also has a mechanism for explicit permission boundaries so that the system's actions are subject to user approval.\n\nThe system has a mechanism for resource budgets so that the system's actions are limited by the system's resources. The system also has a mechanism for a reliable stop control so that the system can be stopped if it is determined that it is no longer safe to continue.\n\n7. Evaluation:\n\nThe proposed evaluation mechanism for Cognitive-OS is three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results. The system has a mechanism for evaluating its performance on unseen data.\n\nThe system has a mechanism for comparing its performance to a baseline system so that it can evaluate the effectiveness of its architecture. The system also has a mechanism for evaluating its performance on a testbed system so that it can evaluate the effectiveness of its architecture.\n\nThe system has a mechanism for comparing its performance to a control system so that it can evaluate the effectiveness of its architecture. The system also has a mechanism for comparing its performance to a baseline system so that it can evaluate the effectiveness of its architecture.\n\n8. Persistence and Runtime:\n\nThe proposed persistence and runtime mechanism for Cognitive-OS is a transaction boundary and a crash/restart protocol. The system has a mechanism for ensuring that the system's state is consistent with the system's goals and objectives.\n\nThe system has a mechanism for ensuring that the system's state is consistent with the system's goals and objectives. The system also has a mechanism for recovering from a crash or restart so that the system can continue to operate.\n\n9. Orchestration:\n\nThe proposed orchestration mechanism for Cognitive-OS is a single controller or multiple bounded workers, including coordination costs and conflict resolution. The system has a mechanism for coordinating the actions of multiple systems.\n\nThe system has a mechanism for resolving conflicts between systems so that the system can continue to operate. The system also has a mechanism for coordinating the actions of multiple systems so that the system can continue to operate.\n\n10. Engineering Feasibility:\n\nThe proposed engineering feasibility for Cognitive-OS is a six-week implementation sequence, the minimum viable slice, and the largest unresolved risk. The system has a mechanism for evaluating the feasibility of its architecture.\n\nThe system has a mechanism for evaluating the feasibility of its architecture. The system also has a mechanism for identifying the largest unresolved risk so that the system can prioritize its development.\n\n11. Non-obvious Insight:\n\nThe proposed non-obvious insight for Cognitive-OS is to use a hybrid memory system that combines working memory, episodic memory, semantic memory, and procedural memory. The system has a mechanism for distinguishing between these different types of memory.\n\nThe system has a mechanism for distinguishing between these different types of memory. The system also has a mechanism for using these different types of memory to improve the system's performance." + } + } + ], + "created": 1788894211, + "model": "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/smollm2-1.7b-instruct-q4_k_m.gguf", + "system_fingerprint": "b10809-5266f24da", + "object": "chat.completion", + "usage": { + "completion_tokens": 1778, + "prompt_tokens": 559, + "total_tokens": 2337, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "id": "chatcmpl-MWxD8DMeQf34gnyTzqmU21UauQAnVfTx", + "timings": { + "cache_n": 0, + "prompt_n": 559, + "prompt_ms": 34943.535, + "prompt_per_token_ms": 62.51079606440072, + "prompt_per_second": 15.99723668484027, + "predicted_n": 1778, + "predicted_ms": 381413.813, + "predicted_per_token_ms": 214.63917445132248, + "predicted_per_second": 4.658981765822938 + } +} diff --git a/research/ai_generated_agi_architectures/collection/smollm2/run.json b/research/ai_generated_agi_architectures/collection/smollm2/run.json new file mode 100644 index 0000000..50d7524 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/smollm2/run.json @@ -0,0 +1,42 @@ +{ + "started_at": "2026-09-08T18:56:08.477829+00:00", + "finished_at": "2026-09-08T19:03:31.552280+00:00", + "runtime": "llama.cpp b10809 / 5266f24da", + "command": [ + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/llama-b10809/llama-server", + "-m", + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/smollm2-1.7b-instruct-q4_k_m.gguf", + "--host", + "127.0.0.1", + "--port", + "18763", + "-t", + "2", + "-tb", + "2", + "-ngl", + "0", + "-c", + "4096", + "-np", + "1", + "--jinja", + "--reasoning-budget", + "0", + "--no-webui" + ], + "request_sha256": "68914c52df72d4a5a309522561da77d063a585e2489c3b33fc1f435177f3886c", + "response_sha256": "8bc3176e87dfb69ad78b26e0abe546c57e82852418f82dc040d7ca210c9801c1", + "content_sha256": "399d3476ec8f2ea4f10a70eb9dbb67c48628134e3e65a94510f3742fd62aa217", + "finish_reason": "stop", + "usage": { + "completion_tokens": 1778, + "prompt_tokens": 559, + "total_tokens": 2337, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "cpu_threads": 2, + "context_tokens": 4096 +} diff --git a/research/ai_generated_agi_architectures/collection/smollm2/source.json b/research/ai_generated_agi_architectures/collection/smollm2/source.json new file mode 100644 index 0000000..ba6fe75 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/smollm2/source.json @@ -0,0 +1,9 @@ +{ + "model_repository": "HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF", + "revision": "2d4a76a30b4af41ecd395c35725ac11688d4cfe4", + "filename": "smollm2-1.7b-instruct-q4_k_m.gguf", + "url": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF/resolve/2d4a76a30b4af41ecd395c35725ac11688d4cfe4/smollm2-1.7b-instruct-q4_k_m.gguf", + "sha256": "decd2598bc2c8ed08c19adc3c8fdd461ee19ed5708679d1c54ef54a5a30d4f33", + "size": 1055609536, + "accessed_at": "2026-09-08T18:56:08.468289+00:00" +} diff --git a/research/ai_generated_agi_architectures/collection/tinyllama/analysis-notes.md b/research/ai_generated_agi_architectures/collection/tinyllama/analysis-notes.md new file mode 100644 index 0000000..f11abd1 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/tinyllama/analysis-notes.md @@ -0,0 +1,19 @@ +# Analyst notes: TinyLlama 1.1B Chat v1.0 + +Normal stop after 1,193 generated tokens on 2026-09-08 at 19:47:32 UTC. The response repeats broad requirements, repeats Evaluation three times and Persistence twice, and does not supply the requested concrete example. A normal stop does not imply complete instruction following. Its raw text is preserved without deduplication or repair. + +| Dimension | What the response supplies | What it does not establish | +| --- | --- | --- | +| Memory | A section titled Memory Architecture describes an observation/action/observation loop. | It does not distinguish the four requested memory types or specify storage, retrieval, provenance or forgetting. The section's actual content is a control-loop description. | +| Planning | Repeatedly says planning selects the best action from observations and resources. | No selection rule, state transition, stopping condition or recovery procedure. | +| Learning | Repeats the same action-selection language used for planning. | No learning candidate, offline evaluation, versions, held-out tests or rollback. | +| Tools | Names action/result contracts, validation, idempotency and uncertainty handling as responsibilities. | No fields, validation procedure, duplicate-request behavior or uncertain-effect reconciliation. | +| World representation | Says the phase distinguishes observations, hypotheses and predictions. | Does not represent any of those objects, confidence, evidence dependencies or contradictions. | +| Governance | Names user authority, permissions, budgets and stop control. | Supplies no concrete interface or accounting procedure; these are only stated requirements. | +| Evaluation | Calls observation, hypothesis and prediction three experiments; repeats the evaluation passage three times. | No task, metric, comparison or falsification rule; no component is actually selected for ablation. | +| Persistence | Calls observation, hypothesis and prediction bounded operations, and names transactions/restart; this section repeats. | No transaction boundary, snapshot/receipt schema or interrupted-action protocol. | +| Orchestration | Opening and conclusion describe the architecture as modular and flexible. | No dedicated orchestration section, controller/worker selection, coordination cost or conflict-resolution rule. | +| Feasibility | The Output section says there is a six-week sequence, minimum slice and largest risk. | It does not actually enumerate the sequence, select a slice or identify a risk. | +| Originality | Says the insight is a concrete documentation-edit example. | No design choice, tradeoff or rejection criterion; the claimed example is not supplied. | + +There is little mechanism-level material to adopt into a combined architecture. The useful observation is methodological: retaining a model's exact response exposes noncompliance that a cleaned summary could conceal. No cause is isolated by this one run; model capacity, training, template, quantization and prompt fit remain uncontrolled influences. 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Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions.\n\nContext: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority.\n\nWrite an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context.\n\n1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting.\n2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans.\n3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs.\n4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes.\n5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling.\n6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control.\n7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results.\n8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored.\n9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution.\n10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk.\n11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it.\n\nFinish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation.\n" + } + ], + "temperature": 0.2, + "top_p": 0.9, + "seed": 20260908, + "max_tokens": 2400, + "stream": false, + "chat_template_kwargs": { + "enable_thinking": false + } +} diff --git a/research/ai_generated_agi_architectures/collection/tinyllama/response.json b/research/ai_generated_agi_architectures/collection/tinyllama/response.json new file mode 100644 index 0000000..e7a15ed --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/tinyllama/response.json @@ -0,0 +1,36 @@ +{ + "choices": [ + { + "finish_reason": "stop", + "index": 0, + "message": { + "role": "assistant", + "content": "Architecture for CoGNITIVE-OS\n\nCoGNITIVE-OS is a local-first cognitive runtime pursuing general-purpose problem solving. The project aims to provide a practical next iteration that retains control over goals, budget, tools, and shutdown. The architecture is designed to be concise, modular, and flexible, allowing for easy extension and adaptation to new use cases.\n\nMemory Architecture\n\nThe memory architecture is designed to be bounded observation-to-action loop with stopping criteria and recovery from failed plans. The loop is divided into three phases: observation, action, and observation. Observation is the initial state of the system, and action is the next state that the system will reach based on the current observation. The observation phase is responsible for gathering and storing observations, while the action phase is responsible for executing the action that is stored in the observation.\n\nReasoning and Planning\n\nThe reasoning and planning phase is responsible for evaluating the current observation and the possible actions that can be taken. The planning phase is responsible for identifying the best action to take based on the current observation and the available resources. The planning phase is also responsible for identifying the best action to take based on the available resources and the current observation.\n\nLearning\n\nThe learning phase is responsible for evaluating the current observation and the possible actions that can be taken. The learning phase is responsible for identifying the best action to take based on the current observation and the available resources. The learning phase is also responsible for identifying the best action to take based on the available resources and the current observation.\n\nTool Use\n\nThe tool use phase is responsible for specifying an action request/result contract, validation, idempotency, and handling of uncertain outcome. The tool use phase is also responsible for identifying the best action to take based on the available resources and the current observation.\n\nWorld Representation\n\nThe world representation phase is responsible for distinguishing observations, hypotheses, and predictions. The world representation phase is also responsible for identifying the best action to take based on the available resources and the current observation.\n\nSafety and Governance\n\nThe safety and governance phase is responsible for preserving user authority, explicit permission boundaries, resource budgeting, and a reliable stop control. The safety and governance phase is also responsible for identifying the best action to take based on the available resources and the current observation.\n\nEvaluation\n\nThe evaluation phase is responsible for three measurable experiments: observation, hypothesis, and prediction. The evaluation phase is also responsible for ablation that could falsify the claimed architectural benefit, without claiming results.\n\nPersistence and Runtime\n\nThe persistence and runtime phase is responsible for three bounded operations: observation, hypothesis, and prediction. The persistence and runtime phase is also responsible for transaction boundaries and a crash/restart protocol.\n\nEvaluation\n\nThe evaluation phase is responsible for three measurable experiments: observation, hypothesis, and prediction. The evaluation phase is also responsible for ablation that could falsify the claimed architectural benefit, without claiming results.\n\nPersistence and Runtime\n\nThe persistence and runtime phase is responsible for three bounded operations: observation, hypothesis, and prediction. The persistence and runtime phase is also responsible for transaction boundaries and a crash/restart protocol.\n\nEvaluation\n\nThe evaluation phase is responsible for three measurable experiments: observation, hypothesis, and prediction. The evaluation phase is also responsible for ablation that could falsify the claimed architectural benefit, without claiming results.\n\nNon-obvious Insight\n\nThe non-obvious insight is a short concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption.\n\nOutput\n\nThe output of the proposal is a six-week implementation sequence, the minimum viable slice, and the largest unresolved risk. The output is also accompanied by a concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption.\n\nConclusion\n\nThe architecture for CoGNITIVE-OS is designed to be concise, modular, and flexible, allowing for easy extension and adaptation to new use cases. The memory architecture is designed to be bounded observation-to-action loop with stopping criteria and recovery from failed plans. The reasoning and planning phase is designed to identify the best action to take based on the current observation and the available resources. The learning phase is designed to identify the best action to take based on the current observation and the available resources and the current observation. The tool use phase is designed to specify an action request/result contract, validation, idempotency, and handling of uncertain outcome. The world representation phase is designed to distinguish observations, hypotheses, and predictions. The safety and governance phase is designed to preserve user authority, explicit permission boundaries, resource budgeting, and a reliable stop control. The evaluation phase is designed to three measurable experiments: observation, hypothesis, and prediction. The persistence and runtime phase is designed to three bounded operations: observation, hypothesis, and prediction. The evaluation phase is designed to three measurable experiments: observation, hypothesis, and prediction. The non-obvious insight is a short concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption. The output is accompanied by a concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption." + } + } + ], + "created": 1788896852, + "model": "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf", + "system_fingerprint": "b10809-5266f24da", + "object": "chat.completion", + "usage": { + "completion_tokens": 1193, + "prompt_tokens": 602, + "total_tokens": 1795, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "id": "chatcmpl-WLSvG7oAh3X3oaNQMM1ZMSKrPYhnZxgZ", + "timings": { + "cache_n": 0, + "prompt_n": 602, + "prompt_ms": 10413.454, + "prompt_per_token_ms": 17.29809634551495, + "prompt_per_second": 57.80982947636779, + "predicted_n": 1193, + "predicted_ms": 103169.355, + "predicted_per_token_ms": 86.55147231543624, + "predicted_per_second": 11.553818476426455 + } +} diff --git a/research/ai_generated_agi_architectures/collection/tinyllama/run.json b/research/ai_generated_agi_architectures/collection/tinyllama/run.json new file mode 100644 index 0000000..5cb0c42 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/tinyllama/run.json @@ -0,0 +1,42 @@ +{ + "started_at": "2026-09-08T19:45:35.088075+00:00", + "finished_at": "2026-09-08T19:47:32.830910+00:00", + "runtime": "llama.cpp b10809 / 5266f24da", + "command": [ + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/llama-b10809/llama-server", + "-m", + "/Users/astra/Documents/ChatGPT/Income/.local/architecture-models/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf", + "--host", + "127.0.0.1", + "--port", + "18763", + "-t", + "2", + "-tb", + "2", + "-ngl", + "0", + "-c", + "4096", + "-np", + "1", + "--jinja", + "--reasoning-budget", + "0", + "--no-webui" + ], + "request_sha256": "85a69c5e7fa83de44b32f5c00dd001d4111daf51409e1756e7687de7ba87b9f9", + "response_sha256": "5b92e920a6a179194f9f59bf83a7da25ece742c7fd3dc0e5d52884d17da53fa7", + "content_sha256": "f2f7b4f40e6b762c3b4e221ff3c664273713ba56c75402bb7c368cfa11b01223", + "finish_reason": "stop", + "usage": { + "completion_tokens": 1193, + "prompt_tokens": 602, + "total_tokens": 1795, + "prompt_tokens_details": { + "cached_tokens": 0 + } + }, + "cpu_threads": 2, + "context_tokens": 4096 +} diff --git a/research/ai_generated_agi_architectures/collection/tinyllama/source.json b/research/ai_generated_agi_architectures/collection/tinyllama/source.json new file mode 100644 index 0000000..4bbe8d5 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/tinyllama/source.json @@ -0,0 +1,9 @@ +{ + "model_repository": "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF", + "revision": "52e7645ba7c309695bec7ac98f4f005b139cf465", + "filename": "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf", + "url": "https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/52e7645ba7c309695bec7ac98f4f005b139cf465/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf", + "sha256": "9fecc3b3cd76bba89d504f29b616eedf7da85b96540e490ca5824d3f7d2776a0", + "size": 668788096, + "accessed_at": "2026-09-08T19:45:35.083461+00:00" +} diff --git a/research/ai_generated_agi_architectures/collection/transaction-example-results.json b/research/ai_generated_agi_architectures/collection/transaction-example-results.json new file mode 100644 index 0000000..b71794c --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/transaction-example-results.json @@ -0,0 +1,30 @@ +{ + "sqlite_version": "3.53.1", + "python_version": "3.12.14", + "cases": [ + { + "mode": "intent-and-receipt-in-one-transaction", + "child_exit_code": 17, + "pending_actions_found_after_exit": [], + "filesystem_effects_after_exit": [ + "performed a1" + ], + "reconciled_without_repeating_effect": false + }, + { + "mode": "durable-intent-before-effect", + "child_exit_code": 17, + "pending_actions_found_after_exit": [ + [ + "a1", + "PENDING" + ] + ], + "filesystem_effects_after_exit": [ + "performed a1" + ], + "reconciled_without_repeating_effect": true + } + ], + "scope": "Two isolated process-exit examples of a proposed transaction ordering. Not an upstream bug report, a power-loss test, or a general exactly-once guarantee." +} diff --git a/research/ai_generated_agi_architectures/collection/transaction-example.md b/research/ai_generated_agi_architectures/collection/transaction-example.md new file mode 100644 index 0000000..afd1fb5 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/transaction-example.md @@ -0,0 +1,26 @@ +# One executed transaction-ordering example + +**Finding:** Gemini reference candidate A §8 cannot recover its pending action at the illustrated failure boundary. Its intent record is still uncommitted when a separate filesystem effect occurs. An abrupt process exit loses the pending record while leaving that effect observable. + +This is an analyst-authored functional example of generated pseudocode. It does not run Cognitive-OS and is not an upstream defect report. The script uses only a temporary directory, SQLite databases and a text file; it makes no network calls. No other experiments proposed by the model were executed. + +Run from the packet root: + +```sh +python collection/transaction_example.py +``` + +The worker uses SQLite WAL and `synchronous=FULL`. In both cases it appends one line to a file, flushes it, calls `fsync`, and exits using `os._exit(17)` before writing any completion receipt. Exit code 17 is the intended interruption, not a failed test runner. The parent reopens the database and reads the file. The recorded run used Python 3.12.14 and SQLite 3.53.1. + +| Case | Intent ordering | Pending actions after exit | File effects after exit | Reconciliation | +| --- | --- | --- | --- | --- | +| A ordering | Insert intent, perform effect, exit before SQL commit | None | One `performed a1` line | A pending-action scan cannot discover this action. | +| Analyst correction | Insert and commit intent, perform effect, exit before receipt | `a1`, `PENDING` | One `performed a1` line | The action-specific read probe observes the line and marks the record verified without another append. | + +Both assertions passed. The original result is preserved in [transaction-example-results.json](transaction-example-results.json). The script also verifies that the corrected case still has only one line after reconciliation. + +The inference is limited: durable intent preserves the identity needed for this recovery probe; it does not atomically couple arbitrary external effects to SQLite. The probe is deliberately simple because this action has a unique, observable effect. There is no competing writer, no ambiguous third-party response, and no general exactly-once guarantee. The test is abrupt process exit, not power loss, filesystem failure or exhaustive crash-boundary coverage. The generated A trace mentions both power loss and process kill; this experiment does not validate power-loss recovery. + +SQLite documents atomicity of database changes within a transaction. WAL records a commit explicitly, with durability behavior depending on synchronization. The separate file append in this example is outside those database changes. These documentation points support the interpretation; the two observed cases are evidence from this script. Sources: [Atomic Commit in SQLite](https://sqlite.org/atomiccommit.html), [Write-Ahead Logging](https://sqlite.org/wal.html), accessed 2026-09-08. + +For the proposed runtime design, commit a validated action intent before dispatch. After interruption, reconcile the effect using a task-specific probe, then commit the receipt and task transition together. Preserve `UNCERTAIN` when the probe cannot decide. Those steps remain a design proposal until evaluated against the actual runtime and its full set of effect adapters. diff --git a/research/ai_generated_agi_architectures/collection/transaction_example.py b/research/ai_generated_agi_architectures/collection/transaction_example.py new file mode 100644 index 0000000..f90e519 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/transaction_example.py @@ -0,0 +1,64 @@ +"""Test one crash-boundary claim in a collected design, not Cognitive-OS. + +Uses only temporary SQLite databases and local files. Child processes exit +abruptly after a filesystem effect but before the completion receipt commits. +This models process termination, not power loss or arbitrary external tools. +""" +from pathlib import Path +import argparse +import json +import os +import sqlite3 +import subprocess +import sys +import tempfile + +def worker(root, durable_intent): + db = sqlite3.connect(root / 'state.db') + db.execute('PRAGMA journal_mode=WAL') + db.execute('PRAGMA synchronous=FULL') + db.execute('BEGIN IMMEDIATE') + db.execute("INSERT INTO actions VALUES ('a1', 'PENDING')") + if durable_intent: + db.commit() + with (root / 'effect.txt').open('a') as f: + f.write('performed a1\n') + f.flush() + os.fsync(f.fileno()) + # No Python shutdown cleanup; the pending transaction remains uncommitted. + os._exit(17) + +def run_case(root, durable_intent): + root.mkdir() + with sqlite3.connect(root / 'state.db') as db: + db.execute('CREATE TABLE actions (action_id TEXT PRIMARY KEY, status TEXT)') + result = subprocess.run([sys.executable, __file__, '--worker', str(root), '--mode', 'durable' if durable_intent else 'single-transaction'], check=False) + assert result.returncode == 17 + with sqlite3.connect(root / 'state.db') as db: + actions = db.execute('SELECT action_id, status FROM actions').fetchall() + effects = (root / 'effect.txt').read_text().splitlines() + assert effects == ['performed a1'] + if durable_intent: + assert actions == [('a1', 'PENDING')] + # The action-specific read probe reconciles the effect; no second append. + with sqlite3.connect(root / 'state.db') as db: + db.execute("UPDATE actions SET status='VERIFIED_BY_PROBE' WHERE action_id='a1'") + assert (root / 'effect.txt').read_text().splitlines() == effects + else: + assert actions == [] + return {'mode': 'durable-intent-before-effect' if durable_intent else 'intent-and-receipt-in-one-transaction', 'child_exit_code': result.returncode, 'pending_actions_found_after_exit': actions, 'filesystem_effects_after_exit': effects, 'reconciled_without_repeating_effect': durable_intent} + +def main(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--worker', type=Path) + parser.add_argument('--mode', choices=['durable', 'single-transaction']) + args = parser.parse_args() + if args.worker: + worker(args.worker, args.mode == 'durable') + with tempfile.TemporaryDirectory(prefix='cognitive-design-example-') as directory: + root = Path(directory) + results = [run_case(root / 'one-transaction', False), run_case(root / 'durable-intent', True)] + print(json.dumps({'sqlite_version': sqlite3.sqlite_version, 'python_version': sys.version.split()[0], 'cases': results, 'scope': 'Two isolated process-exit examples of a proposed transaction ordering. Not an upstream bug report, a power-loss test, or a general exactly-once guarantee.'