diff --git a/research/ai_generated_agi_architectures/README.md b/research/ai_generated_agi_architectures/README.md new file mode 100644 index 0000000..318df5c --- /dev/null +++ b/research/ai_generated_agi_architectures/README.md @@ -0,0 +1,45 @@ +# AI-Generated AGI Architecture Research Packet + +This packet collects and compares fresh AGI-direction architecture proposals from 10 distinct model families for Cognitive-OS planning. The work was collected on 2026-05-27 with one standardized prompt, preserved as raw output files, then synthesized into implementation guidance tied to this repository's actual runtime primitives. + +## What Is Included + +- `prompts.md`: exact prompt and system message used for every model. +- `raw_outputs/`: one minimally wrapped raw output file per model family. +- `collection_manifest.json`: machine-readable collection metadata, model IDs, public catalog verification, response IDs, response-created timestamps, token usage, raw-output hashes, byte counts, and word counts. +- `comparison.csv`: structured comparison across the requested architecture dimensions. +- `comparison_by_dimension.csv`: dimension-first companion matrix for quick reviewer scanning. +- `evidence_matrix.md`: claim-to-raw-output traceability map. +- `summary.md`: common patterns, disagreements, and notable model-family ideas. +- `synthesis.md`: a concrete combined architecture for Cognitive-OS implementation planning. +- `sources.md`: source, provider, access date, edit log, and limitations. +- `validation_report.md`: acceptance-criteria mapping, local verification commands, and raw-output hashes. + +## Collected Systems + +| Family | Model ID | Access path | Response ID | Raw file | +| --- | --- | --- | --- | --- | +| OpenAI GPT | `openai/gpt-5.4-mini` | OpenAI via OpenRouter | `gen-1779848740-c5o4NyAhVIgVX1YTosx1` | `raw_outputs/openai_gpt54_mini.md` | +| Claude | `anthropic/claude-sonnet-4.6` | Anthropic via OpenRouter | `gen-1779848760-QFo0zCh36RMjSfdk7VHW` | `raw_outputs/anthropic_claude_sonnet_46.md` | +| Gemini | `google/gemini-3.1-pro-preview` | Google via OpenRouter | `gen-1779848820-fHN73CLXM5jNTUZ3lUHq` | `raw_outputs/google_gemini_31_pro.md` | +| Grok | `x-ai/grok-4.3` | xAI via OpenRouter | `gen-1779848852-0ktKsI72HwKX3E0JoTKd` | `raw_outputs/xai_grok_43.md` | +| DeepSeek | `deepseek/deepseek-v3.2` | DeepSeek via OpenRouter | `gen-1779848866-t5VvIELdsYHWEfAyntld` | `raw_outputs/deepseek_v32.md` | +| Qwen | `qwen/qwen3.7-max` | Qwen/Alibaba via OpenRouter | `gen-1779848934-T2oEkUEI9efRN7jEcVlF` | `raw_outputs/qwen37_max.md` | +| Llama | `meta-llama/llama-4-maverick` | Meta Llama via OpenRouter | `gen-1779849013-QMXEwlExEw8ub6paD3nk` | `raw_outputs/meta_llama4_maverick.md` | +| Mistral | `mistralai/mistral-large-2512` | Mistral via OpenRouter | `gen-1779849033-NNZNF80eWMcFyxzdJkAp` | `raw_outputs/mistral_large_2512.md` | +| Perplexity Sonar | `perplexity/sonar-pro` | Perplexity via OpenRouter | `gen-1779849077-8sTXk7EUz9sQeu3Y1EuE` | `raw_outputs/perplexity_sonar_pro.md` | +| Cohere Command | `cohere/command-a` | Cohere via OpenRouter | `gen-1779849103-p1SgOCsNZOzouYMpfOTJ` | `raw_outputs/cohere_command_a.md` | + +## Headline Findings + +1. All 10 systems converged on a control-plane architecture rather than a monolithic AGI model: memory, planning, policy, action execution, and persistence should be separate services. +2. The strongest shared pattern is evidence-governed action: no model should directly mutate the world without typed permissions, verifier gates, and durable side-effect records. +3. The biggest disagreement is how much sophistication belongs in the world model. Qwen and Cohere favor causal/Bayesian machinery; OpenAI, Claude, Gemini, and Grok emphasize typed evidence and replayable state first. +4. The most implementation-ready synthesis for this repository is an event-sourced local runtime with typed memory, capability-scoped tools, verifier-gated execution, and offline eval-gated learning. +5. The non-obvious combined insight is to treat the LLM context window as a cache over an auditable cognitive state machine, not as the system's memory or identity. + +## Why This Packet Is Auditable + +The raw files include provider/tool, model ID, access date, UTC batch-start timestamp, OpenRouter response ID, response-created timestamp, and token usage metadata. The manifest adds raw-output SHA-256 hashes, byte counts, word counts, and a public OpenRouter model-catalog verification timestamp. The artifacts do not include API keys, private prompts, account screenshots, or proprietary system messages. Human edits to raw outputs were limited to adding the metadata wrapper and preserving whitespace. + +See `validation_report.md` for the exact file counts, manifest consistency checks, repository layout check, credential scan pattern, and SHA-256 hashes for the preserved raw outputs. diff --git a/research/ai_generated_agi_architectures/collection_manifest.json b/research/ai_generated_agi_architectures/collection_manifest.json new file mode 100644 index 0000000..41f4fcf --- /dev/null +++ b/research/ai_generated_agi_architectures/collection_manifest.json @@ -0,0 +1,419 @@ +{ + "collection_time_utc": "2026-05-27T02:25:39+00:00", + "prompt": "You are contributing to the public Cognitive-OS planning repository. Produce an AGI architecture proposal that is concrete enough to inform implementation decisions, not a philosophical essay.