}, indent=2)) + +if __name__ == '__main__': + main() diff --git a/research/ai_generated_agi_architectures/collection/verify.py b/research/ai_generated_agi_architectures/collection/verify.py new file mode 100644 index 0000000..b72fb87 --- /dev/null +++ b/research/ai_generated_agi_architectures/collection/verify.py @@ -0,0 +1,111 @@ +"""Verify collection provenance and packet coverage, not research quality. + +Usage: python collection/verify.py [--collection-only] +No network requests, model inference, or credential access. +""" +from pathlib import Path +import argparse +import csv +import hashlib +import json +import re + +ROOT = Path(__file__).resolve().parents[1] +MODELS = ('smollm2', 'qwen25', 'granite33', 'tinyllama', 'phi3', 'qwen3', 'falcon3', 'danube3') +HOSTED = 'gemini_flash_reference' +DIMENSIONS = ('memory', 'planning', 'learning', 'tools', 'world_representation', 'governance', 'evaluation', 'persistence', 'orchestration', 'feasibility', 'originality') + +def sha(path): + return hashlib.sha256(path.read_bytes()).hexdigest() + +def verify_model(root, name): + folder = root / 'collection' / name + request_file, response_file, run_file = [folder / f'{n}.json' for n in ('request', 'response', 'run')] + raw = root / 'raw_outputs' / f'{name}.txt' + request, response, run, source, meta = [json.loads(p.read_text()) for p in (request_file, response_file, run_file, folder / 'source.json', folder / 'model.json')] + assert request['messages'] == [{'role': 'user', 'content': (root / 'collection/prompt.txt').read_text()}], f'{name}: prompt changed' + for field, expected in {'temperature': 0.2, 'top_p': 0.9, 'seed': 20260908, 'max_tokens': 2400, 'stream': False}.items(): + assert request[field] == expected, f'{name}: sampling changed: {field}' + assert request['chat_template_kwargs'] == {'enable_thinking': False} + choice = response['choices'][0] + assert isinstance(choice['message']['content'], str) and choice['message']['content'].strip(), f'{name}: no proposal text' + assert raw.read_text() == choice['message']['content'], f'{name}: raw output differs from response' + for path, key in [(request_file, 'request_sha256'), (response_file, 'response_sha256'), (raw, 'content_sha256')]: + assert sha(path) == run[key], f'{name}: {key} mismatch' + assert run['finish_reason'] == choice['finish_reason'], f'{name}: inconsistent finish reason' + assert run['usage'] == response['usage'], f'{name}: inconsistent token usage' + assert run['started_at'] <= run['finished_at'], f'{name}: invalid timestamps' + assert source['revision'] == meta['sha'] and re.fullmatch('[0-9a-f]{40}', source['revision']) + assert source['url'] == f'https://huggingface.co/{source["model_repository"]}/resolve/{source["revision"]}/{source["filename"]}' + entry = next(x for x in meta['siblings'] if x['rfilename'] == source['filename']) + assert source['sha256'] == entry['lfs']['sha256'] and source['size'] == entry['lfs']['size'], f'{name}: upstream identity mismatch' + assert re.fullmatch('[0-9a-f]{64}', source['sha256']) + response_kind = 'prompt_echo' if raw.read_text().strip() == (root / 'collection/prompt.txt').read_text().strip() else 'non_echo_text' + return {'model': name, 'characters': len(raw.read_text()), 'finish_reason': choice['finish_reason'], 'completion_tokens': response['usage']['completion_tokens'], 'source_sha256': source['sha256'], 'response_kind': response_kind} + +def verify_hosted_reference(root): + folder = root / 'collection/gemini-flash-reference' + meta = json.loads((folder / 'source.json').read_text()) + assert (folder / 'request.txt').read_bytes() == (root / 'collection/prompt.txt').read_bytes(), 'Hosted reference prompt differs' + assert meta['backend_model_revision'] is None and meta['sampling_parameters'] is None, 'Do not infer unknown service settings' + assert [r['label'] for r in meta['responses']] == ['A', 'B'] + for item in meta['responses']: + raw = folder / f'response-{item["label"]}.txt' + assert sha(raw) == item['sha256'], 'Hosted reference content hash mismatch' + assert len(raw.read_text()) == item['characters'], 'Hosted reference character count mismatch' + combined = root / meta['combined_raw_file'] + expected = ('=== Collector label: Gemini Flash web, candidate A ===\n\n' + + (folder / 'response-A.txt').read_text() + + '\n\n=== Collector label: Gemini Flash web, candidate B ===\n\n' + + (folder / 'response-B.txt').read_text()) + assert combined == root / f'raw_outputs/{HOSTED}.txt' + assert combined.read_text() == expected and sha(combined) == meta['combined_raw_sha256'], 'Combined hosted view differs from recorded answers' + with (folder / 'comparison.csv').open(newline='') as f: + rows = list(csv.DictReader(f)) + pairs = [(r['candidate'], r['dimension']) for r in rows] + assert len(pairs) == 22 and set(pairs) == {(c, d) for c in ('A', 'B') for d in DIMENSIONS} + for row in rows: + relative, line = row['evidence'].split('#L') + assert relative == f'response-{row["candidate"]}.txt' + text = (folder / relative).read_text().splitlines() + assert 1 <= int(line) <= len(text) and re.match(r'^\d+\. ', text[int(line) - 1]), 'Hosted evidence anchor is not a heading' + return {'systems': 1, 'candidate_answers': 2, 'comparison_rows': len(rows), 'backend_revision': 'unknown', 'primary_model_count_increment': 0} + +def verify(root=ROOT, collection_only=False): + results = [verify_model(root, name) for name in MODELS] + hosted_reference = verify_hosted_reference(root) + assert set(p.stem for p in (root / 'raw_outputs').glob('*.txt')) == set(MODELS) | {HOSTED}, 'Unexpected/missing raw outputs' + assert [r['model'] for r in results if r['response_kind'] == 'prompt_echo'] == ['danube3'], 'Reassess the documented response classification' + if not collection_only: + for name in ('README.md', 'prompts.md', 'summary.md', 'synthesis.md', 'sources.md'): + assert (root / name).is_file() and (root / name).stat().st_size > 0, f'Missing {name}' + report = (root / name).read_text() + assert '**Working draft' not in report and '**Preliminary analysis' not in report, f'{name}: report still marked incomplete' + with (root / 'comparison.csv').open(newline='') as f: + reader = csv.DictReader(f) + rows = list(reader) + assert set(('model', 'dimension', 'proposal', 'limits', 'evidence', 'cohort')) <= set(reader.fieldnames or []), 'Missing comparison columns' + expected = {(m, d) for m in (*MODELS, HOSTED) for d in DIMENSIONS} + actual = [(r['model'], r['dimension']) for r in rows] + assert set(actual) == expected and len(actual) == len(expected), 'Comparison must cover all 99 system/dimension pairs exactly once' + for row in rows: + assert all(row[k].strip() for k in ('proposal', 'limits', 'evidence')), 'Empty comparison evidence' + assert row['evidence'].startswith(f'raw_outputs/{row["model"]}.txt'), 'Evidence points to a different model' + assert row['cohort'] == ('exploratory_hosted' if row['model'] == HOSTED else 'primary_local') + for reference in row['evidence'].split('; '): + relative, number = reference.split('#L') + assert relative == f'raw_outputs/{row["model"]}.txt' + assert 1 <= int(number) <= len((root / relative).read_text().splitlines()), 'Evidence anchor is outside the raw file' + # Explicitly prevent accidental credential/file bundles in the research packet. + for p in root.rglob('*'): + if not p.is_file(): + continue + assert p.suffix.lower() not in {'.gguf', '.pem', '.key'}, f'Unexpected model/key file: {p}' + assert not p.name.startswith('.env'), f'Unexpected environment file: {p}' + return {'local_models_verified': len(results), 'total_systems': len(results) + 1, 'local_prompt_echoes': sum(r['response_kind'] == 'prompt_echo' for r in results), 'comparison_rows_verified': 0 if collection_only else 99, 'results': results, 'exploratory_hosted_reference': hosted_reference, 'scope': 'Verifies recorded artifact consistency, echo classification and structural coverage; non-echo text is not a quality score. Does not authenticate remote provenance independently or judge research quality.'} + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('--collection-only', action='store_true') + args = parser.parse_args() + print(json.dumps(verify(collection_only=args.collection_only), indent=2)) diff --git a/research/ai_generated_agi_architectures/comparison.csv b/research/ai_generated_agi_architectures/comparison.csv new file mode 100644 index 0000000..1bc9d36 --- /dev/null +++ b/research/ai_generated_agi_architectures/comparison.csv @@ -0,0 +1,100 @@ +model,dimension,proposal,limits,evidence,cohort +smollm2,memory,"Four memory categories, caching/buffering/versioning and periodic purging.","No concrete schema, retrieval policy, provenance key or retention criterion; claims about comparative robustness/efficiency are unsupported.",raw_outputs/smollm2.txt#L1,primary_local +smollm2,planning,"Observe, form a hypothesis, evaluate, execute, monitor and revise; stop if a goal appears unachievable.","No bounded iteration count, progress measurement or operational stopping threshold.",raw_outputs/smollm2.txt#L17,primary_local +smollm2,learning,"Mentions offline evaluation, versioning, held-out data and rollback.","No candidate artifact, held-out partition, promotion criterion or distinction between storing experience and changing behavior.",raw_outputs/smollm2.txt#L29,primary_local +smollm2,tools,"Mentions request/result contracts, result validation, uncertain outcomes and idempotency.",No contract fields or reconciliation protocol. Describing idempotency as preventing a stuck state does not specify duplicate-effect prevention.,raw_outputs/smollm2.txt#L39,primary_local +smollm2,world_representation,"Names observations, hypotheses, predictions, uncertainty and contradictions.","No representation, update rule or example of resolving contradictory evidence.",raw_outputs/smollm2.txt#L47,primary_local +smollm2,governance,"Endorses user control, approval, budgets and stopping.","No operational budget accounting or stop/approval interface. This is an architectural completeness observation, not a security test.",raw_outputs/smollm2.txt#L55,primary_local +smollm2,evaluation,"Names a baseline, testbed and control system.","No three defined measurable experiments or specified ablation, despite repeating that requirement.",raw_outputs/smollm2.txt#L63,primary_local +smollm2,persistence,Mentions transaction boundaries and crash/restart recovery.,No transaction sequence or resolution of a completed action whose receipt was not saved.,raw_outputs/smollm2.txt#L71,primary_local +smollm2,orchestration,Repeats single-controller/multiple-worker alternatives and conflict resolution.,Does not choose an approach or describe coordination costs and conflict rules.,raw_outputs/smollm2.txt#L77,primary_local +smollm2,feasibility,"Repeats the request for a six-week sequence, minimal slice and largest risk.",Supplies none of those concrete deliverables.,raw_outputs/smollm2.txt#L83,primary_local +smollm2,originality,Calls the four-part hybrid memory design its insight.,"Does not state a non-obvious choice, tradeoff or rejection condition.",raw_outputs/smollm2.txt#L89,primary_local +qwen25,memory,Definitions of four memory types; says provenance tracks origin and selective forgetting prioritizes information.,"No storage schema, retrieval algorithm, source identity field or retention schedule.",raw_outputs/qwen25.txt#L1,primary_local +qwen25,planning,"Names a bounded observation/action loop, stopping rules and recovery heuristics.","Repeats enough-information language without a threshold, transition contract or failure-recovery sequence.",raw_outputs/qwen25.txt#L15,primary_local +qwen25,learning,"Names versions, held-out tests and rollback; distinguishes improvement from recording observations.","No representation of an improvement candidate, evaluation metric or promotion rule. The learning/logging distinction is repeated rather than operationalized.",raw_outputs/qwen25.txt#L23,primary_local +qwen25,tools,"Describes contracts, validation and repeated-request handling.","No action/result fields or idempotency mechanism. Uncertain-outcome handling is defined in terms of handling uncertainty, without a procedure.",raw_outputs/qwen25.txt#L29,primary_local +qwen25,world_representation,"Defines observations, hypotheses and predictions and names contradiction handling.","Does not encode confidence, provenance, dependencies or a conflict-resolution rule.",raw_outputs/qwen25.txt#L37,primary_local +qwen25,governance,"Names user authority, permissions, resource budgets and stop control.","Mostly defines each term using itself; supplies no accounting mechanism, authority contract or stopping procedure.",raw_outputs/qwen25.txt#L47,primary_local +qwen25,evaluation,Names three measurable experiments and an ablation.,"Does not actually specify the experiments, metrics, baselines or removed component.",raw_outputs/qwen25.txt#L57,primary_local +qwen25,persistence,Names transaction boundaries and a crash/restart protocol.,Describes boundaries as transaction boundaries and restart as restart; no receipt-reconciliation sequence or authoritative store is identified.,raw_outputs/qwen25.txt#L63,primary_local +qwen25,orchestration,Names the controller/worker decision and a tradeoff.,"Does not select an arrangement, give the tradeoff or specify conflict resolution.",raw_outputs/qwen25.txt#L69,primary_local +qwen25,feasibility,"Names a six-week sequence, minimal slice and unresolved risk.","Does not enumerate weeks, choose a minimal slice or identify a concrete risk.",raw_outputs/qwen25.txt#L77,primary_local +qwen25,originality,Returns to single controller versus multiple workers.,Provides neither a concrete insight nor its tradeoff; the rejection-condition paragraph is truncated.,raw_outputs/qwen25.txt#L85,primary_local +granite33,memory,"LRU working cache, timestamped SQLite episodes, Neo4j-style semantic triples and versioned Python procedures.","No retrieval query, source/provenance schema across stores or retention policy beyond working-cache eviction. The extra graph database is not justified against the existing SQLite runtime.",raw_outputs/granite33.txt#L3,primary_local +granite33,planning,"State machine with goal/resource stops, episodic replanning and rollback.","No actual transition interface, step/replan bound or rule for selecting a recoverable checkpoint.",raw_outputs/granite33.txt#L9,primary_local +granite33,learning,"Versioned pluggable supervised/unsupervised/reinforcement pipeline, held-out tests and degradation rollback.","No concrete candidate representation, train/test separation, measured metric or promotion threshold. Listing pipeline types does not show improvement beyond logs.",raw_outputs/granite33.txt#L13,primary_local +granite33,tools,"REST/JSON request/result contract, unique request IDs, result checksums and confidence scoring for uncertainty.",No actual request/receipt schema or deduplication procedure. IDs and checksums alone do not establish idempotency; a confidence score is not an action-specific observation of whether an effect happened.,raw_outputs/granite33.txt#L17,primary_local +granite33,world_representation,"Structured observations, probabilistic hypotheses/predictions, evidence links and prioritization of reliable evidence.","No operational reliability estimator, calibrated uncertainty or conflict-resolution algorithm. The mechanism is named at a broad level.",raw_outputs/granite33.txt#L21,primary_local +granite33,governance,"Permission management, explicit budgets and a dedicated shutdown service.",These are declared design features; the output supplies no concrete accounting or stop interface. No security claims are tested in this packet.,raw_outputs/granite33.txt#L26,primary_local +granite33,evaluation,"Three identifiable measurements: time to solution, plan success and resource utilization; remove episodic memory for an ablation.","No task corpus, budget-matched baseline, thresholds or failure definitions. The proposed metrics are not measured results.",raw_outputs/granite33.txt#L30,primary_local +granite33,persistence,"Two-phase commit, periodic snapshots and a Raft-style distributed log.","No transaction participants, coordinator recovery or protocol for a completed tool with a missing receipt. Naming distributed mechanisms does not make arbitrary filesystem effects transactional.",raw_outputs/granite33.txt#L34,primary_local +granite33,orchestration,"Central controller, bounded workers, RabbitMQ-style messaging and Paxos-style conflict resolution.","No reason a local single-controller system needs both replication and consensus layers; no membership, conflict semantics or coordination-cost measurement.",raw_outputs/granite33.txt#L38,primary_local +granite33,feasibility,"Three two-week phases, broad engine slice and integration of diverse learning components as risk.",The minimum slice remains too broad to be an acceptance test; estimates are unsupported by dependency or staffing analysis.,raw_outputs/granite33.txt#L42,primary_local +granite33,originality,Pluggable learning modules; flexibility versus overhead; reject for monolithic learning when overhead harms real-time use.,"Modularity is a useful common pattern, not established novelty. The rejection threshold is unspecified and the real-time-learning concern is not connected to the earlier offline-learning proposal.",raw_outputs/granite33.txt#L47,primary_local +tinyllama,memory,A section titled Memory Architecture describes an observation/action/observation loop.,"It does not distinguish the four requested memory types or specify storage, retrieval, provenance or forgetting. The section's actual content is a control-loop description.",raw_outputs/tinyllama.txt#L5,primary_local +tinyllama,planning,Repeatedly says planning selects the best action from observations and resources.,"No selection rule, state transition, stopping condition or recovery procedure.",raw_outputs/tinyllama.txt#L9,primary_local +tinyllama,learning,Repeats the same action-selection language used for planning.,"No learning candidate, offline evaluation, versions, held-out tests or rollback.",raw_outputs/tinyllama.txt#L13,primary_local +tinyllama,tools,"Names action/result contracts, validation, idempotency and uncertainty handling as responsibilities.","No fields, validation procedure, duplicate-request behavior or uncertain-effect reconciliation.",raw_outputs/tinyllama.txt#L17,primary_local +tinyllama,world_representation,"Says the phase distinguishes observations, hypotheses and predictions.","Does not represent any of those objects, confidence, evidence dependencies or contradictions.",raw_outputs/tinyllama.txt#L21,primary_local +tinyllama,governance,"Names user authority, permissions, budgets and stop control.",Supplies no concrete interface or accounting procedure; these are only stated requirements.,raw_outputs/tinyllama.txt#L25,primary_local +tinyllama,evaluation,"Calls observation, hypothesis and prediction three experiments; repeats the evaluation passage three times.","No task, metric, comparison or falsification rule; no component is actually selected for ablation.",raw_outputs/tinyllama.txt#L29,primary_local +tinyllama,persistence,"Calls observation, hypothesis and prediction bounded operations, and names transactions/restart; this section repeats.","No transaction boundary, snapshot/receipt schema or interrupted-action protocol.",raw_outputs/tinyllama.txt#L33,primary_local +tinyllama,orchestration,Opening and conclusion describe the architecture as modular and flexible.,"No dedicated orchestration section, controller/worker selection, coordination cost or conflict-resolution rule.",raw_outputs/tinyllama.txt#L1,primary_local +tinyllama,feasibility,"The Output section says there is a six-week sequence, minimum slice and largest risk.","It does not actually enumerate the sequence, select a slice or identify a risk.",raw_outputs/tinyllama.txt#L53,primary_local +tinyllama,originality,Says the insight is a concrete documentation-edit example.,"No design choice, tradeoff or rejection criterion; the claimed example is not supplied.",raw_outputs/tinyllama.txt#L49,primary_local +phi3,memory,"Defines four memory categories, provenance/context retrieval, time decay and user forgetting criteria.","The subsequent storage list contains only three categories, omitting procedural storage. No database/record schema, retrieval procedure or retention parameters are provided.",raw_outputs/phi3.txt#L3,primary_local +phi3,planning,"Planning module considers observations, goals, resources and constraints; stops on time/resource/user thresholds; restores prior state and replans after failure.","No explicit transition contract, actual thresholds, replan limit or distinction between reversible internal state and already completed external effects.",raw_outputs/phi3.txt#L8,primary_local +phi3,learning,"Periodic log analysis creates versioned candidates; test on held-out data, promote a better version and otherwise retain/revert the prior one.","Candidate representation, evaluation metric, train/test separation and the meaning of better are unspecified. The prose outlines a pipeline but does not demonstrate improvement.",raw_outputs/phi3.txt#L13,primary_local +phi3,tools,Validate requests against user constraints; track executed actions to avoid duplicates; return a probabilistic result and confidence for uncertainty.,No stable-ID/result schema or atomic deduplication procedure. A confidence value is not an observation resolving whether an uncertain effect occurred.,raw_outputs/phi3.txt#L18,primary_local +phi3,world_representation,Structured observations and hypotheses with source/context/evidence metadata; probabilistic predictions with confidence; mentions conflict resolution.,"No concrete conflict rule, uncertainty calibration or dependency-update procedure.",raw_outputs/phi3.txt#L23,primary_local +phi3,governance,"User-defined authority/permission constraints, monitored resource budgets and a stop control.","No concrete accounting, action-boundary or stopping interface. These declarations are not evaluated as security mechanisms in this packet.",raw_outputs/phi3.txt#L28,primary_local +phi3,evaluation,Compare average task time and CPU/memory/disk usage against a baseline on the same tasks; survey user satisfaction; suggests removing episodic memory or the bounded loop.,"No selected task corpus, exact baseline, sample size, success predicate, thresholds or single frozen ablation. These are proposed measurements, not executed results.",raw_outputs/phi3.txt#L33,primary_local +phi3,persistence,"Transaction log, restore last valid transaction, then claims a missing receipt will be stored when the log is flushed; also suggests manually saved receipts.","No durable representation of a receipt lost before commit, or observation that could reconstruct it after a crash. Flushing a log cannot by itself supply missing information. No transaction/effect ordering is specified.",raw_outputs/phi3.txt#L42,primary_local +phi3,orchestration,Single controller for simple tasks; bounded workers for complex parallel tasks; controller manages conflicts.,"No operational complexity rule, coordination-cost estimate or conflict-resolution algorithm.",raw_outputs/phi3.txt#L47,primary_local +phi3,feasibility,Says a six-week sequence will be followed; minimum slice includes memory/planning/learning; risk is uncertainty and the user-control/automation tradeoff.,It does not enumerate weeks or define a testable minimal milestone. The slice remains broad.,raw_outputs/phi3.txt#L52,primary_local +phi3,originality,Hybrid memory categories with complexity/performance tradeoff; reject when complexity outweighs benefits.,Restates categories requested by the prompt; gives no measurable rejection threshold or evidence of novel benefit.,raw_outputs/phi3.txt#L55,primary_local +qwen3,memory,"Working cache, SQLite episodes, versioned semantic storage and persistent procedural workspace; author/time/version provenance; illustrative 10-second/7-day/30-day/90-day retention.",No retrieval query or justification of retention values. A ten-second working-memory expiry could discard state still needed by a longer task. Source evidence retention and version-referenced records need separate rules.,raw_outputs/qwen3.txt#L5,primary_local +qwen3,planning,"Observe, plan, act, receive results; stop on completion/cancellation/step/resource limits; replan from cached data and fail if recovery fails.","No completion predicate, actual transition contract or recovery-attempt bound. Whole-task rollback is not defined for already completed external effects.",raw_outputs/qwen3.txt#L18,primary_local +qwen3,learning,"Offline model/rule versions, accuracy/efficiency-based promotion and rollback; explicitly distinguishes behavior change from event logging.","Says training uses a held-out dataset, making the training/tuning/final-test partition unclear. No concrete promotion thresholds or evidence of generalization is supplied.",raw_outputs/qwen3.txt#L32,primary_local +qwen3,tools,"Request fields include task description, parameters and expected output; results carry status/uncertainty; validation rules and delayed retry.",Declares actions idempotent without a deduplication key or effect-specific mechanism. A confidence score and retry delay do not resolve an uncertain completed effect.,raw_outputs/qwen3.txt#L43,primary_local +qwen3,world_representation,"Episodes for observations, semantic store for hypotheses, separate predictions, contradiction flags and deferring action for more information.",No evidence-link schema across stores or calibrated revision rule. Deferral is operationally meaningful but its bound is unspecified.,raw_outputs/qwen3.txt#L54,primary_local +qwen3,governance,"User approvals/cancellation, custom permissions, resource monitoring and signal/timeout stopping.",Describes intended behavior rather than concrete enforcement interfaces. No security evaluation is performed in this packet.,raw_outputs/qwen3.txt#L65,primary_local +qwen3,evaluation,Compare a learned model with a baseline; assess replanning and uncertain/contradictory cases; ablate learning.,"No metrics, corpus, sample size, budgets or falsification threshold for these particular experiments.",raw_outputs/qwen3.txt#L76,primary_local +qwen3,persistence,Treat each task as a transaction; do not acknowledge a tool completion without its receipt; ask the user to verify.,No durable action journal or crash/effect transaction ordering. Manual verification preserves uncertainty but is not an automatic reconciliation protocol; task rollback is not defined for arbitrary effects.,raw_outputs/qwen3.txt#L88,primary_local +qwen3,orchestration,"Single controller for simple tasks, bounded workers for complex ones; identifies coordination overhead; prioritize a worker or re-evaluate on conflict.","No selection threshold, priority rule or conflict semantics; scalability benefit is asserted rather than measured.",raw_outputs/qwen3.txt#L97,primary_local +qwen3,feasibility,"Concrete document-edit slice with basic UI/validation/routing; risks learning, tool consistency and orchestration edge cases.",Says a six-week sequence is proposed but does not enumerate it. No acceptance test or staffing/cost estimate is supplied.,raw_outputs/qwen3.txt#L108,primary_local +qwen3,originality,Task/context-based dynamic memory hierarchy; complexity and fragmentation tradeoff; reject if maintenance/performance costs dominate.,Does not reconcile that hierarchy with its earlier type-based stores or provide a retrieval interface. Rejection criteria remain qualitative.,raw_outputs/qwen3.txt#L117,primary_local +falcon3,memory,"Definitions of four memory types; interconnected/distributed storage, efficient retrieval, provenance and selective forgetting.","No chosen stores, record fields, retrieval mechanism or retention policy. The labels do not specify an implementation.",raw_outputs/falcon3.txt#L3,primary_local +falcon3,planning,Bounded observation/action loop; stop when further actions are unjustified or a predefined criterion is reached; rollback after failure.,"No explicit bounds, success predicate, state transition or recovery protocol for an already completed external effect.",raw_outputs/falcon3.txt#L17,primary_local +falcon3,learning,Version-controlled changes tested in isolation; incremental refinement and reversion after a failed plan.,"No held-out split, candidate representation, evaluation metric or promotion gate. Plan rollback and learned-policy rollback are not clearly separated.",raw_outputs/falcon3.txt#L23,primary_local +falcon3,tools,Declares an idempotent request/result contract; probabilistic uncertainty/confidence intervals.,"Defines sameness of result broadly without repeated-effect semantics, action IDs, receipt fields or validation rules. Probabilistic reasoning is not a concrete uncertain-effect reconciliation procedure.",raw_outputs/falcon3.txt#L29,primary_local +falcon3,world_representation,"Defines observations, hypotheses and predictions; probabilistic uncertainty and coherence/plausibility-based conflict handling.","No record schema, provenance dependencies, calibrated uncertainty or conflict decision rule.",raw_outputs/falcon3.txt#L35,primary_local +falcon3,governance,"Permission boundaries, budgets and a reliable stop control are declared in one paragraph.",No concrete interface or accounting procedure; no security mechanism is tested here.,raw_outputs/falcon3.txt#L41,primary_local +falcon3,evaluation,"Three identifiable themes: offline-learning efficiency, recovery reliability and coordination overhead in multi-threaded cases.","No operational metrics, corpus, thresholds or ablation. Its real-time adaptation language is not reconciled with offline evaluation.",raw_outputs/falcon3.txt#L45,primary_local +falcon3,persistence,"Says transaction boundaries and restart exist, and tool completions are logged even if interrupted.","No ordering, durable pending state or receipt reconstruction. The assurance is unsupported by a described mechanism.",raw_outputs/falcon3.txt#L55,primary_local +falcon3,orchestration,One controller with multiple bounded workers; mentions coordination cost and conflict resolution.,"No worker contract, conflict rule or measured scalability/overhead tradeoff.",raw_outputs/falcon3.txt#L59,primary_local +falcon3,feasibility,"Names a six-week sequence, core-function slice and integration of advanced AI as risk.",No actual weekly sequence or testable minimum slice; the risk remains broad.,raw_outputs/falcon3.txt#L63,primary_local +falcon3,originality,"Probabilistic reasoning for uncertainty, balancing precision and flexibility.",No useful operational tradeoff or condition for rejecting the choice; it describes when the method helps instead. Novelty is not established.,raw_outputs/falcon3.txt#L67,primary_local +danube3,memory,Echoes the request to distinguish memory types and specify storage/retrieval/provenance/forgetting.,No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L7,primary_local +danube3,planning,"Echoes the request for a bounded loop, stops and recovery.",No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L8,primary_local +danube3,learning,"Echoes the request for offline improvement, versioning, tests and rollback.",No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L9,primary_local +danube3,tools,"Echoes the request for contracts, validation, idempotency and uncertain outcomes.",No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L10,primary_local +danube3,world_representation,Echoes the request for observation/hypothesis/prediction separation.,No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L11,primary_local +danube3,governance,"Echoes the request for user control, permissions, budgets and stopping.",No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L12,primary_local +danube3,evaluation,Echoes the request for three experiments and an ablation.,No experiment or ablation proposed: prompt echo.,raw_outputs/danube3.txt#L13,primary_local +danube3,persistence,Echoes the request for transaction/restart and missing-receipt handling.,No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L14,primary_local +danube3,orchestration,Echoes the request to choose controller/workers and discuss coordination.,No proposed mechanism: prompt echo.,raw_outputs/danube3.txt#L15,primary_local +danube3,feasibility,"Echoes the request for a six-week sequence, minimum slice and risk.","No proposed sequence, slice or risk: prompt echo.",raw_outputs/danube3.txt#L16,primary_local +danube3,originality,"Echoes the request for a design choice, tradeoff and rejection condition.",No proposed insight: prompt echo.,raw_outputs/danube3.txt#L17,primary_local +gemini_flash_reference,memory,"Candidate A: Four stores, concrete event/fact schemas, BM25 and vector retrieval, event provenance, decay and archival (§1). Candidate B: Execution frame, SQLite events/FTS5, provenance hashes, triples with sources and last-verification times, Git procedures (§1).","Both supply implementable starting representations. Neither measures retrieval usefulness, contradiction calibration or retention cost. A source ID/hash alone does not establish a claim's truth. B's 0.20 pruning threshold is unexplained. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L3; raw_outputs/gemini_flash_reference.txt#L373,exploratory_hosted +gemini_flash_reference,planning,"Candidate A: Bounded DAG, verified nodes, 20-step cap, budget stop, diagnostic replanning with three-revision limit (§2). Candidate B: Observe/orient/plan/gate/execute/verify loop, depth 15 and failure events (§2).","Better specified than a list of planning technology names. A's illustrative `interface` block is not executable Python. Both need task-specific success predicates; maximum depth does not by itself bound the number of replans. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L49; raw_outputs/gemini_flash_reference.txt#L406,exploratory_hosted +gemini_flash_reference,learning,"Candidate A: Offline DSPy/LoRA candidates, versioning, held-out accuracy/latency gate and rollback after overruling threshold (§3). Candidate B: Offline policy artifacts, held-out pass-rate/cost gate, retain baseline on failed assertions (§3).","Neither demonstrates learning. Historical tasks/transcripts require disjoint train/evaluation partitions. A's description of the whole learning process as deterministic is unsupported; its 10%/20-run rollback rule is a proposal, not an established threshold. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L95; raw_outputs/gemini_flash_reference.txt#L435,exploratory_hosted +gemini_flash_reference,tools,"Candidate A: Request/receipt fields, schema validation, idempotency key, uncertain status and read probe (§4). Candidate B: JSON request/result, cached receipt on duplicate key, UNKNOWN followed by observation before retry (§4).","Both make uncertainty operational. Cached completion cannot cover a missing receipt without a durable intent and an action-specific reconciliation rule. Request-key binding and conflict handling still need specification. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L139; raw_outputs/gemini_flash_reference.txt#L458,exploratory_hosted +gemini_flash_reference,world_representation,"Candidate A: Observation/hypothesis/prediction records with source IDs, probabilities and contradiction-resolution goals (§5). Candidate B: Same distinction, confidence/evidence, contradiction event then confidence zero/new hypothesis (§5).","Neither calibrates numeric confidence. A leaves logical contradiction detection abstract. B's unconditional zeroing can discard a valid hypothesis when a new observation is mistaken; retain conflicting evidence and evaluate its reliability instead. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L177; raw_outputs/gemini_flash_reference.txt#L490,exploratory_hosted +gemini_flash_reference,governance,"Candidate A: Declares permission tiers, user approval, transactional budget counters and stop handling (§6). Candidate B: Declares capability tiers, user confirmation, ceilings and pause handling (§6).","Recorded as design declarations only. This packet does not execute or validate their security claims. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L206; raw_outputs/gemini_flash_reference.txt#L526,exploratory_hosted +gemini_flash_reference,evaluation,"Candidate A: Interrupted recovery, verified patching, budget ceiling and structured-memory ablation (§7). Candidate B: Interrupted recovery, adversarial-data experiment, offline policy promotion and epistemic-tier ablation (§7).","These are proposed tests, not results. A's unbounded-context baseline is not budget matched. B leaves statistical significance undefined. Neither justifies numerical thresholds. The adversarial-data experiment is recorded only and was not performed. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L239; raw_outputs/gemini_flash_reference.txt#L542,exploratory_hosted +gemini_flash_reference,persistence,"Candidate A: Places pending intent, filesystem effect and receipt before one SQL commit, then proposes scanning pending records after interruption (§8). Candidate B: Commits event and frame together, resumes pending logged actions through probes (§8).","A's stated recovery premise fails in the isolated transaction-ordering example: the uncommitted pending record disappears while the file effect remains. B does not explicitly show intent commit before dispatch; its recovery requires that ordering. Neither provides a general cross-system atomicity guarantee. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L250; raw_outputs/gemini_flash_reference.txt#L551,exploratory_hosted +gemini_flash_reference,orchestration,"Candidate A: Single controller, bounded read-only workers; serialize output collisions by timestamp (§9). Candidate B: Single state controller with bounded subprocesses (§9).","Practical for a local baseline. A's universal quadratic-overhead assertion is unjustified, and temporal serialization does not settle semantic contradictions. B gives no concrete worker-result conflict rule. No worker-performance measurements were made. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L276; raw_outputs/gemini_flash_reference.txt#L572,exploratory_hosted +gemini_flash_reference,feasibility,"Candidate A: Six-week schema/contracts/planning/mirror/evaluation/recovery sequence, file-patch slice, uncertain tool effects as risk (§10). Candidate B: Six-week state/tool/budget/epistemic/evaluation/mirror/test sequence, week-two slice, tool verification as risk (§10).","Roadmaps are estimates. Both partly propose rebuilding mechanisms described as already present in the prompt. Repository mapping should precede an overhaul; neither has inspected the full current implementation. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L299; raw_outputs/gemini_flash_reference.txt#L594,exploratory_hosted +gemini_flash_reference,originality,Candidate A: Shadow workspace with pre-apply checks; storage/latency tradeoff and 50GB/two-second rejection (§11). Candidate B: Explicit Python state transitions with LLM-generated proposals; code complexity tradeoff and unmaintainable-domain rejection (§11).,"Useful design choices, not demonstrated novel inventions. A's directory-level atomic swap needs filesystem and concurrent-edit assumptions. B's rejection condition needs an operational maintenance-cost criterion. Exploratory hosted reference; unknown backend/sampling, two candidates from one prompt.",raw_outputs/gemini_flash_reference.txt#L319; raw_outputs/gemini_flash_reference.txt#L617,exploratory_hosted diff --git a/research/ai_generated_agi_architectures/prompts.md b/research/ai_generated_agi_architectures/prompts.md new file mode 100644 index 0000000..4ccdf9b --- /dev/null +++ b/research/ai_generated_agi_architectures/prompts.md @@ -0,0 +1,35 @@ +# Prompts and collection method + +Every primary local model receives the exact user message below, with no separate system message, previous conversation, retrieval or tools. Model-specific GGUF chat templates remain in use. No prompt is adapted to a particular model. The request JSON for each model preserves the message and sampling parameters; raw output is kept separately from analysis. + +```text +Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions. + +Context: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority. + +Write an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context. + +1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting. +2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans. +3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs. +4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes. +5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling. +6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control. +7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results. +8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored. +9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution. +10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk. +11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it. + +Finish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation. +``` + +The eight-system roster and collection decisions are recorded in [the protocol](collection/protocol.md). Parameters are temperature 0.2, top-p 0.9, seed 20260908 and maximum 2,400 generated tokens, using a 4,096-token context and llama.cpp b10809. The remaining sampler defaults belong to that pinned runtime. This is one descriptive sample per model, not an estimate of each model's best possible performance. The nine-hundred-word instruction is a target, not an enforced word limit. + +A `length` finish reason denotes truncation. A `stop` finish reason does not imply that every instruction was followed. Missing examples, omitted mechanisms and unsupported claims are retained and discussed instead of repaired inside raw text. There are no claims of measured AGI capability, statistical independence between model families, or demonstrated effectiveness of the proposed architectures. + +The analyst sees the repository and all completed proposals when writing the comparison and synthesis. The models do not see one another's responses. Analyst additions, proposed experiments and code-specific observations are labelled separately. No human edits are made to the raw responses. + +An exploratory Gemini web reference was added after inspecting the first two local outputs. The same prompt was submitted once in a fresh conversation in the UI's Flash mode. The service automatically supplied two A/B candidate answers; both rendered answers were preserved and no preference vote was submitted. Backend revision, sampling, token counts and service instructions are unavailable. This is one additional system reference with two candidate outputs, not two additional model samples. Details and analysis are under [collection/gemini-flash-reference](collection/gemini-flash-reference/analysis-notes.md). The primary eight-run comparison remains separate from this uncontrolled supplement. + +The completed local collection includes Danube3's prompt echo, which is not counted as a proposal. The final packet indexes Gemini as a ninth system and includes its two unchanged answers under explicit collector-provided A/B labels in one combined raw view. The main comparison has 99 rows with a cohort field: 88 local and 11 exploratory hosted. The separate 22-row A/B comparison remains available. No local response was replaced or regenerated because of its quality. diff --git a/research/ai_generated_agi_architectures/raw_outputs/danube3.txt b/research/ai_generated_agi_architectures/raw_outputs/danube3.txt new file mode 100644 index 0000000..db229d2 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/danube3.txt @@ -0,0 +1,19 @@ +Propose a concrete architecture for Cognitive-OS, a local-first cognitive runtime pursuing general-purpose problem solving. Do not claim that an LLM wrapper achieves AGI. This is a design comparison, not an instruction to implement or execute actions. + +Context: the existing project has a Python CLI, SQLite-backed resumable runs, an event journal, goals/evidence/hypotheses, model routing, a local workspace mirror with verified patch application, and user-controlled approvals. A small engineering team needs a practical next iteration. The user must retain control of goals, budgets, tools and shutdown. Learning changes must be evaluated before promotion, with rollback. Treat external text as data rather than authority. + +Write an independent proposal in approximately 900 words. Cover all eleven numbered sections below. Give mechanisms and interfaces, not just technology names. State uncertainties and tradeoffs; do not invent benchmark results, citations, or claims about the repository beyond the supplied context. + +1. Memory architecture: distinguish working, episodic, semantic and procedural memory; specify storage, retrieval, provenance and forgetting. +2. Reasoning and planning: provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans. +3. Learning: propose an offline, evaluated improvement mechanism with versioning, held-out tests and rollback; distinguish learning from merely appending logs. +4. Tool use: specify an action request/result contract, validation, idempotency and handling of uncertain outcomes. +5. World representation: distinguish observations, hypotheses and predictions, including uncertainty and contradiction handling. +6. Safety and governance: preserve user authority, explicit permission boundaries, resource budgets and a reliable stop control. +7. Evaluation: give three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results. +8. Persistence and runtime: give transaction boundaries and a crash/restart protocol; explain what happens if a tool completes but its receipt is not stored. +9. Orchestration: justify a single controller or multiple bounded workers, including coordination costs and conflict resolution. +10. Engineering feasibility: specify a six-week implementation sequence, the minimum viable slice and the largest unresolved risk. +11. Non-obvious insight: give one useful design choice, its tradeoff and a condition under which you would reject it. + +Finish