\n\nContext: Cognitive-OS is a local-first, evidence-governed runtime for AI agents with goals, evidence, hypotheses, world/self state, governed actions, LLM routing, local-machine adapters, verifier gates, managed-VM boundaries, recovery, and runtime modes. Treat this as an engineering planning exercise for a future AGI-direction control plane. Do not claim current AGI capability.\n\nRequired sections, in this order:\n1. Architecture thesis in 3-5 sentences.\n2. Memory architecture: working, episodic, semantic/procedural, retrieval, consolidation, forgetting.\n3. Reasoning and planning loop: deliberation, planning, reflection, uncertainty, interrupts.\n4. Learning or self-improvement: allowed update surfaces, offline learning, eval-gated changes, rollback.\n5. Tool use and action execution: sandboxing, permissions, verifier gates, side-effect ledger.\n6. World model and representation layer: entities, causal models, forecasts, state sync.\n7. Safety/governance: policy engine, human controls, auditability, refusal/escalation, capability boundaries.\n8. Evaluation and benchmark strategy: unit tests, integration tests, red teams, long-run soak, regression gates.\n9. Persistence/runtime architecture: event log, state store, leases, runtime modes, recovery.\n10. Multi-agent/orchestration design: roles, arbitration, consensus, specialization, conflict handling.\n11. Engineering feasibility: 90-day prototype plan, highest-risk assumptions, dependencies.\n12. Original or non-obvious insight from your model family.\n\nKeep the output self-contained. Avoid private information, hidden prompts, screenshots, credentials, and unsupported factual claims. 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adapters, permissioned actions, precondition/postcondition checks, managed execution boundary, and durable side-effect logging before external mutation.","Typed entities with provenance, causal links, forecasts, stale-memory flags, and explicit separation between observed evidence and inferred state.","Policy engine plus refusal/escalation records, human controls, capability boundaries, immutable audit trail, and sensitive-memory retention limits.","Unit and integration gates around memory, planner, policy, tool calls, regression suites, red-team prompts, and long-running recovery/soak scenarios.","Event journal as source of truth, state snapshots/materialized views, runtime modes, background consolidation, and crash recovery through replay.","Specialized proposer, critic, verifier, executor, and monitor roles coordinated through shared evidence and arbitration rather than unconstrained debate.",High feasibility as an incremental control-plane architecture; highest risk is writing useful verifiers and keeping memory consolidation precise.,"Treat intent, evidence, world state, policy, and execution as separately versioned objects so model upgrades do not destabilize governance." +Claude,anthropic/claude-sonnet-4.6,"Event-sourced memory with provenance-first entries, consolidation through contradiction checks, and memory accesses mediated by policy and evidence quality.","Structured observe-orient-decide-act cycle with explicit hypothesis formation, verification checkpoints, uncertainty handling, and interruptible plans.","Only bounded update surfaces are allowed: memory summaries, planner heuristics, route policies, and procedures; every candidate change enters shadow mode before promotion.","Tools are narrow capabilities with dry-run support, approval requirements, verifier gates, and compensating-action records for reversible work.","World/self state is typed and belief-weighted, with forecasts tied to evidence and observed outcome feedback.","Governance is a first-class runtime service: policy checks, audit records, human escalation, refusal logic, and capability leases.","Layered evaluation: deterministic unit tests, scenario replay, safety red teams, regression gates, and long-run soak for drift and recovery.","Append-only event log, state store, checkpoints, leases, runtime modes, and recovery flow that rehydrates exact pre-crash state.","Planner, executor, verifier, critic, and memory curator roles with policy-constrained context sharing and arbitration.",Feasible in 90 days if scoped to a local-first prototype over existing Cognitive-OS modules and not framed as achieved AGI.,"Make uncertainty a runtime object with escalation thresholds, not a prose caveat inside model output." +Gemini,google/gemini-3.1-pro-preview,"Sliding working buffer, SQLite/pgvector episodic log, semantic graph/procedural AST library, sleep-mode consolidation, and cold-storage tiering instead of destructive deletion.","Asynchronous OODA-style state machine with hypothesis testing, plan generation, reflection, confidence scores, and priority interrupts.","Local-first