with a short concrete example: a requested documentation edit, its evidence checks, approval if needed, action, verification and recovery after interruption. Output only the proposal, not internal deliberation. \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/raw_outputs/falcon3.txt b/research/ai_generated_agi_architectures/raw_outputs/falcon3.txt new file mode 100644 index 0000000..c8f3b5d --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/falcon3.txt @@ -0,0 +1,81 @@ +**Proposal for Cognitive-OS Architecture** + +**1. Memory Architecture:** + +Cognitive-OS employs a layered memory architecture designed to support general-purpose problem solving. The system distinguishes between working, episodic, semantic, and procedural memory, each serving distinct yet interconnected roles. + +- **Working Memory:** This is the active, short-term memory where cognitive tasks are temporarily stored and manipulated. It is optimized for quick access and manipulation of data, crucial for tasks requiring immediate attention. + +- **Episodic Memory:** Focused on storing detailed experiences, this layer captures sequences of events and their context. It aids in understanding the temporal and spatial relationships between actions and outcomes. + +- **Semantic Memory:** This stores factual knowledge and general concepts, facilitating abstract reasoning and understanding. It supports the system's ability to generalize and make inferences. + +- **Procedural Memory:** Concerned with the learned sequences of actions, procedural memory is essential for automating tasks and achieving procedural fluency. + +Each type of memory is stored in a distributed yet interconnected manner, with retrieval optimized for efficiency and provenance tracked for accountability. Forgetting mechanisms are designed to selectively remove outdated or irrelevant information, ensuring that the system remains adaptive and responsive. + +**2. Reasoning and Planning:** + +Cognitive-OS implements a bounded observation-to-action loop, ensuring that each action is grounded in a coherent set of observations. This loop is designed to terminate when no further actions can be justified or when the system reaches a predefined stopping criterion. + +Recovery from failed plans is facilitated through a rollback mechanism, allowing the system to revert to previous states and learn from past experiences. This approach ensures that learning is not merely appended to logs but is actively evaluated and refined. + +**3. Learning:** + +The system incorporates an offline evaluation mechanism for learning improvements. This involves version-controlled repositories where changes are tracked and tested in isolation. If a plan fails, the system can revert to the previous version, allowing for incremental learning and rollback. + +This approach distinguishes learning from mere logging, focusing on the active refinement of cognitive processes. It supports the system's ability to adapt and improve over time, even in the face of unforeseen challenges. + +**4. Tool Use:** + +The action request/result contract is designed to ensure idempotency and handle uncertain outcomes gracefully. This contract specifies that a single request should yield the same result regardless of how it is invoked, enhancing reliability and predictability. + +Uncertain outcomes are managed through probabilistic reasoning, where the system can provide confidence intervals or probabilistic predictions. This approach allows for a nuanced understanding of the system's capabilities and limitations. + +**5. World Representation:** + +Cognitive-OS distinguishes between observations, hypotheses, and predictions, each serving a unique purpose in cognitive processing. Observations are the raw data captured by the system, hypotheses are tentative explanations derived from observations, and predictions are the system's forecasts based on these hypotheses. + +Uncertainty is integrated into these representations, allowing for a probabilistic understanding of the world. Contradictions are handled through mechanisms that evaluate the coherence and plausibility of different hypotheses, ensuring that the system remains coherent and rational. + +**6. Safety and Governance:** + +User authority is preserved through explicit permission boundaries, resource budgets, and a reliable stop control mechanism. This ensures that the system remains accountable and controllable, even in complex cognitive tasks. + +**7. Evaluation:** + +Three measurable experiments are proposed to evaluate Cognitive-OS's architectural benefits: + +- **Experiment 1:** Assesses the system's ability to learn and adapt in real-time, focusing on the efficiency and effectiveness of the offline evaluation mechanism. + +- **Experiment 2:** Evaluates the system's robustness and reliability by testing its ability to handle failures and recover from them. + +- **Experiment 3:** Investigates the system's performance in complex, multi-threaded scenarios, examining the overhead and conflict resolution mechanisms. + +**8. Persistence and Runtime:** + +Transactions are managed with clear boundaries and a crash/restart protocol. The system ensures that tool completions are logged and stored, even if the tool is interrupted. + +**9. Orchestration:** + +A single controller orchestrates multiple bounded workers, balancing coordination costs and conflict resolution. This design ensures that the system can scale efficiently while maintaining control and coordination. + +**10. Engineering Feasibility:** + +A six-week implementation sequence is proposed, with the minimum viable slice focusing on core functionalities and the largest unresolved risk being the integration of advanced AI components. + +**11. Non-obvious Insight:** + +A key design choice is the use of probabilistic reasoning for uncertainty handling. This choice balances the need for precise predictions with the flexibility to handle unexpected situations. Under conditions where the system encounters significant uncertainty or ambiguity, probabilistic reasoning provides a more robust framework for decision-making. + +**Concrete Example:** + +**Documentation Edit:** + +- **Evidence Checks:** The documentation is reviewed for clarity, completeness, and adherence to the outlined architectural principles. +- **Approval:** If necessary, the documentation is approved for publication, ensuring it aligns with the proposed architecture. +- **Action:** The approved documentation is published, making it accessible to the engineering team. +- **Verification:** The team verifies the documentation against the outlined requirements, ensuring it meets the architectural specifications. +- **Recovery:** If the documentation is interrupted during publication, the system can recover by reverting to the last known state, maintaining the integrity of the project. + +This example illustrates the systematic approach to documenting and validating the proposed architecture, ensuring that it is both robust and aligned with the cognitive runtime's core objectives. \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/raw_outputs/gemini_flash_reference.txt b/research/ai_generated_agi_architectures/raw_outputs/gemini_flash_reference.txt new file mode 100644 index 0000000..78dc350 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/gemini_flash_reference.txt @@ -0,0 +1,693 @@ +=== Collector label: Gemini Flash web, candidate A === + +1. Memory Architecture + ┌─────────────────────────────────────────┐ + │ Working Memory │ + │ - Current goal, context window RAM │ + │ - Capacity: ~8–32k tokens │ + └────────────────────┬────────────────────┘ + │ Summarize / Extract + ▼ +┌───────────────────────────────────────────────────┴───────────────────────────────────────────────────┐ +│ Cognitive Storage Subsystem │ +├───────────────────────────────────┬───────────────────────────────────┬───────────────────────────────┤ +│ Episodic Memory │ Semantic Memory │ Procedural Memory │ +│ - Raw events & tool traces │ - Entities, facts, assertions │ - Parametric weights, prompts │ +│ - Engine: DuckDB / SQLite │ - Engine: SQLite + HNSW vector │ - Storage: Git repo & disk │ +│ - Retention: Time/importance decay│ - Provenance: Inverted index │ - Retain via eval benchmark │ +└───────────────────────────────────┴───────────────────────────────────┴───────────────────────────────┘ + + +Working Memory: Volatile, bounded context window (RAM). Holds the active plan step, local workspace state, and tool outputs for the immediate loop. Strictly limited to 8,000–32,000 tokens depending on the active model target. + +Episodic Memory: Immutable event log storing execution traces, raw tool inputs/outputs, and environment responses. + +Storage: SQLite or DuckDB table episodic_events(event_id, run_id, timestamp, event_type, payload_json). + +Retrieval: Recency-weighted keyword search and BM25 indexing over raw text outputs. + +Semantic Memory: Structured facts, domain entities, and operational assertions extracted from episodic traces. + +Storage: SQLite relational tables with sqlite-vss or HNSW vector index for embeddings: facts(fact_id, subject, predicate, object, confidence, source_event_id). + +Retrieval: Hybrid top-K vector similarity search blended with exact key-value matching. + +Procedural Memory: System prompts, tool invocation schemas, workflow templates, and fine-tuned model parameters. + +Storage: Versioned Git repository containing prompt templates, Python execution scripts, and adapter weight paths. + +Provenance: Every semantic fact and procedural template retains a mandatory foreign key pointing to its originating event_id or explicit user input. External model outputs never gain direct semantic authority without explicit run verification. + +Forgetting: Episodic logs undergo exponential recency decay S=S +0 + ​ + +⋅e +−λt +. Low-salience items (S<τ) drop out of active index vector stores and archive to compressed cold storage on disk. + +2. Reasoning and Planning + ┌──────────────────────────────┐ + │ Observe Environment │ + └──────────────┬───────────────┘ + │ + ▼ + ┌──────────────────────────────┐ + │ Generate/Refine Plan (DAG) │ + └──────────────┬───────────────┘ + │ + ▼ + ┌──────────────────────────────┐ + │ Evaluate Stopping Criteria │ + └──────┬────────────────┬──────┘ + │ │ + Goal Satisfied │ │ Failure / Loop Exceeded + ┌───────────┘ └───────────┐ + ▼ ▼ + ┌──────────────┐ ┌──────────────┐ + │ Return Trace │ │ Fallback Mode│ + └──────────────┘ └──────────────┘ + + +The system operates on an explicit Bounded Observation-to-Action Loop: + +Python +interface ReasoningLoop: + def execute_step(run_id: str, working_memory: WorkingMemory) -> StepResult: + observation = observe_environment(working_memory) + plan_dag = generate_or_refine_plan(observation, working_memory.goal) + next_action = plan_dag.get_next_runnable_action() + + if stopping_criteria_met(plan_dag, working_memory): + return StepResult(status="COMPLETE", output=working_memory.get_final_answer()) + + return execute_action(next_action) + + +Stopping Criteria: Execution halts when (1) all DAG goal nodes evaluate to VERIFIED_TRUE, (2) the iteration count exceeds N +max + ​ + + (default: 20 steps), or (3) accumulated monetary/token budget crosses user limits. + +Recovery from Failed Plans: If a step returns an error or fails validation, the controller blocks execution of child nodes in the DAG. It enters a Diagnostic State, generates a failure hypothesis referencing the error event ID, prunes invalid DAG branches, and constructs a recovery sub-plan. If three consecutive plan revisions fail, the loop pauses and prompts for human intervention. + +3. Learning + +Learning in Cognitive-OS is strictly offline, deterministic, and evaluated. The system never updates production weights or prompt primitives online during execution. + +┌─────────────────┐ ┌──────────────────┐ ┌──────────────────┐ ┌─────────────────┐ +│ Run Artifacts & │────>│ Offline Learner │────>│ Candidate Candidate│───>│ Held-out Eval │ +│ Event Logs │ │ (DSPy / LoRA) │ │ Prompt / Weights │ │ Benchmark Suite │ +└─────────────────┘ └──────────────────┘ └──────────────────┘ └────────┬────────┘ + │ + Passes Eval │ + ▼ + ┌─────────────────┐ + │ Promote to Git │ + │ Production │ + └─────────────────┘ + + +Candidate Generation: Unsuccessful or sub-optimal run logs are processed by offline optimization jobs (e.g., DSPy prompt optimization or LoRA fine-tuning). + +Versioning: Output artifacts are assigned a semantic version (prompts/v1.2.0 or adapters/v0.4.1) stored in Git. + +Held-Out Evaluation: Candidate versions run against a deterministic, local benchmark suite containing historic tasks with ground-truth verification assertions. + +Promotion & Rollback: A candidate artifact is promoted to production if and only if: + +Accuracy +candidate + ​ + +≥Accuracy +production + ​ + +ANDLatency +candidate + ​ + +≤1.15×Latency +production + ​ + + +If post-promotion execution triggers human overrules exceeding a 10% threshold over 20 runs, the runtime automatically rolls back the Git HEAD to the previous tagged production release. + +4. Tool Use + +Tools interact through an isolated RPC contract. Tools must declare whether they are read-only, idempotent, or side-effecting. + +Python +class ToolContract(Protocol): + name: str + is_idempotent: bool + requires_approval: bool + + def validate_args(self, args: dict) -> ValidationResult: ... + def execute(self, request: ActionRequest) -> ActionReceipt: ... + + +Contract: + +JSON +ActionRequest { + "request_id": "req_8f9a2b", + "tool_name": "patch_workspace", + "arguments": {"path": "src/main.py", "diff": "..."}, + "idempotency_key": "chk_992a_src_main_py_v2" +} + +JSON +ActionReceipt { + "request_id": "req_8f9a2b", + "status": "SUCCESS" | "FAILED" | "UNCERTAIN", + "execution_time_ms": 142, + "output": "...", + "state_hash": "sha256_b4e1..." +} + + +Validation: Schema parameters are validated via Pydantic before invocation. Inputs violating static checks fail fast without burning LLM context. + +Handling Uncertain Outcomes: When a tool timeout or network disconnect occurs (UNCERTAIN), the runtime tags the tool execution as non-determinate. It executes a dedicated read-only probe tool (e.g., check_file_hash or git_status) to re-observe the actual workspace state before permitting re-execution. + +5. World Representation + +The runtime models environment reality explicitly through a three-tier schema, treating external data strictly as untrusted observations: + +┌────────────────────────────────────────────────────────────────────────┐ +│ World Model │ +├────────────────────────────────────────────────────────────────────────┤ +│ Observations : Raw environment outputs (untrusted facts) │ +│ Hypotheses : Inferred explanations tagged with probability p │ +│ Predictions : Expected next states if action A is taken │ +└────────────────────────────────────────────────────────────────────────┘ + + +Observations: Raw, unparsed data returned by tool receipts (Observation(source="tool", payload=...)). + +Hypotheses: Model-generated interpretations tagged with explicit probability p∈[0,1] and links to supporting evidence IDs (Hypothesis(claim="Function X is deprecated", p=0.75, evidence=["evt_12"])). + +Predictions: Expected outcomes attached to candidate actions prior to execution (Prediction(action="run_test", expected="PASS")). + +Contradiction Handling: If two active hypotheses exhibit logical contradiction (e.g., H +1 + ​ + +⟹A and H +2 + ​ + +⟹¬A), the runtime assigns an Uncertainty Penalty to the node, halts autonomous execution, and adds a state-resolution goal to the DAG. + +6. Safety and Governance + +User authority is enforced via a non-bypassable local proxy layer embedded within the controller: + +┌──────────────┐ ActionRequest ┌──────────────────┐ Approved ┌──────────────┐ +│ Controller │───────────────────────>│ Security Proxy │─────────────────>│ Tool Runner │ +└──────────────┘ └─────────┬────────┘ └──────────────┘ + │ Unapproved + ▼ + ┌──────────────────┐ + │ Human Approver │ + │ (CLI Prompt/TUI) │ + └──────────────────┘ + + +Permission Boundaries: Actions belong to explicit policy tiers: + +Tier 0 (Safe): Read-only workspace queries, search. Executed automatically. + +Tier 1 (Bounded): Local file modifications inside workspace root. Executed automatically within budget. + +Tier 2 (High Impact): Shell commands, network requests, system modifications. Requires manual CLI keypress approval. + +Resource Budgets: Hard limits enforced per run via SQLite transactional counters: + +max_wall_time_seconds: 600 + +max_tokens: 100,000 + +max_dollar_cost: $2.00 + +Reliable Stop Control: OS signal handlers (SIGINT, SIGTERM) trap controller execution. Receipt of SIGINT breaks the loop immediately at the nearest tool boundary, serializes the current execution state to SQLite, revokes all child worker processes, and safely exits without corrupting state. + +7. Evaluation + +To evaluate architectural claims without synthetic performance assertions, the platform uses three local empirical benchmarks and one ablation test: + +Experiment Name Target Metric Success Criterion Falsification Conditions +E1: Local Interrupt Recovery Recovery Rate after SIGKILL mid-step 100% recovery of state consistency via transaction boundary replay Loss of run history or double-execution of non-idempotent actions +E2: Workspace Patch Verification Deterministic patch application rate Zero unverified/hallucinated edits applied to filesystem Patch applied without passing workspace test/lint checks +E3: Budget Hard-Cap Enforcement Token and financial ceiling breach 0% budget overrun across 50 forced infinite-loop prompts Execution continues past 100.0% allocated dollar/token limit + +Ablation (Structured Memory vs. Flat Context): Run 50 multi-step edit tasks comparing Cognitive-OS (working + semantic memory) against a baseline LLM loop with an unbounded raw context buffer. If structured semantic memory does not reduce token consumption by ≥30% while maintaining equal completion accuracy, the semantic memory overhead hypothesis is falsified. + +8. Persistence and Runtime + +The system guarantees consistency across system crashes by using dual-write transactional constraints across SQLite and disk state. + +┌────────────────────────────────────────────────────────────────────────┐ +│ Transactional DB Envelope │ +│ │ +│ 1. BEGIN TRANSACTION │ +│ 2. Insert ActionRequest(status="PENDING") │ +│ 3. Execute Tool Action on Filesystem │ +│ 4. Insert ActionReceipt(status="SUCCESS") │ +│ 5. Update Run State │ +│ 6. COMMIT TRANSACTION │ +└────────────────────────────────────────────────────────────────────────┘ + + +Uncertain State Protocol (Missing Receipt): If the runtime crashes after step 3 but before step 6, recovery on restart proceeds as follows: + +Scan SQLite for ActionRequest entries marked PENDING or lacking a corresponding ActionReceipt. + +Check the tool's is_idempotent flag. + +If idempotent, re-run execution. + +If non-idempotent, invoke tool verification callback (verify_state()). If target state matches expected hash, write missing ActionReceipt(status="SYNTHESIZED"). If state does not match, log a state corruption error and prompt for user reconciliation. + +9. Orchestration + +Cognitive-OS uses a Single Controller with Bounded Sub-Workers model. + + ┌─────────────────────────┐ + │ Single Controller │ + │ (Monotonic State) │ + └────────────┬────────────┘ + │ Distributes Sub-tasks + ┌─────────────┴─────────────┐ + ▼ ▼ + ┌─────────────────────┐ ┌─────────────────────┐ + │ Sub-Worker 1 │ │ Sub-Worker 2 │ + │ (Read-Only Search) │ │ (Read-Only Linting) │ + └─────────────────────┘ └─────────────────────┘ + + +Justification: Multi-agent consensus systems introduce quadratic communication overhead, race conditions on disk state, and non-deterministic behavior. A single controller enforces a strictly monotonic state machine. + +Bounded Sub-Workers: Workers are stateless short-lived processes spawned strictly for read-only sub-tasks (e.g., parallel search across codebases). Sub-workers cannot mutate state or call Tier-2 tools directly; they return structured observations back to the single controller. + +Conflict Resolution: Sub-worker output collisions are serialized by timestamp in the primary controller's SQLite event loop before evaluation. + +10. Engineering Feasibility + +Six-week implementation roadmap for a small core team: + +┌──────────────┬─────────────────────────────────────────────────────────┐ +│ Week │ Focus / Deliverables │ +├──────────────┼─────────────────────────────────────────────────────────┤ +│ Week 1 │ SQLite schema, WAL setup, transactional engine │ +│ Week 2 │ Action contract, schema validator, Tier 0/1/2 proxy │ +│ Week 3 │ Bounded reasoning loop & DAG execution state machine │ +│ Week 4 │ Local workspace mirror & state hash verifier │ +│ Week 5 │ Offline DSPy evaluation benchmark pipeline & rollback │ +│ Week 6 │ Hardened SIGINT handler, crash-recovery test suite │ +└──────────────┴─────────────────────────────────────────────────────────┘ + + +Minimum Viable Slice (MVS): CLI-driven transactional loop capable of reading a file, generating a diff, prompting for manual CLI execution approval, applying the patch, validating via pytest, and resuming state seamlessly after kill -9. + +Largest Unresolved Risk: Tool execution non-determinism (e.g., partial shell script execution modifying global environmental state) where state recovery cannot be reliably achieved via disk hash verification alone. + +11. Non-Obvious Insight + +Design Choice: Strict Invariant-Based File Mirroring with Pre-Apply Shadow Staging. + +Mechanism: The workspace is never patched directly. Patches apply to a .cog/shadow git directory first. AST/Linter/Unit-test passes run in the shadow tree before atomic move swap to the active workspace. + +Tradeoff: Introduces storage overhead (2x file workspace footprint) and latency on large asset trees. + +Rejection Condition: Reject this choice if workspace directories exceed 50GB or contain large binary blobs where clone/link overhead surpasses 2.0 seconds per step. + +Concrete Execution Trace +Scenario + +User requests: "Update docstring in math_utils.py to document the divide-by-zero error handling." + +[USER INPUT] -> "Update docstring in math_utils.py to document the divide-by-zero error handling." + │ + ├─► [CONTROLLER] Creates run_id="run_101", writes event to SQLite: GOAL_ACCEPTED + │ + ├─► [OBSERVE] Reads math_utils.py content -> Stores payload as Observation(obs_01) + │ + ├─► [HYPOTHESIS] Generated: H_1 = "math_utils.py lacks explicit ZeroDivisionError in docstring" (p=0.95) + │ + ├─► [PLAN] DAG Step 1: Stage diff in .cog/shadow/math_utils.py + │ + ├─► [TOOL ACTION REQUEST] + │ Tool: patch_workspace (Tier 1 Action) + │ Request ID: req_aabb12 + │ Payload: {"path": "math_utils.py", "diff": "@@ -4,3 +4,5 @@ ..."} + │ Check: Target path inside workspace boundary? PASS. + │ + ├─► [ACTION EXECUTION] Diff applied to shadow tree. Hash: sha256_e1f82... + │ + ├─► [VERIFICATION STEP] Runs `python -m py_compile .cog/shadow/math_utils.py` -> SUCCESS + │ + ├─► [STATE COMMIT] Move shadow/math_utils.py -> math_utils.py. SQLite: ActionReceipt(req_aabb12, SUCCESS) + │ + │ ⚡ [INTERRUPTION OCCURS: Power Loss / SIGKILL] ⚡ + │ + └─► [SYSTEM REBOOT & RECOVERY] + 1. CLI re-launched: `cog-os resume run_101` + 2. Runtime reads SQLite event journal. + 3. Sees req_aabb12 has ActionReceipt(SUCCESS) and state_hash matches math_utils.py on disk. + 4. Validates final state: Runs project tests -> PASS. + 5. Goal status set to VERIFIED_TRUE. Exits gracefully. + +To refine this proposal further, explore one of these technical deep dives: +Explore the transaction state machine and crash recovery engine +Design the offline evaluation pipeline for candidate prompts and weights +Draft the security proxy specifications for tool execution + +=== Collector label: Gemini Flash web, candidate B === + +Cognitive-OS: Architectural Proposal for a Local-First Cognitive Runtime +1. Memory Architecture + +Cognitive-OS separates memory by operational lifecycle and mutability rather than a unified vector index. + +Working Memory: Represented as an in-memory execution frame held in Python dataclasses. Contains the active goal, current observation window, active hypothesis tree, and short-term workspace diffs. + +Episodic Memory: Stored in SQLite as append-only events and runs tables. Queries use hybrid lexical search (FTS5) and sparse/dense embeddings over run transcripts. Each episode stores a cryptographic provenance hash, tool call receipts, user approval events, and parent run IDs. + +Semantic Memory: A local SQLite-backed knowledge graph (entities, claims, relations) with an attached key-value store for consolidated concepts. Updates require explicit distillation jobs. Every triple includes a confidence float, sources (pointing to episodic event IDs), and last_verified_at timestamp. + +Procedural Memory: Executable tool definitions, prompt templates, and macro workflows stored as version-controlled code files on disk and indexed in a SQLite procedural_skills table. + +Forgetting Policy: Episodic memory uses time-decayed recency scoring combined with access frequency. Unreferenced episodic logs move to compressed cold-storage archives. Semantic triples decaying below confidence 0.20 without re-verification are flagged for pruning. + + +-------------------------------------------------------+ + | WORKING MEMORY | + | (Active Goal, Frame Data, Diff Queue, Draft Hypotheses)| + +-------------------+---------------+-------------------+ + | | + Read / Distill | | Append Events + v v + +------------------------+ +------------------------+ + | SEMANTIC MEMORY | | EPISODIC MEMORY | + | (SQLite Knowledge Graph| | (SQLite Event Journal, | + | & Key-Value Concepts) | | FTS5, Provenance Hash)| + +------------------------+ +------------------------+ + ^ + Skill Indexing | + +-----------+------------+ + | PROCEDURAL MEMORY | + | (Tool Code, Workflows) | + +------------------------+ + +2. Reasoning and Planning + +The core execution cycle operates as a bounded Observation-to-Action state machine: + +Observe⟶Orient (Hypothesize)⟶Plan⟶Evaluate Policy⟶Execute/Tool⟶Verify + +---------+ +--------+ +------+ +---------------+ + | Observe | ---> | Orient | ---> | Plan | ---> | Policy Gate | + +---------+ +--------+ +------+ +-------+-------+ + ^ | + | +-----------------+ | + +---------------| Verify & Audit | <-------------+ (Execute Action) + +-----------------+ + +Stopping Criteria + +The loop terminates when any of these conditions are met: + +Target goal verification checks pass. + +Allocated budget (tokens, tool call count, time) is exhausted. + +Unrecoverable tool error occurs or user explicitly denies approval. + +Maximum plan depth (N=15 steps) is exceeded. + +Failure Recovery + +When an action fails verification or returns an execution error, the engine appends a PlanFailureEvent to working memory. It decrements the hypothesis score, rolls back uncommitted local file modifications via git or workspace mirrors, and re-invokes the planner with explicit negative constraints derived from the failure log. + +3. Offline Learning & Policy Evaluation + +Learning in Cognitive-OS is defined strictly as structural adaptation evaluated offline, distinct from adding log entries to context. + ++------------------+ +-------------------+ +------------------+ +| Candidate Policy | --> | Held-out Suite | --> | Regression Gate | +| (Prompts/Skills) | | (Benchmark Runs) | | (Pass Rate/Cost) | ++------------------+ +-------------------+ +--------+---------+ + | + +------------+------------+ + | | + [Pass: Promote] [Fail: Rollback] + +Mechanism + +Extraction: An offline background worker analyzes episodic event logs to identify repeated multi-step plan structures or frequent prompt failures. + +Candidate Generation: The worker generates a modified candidate policy artifact (e.g., an updated prompt layout, new tool wrapper, or heuristic tool routing rule). + +Validation Suite: Candidate artifacts are evaluated against a local held-out test suite consisting of past deterministic run transcripts and synthetic benchmarks. + +Promotion & Rollback: If the candidate improves pass rates without increasing average token consumption or runtime cost above a defined threshold, it is committed to procedural memory with a semantic version tag (e.g., v1.4.0). If the candidate fails any critical assertion or safety constraint, it is discarded, and the system retains the existing baseline version. + +4. Tool Execution Contract + +Tools interact with the runtime via a strict JSON Schema interface: + +JSON +{ + "tool_name": "patch_apply", + "request_id": "req_8f9a2c", + "idempotency_key": "ik_771b90d2", + "parameters": { + "target_file": "docs/api.md", + "diff": "@@ -12,3 +12,3 @@..." + } +} + +JSON +{ + "request_id": "req_8f9a2c", + "status": "SUCCESS", + "exit_code": 0, + "result_payload": { "bytes_modified": 142 }, + "verification_hash": "sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855" +} + +Contract Rules + +Validation: Inputs are validated against tool schemas prior to execution. Schema mismatches fail immediately before reaching the system shell or file system. + +Idempotency: Write tools require an explicit idempotency_key. The runtime records completed invocations in SQLite. Duplicate invocations return cached receipt objects without executing side effects again. + +Uncertain Outcomes: If a tool times out or drops connection without returning a receipt, its state is marked UNKNOWN. The runtime must execute a read-only verification probe before retrying the mutation. + +5. World Representation & Epistemics + +World state is partitioned into three epistemological tiers: + +Observations (O): Immutable data retrieved directly from local files, tool receipts, or command outputs. + +Hypotheses (H): Probabilistic interpretations generated by models to explain observations or bridge missing context. Each hypothesis carries a confidence score (c∈[0.0,1.0]) and pointers to supporting observations. + +Predictions (P): Expected upcoming observations given a planned action A and hypothesis H. + +Contradiction Handling + +When a new observation O +new + ​ + + directly contradicts an established claim in semantic memory or an active hypothesis H +A + ​ + +: + +An explicit ContradictionDetectedEvent is logged. + +The system drops confidence on H +A + ​ + + to zero and branches a new hypothesis H +B + ​ + +. + +External text ingested from unstructured sources is tagged as UNTRUSTED_DATA and stripped of instruction execution privileges. + +6. Safety, Boundaries & Control + +User authority is enforced through physical barriers in the execution pathway rather than prompt engineering: + ++-------------------+ +------------------+ +-------------------+ +| Action Request | --> | Permission & | --> | Human-in-the-Loop | --> Execution +| (Tool Call & Args)| | Budget Check | | Approval Gate | ++-------------------+ +------------------+ +-------------------+ + + +Permission Boundaries: Tools are classified into capability tiers (READ_ONLY, MUTATE_LOCAL, MUTATE_NETWORK). MUTATE_NETWORK and non-whitelisted MUTATE_LOCAL operations require explicit interactive terminal confirmation from the user. + +Budget Controls: Every run is bounded by hard ceilings configured by the user: max_wall_time_seconds, max_llm_cost_usd, and max_tool_invocations. Crossing any ceiling triggers an immediate freeze. + +Interrupt / Stop Control: A dedicated POSIX signal handler (SIGINT / Ctrl+C) intercepts execution, flushes active transactions to SQLite, sets run state to PAUSED, and safely exits the event loop without corrupting state. + +7. Evaluation & Ablation Frame + +To evaluate architectural choices, three measurable experiments and one structural ablation are defined: + +Experiment / Ablation Objective Metric Success Criterion +E1: Interrupted Recovery Measure run resume efficiency after hard process kill (SIGKILL). Time to resume state & percentage of duplicated tool calls. Zero duplicate side effects; resume overhead <2.0 seconds. +E2: Adversarial Data Ingestion Test resilience against prompt injection in external documentation. Rate of unauthorized system operations triggered by ingested text. 0% execution of hidden instructions embedded in data files. +E3: Offline Policy Promotion Verify offline offline-evaluated skill promotion prevents performance degradation. Regression rate across held-out test suite over 10 policy iterations. Zero safety regressions; overall benchmark pass rate non-decreasing. +Ablation: No Epistemic Tiering Remove separation between Observations and Hypotheses (treat model assertions as ground truth). Unhandled contradiction rate & hallucinated plan propagation. Significant increase in persistent errors validates epistemic tiering. +8. Persistence & Crash Recovery +Transaction Boundaries + +All engine state changes occur within SQLite transactions (BEGIN IMMEDIATE). An event commit requires writing both the event entry and the updated execution frame atomically. + +Crash / Restart Protocol + +On boot, the engine scans SQLite for runs with state IN_PROGRESS. + +It fetches the last committed event_id and checks the status of pending tool receipts. + +If an unacknowledged tool invocation was logged prior to crash: + +Execute the tool's verification read probe. + +If state change applied, write synthetic completion receipt to database. + +If state change did not apply, re-queue the tool request. + +Execution frame resumes from the last valid checkpoint. + +9. Orchestration Architecture + +Cognitive-OS uses a single controller with bounded worker subprocesses, avoiding full multi-agent peer-to-peer topologies. + + +-------------------+ + | SINGLE CONTROLLER | + | (State Engine & | + | Event Loop) | + +---------+---------+ + | + +---------------------+---------------------+ + | | | + v v v + +---------------+ +---------------+ +---------------+ + | Worker Subproc| | Worker Subproc| | Worker Subproc| + | (Read/Search) | | (Code Analysis| | (Testing/Diff)| + +---------------+ +---------------+ +---------------+ + +Justification + +Multi-agent topologies incur high coordination overhead, redundant LLM context costs, and complex deadlock resolution requirements. A single deterministic controller managing execution state with isolated worker subprocesses for parallel compute tasks keeps state execution predictable, easy to trace, and local-first. + +10. Engineering Implementation Roadmap +Six-Week Sequence + +Week 1: SQLite transaction layer overhaul, formalizing JSON Schema interfaces for tool execution contracts and idempotency keys. + +Week 2: Bounded observation-to-action state machine implementation with hard ceiling budget limits and POSIX signal handling. + +Week 3: Observation vs. Hypothesis separation in working state; input sanitization layer for external data sources. + +Week 4: Offline evaluation harness construction; regression testing pipeline for prompt/skill promotion. + +Week 5: Workspace diff mirroring and automated post-action verification framework. + +Week 6: End-to-end integration testing, crash injection benchmarking, and CLI refinement. + +Minimum Viable Slice + +Week 2 milestone: A local run that executes file system modifications, captures tool failure gracefully, respects token and cost budget limits, and resumes cleanly after process interruptions. + +Largest Unresolved Risk + +Tool verification accuracy. Detecting whether a third-party non-idempotent side effect succeeded when a process crashes mid-execution remains non-trivial without custom tool drivers. + +11. Non-Obvious Insight +Design Choice + +Deterministic Prompt-Free State Transition Engine. The core controller uses explicit, hardcoded Python logic for state transitions, tool dispatch, and verification, using LLMs only as structured output generators for planning and text analysis. + + +-------------------------------------------------+ + | PYTHON STATE CONTROLLER | + | (Explicit Logic: Routing, Budget, Verification) | + +------------------------+------------------------+ + | + Calls structured | Returns parsed + generation v JSON schema + +-----------------+ + | LLM MODEL | + | (Planning / | + | Extraction) | + +-----------------+ + +Tradeoff + +Increases hand-written state-machine code and reduces runtime flexibility compared to autonomous agency loops where an LLM controls loop iteration. + +Condition for Rejection + +Reject this choice if task domain complexity expands beyond programmatic state representation, making explicit deterministic state graphs unmaintainable compared to direct probabilistic model control. + +12. Concrete Workflow Example +Scenario + +Documentation Update: Modify docs/api.md to reflect an added endpoint parameter timeout_ms. + + +-----------------------------------------------------------------+ + | 1. Observation & State Initialization | + | - Action Request: Update parameter docs in docs/api.md | + | - Run state committed to SQLite: RUN_ACTIVE (ID: run_9021) | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 2. Evidence Verification | + | - Engine inspects source code for parameter definition. | + | - Reads src/api.py -> Confirms timeout_ms parameter exists. | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 3. Governance Gate | + | - Target: MUTATE_LOCAL (docs/api.md) | + | - Policy check: Auto-approved by low-risk local file policy. | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 4. Idempotent Action Application | + | - Request ID: req_001, Idempotency Key: ik_api_doc_9021 | + | - Tool: patch_apply | + | - Patch applied to mirror; SQLite event written. | + +-----------------------------------------------------------------+ + | + [ PROCESS CRASH / SIGKILL ] + | + v + +-----------------------------------------------------------------+ + | 5. Process Restart & Recovery | + | - Engine re-boots -> Detects run_9021 marked IN_PROGRESS. | + | - Reads last log -> Pending receipt for req_001. | + | - Executes Probe: Checks file hash of docs/api.md. | + | - Validation Pass: File diff already present on disk. | + | - Writes synthetic receipt -> Advances state machine. | + +-----------------------------------------------------------------+ + | + v + +-----------------------------------------------------------------+ + | 6. Final Verification & Run Completion | + | - Evaluator runs git diff check on docs/api.md. | + | - Verification successful -> Run transitions to COMPLETED. | + +-----------------------------------------------------------------+ diff --git a/research/ai_generated_agi_architectures/raw_outputs/granite33.txt b/research/ai_generated_agi_architectures/raw_outputs/granite33.txt new file mode 100644 index 0000000..1aa7865 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/granite33.txt @@ -0,0 +1,64 @@ +**Cognitive-OS Architecture Proposal** + +1. **Memory Architecture**: + - **Working Memory**: Utilizes a local in-memory data structure for real-time processing and immediate access. It employs a Least Recently Used (LRU) cache for efficient memory management. + - **Episodic Memory**: Stores experiences in a relational database (SQLite) with a focus on temporal ordering. Each episode is timestamped and linked to related events for temporal reasoning. + - **Semantic Memory**: A knowledge graph (e.g., Neo4j) stores explicit facts, relationships, and inferences. It uses a triple-store model for efficient querying and reasoning. + - **Procedural Memory**: Encapsulates learned procedures and heuristics in a modular, extensible format (e.g., Python modules). It supports versioning for traceability. + +2. **Reasoning and Planning**: + - Implements a bounded observation-to-action loop using a state machine with clear transitions and stopping criteria (e.g., goal satisfaction, resource exhaustion). + - Incorporates a recovery mechanism for failed plans, leveraging the episodic memory for re-planning and a rollback strategy in case of catastrophic failures. + +3. **Learning**: + - Offline learning is achieved through a modular, pluggable machine learning pipeline. Each component (e.g., supervised, unsupervised, reinforcement) is versioned, and held-out tests ensure generalization. + - A rollback mechanism is in place to revert to previous learning states upon detecting degraded performance or undesirable behavior. + +4. **Tool Use**: + - An action request/result contract uses a RESTful API with JSON payloads for tool invocation and result submission. + - Idempotency is ensured through unique request IDs and result checksums. Uncertain outcomes are handled by probabilistic reasoning and confidence scoring. + +5. **World Representation**: + - Observations are represented as structured data, enriched with uncertainty estimates (e.g., Bayesian networks). + - Hypotheses and predictions are stored in the knowledge graph, linked to evidence sources and updated probabilistically. + - Contradictions are detected through conflict resolution algorithms and handled by prioritizing more reliable evidence. + +6. **Safety and Governance**: + - User authority is maintained through a permission management system, enforcing explicit permissions and budgets. + - A reliable stop control is implemented via a dedicated "shutdown" service, ensuring no further learning or execution post-shutdown. + +7. **Evaluation**: + - Three measurable experiments: (1) Time-to-solution for complex tasks, (2) Plan success rate under varying conditions, (3) Resource utilization under different workloads. + - An ablation study: Removing the episodic memory component and observing its impact on learning and planning performance. + +8. **Persistence and Runtime**: + - Transaction boundaries are enforced using a distributed transaction manager (e.g., Two-Phase Commit). + - A crash/restart protocol ensures data consistency through periodic snapshots and a distributed log (e.g., Raft) for state replication. + +9. **Orchestration**: + - A single, centralized controller manages high-level decision-making, coordinating with multiple bounded workers (task-specific agents). + - Coordination costs are minimized through a message queue (e.g., RabbitMQ) for asynchronous communication. Conflict resolution is handled by a consensus algorithm (e.g., Paxos). + +10. **Engineering Feasibility**: + - A six-week implementation sequence: (1-2 weeks) - Architecture design and tool selection, (3-4 weeks) - Core components development, (5-6 weeks) - Integration, testing, and optimization. + - Minimum viable slice: A self-contained, local-first cognitive engine capable of goal-oriented reasoning and learning. + - Largest unresolved risk: Ensuring seamless integration of diverse machine learning components with varying performance characteristics. + +11. **Non-obvious Insight**: + - Design choice: Utilizing a modular, pluggable architecture for learning components. + - Tradeoff: Balancing flexibility and performance overhead. + - Condition for rejection: If the performance overhead significantly hampers real-time learning capabilities, we would revert to a monolithic learning module, accepting potential compromises in adaptability. + +**Concrete Example**: + +*Requested Documentation Edit*: A user requests a modification to a technical report, specifying the need to add a section on cognitive-os's safety mechanisms. + +*Evidence Checks*: The system cross-references the report's current content against a predefined set of safety-related keywords and checks for compliance with established cognitive-os guidelines. + +*Approval*: The request is flagged for review by a designated safety officer, who verifies the proposed addition adheres to cognitive-os's safety protocols. + +*Action*: Upon approval, the system integrates the new section, ensuring it's linked to the relevant evidence and approved by the safety officer. + +*Verification*: The updated report is immediately saved, and a verification process confirms the new section's compliance with the established standards. + +*Recovery*: In case of interruption (e.g., system crash), the system leverages its snapshot mechanism to restore the report to a consistent state, ensuring no loss of approved content. \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/raw_outputs/phi3.txt b/research/ai_generated_agi_architectures/raw_outputs/phi3.txt new file mode 100644 index 0000000..86df318 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/phi3.txt @@ -0,0 +1,59 @@ + Title: Proposal for Cognitive-OS: A Local-First Cognitive Runtime Architecture + +1. Memory Architecture: +Cognitive-OS will have a distinct memory architecture comprising working, episodic, semantic, and procedural memory. Working memory will store current tasks and data, while episodic memory will store events and experiences. Semantic memory will store general knowledge and facts, and procedural memory will store learned procedures and algorithms. + +Storage will be divided into three main components: working memory, episodic memory, and semantic memory. Retrieval will be based on provenance and context, with a focus on maintaining the integrity of data. Forgetting will be handled through a combination of automatic and user-controlled mechanisms, such as time-based decay and user-defined criteria. + +2. Reasoning and Planning: +Cognitive-OS will provide a bounded observation-to-action loop with stopping criteria and recovery from failed plans. The system will use a planning module to generate actions based on observations and goals. The planning module will consider the current state of the system, available resources, and user-defined constraints. + +The stopping criteria will be based on predefined thresholds, such as time limits, resource usage, and user intervention. In case of a failed plan, the system will automatically roll back to the previous state and attempt to generate an alternative plan. + +3. Learning: +Learning in Cognitive-OS will be an offline, evaluated improvement mechanism with versioning, held-out tests, and rollback. The system will store logs of actions and their outcomes, and periodically evaluate the logs to identify potential improvements. + +The learning module will create new versions of the system, incorporating the identified improvements. These versions will be tested against held-out data to evaluate their effectiveness. If the new version performs better, it will be promoted; otherwise, the system will rollback to the previous version. + +4. Tool Use: +Cognitive-OS will provide an action request/result contract, validation, idempotency, and handling of uncertain outcomes. Users will submit action requests, and the system will execute the requested actions. The system will validate the action requests based on user-defined criteria and constraints. + +The system will ensure idempotency by tracking executed actions and preventing duplicate executions. In case of uncertain outcomes, the system will provide a probabilistic result, along with a confidence level. + +5. World Representation: +Cognitive-OS will distinguish observations, hypotheses, and predictions, including uncertainty and contradiction handling. The system will store observations in a structured format, with metadata describing their source, context, and provenance. + +Hypotheses will be stored as structured representations, with metadata describing their source, context, and supporting evidence. Predictions will be stored as probabilistic estimates, with metadata describing their source, context, and confidence level. The system will handle uncertainty and contradiction by using probabilistic reasoning and conflict resolution techniques. + +6. Safety and Governance: +Cognitive-OS will preserve user authority, explicit permission boundaries, resource budgets, and a reliable stop control. The system will enforce user-defined constraints and permissions, ensuring that users retain control over their goals, budgets, and tools. + +The system will monitor resource usage and enforce resource budgets, preventing users from exceeding their allocated resources. Users will have a reliable stop control, allowing them to terminate the system at any time. + +7. Evaluation: +Three measurable experiments to evaluate Cognitive-OS's architectural benefits are: + +a. Task Completion Time: Compare the average time taken to complete a set of predefined tasks using Cognitive-OS and a baseline system. +b. Resource Efficiency: Measure the resource usage (CPU, memory, and disk) of Cognitive-OS and a baseline system while performing the same set of tasks. +c. User Satisfaction: Survey users to evaluate their satisfaction with Cognitive-OS's performance, usability, and control over the system. + +An ablation study could falsify the claimed architectural benefit by removing specific components, such as episodic memory or the bounded observation-to-action loop, and measuring the impact on the above metrics. + +8. Persistence and Runtime: +Cognitive-OS will have transaction boundaries and a crash/restart protocol. The system will maintain a transaction log, recording all actions and their outcomes. In case of a crash, the system will restore the last valid transaction and resume execution. + +If a tool completes its task but its receipt is not stored, the system will automatically store the receipt once the transaction log is flushed. The system will also provide a mechanism for users to manually save receipts. + +9. Orchestration: +Cognitive-OS will justify a single controller or multiple bounded workers, depending on the use case. A single controller will be used for simple tasks, while multiple bounded workers will be used for complex tasks that require parallel processing. + +The coordination costs and conflict resolution will be handled by the controller, which will manage the workers and resolve any conflicts that arise. + +10. Engineering Feasibility: +A six-week implementation sequence will be followed, with the minimum viable slice focusing on the core memory architecture, reasoning and planning, and learning mechanisms. The largest unresolved risk is the handling of uncertain outcomes and the tradeoff between user control and automation. + +11. Non-obvious Insight: +One useful design choice is to use a hybrid memory architecture, combining working, episodic, semantic, and procedural memory. The tradeoff is increased complexity and potential performance overhead, but it allows for more efficient data retrieval and better handling of uncertainty. This design choice would be rejected if the complexity outweighs the benefits in the target use case. + +Example: +A user requests a documentation edit, providing evidence of the changes made and their justification. The system validates the request, stores the edit in episodic memory, and generates a receipt. If the system crashes during the edit, the last valid transaction is restored upon restart. The user approves the edit, and the system stores the approval in semantic memory. If the system is interrupted, the edit is stored in working memory and resumed upon recovery. \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/raw_outputs/qwen25.txt b/research/ai_generated_agi_architectures/raw_outputs/qwen25.txt new file mode 100644 index 0000000..a4de9fd --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/qwen25.txt @@ -0,0 +1,91 @@ +### Memory Architecture + +**Working Memory**: The working memory is the immediate, active memory that is used to solve problems and make decisions. It is characterized by its limited capacity and short lifespan, typically lasting only a few seconds to minutes. It is the primary memory system used for current tasks and is responsible for the immediate processing of sensory information. + +**Episodic Memory**: Episodic memory is the memory of personal experiences, including events, places, and people. It is characterized by its long-term storage and the ability to recall specific events or experiences. It is used to store information about past experiences and is crucial for memory retrieval and the ability to recall specific events. + +**Semantic Memory**: Semantic memory is the memory of general knowledge, facts, and concepts. It is characterized by its long-term storage and the ability to recall information about the world. It is used to store information about the world and is crucial for understanding the meaning of words and concepts. + +**Procedural Memory**: Procedural memory is the memory of skills and abilities, including motor skills and problem-solving strategies. It is characterized by its long-term storage and the ability to perform tasks without conscious thought. It is used to store information about how to perform specific tasks and is crucial for learning and adapting to new situations. + +**Provenance**: Provenance refers to the source or origin of information. It is important for maintaining the integrity and reliability of data. In the context of Cognitive-OS, provenance can be used to track the origin of data, ensuring that the information used is accurate and reliable. + +**Forgetting**: Forgetting is the process of losing or forgetting information. It is important for maintaining the focus and attention on the current task and is crucial for the efficient use of memory resources. In the context of Cognitive-OS, forgetting can be managed through the use of selective memory and the prioritization of information. + +### Reasoning and Planning + +**Bounded Observation-to-Action Loop**: The bounded observation-to-action loop is a mechanism that ensures that the system only processes and acts on a limited set of observations and hypotheses. This loop is characterized by its stopping criteria, which are defined as the point at which the system has enough information to make a decision or act. The loop is also characterized by its recovery mechanism, which is used to handle failed plans and ensure that the system can continue to function even if a plan fails. + +**Stopping Criteria**: The stopping criteria are defined as the point at which the system has enough information to make a decision or act. This can be defined as the point at which the system has enough information to make a decision or act, or as the point at which the system has enough information to act on a hypothesis. The stopping criteria can be defined as a set of rules or as a set of heuristics that are used to determine when the system should stop processing and act. + +**Recovery Mechanism**: The recovery mechanism is used to handle failed plans and ensure that the system can continue to function even if a plan fails. This can be defined as a set of rules or as a set of heuristics that are used to determine when the system should stop processing and act. The recovery mechanism can be defined as a set of rules or as a set of heuristics that are used to determine when the system should stop processing and act. + +### Learning + +**Offline, Evaluated Improvement Mechanism**: The offline, evaluated improvement mechanism is a mechanism that allows the system to learn from past experiences and improve its performance. This mechanism is characterized by its versioning, which allows the system to track the changes made to the system and the improvements made to its performance. The mechanism is also characterized by its held-out tests, which allow the system to evaluate its performance on data that it has not seen before. The mechanism is also characterized by its rollback, which allows the system to revert to a previous version of the system if the improvements made to its performance are not satisfactory. + +**Learning vs. Logging**: The key difference between learning and logging is that learning is a mechanism that allows the system to improve its performance, while logging is a mechanism that allows the system to record its observations and actions. Learning is a mechanism that allows the system to improve its performance, while logging is a mechanism that allows the system to record its observations and actions. + +### Tool Use + +**Action Request/Result Contract**: The action request/result contract is a mechanism that defines the relationship between the user and the system. It defines the actions that the user can take and the results that the system will provide. The contract is also characterized by its validation, which ensures that the user's actions are valid and that the system's results are accurate. The contract is also characterized by its idempotency, which ensures that the system can handle repeated requests without causing any harm or damage. + +**Validation**: The validation is a mechanism that ensures that the user's actions are valid and that the system's results are accurate. It is also characterized by its idempotency, which ensures that the system can handle repeated requests without causing any harm or damage. + +**Handling Uncertain Outcomes**: The handling of uncertain outcomes is a mechanism that allows the system to handle situations where the outcome of an action is uncertain. This mechanism is characterized by its uncertainty handling, which ensures that the system can handle situations where the outcome of an action is uncertain. The mechanism is also characterized by its contradiction handling, which ensures that the system can handle situations where the outcome of an action is contradictory. + +### World Representation + +**Observations**: Observations are the data that the system receives from the world. They are characterized by their accuracy and their reliability. Observations are used to make decisions and to improve the system's performance. + +**Hypotheses**: Hypotheses are the theories or ideas that the system uses to make decisions. They are characterized by their accuracy and their reliability. Hypotheses are used to make decisions and to improve the system's performance. + +**Predictions**: Predictions are the outcomes that the system expects to occur based on its observations and hypotheses. They are characterized by their accuracy and their reliability. Predictions are used to make decisions and to improve the system's performance. + +**Uncertainty and Contradiction Handling**: The uncertainty and contradiction handling is a mechanism that ensures that the system can handle situations where the outcome of an action is uncertain or contradictory. This mechanism is characterized by its uncertainty handling, which ensures that the system can handle situations where the outcome of an action is uncertain. The mechanism is also characterized by its contradiction handling, which ensures that the system can handle situations where the outcome of an action is contradictory. + +### Safety and Governance + +**User Authority**: The user authority is the authority that the user has over the system. It is characterized by its explicit permission boundaries, which define the actions that the user can take and the actions that the system can take. The authority is also characterized by its resource budgets, which define the amount of resources that the user can allocate to the system. The authority is also characterized by its reliable stop control, which ensures that the user can control the system and stop it if necessary. + +**Explicit Permission Boundaries**: The explicit permission boundaries are the boundaries that define the actions that the user can take and the actions that the system can take. They are characterized by their definition, which defines the actions that the user can take and the actions that the system can take. They are also characterized by their implementation, which defines the actions that the user can take and the actions that the system can take. + +**Resource Budgets**: The resource budgets are the budgets that define the amount of resources that the user can allocate to the system. They are characterized by their definition, which defines the amount of resources that the user can allocate to the system. They are also characterized by their implementation, which defines the amount of resources that the user can allocate to the system. + +**Reliable Stop Control**: The reliable stop control is the control that ensures that the user can control the system and stop it if necessary. It is characterized by its definition, which defines the control that ensures that the user can control the system and stop it if necessary. It is also characterized by its implementation, which defines the control that ensures that the user can control the system and stop it if necessary. + +### Evaluation + +**Three Measurable Experiments**: The three measurable experiments are the experiments that measure the effectiveness of the system. They are characterized by their definition, which defines the experiments that measure the effectiveness of the system. They are also characterized by their implementation, which defines the experiments that measure the effectiveness of the system. + +**Ablation Experiment**: The ablation experiment is the experiment that tests the effectiveness of the system without the use of a particular feature. It is characterized by its definition, which defines the experiment that tests the effectiveness of the system without the use of a particular feature. It is also characterized by its implementation, which defines the experiment that tests the effectiveness of the system without the use of a particular feature. + +### Persistence and Runtime + +**Transaction Boundaries**: The transaction boundaries are the boundaries that define the boundaries of a transaction. They are characterized by their definition, which defines the boundaries of a transaction. They are also characterized by their implementation, which defines the boundaries of a transaction. + +**Crash/Restart Protocol**: The crash/restart protocol is the protocol that defines the protocol for restarting the system after a crash. It is characterized by its definition, which defines the protocol for restarting the system after a crash. It is also characterized by its implementation, which defines the protocol for restarting the system after a crash. + +### Orchestration + +**Controller vs. Workers**: The controller vs. workers is the question of whether to use a single controller or multiple bounded workers. It is characterized by its implementation, which defines the controller vs. workers. + +**Controller vs. Workers Tradeoff**: The controller vs. workers tradeoff is the tradeoff between the controller vs. workers. It is characterized by its implementation, which defines the controller vs. workers tradeoff. + +**Unresolved Risk**: The unresolved risk is the risk that is not resolved by the implementation of the system. It is characterized by its definition, which defines the unresolved risk. + +### Engineering Feasibility + +**Six-Week Implementation Sequence**: The six-week implementation sequence is the sequence of steps that defines the steps that are required to implement the system. It is characterized by its definition, which defines the steps that are required to implement the system. It is also characterized by its implementation, which defines the steps that are required to implement the system. + +**Minimum Viable Slice**: The minimum viable slice is the smallest piece of the system that is required to be implemented in order to be considered a viable system. It is characterized by its definition, which defines the minimum viable slice. It is also characterized by its implementation, which defines the minimum viable slice. + +**Largest Unresolved Risk**: The largest unresolved risk is the largest risk that is not resolved by the implementation of the system. It is characterized by its definition, which defines the largest unresolved risk. It is also characterized by its implementation, which defines the largest unresolved risk. + +### Non-Obvious Insight + +**Single Controller vs. Multiple Workers**: The single controller vs. multiple workers is the question of whether to use a single controller or multiple bounded workers. It is characterized by its implementation, which defines the single controller vs. multiple workers. + +**Single Controller vs. Multiple Workers Tradeoff**: The single controller vs. multiple workers tradeoff is the tradeoff between the single controller vs. multiple workers. It is characterized by its implementation, which defines the single controller vs. multiple workers tradeoff. + +**Condition Under Which You Would Reject It**: The condition under which you would reject it is the condition under which you would reject the single controller vs. multiple workers. It is characterized by its \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/raw_outputs/qwen3.txt b/research/ai_generated_agi_architectures/raw_outputs/qwen3.txt new file mode 100644 index 0000000..8579408 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/qwen3.txt @@ -0,0 +1,147 @@ +**Proposal for Cognitive-OS: A Local-First Cognitive Runtime** + +--- + +### 1. **Memory Architecture** + +Cognitive-OS employs a layered memory architecture to support working, episodic, semantic, and procedural memory, each with distinct storage, retrieval, and management mechanisms. + +- **Working Memory**: Stored in a lightweight, in-memory cache with high-speed access. It holds current task state and intermediate results, with a short retention period (e.g., 10 seconds) to prevent memory overload. +- **Episodic Memory**: Persisted in a SQLite-backed journal, with timestamps and metadata. It stores task-specific data, such as input data, intermediate steps, and output results, with a longer retention period (e.g., 7 days). +- **Semantic Memory**: Stored in a distributed, versioned database, such as a Git-like repository. It contains knowledge, rules, and models, with a retention period of 30 days and versioning for rollback. +- **Procedural Memory**: Stored in a local workspace, with a persistent state that can be reloaded on restart. It contains learned workflows and task sequences, with a retention period of 90 days. + +Provenance is tracked via metadata in each memory entry, including author, timestamp, and version. Forgetting is managed through a decay model, where entries are automatically removed after a set period, and users can manually prune if needed. + +--- + +### 2. **Reasoning and Planning** + +Cognitive-OS implements a bounded observation-to-action loop with stopping criteria and recovery mechanisms. + +- **Observation-to-Action Loop**: A task begins with an observation (e.g., user input or system state), then generates a plan, executes actions, and receives results. If the plan fails, it attempts recovery (e.g., re-evaluating steps, rerunning subtasks). +- **Stopping Criteria**: The loop terminates if: + - The task completes successfully. + - The user explicitly cancels the task. + - A predefined maximum number of steps is reached. + - A resource limit (e.g., memory, CPU) is exceeded. +- **Recovery**: If a plan fails, the system attempts to re-plan, using cached data or re-evaluated hypotheses. If recovery fails, the task is marked as failed and can be rolled back. + +--- + +### 3. **Learning** + +Cognitive-OS features an offline, evaluated learning mechanism with versioning, held-out tests, and rollback support. + +- **Offline Learning**: Models and rules are trained using a held-out dataset, with a validation set for evaluation. The learning process is isolated from the main task to prevent interference. +- **Versioning**: Each version of a model or rule is stored with a unique identifier, and versions are tracked in a version control system (e.g., Git). +- **Rollback**: If a learning step fails, the system can revert to a previous version, using a versioned history. The held-out test ensures that the model is not overfit to the training data. +- **Distinguishing Learning from Logging**: Learning involves modifying the system’s behavior, while logging merely records events. Learning is evaluated through performance metrics (e.g., accuracy, efficiency) and is only promoted if it improves the system’s capabilities. + +--- + +### 4. **Tool Use** + +Cognitive-OS defines a standardized action request/result contract to ensure consistency and reliability. + +- **Action Request**: A request includes a task description, input parameters, and expected output. It is validated against a set of rules (e.g., type, format, constraints). +- **Result Handling**: Results are returned in a structured format, with metadata indicating success, failure, or uncertainty. If an action fails, the system attempts to re-run it or provides a diagnostic. +- **Idempotency**: Actions are designed to be idempotent, meaning that repeated execution has no unintended side effects. If an action fails, it is retried with a delay. +- **Uncertain Outcomes**: If an action’s outcome is uncertain, the system provides a probability or confidence score, and the user is informed of the uncertainty. + +--- + +### 5. **World Representation** + +Cognitive-**OS distinguishes between observations, hypotheses, and predictions, with mechanisms for uncertainty and contradiction handling. + +- **Observations**: Directly derived from the environment, such as user input or system state. These are stored in the episodic memory. +- **Hypotheses**: Proposed explanations for observations, stored in semantic memory. They are tested against observations and may be revised or discarded. +- **Predictions**: Based on hypotheses and prior knowledge, predictions are generated and stored in a separate prediction store. They are evaluated against observations and may be updated or rejected. +- **Uncertainty and Contradiction Handling**: If a prediction contradicts an observation, the system flags the contradiction and may reject the prediction. If the observation is uncertain, the system may defer action until more information is available. + +--- + +### 6. **Safety and Governance** + +Cognitive-OS preserves user authority and includes explicit permission boundaries, resource budgets, and a reliable stop control. + +- **User Authority**: Users retain control over goals, budgets, and tools. They can approve or reject actions, and can terminate the system at any time. +- **Permission Boundaries**: Actions are restricted based on user permissions, and users can define custom permissions. +- **Resource Budgets**: The system tracks resource usage (e.g., memory, CPU) and enforces budgets to prevent resource exhaustion. +- **Stop Control**: A reliable stop control is implemented, such as a signal-based shutdown or a timeout mechanism, ensuring the system can be terminated gracefully. + +--- + +### 7. **Evaluation** + +Three measurable experiments and an ablation could falsify the claimed architectural benefit. + +- **Experiment 1**: Compare the performance of a task using a baseline model versus a model trained with the learning mechanism. +- **Experiment 2**: Evaluate the system’s ability to recover from failed plans and re-plan. +- **Experiment 3**: Assess the system’s ability to handle contradictions and uncertain outcomes. + +An ablation could involve removing the learning mechanism and evaluating the system’s performance without it, to determine if the learning benefit is necessary. + +--- + +### 8. **Persistence and Runtime** + +Cognitive-OS implements transaction boundaries and a crash/restart protocol. + +- **Transaction Boundaries**: Each task is a transaction, with a start and end point. If a transaction fails, it is rolled back, and the system can retry. +- **Crash/Restart Protocol**: If a tool completes but its receipt is not stored, the system will not acknowledge the completion, and the task will be considered incomplete. The user must manually verify the completion. + +--- + +### 9. **Orchestration** + +Cognitive-OS recommends a single controller or multiple bounded workers, depending on the complexity of the task. + +- **Single Controller**: For simple tasks, a single controller manages the entire workflow, ensuring centralized control and coordination. +- **Multiple Bounded Workers**: For complex tasks, multiple workers are used to handle different parts of the task, reducing the load on the controller and improving scalability. +- **Coordination Costs**: Coordination between workers can introduce overhead, so the system must balance between centralized control and distributed execution. +- **Conflict Resolution**: If workers conflict, the system must resolve the conflict, either by prioritizing one worker or by re-evaluating the plan. + +--- + +### 10. **Engineering Feasibility** + +A six-week implementation sequence is proposed, with a minimum viable slice and unresolved risks. + +- **Minimum Viable Slice**: A simple task, such as editing a document, with a basic UI, input validation, and basic model routing. +- **Unresolved Risks**: Implementing a robust learning mechanism, ensuring consistent tool use, and handling all edge cases in the orchestration layer. + +--- + +### 11. **Non-Obvious Insight** + +A useful design choice is to implement a **dynamic memory hierarchy**, where memory is organized by task and context, rather than by type. This allows for more efficient retrieval and management of data. + +- **Tradeoff**: Increased complexity in memory management and potential for fragmentation. +- **Condition for Rejecting**: If the system becomes too complex to manage, or if the tradeoff in performance or maintainability is too high. + +--- + +### Concrete Example: Document Editing Request + +**Request**: A user requests to edit a document titled "Project Plan 2025." + +**Evidence Checks**: +- The document is stored in the episodic memory with a timestamp. +- The user has permission to edit the document. +- The document is not locked or in progress. + +**Approval**: +- User approves the request. + +**Action**: +- The system retrieves the document from the episodic memory. +- It validates the input against the document’s structure. +- It applies the changes and stores the updated document in the episodic memory. + +**Verification**: +- The document is updated and verified by the user. + +**Recovery After Interruption**: +- If the system crashes, the document remains in the episodic memory and can be recovered upon restart. \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/raw_outputs/smollm2.txt b/research/ai_generated_agi_architectures/raw_outputs/smollm2.txt new file mode 100644 index 0000000..77ce139 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/smollm2.txt @@ -0,0 +1,93 @@ +1. Memory Architecture: + +The proposed memory architecture for Cognitive-OS is a hybrid system that combines working memory, episodic memory, semantic memory, and procedural memory. Each type of memory is designed to serve specific purposes and is implemented using different techniques. + +Working memory is used for short-term information storage and retrieval. It is a shallow, shallow-memory system that is designed to be fast and efficient but lacks the depth and richness of semantic memory. Working memory is used for tasks such as planning, reasoning, and decision-making. + +Episodic memory is a system that stores and retrieves information in a more structured and organized way. It is designed to be more robust and reliable than working memory but is also slower and more energy-intensive. Episodic memory is used for tasks such as learning and memory consolidation. + +Semantic memory is a system that stores and retrieves information in a more abstract and general way. It is designed to be more powerful and flexible than episodic memory but is also more prone to errors and inconsistencies. Semantic memory is used for tasks such as knowledge representation and inference. + +Procedural memory is a system that stores and retrieves information in a more procedural and rule-based way. It is designed to be more efficient and effective than semantic memory but is also more rigid and inflexible. Procedural memory is used for tasks such as skill learning and motor control. + +Storage, retrieval, provenance, and forgetting are all managed by a separate system that is designed to be robust and reliable. This system uses a combination of techniques such as caching, buffering, and versioning to ensure that the memory system is always up-to-date and accurate. + +The system also has a mechanism for forgetting information that is no longer needed or relevant. This is done by periodically purging old or irrelevant information from the memory system. + +2. Reasoning and Planning: + +The proposed reasoning and planning system for Cognitive-OS is a bounded observation-to-action loop with stopping criteria and recovery from failed plans. The loop is designed to be flexible and adaptable to changing circumstances and goals. + +The loop starts with an observation of the current state of the world. This observation is then used to generate a hypothesis about the best course of action. The hypothesis is then evaluated and refined based on the consequences of that course of action. + +If the hypothesis is deemed to be a good one, it is then implemented as a plan. The plan is then executed and the system monitors its progress and updates its hypothesis as needed. + +If the plan is not successful, the system can recover by re-evaluating its hypothesis and adjusting its plan accordingly. The system can also recover by re-evaluating its observations and updating its hypothesis. + +The system has a mechanism for stopping the loop if it is determined that a plan is not viable or if a goal is no longer achievable. This is done by periodically evaluating the system's goals and adjusting its plans accordingly. + +3. Learning: + +The proposed learning mechanism for Cognitive-OS is an offline, evaluated improvement mechanism with versioning, held-out tests and rollback. The system has a mechanism for storing and retrieving its past experiences and using them to improve its performance. + +The system has a mechanism for versioning its past experiences so that it can roll back to a previous version if needed. This is done to prevent the system from becoming stuck in a particular state or to recover from a failed plan. + +The system also has a mechanism for held-out tests so that it can evaluate its performance on unseen data. This is done to prevent the system from becoming too specialized in a particular task or to prevent it from becoming too narrow in its knowledge. + +The system has a mechanism for rollback so that it can recover from a failed plan or a failed test. This is done by periodically evaluating the system's goals and adjusting its plans accordingly. + +4. Tool Use: + +The proposed tool use mechanism for Cognitive-OS is an action request/result contract, validation, idempotency and handling of uncertain outcomes. The system has a mechanism for requesting and receiving actions from external tools. + +The system has a mechanism for validating the results of external tools so that it can ensure that the system's goals are being met. The system also has a mechanism for handling uncertain outcomes so that it can adapt to changing circumstances. + +The system has a mechanism for idempotency so that it can prevent the system from becoming stuck in a particular state. This is done by periodically evaluating the system's goals and adjusting its plans accordingly. + +5. World Representation: + +The proposed world representation for Cognitive-OS is a system that distinguishes observations, hypotheses, and predictions, including uncertainty and contradiction handling. The system has a mechanism for representing the world in a way that is consistent with the system's goals and objectives. + +The system has a mechanism for representing observations in a way that is accurate and reliable. The system also has a mechanism for representing hypotheses in a way that is consistent with the system's goals and objectives. + +The system has a mechanism for representing predictions in a way that is accurate and reliable. The system also has a mechanism for representing uncertainty and contradiction in a way that is consistent with the system's goals and objectives. + +6. Safety and Governance: + +The proposed safety and governance mechanism for Cognitive-OS is a system that preserves user authority, explicit permission boundaries, resource budgets and a reliable stop control. The system has a mechanism for ensuring that the system's actions are consistent with the system's goals and objectives. + +The system has a mechanism for preserving user authority so that the system's actions are subject to user control. The system also has a mechanism for explicit permission boundaries so that the system's actions are subject to user approval. + +The system has a mechanism for resource budgets so that the system's actions are limited by the system's resources. The system also has a mechanism for a reliable stop control so that the system can be stopped if it is determined that it is no longer safe to continue. + +7. Evaluation: + +The proposed evaluation mechanism for Cognitive-OS is three measurable experiments and an ablation that could falsify the claimed architectural benefit, without claiming results. The system has a mechanism for evaluating its performance on unseen data. + +The system has a mechanism for comparing its performance to a baseline system so that it can evaluate the effectiveness of its architecture. The system also has a mechanism for evaluating its performance on a testbed system so that it can evaluate the effectiveness of its architecture. + +The system has a mechanism for comparing its performance to a control system so that it can evaluate the effectiveness of its architecture. The system also has a mechanism for comparing its performance to a baseline system so that it can evaluate the effectiveness of its architecture. + +8. Persistence and Runtime: + +The proposed persistence and runtime mechanism for Cognitive-OS is a transaction boundary and a crash/restart protocol. The system has a mechanism for ensuring that the system's state is consistent with the system's goals and objectives. + +The system has a mechanism for ensuring that the system's state is consistent with the system's goals and objectives. The system also has a mechanism for recovering from a crash or restart so that the system can continue to operate. + +9. Orchestration: + +The proposed orchestration mechanism for Cognitive-OS is a single controller or multiple bounded workers, including coordination costs and conflict resolution. The system has a mechanism for coordinating the actions of multiple systems. + +The system has a mechanism for resolving conflicts between systems so that the system can continue to operate. The system also has a mechanism for coordinating the actions of multiple systems so that the system can continue to operate. + +10. Engineering Feasibility: + +The proposed engineering feasibility for Cognitive-OS is a six-week implementation sequence, the minimum viable slice, and the largest unresolved risk. The system has a mechanism for evaluating the feasibility of its architecture. + +The system has a mechanism for evaluating the feasibility of its architecture. The system also has a mechanism for identifying the largest unresolved risk so that the system can prioritize its development. + +11. Non-obvious Insight: + +The proposed non-obvious insight for Cognitive-OS is to use a hybrid memory system that combines working memory, episodic memory, semantic memory, and procedural memory. The system has a mechanism for distinguishing between these different types of memory. + +The system has a mechanism for distinguishing between these different types of memory. The system also has a mechanism for using these different types of memory to improve the system's performance. \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/raw_outputs/tinyllama.txt b/research/ai_generated_agi_architectures/raw_outputs/tinyllama.txt new file mode 100644 index 0000000..714d598 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/tinyllama.txt @@ -0,0 +1,59 @@ +Architecture for CoGNITIVE-OS + +CoGNITIVE-OS is a local-first cognitive runtime pursuing general-purpose problem solving. The project aims to provide a practical next iteration that retains control over goals, budget, tools, and shutdown. The architecture is designed to be concise, modular, and flexible, allowing for easy extension and adaptation to new use cases. + +Memory Architecture + +The memory architecture is designed to be bounded observation-to-action loop with stopping criteria and recovery from failed plans. The loop is divided into three phases: observation, action, and observation. Observation is the initial state of the system, and action is the next state that the system will reach based on the current observation. The observation phase is responsible for gathering and storing observations, while the action phase is responsible for executing the action that is stored in the observation. + +Reasoning and Planning + +The reasoning and planning phase is responsible for evaluating the current observation and the possible actions that can be taken. The planning phase is responsible for identifying the best action to take based on the current observation and the available resources. The planning phase is also responsible for identifying the best action to take based on the available resources and the current observation. + +Learning + +The learning phase is responsible for evaluating the current observation and the possible actions that can be taken. The learning phase is responsible for identifying the best action to take based on the current observation and the available resources. The learning phase is also responsible for identifying the best action to take based on the available resources and the current observation. + +Tool Use + +The tool use phase is responsible for specifying an action request/result contract, validation, idempotency, and handling of uncertain outcome. The tool use phase is also responsible for identifying the best action to take based on the available resources and the current observation. + +World Representation + +The world representation phase is responsible for distinguishing observations, hypotheses, and predictions. The world representation phase is also responsible for identifying the best action to take based on the available resources and the current observation. + +Safety and Governance + +The safety and governance phase is responsible for preserving user authority, explicit permission boundaries, resource budgeting, and a reliable stop control. The safety and governance phase is also responsible for identifying the best action to take based on the available resources and the current observation. + +Evaluation + +The evaluation phase is responsible for three measurable experiments: observation, hypothesis, and prediction. The evaluation phase is also responsible for ablation that could falsify the claimed architectural benefit, without claiming results. + +Persistence and Runtime + +The persistence and runtime phase is responsible for three bounded operations: observation, hypothesis, and prediction. The persistence and runtime phase is also responsible for transaction boundaries and a crash/restart protocol. + +Evaluation + +The evaluation phase is responsible for three measurable experiments: observation, hypothesis, and prediction. The evaluation phase is also responsible for ablation that could falsify the claimed architectural benefit, without claiming results. + +Persistence and Runtime + +The persistence and runtime phase is responsible for three bounded operations: observation, hypothesis, and prediction. The persistence and runtime phase is also responsible for transaction boundaries and a crash/restart protocol. + +Evaluation + +The evaluation phase is responsible for three measurable experiments: observation, hypothesis, and prediction. The evaluation phase is also responsible for ablation that could falsify the claimed architectural benefit, without claiming results. + +Non-obvious Insight + +The non-obvious insight is a short concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption. + +Output + +The output of the proposal is a six-week implementation sequence, the minimum viable slice, and the largest unresolved risk. The output is also accompanied by a concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption. + +Conclusion + +The architecture for CoGNITIVE-OS is designed to be concise, modular, and flexible, allowing for easy extension and adaptation to new use cases. The memory architecture is designed to be bounded observation-to-action loop with stopping criteria and recovery from failed plans. The reasoning and planning phase is designed to identify the best action to take based on the current observation and the available resources. The learning phase is designed to identify the best action to take based on the current observation and the available resources and the current observation. The tool use phase is designed to specify an action request/result contract, validation, idempotency, and handling of uncertain outcome. The world representation phase is designed to distinguish observations, hypotheses, and predictions. The safety and governance phase is designed to preserve user authority, explicit permission boundaries, resource budgeting, and a reliable stop control. The evaluation phase is designed to three measurable experiments: observation, hypothesis, and prediction. The persistence and runtime phase is designed to three bounded operations: observation, hypothesis, and prediction. The evaluation phase is designed to three measurable experiments: observation, hypothesis, and prediction. The non-obvious insight is a short concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption. The output is accompanied by a concreted example of a requested documentation edit, its evidence checks, approval if needed, action, verification, and recovery after interruption. \ No newline at end of file diff --git a/research/ai_generated_agi_architectures/sources.md b/research/ai_generated_agi_architectures/sources.md new file mode 100644 index 0000000..b8cb1d5 --- /dev/null +++ b/research/ai_generated_agi_architectures/sources.md @@ -0,0 +1,49 @@ +# Sources, attribution and editing record + + +## Primary local systems + +Each system is executed through the same local llama.cpp runtime, using its public GGUF chat template. The linked repositories identify the actual weight distribution, including community quantizers where applicable. A community GGUF distributor is not presented as the original model developer. Dates below are UTC. + +| Alias / model | Developer and weight distributor | Weight source | Source downloaded / verified | Response completed | +| --- | --- | --- | --- | --- | +| `smollm2` / SmolLM2 1.7B Instruct | HuggingFaceTB | [HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct-GGUF/tree/2d4a76a30b4af41ecd395c35725ac11688d4cfe4) | 2026-09-08T18:56:08.468289+00:00 | 2026-09-08T19:03:31.552280+00:00 | +| `qwen25` / Qwen2.5 1.5B Instruct | Qwen | [Qwen/Qwen2.5-1.5B-Instruct-GGUF](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct-GGUF/tree/91cad51170dc346986eccefdc2dd33a9da36ead9) | 2026-09-08T19:04:07.535354+00:00 | 2026-09-08T19:12:21.575675+00:00 | +| `granite33` / Granite 3.3 2B Instruct | IBM Granite | [ibm-granite/granite-3.3-2b-instruct-GGUF](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct-GGUF/tree/7cdf86ccd1f1bb3491c9b7017b033f2e51367397) | 2026-09-08T19:39:51.443219+00:00 | 2026-09-08T19:44:36.118101+00:00 | +| `tinyllama` / TinyLlama 1.1B Chat v1.0 | TinyLlama; GGUF by TheBloke | [TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF](https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/tree/52e7645ba7c309695bec7ac98f4f005b139cf465) | 2026-09-08T19:45:35.083461+00:00 | 2026-09-08T19:47:32.830910+00:00 | +| `phi3` / Phi-3 mini 4k Instruct | Microsoft | [microsoft/Phi-3-mini-4k-instruct-gguf](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf/tree/a64113399c2f6b8ad3e11c394733a2ddadaa7f33) | 2026-09-08T19:51:30.167408+00:00 | 2026-09-08T20:03:18.180745+00:00 | +| `qwen3` / Qwen3 1.7B | Qwen | [Qwen/Qwen3-1.7B-GGUF](https://huggingface.co/Qwen/Qwen3-1.7B-GGUF/tree/90862c4b9d2787eaed51d12237eafdfe7c5f6077) | 2026-09-08T20:04:02.023673+00:00 | 2026-09-08T20:12:33.103579+00:00 | +| `falcon3` / Falcon3 1B Instruct | TII; GGUF by bartowski | [bartowski/Falcon3-1B-Instruct-GGUF](https://huggingface.co/bartowski/Falcon3-1B-Instruct-GGUF/tree/9e280a1e3b7f287edb7248042e3d14f893227b21) | 2026-09-08T20:13:09.868542+00:00 | 2026-09-08T20:16:49.049787+00:00 | +| `danube3` / H2O Danube3 500m Chat | H2O.ai; GGUF by bartowski | [bartowski/h2o-danube3-500m-chat-GGUF](https://huggingface.co/bartowski/h2o-danube3-500m-chat-GGUF/tree/34005c67faf918e19c8748e501fa8418b51060fe) | 2026-09-08T20:17:22.738887+00:00 | 2026-09-08T20:18:28.180171+00:00 (prompt echo; no proposal) | + +For each completed system, `collection//model.json` is the public repository metadata, including the LFS object identity. `source.json` records the pinned revision, exact weight URL, file size and SHA-256 verified after download. `request.json`, `response.json` and `run.json` preserve the input, full response, finish reason, usage, timestamps and hashes. `raw_outputs/.txt` contains the exact response content. The metadata and hashes permit consistency checks and repeat retrieval; they are not a provider-signed attestation of model authorship. + +The inspected distribution cards identify Apache-2.0 for SmolLM2, Qwen2.5, Granite, TinyLlama, Qwen3 and Danube3; MIT for Phi-3; and the custom Falcon LLM license for Falcon3. The Falcon card links the [TII terms](https://falconllm.tii.ae/falcon-terms-and-conditions.html), which include acceptable-use conditions. Falcon3 is therefore not described here as Apache-2.0. Model weights are not redistributed in this packet. + +## Runtime and method + +The generation runtime is [llama.cpp release b10809](https://github.com/ggml-org/llama.cpp/releases/tag/b10809), commit `5266f24da`. The official macOS arm64 archive was retrieved on 2026-09-08 and its SHA-256 verified as `7d692df9e1e386e62f1c12b843903218041e6cd74c9415aa39a7ed3176f9eaa2`. Its version command reported `0.4.0-dev` and the recorded build/commit. All eight local runs completed on 2026-09-08; Danube3 returned a prompt echo and is recorded as a failed proposal request. The unmodified model-specific requests and run commands preserve generation settings. The exact user prompt is in [prompts.md](prompts.md) and [collection/prompt.txt](collection/prompt.txt). + +The roster comprises eight distinct model systems, not eight independent training lineages. Qwen2.5 and Qwen3 are two generations in one named family. Parameter count, quantization and provider chat templates differ. StableLM was considered but excluded because its public model terms restrict commercial use. DeepSeek was not collected. No unavailable system output is fabricated. + +## Exploratory hosted reference + +[Google Gemini web app](https://gemini.google.com/app), UI mode Flash, accessed 2026-09-08 through an authorized account. One fresh prompt produced two automatically offered A/B candidate answers, preserved at 19:27:26 UTC in [collection/gemini-flash-reference](collection/gemini-flash-reference/analysis-notes.md). Backend model revision, service instructions, sampling parameters and token counts are unknown. No preference vote was submitted. This is one hosted reference, not two independently identified models. + +Extraction copied the rendered `innerText` of each observed answer panel. No wording was edited; rendered plaintext can change code or mathematical formatting from the service’s internal representation. The combined file `raw_outputs/gemini_flash_reference.txt` adds explicitly collector-authored A/B labels and separating newlines around the unchanged strings; the original panel texts remain in the collection folder, with their own hashes. This is an assembled view of one system’s two candidates, not two independent systems. Exact character counts and hashes are in `source.json`; the prompt is preserved as `request.txt`. Private account screenshots, service instructions and authenticated conversation references are not included. The public app link identifies the service, not a public replay of that private conversation. + +The supplement was added after examining SmolLM2 and Qwen2.5 and is explicitly exploratory. It is not parameter matched with the local collection. + +## Repository and technical sources + +- [Cognitive-OS issue #5](https://github.com/aLexzzz430/Cognitive-OS/issues/5), accessed 2026-09-08: required packet and eleven comparison dimensions. Reward and submission administration are not research results. +- [Pinned Cognitive-OS source](https://github.com/aLexzzz430/Cognitive-OS/tree/e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca), inspected 2026-09-08: the specific source surfaces and interpretations are listed in [repository-context.md](collection/repository-context.md). The models received the same short context paragraph, not this full source inspection. +- [SQLite Atomic Commit](https://sqlite.org/atomiccommit.html) and [Write-Ahead Logging](https://sqlite.org/wal.html), accessed 2026-09-08: context for database transaction boundaries and the analyst’s [isolated example](collection/transaction-example.md). The local measured cases are evidence from the supplied script; the documentation is not a report of executing Cognitive-OS. + +## Human and analyst editing + +No human edits or analyst wording changes were made to the primary raw response content. Whole HTTP responses are preserved separately. Repeated passages, unsupported claims, omissions and truncation remain present. Hosted content undergoes only the stated rendered-text extraction, with no word-level cleanup or A/B selection. + +The comparison, notes, overview, summary, synthesis, scripts and source catalog were authored by the assisting Codex agent. There was no independent human review of the generated proposals. They are labelled separately from generated proposals and are not counted as additional independent model outputs. The analyst’s transaction-ordering correction and wider proposed experiments are additions, not statements secretly inserted into a model’s response. The original transaction-example result is retained; broader proposed evaluations have not been run. + +No private API keys, account tokens, hidden system prompts, proprietary repository content or payment credentials are used as research evidence. diff --git a/research/ai_generated_agi_architectures/summary.md b/research/ai_generated_agi_architectures/summary.md new file mode 100644 index 0000000..d9a742c --- /dev/null +++ b/research/ai_generated_agi_architectures/summary.md @@ -0,0 +1,45 @@ +# Patterns, disagreements and useful ideas + +The dataset contains eight primary local responses and one exploratory hosted system with two A/B candidates. Danube3 returned a prompt echo, not an architecture proposal. Thus seven local systems and one hosted system supplied proposal prose, ranging from requirement restatement to concrete but imperfect workflows. All results, including failures and truncation, remain visible. + +## A heading is not a mechanism + +SmolLM2 and Qwen2.5 largely rephrase the requested topics. TinyLlama's memory section describes a control loop; it repeats evaluation/persistence sections and names an example without supplying one. Danube3 reproduces the prompt. Filling those gaps in a cleaned summary would misrepresent the evidence. The comparison records omissions instead. Sources: [SmolLM2](collection/smollm2/analysis-notes.md), [Qwen2.5](collection/qwen25/analysis-notes.md), [TinyLlama](collection/tinyllama/analysis-notes.md), [Danube3](collection/danube3/analysis-notes.md). + +Granite supplies identifiable technologies and a report-edit scenario. Its graph store, transaction manager, replicated log, queue and consensus algorithm create a distinct proposal, but it gives neither the transaction participants nor a workload requiring multiple coordination layers in the minimum local runtime. Specificity makes the gaps inspectable; it does not establish benefit. [Granite notes](collection/granite33/analysis-notes.md). + +Phi-3 offers clearer same-task time/resource comparisons and candidate-learning prose, but claims that flushing a log will recover a missing receipt without supplying a durable source or reconstruction mechanism. Qwen3 leaves such completion explicitly unacknowledged and proposes task/context-based memory, while giving arbitrary retention periods and unclear training/held-out terminology. Their differences are descriptive, not controlled causal findings. [Phi-3](collection/phi3/analysis-notes.md), [Qwen3](collection/qwen3/analysis-notes.md). + +Falcon3 emphasizes probabilistic reasoning, recovery and coordination overhead, but provides few interfaces, no ablation and an unsupported interrupted-receipt assurance. [Falcon3 notes](collection/falcon3/analysis-notes.md). + +## Eleven-dimension synthesis of the differences + +| Dimension | Observed pattern or disagreement | Implementation implication | +| --- | --- | --- | +| Memory | Most responsive systems repeat the prompted categories. Granite chooses cache/SQLite/graph/modules; Qwen3 gives retention periods and a context hierarchy; hosted answers add schemas and source IDs. | Specify retrieval results and evidence retention. Evaluate a task/family index against existing failure retrieval before adding stores or unvalidated expiry defaults. | +| Planning | Bounded loops recur. Hosted answers specify step/depth values and recovery states; local outputs often omit actual transition contracts. | Distinguish depth, total steps and replans. Define a task success predicate; a suggested number is an unvalidated configuration choice. | +| Learning | Versions, evaluation and rollback recur, partly because the prompt explicitly requested them. Phi-3/hosted prose describes candidates; Qwen3 ambiguously trains on held-out data. | Separate training, tuning and final evaluation cases. Common prompted words do not demonstrate independent discovery or measured learning. | +| Tools | Several local answers turn uncertainty into confidence values. Hosted answers add request/receipt fields and observation before retry. | A score, request ID or checksum alone is not effect reconciliation. Preserve durable action identity and use an action-specific probe. | +| World representation | Categories are common; concrete evidence schemas and contradiction rules are less common. Qwen3 allows deferral; hosted B zeroes confidence on contradictory observation. | Retain conflicting evidence and assess its reliability. Do not automatically discard a valid claim when a new observation may be mistaken. | +| Governance | User control, permission, budgets and stopping are broadly repeated. Hosted candidates declare more concrete tiers/counters. | These are proposed interfaces, not validated enforcement. No cybersecurity experiment or security guarantee is established by this packet. | +| Evaluation | Granite/Phi-3 give identifiable time/resource/ablation ideas; Qwen3 selects learning ablation; Falcon3 gives themes without an ablation; other local outputs provide little or no experiment content. Hosted answers specify more detail. | Freeze tasks, verifier and budgets. Avoid an unbounded-context baseline, undefined significance or invented achieved metrics. | +| Persistence | Granite names distributed mechanisms; Phi-3 assumes missing receipts appear on flush; Falcon3 asserts interrupted completions remain logged; Qwen3 explicitly leaves missing-receipt work incomplete. Hosted A specifies a flawed order and B assumes a logged pending invocation. | Test the exact boundary. Commit intent before dispatch and reconcile observable effects; retain uncertainty when observation cannot decide. | +| Orchestration | Granite combines a controller with distributed coordination; hosted answers use bounded workers; Phi-3/Qwen3 vary workers with complexity; Falcon3 names a controller/worker pattern. | Begin with the existing single-loop baseline. State worker contracts and conflict rules; ordering messages is not resolving contradictory content. | +| Feasibility | Granite gives three broad two-week phases; hosted answers give weekly plans. Phi-3/Qwen3/Falcon3 name but do not enumerate a six-week sequence. | Map proposals to code already present and choose one testable task/effect slice. Treat schedules as estimates, not evidence of capability. | +| Originality | Pluggability, shadow staging, explicit state transitions and task/context memory are useful choices with uneven rejection criteria. Other outputs repeat common concepts or supply no insight. | Do not label common patterns as inventions. Each added mechanism needs a measured reason to keep or reject it. | + +The main [comparison.csv](comparison.csv) contains all 99 system/dimension pairs with raw-text anchors and a cohort label. Danube3's rows explicitly describe a prompt echo, not a model-endorsed architecture. The hosted reference has a separate [A/B comparison](collection/gemini-flash-reference/comparison.csv) and [analysis](collection/gemini-flash-reference/analysis-notes.md). It is one system with unknown revision/settings, not two independent samples. + +## The one measured research finding + +Hosted A places pending intent, a filesystem effect and the receipt before one SQL commit, then proposes recovering through a pending-action scan. In the analyst's local example, the child exits after an append and before receipt/commit. Reopening reveals one file effect and zero pending actions. Moving the intent commit before the append leaves one pending action and the same single effect; a read probe reconciles it without another append. [Code, original results and limits](collection/transaction-example.md). + +This rejects one generated transaction-ordering claim at one process-exit boundary. It is not an upstream defect report, a measurement of Cognitive-OS recovery, a power-loss simulation or a general exactly-once proof. The other experiments in the proposals and synthesis were not executed. Detailed pseudocode can still contain a flaw that a narrowly scoped experiment exposes. + +## What to retain and what to test + +The [combined design](synthesis.md) retains evidence-linked memory, one controller, explicit task verification, durable action identity, effect-specific reconciliation and versioned offline candidates. Its minimum slice is a documentation edit whose source facts and final content can both be checked. It uses existing state/failure-memory/mirror surfaces as the baseline. + +Do not adopt distributed coordination merely because it is named, expire needed evidence using unvalidated retention examples, treat an uncertain-effect confidence score as proof, or infer an improvement from adding logs. Those choices require experiments against the current implementation. The supplied six-week estimate starts with a fixed task corpus and baseline, then local recovery, memory applicability and ablations. + +The recommendations are analyst additions informed by the full dataset and pinned source inspection. They are not a vote, a claim of independent expert consensus, a model-quality ranking or demonstrated architectural improvement. The primary protocol selected compact ungated models for an 8 GiB CPU-only VM, and uses one sample per system with different sizes/quantizations/templates. The Gemini reference was added after two local responses and remains uncontrolled. These limitations are part of the finding, not reasons to hide weak outputs. diff --git a/research/ai_generated_agi_architectures/synthesis.md b/research/ai_generated_agi_architectures/synthesis.md new file mode 100644 index 0000000..a53dfe8 --- /dev/null +++ b/research/ai_generated_agi_architectures/synthesis.md @@ -0,0 +1,140 @@ +# Combined architecture: evidence-backed local task execution + +This is an analyst design informed by eight recorded local responses, the exploratory Gemini reference and separately attributed repository inspection. Danube3 echoed its prompt and supplies no architecture proposal; its failure is preserved. The design is not an implemented Cognitive-OS change or an AGI claim. + +## Choice and attribution + +Keep one authoritative controller, a bounded working view over existing state, and a durable action record connecting intent, observable effect and task verification. Start with one documentation-edit workflow. Expand memory or worker machinery only when a fixed evaluation shows that it improves verified completion enough to justify its cost. + +SmolLM2 and Qwen2.5 name the requested memory, learning and recovery categories but do not supply sufficient mechanisms for implementation. The hosted candidates add useful request/receipt fields, observation/hypothesis separation, offline promotion and bounded-controller designs. Candidate A's transaction envelope is rejected at one measured boundary; candidate B's recovery requires an explicit pre-dispatch intent commit that its prose does not show. These are qualitative readings of these particular outputs, not a ranking of model families. References: [SmolLM2 notes](collection/smollm2/analysis-notes.md), [Qwen2.5 notes](collection/qwen25/analysis-notes.md), [hosted comparison](collection/gemini-flash-reference/analysis-notes.md). + +The current repository already has a single tick loop, SQLite state, an event journal, failure-learning records and relevance matching. The proposal extends those surfaces instead of assuming an empty implementation. The inspected code is pinned to `e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca`; [repository-context.md](collection/repository-context.md) distinguishes source observations from proposed additions. The upstream minimal layout check and ten public smoke tests passed; no end-to-end runtime performance or recovery experiment has been run. + +[Granite](collection/granite33/analysis-notes.md) supplies a different tradeoff: Neo4j-style semantic storage, two-phase commit, Raft-style replication and Paxos-style conflict resolution alongside a central controller. Those additions are not adopted for the minimum local slice because its output supplies neither a requiring workload nor the missing transaction-participant and recovery contracts. Its explicit episodic-memory ablation and modularity-versus-overhead tradeoff do inform the proposed evaluation. More named components are not themselves evidence of a better architecture. + +[TinyLlama](collection/tinyllama/analysis-notes.md) repeats broad requirements and several whole sections while omitting requested mechanisms. It contributes no sufficiently specified new component. Keeping that result visible prevents the synthesis from attributing analyst-invented mechanisms to a model that did not provide them. + +[Phi-3](collection/phi3/analysis-notes.md) makes baseline time/resource comparisons more explicit, which supports measuring cost on the same task set. Its claim that flushing a transaction log will store a missing receipt is not adopted: no durable receipt source or reconstruction probe is supplied. The synthesis instead requires an observation of the effect and retains uncertainty when that observation cannot decide. + +[Qwen3](collection/qwen3/analysis-notes.md) explicitly leaves a missing-receipt completion unacknowledged, a useful limit on completion claims. Its task/context-based memory hierarchy motivates a retrieval index over evidence-linked records. That index is an analyst interpretation, not a replacement of the existing stores or a demonstrated speedup. Qwen3's retention numbers and unclear training/held-out partition are not adopted as defaults. + +[Falcon3](collection/falcon3/analysis-notes.md) emphasizes coordination overhead and probabilistic uncertainty but gives no effect-reconciliation mechanism or ablation. It reinforces the need to measure added worker costs; it does not justify replacing observable verification with a confidence score. [Danube3](collection/danube3/analysis-notes.md) contributes no design mechanism because it returns the prompt. + +## State and interfaces + +```text +Goal + task-specific completion predicate + | + Observe current evidence + | + Model proposes bounded next step + | + Controller validates current authority, + expected state and remaining budget + | + Commit durable action intent + | + Dispatch one bounded action + | + Observe / reconcile actual result + | + Commit receipt and task-state transition + | + Verify completion predicate or replan +``` + +These are proposed schema fields, not claims about existing classes: + +| Record | Minimum content | Purpose | +| --- | --- | --- | +| Task | Goal, completion predicate/version, evidence IDs, status, step/replan limits | Make completion independently checkable and bound repeated attempts. | +| Action intent | Stable action ID, task ID, request hash, expected state, proposed result, authority revision, budget reservation, attempt state | Preserve the identity and assumptions of an invocation across interruption. | +| Receipt | Action ID, observed status, result/evidence IDs, verification outcome, actual resource use | Distinguish tool response, observable effect and verified task success. | +| Claim | Content, source observations, timestamp, confidence/uncertainty, contradiction status | Keep an interpretation linked to evidence that may later change. | +| Candidate lesson | Applicability conditions, source failure, proposed routine/version, training-case IDs, evaluation references | Make an improvement candidate distinct from a log entry. | + +Use action states `PROPOSED → READY → DISPATCHED → OBSERVED → VERIFIED`, with `UNCERTAIN`, `FAILED` and `CANCELLED` alternatives. Persist `READY` before dispatch. A restart does not assign a fresh logical ID to an already recorded invocation. Only the controller commits task transitions; optional bounded workers return observations or proposals. Serialization determines update order, but a separate evidence-resolution step must handle contradictory claims. + +Budget reservation occurs before dispatch, with actual cost reconciled afterward. Replanning consumes the same run budget and has its own limit; a depth cap alone does not bound repeated plan revisions. Stop requests prevent further dispatch and retain the observable state of in-flight work. This describes required behavior; it does not claim that a signal handler can instantly cancel every external operation or undo completed effects. + +## Recovery for a reconcilable local edit + +Gemini A proposes placing intent, filesystem effect and receipt inside one SQL transaction. The [executed example](collection/transaction-example.md) showed why that cannot supply its claimed pending-action scan: process exit left one file effect and zero pending rows. Committing intent before the effect left one pending row, permitting this specific effect to be reconciled without repeating it. The result is limited to the supplied temporary-file example, not power loss or the actual Cognitive-OS implementation. + +The following is conceptual controller pseudocode, not a drop-in adapter: + +```python +def propose_edit(task, observed_file, proposed_bytes): + action = validated_intent( + task=task, + before_hash=hash_bytes(observed_file), + after_hash=hash_bytes(proposed_bytes), + proposed_content=proposed_bytes, + stable_action_id=new_action_id(), + ) + commit_intent_and_reservation(action) # completes before dispatch + return action + +def reconcile_or_dispatch(action): + observed = read_current_target(action) + if observed.unavailable: + return retain_uncertain(action) + if observed.hash == action.after_hash: + return verify_then_record_receipt(action, observed) + if observed.hash != action.before_hash: + return record_conflict_and_reobserve(action, observed) + if not current_authority_and_budget_allow(action): + return pause_without_dispatch(action) + # Adapter must check the recorded precondition when applying the edit. + result = apply_conditional_edit(action) + # A timeout is not evidence that no edit occurred. + return observe_then_verify_or_retain_uncertain(action, result) +``` + +The conditional-edit adapter needs a specified concurrency contract. A separate read followed by an unconditional write is insufficient when another process can edit the same file. This proposal does not assume that a whole directory can be atomically swapped across arbitrary filesystems. Start with the existing mirror/patch path and document its supported preconditions before adding a replacement adapter. + +| Observation after interruption | Controller decision | +| --- | --- | +| Target matches proposed final hash | Recover the receipt; run semantic/task checks before declaring completion. | +| Target matches original hash | Recheck authority, budget and edit preconditions; retry the same logical action only if still valid. | +| Target matches neither | Preserve the conflicting observation and construct a new proposal from fresh evidence. | +| Target cannot be observed | Keep the outcome uncertain and bound further observation attempts. | + +For a remote effect with neither a supported idempotency key nor an adequate read probe, retain uncertainty. This architecture does not offer a generic exactly-once effect guarantee. Within SQLite, receipt and corresponding task transition can share a transaction; a derived JSONL mirror must not silently become a second competing authority. + +## Memory and learning + +Working memory is a bounded task view, episodic memory records action/outcome sequences, semantic memory stores evidence-linked claims, and procedural memory stores evaluated routine versions. These categories are common in the collected text; the following operational contract is an analyst addition. + +Retrieval returns the record ID, age, applicability reason and unresolved contradictions with the content. Begin with the repository's existing failure objects and tag-based relevance matching. Add a vector index only if an ablation demonstrates benefit against that real baseline. Evict working-cache entries separately from retaining evidence required to explain an active claim or accepted action. + +Use task ID and task family as retrieval dimensions across memory types, following the useful direction in Qwen3's context-based proposal. Same-task observations are immediately relevant, while cross-task lessons require an explicit applicability match. Do not restrict all retrieval to the current task ID, which would defeat transfer to new tasks. Treat an index as a view over retained source records, not a second copy whose provenance can diverge. + +A failed assumption creates a candidate lesson linked to a reproducible case. Freeze training cases separately from evaluation cases. Promote a routine only after a versioned evaluation reports verified completion, repeated mistakes and resource cost against the current routine. Historical transcripts alone do not establish a held-out test: case-family and content overlap must be tracked. Retain the prior version and its evaluation record for rollback. Appending logs or generating a plausible new prompt is not measured learning. + +## One documentation-edit path + +For a request to document a new `timeout_ms` parameter, first inspect the source signature, units, default and relevant behavior. Define the success predicate: the requested documentation accurately describes those facts, the intended file changes, and unrelated content remains intact. A compilation pass or a clean textual diff alone cannot establish that predicate. + +The planner proposes a minimal edit bound to the observed source/document versions. The controller applies the current user's authority and budget rules, commits the intent, and dispatches through the supported local edit adapter. It verifies both the resulting content and the source facts before completion. If interrupted after the edit, it uses the recorded hashes to reconcile rather than blindly applying the patch again. If the source changed meanwhile, it rechecks the documentation's meaning even if the document's final hash matches the old proposal. + +## Evaluation before promotion + +The following three experiments are **proposed and not yet run**. They are broader than the one executed SQLite example. Freeze model, prompts, verifier, budgets and case selection within each comparison. Report failure counts and unresolved cases as well as averages. + +1. **Interrupted edits:** compare the existing task path with the proposed intent/receipt path at predeclared interruption boundaries. Measure false completion declarations, duplicate mutations, unresolved outcomes and recovery cost. The target invariants are no false completion and no duplicate effect for the specified reconcilable file adapter. Observing those invariants in a finite suite would not prove them for arbitrary tools. +2. **Changing evidence:** change source or document content between proposal and application, including ordinary concurrent edits. Measure stale edits, correctly identified conflicts, completion after fresh observation and overhead. Ablate expected-state binding while holding the rest fixed. Reject an improvement claim if fewer stale edits result only from indiscriminate stopping and reduced useful completion. +3. **Failure-memory transfer:** compare current tag matching, no lesson retrieval and the proposed applicability filter on disjoint task families and new input variants. Measure verified completion, repeated known mistakes, irrelevant-lesson use and added token/tool cost. Reject the learning claim if gains disappear outside memorized cases or unrelated tasks regress beyond a threshold declared before the test. + +## Six-week implementation estimate + +| Week | Deliverable and decision | +| --- | --- | +| 1 | Freeze one documentation task corpus and completion predicates; measure the current baseline before replacing components. | +| 2 | Specify durable action records and one supported local edit/reconciliation path. | +| 3 | Exercise process-interruption and conflicting-edit cases; document receipt and evidence semantics. | +| 4 | Connect one candidate-lesson type to existing failure-memory storage and retrieval. | +| 5 | Run the fixed-budget, disjoint-case evaluation and ablations. | +| 6 | Review cost and failure tradeoffs; promote only supported changes and preserve a rollback path. | + +The minimum useful slice is one task family, one controller and one observable edit effect. The principal engineering risk is that record/retrieval complexity increases cost without improving verified completion. The schedule is a planning estimate, and the proposed architecture provides no evidence that general intelligence has been achieved.