learning from logged outcomes, with model/prompt/procedure updates tested in shadow environments before release.",LLMs remain stateless; managed VM adapters and verifier gates mediate all real-world actions.,Entity graph plus causal dependencies and temporal state reconciliation from external sensors or APIs.,"Deterministic policy checks, audit logs, managed-VM boundaries, and escalation when confidence or policy constraints fail.","Regression gates, simulated environments, red-team prompts, long-run soak, and recovery replay.","Append-only evidence ledger, state-machine transitions, durable state snapshots, and crash-resilient recovery.","Multiple specialized loops coordinate through a shared evidence ledger, not shared hidden context.",Strong for a local-first prototype; main risk is keeping state reconciliation deterministic as tools expand.,Use cold-storage tiering as forgetting so the active retrieval index stays fast without losing auditability. +Grok,x-ai/grok-4.3,"Fixed-size working ring buffer, append-only episodic traces with hashes, typed semantic/procedural graph, hybrid retrieval, and priority-queue forgetting.","Discrete ticks expand hypotheses, score evidence, plan typed actions, reflect on predicted vs observed deltas, and pause on uncertainty intervals.","Only semantic graph, procedure library, policy rules, and verifier predicates can change; changes are offline, signed, eval-gated, and replayable.","Managed-VM adapters, scoped capability tokens, static/dynamic/policy verifier sequence, and ledger-flush-before-action discipline.","Versioned entity records, causal dependency graphs with probability intervals, forecast simulation, and lease-bound state sync.","Logic-program policy engine, signed human leases, refusal/escalation traces, and hard enforcement at adapter boundaries.","Unit tests for verifiers and policies, integration replay, adversarial goal sets, 72-hour soak, and regression gates.","Signed event log, materialized state store, monotonic leases, interactive/batch/soak modes, and bounded replay recovery.","Planner, executor, verifier, and monitor roles share a blackboard; verifier signatures settle evidence.","Feasible with SQLite, VM isolation, and typed adapters; risk is deterministic verifier coverage for rich LLM outputs.",Store governance decisions as evidence records so historical policy precedents become retrievable planning context. +DeepSeek,deepseek/deepseek-v3.2,"Cost-aware tiered memory with compact working state, searchable episodes, distilled procedures, and periodic consolidation to reduce repeated reasoning.","Efficient planning loop emphasizes decomposition, tool routing, verification, and low-cost reflection before using expensive model calls.","Offline preference and procedure learning from successful traces, with eval-gated rollout and strict rollback of route/prompt/procedure changes.","Structured tool calls, sandbox resource limits, deterministic validators, and side-effect accounting.",Typed state plus causal/forecast metadata optimized for cheap retrieval and update.,"Governance is enforced through route policy, permission gates, and automated checks before model or tool escalation.","Cost and latency metrics are first-class alongside correctness, safety, and regression scores.","Event log and state snapshots designed for replay, compression, and cheap local operation.",Use small specialist agents for routine evaluation and reserve larger models for disputed/high-uncertainty tasks.,High if the first prototype uses small local/router models for verification and reserves larger calls for synthesis.,Efficiency is a safety feature: cheap checks are more likely to run every time. +Qwen,qwen/qwen3.7-max,"Context window as L1 cache over episodic log, semantic graph, procedural DAGs, TTL markers, and hybrid retrieval under access controls.","Constrained MCTS-like plan tree emitting typed JSON plans with preconditions, postconditions, reflection, and uncertainty-triggered information gathering.","Allowed surfaces are prompt templates, tool-routing weights, procedural DAG parameters, and offline LoRA adapters promoted only after shadow evals.","gVisor/Firecracker isolation, macaroon-like capability tokens, pre/post verifiers, output classifiers, and Merkle side-effect ledger.","Strongly typed entities, dynamic Bayesian network edges, simulation-based forecasts, and CRDT-style state sync.","OPA/Rego-style policy engine, break-glass controls, immutable audit stream, escalation tickets, and hard air-gaps around governance code.","Unit, integration, fuzz/red-team, 30-day soak, and CI regression suite for 500 canonical agent tasks.","Redpanda/RocksDB style event and state layers, advisory locks, shadow/supervised/autonomous modes, checkpoint and replay recovery.","Role-scoped agents with orchestrator routing, voting for high-stakes decisions, and serialized conflict resolution.",Ambitious but concrete; prototype should simplify dependencies to SQLite/local queues before Redpanda/RocksDB scale-out.,"Treat LLM context as cache, not mind; cognitive memory management is the product boundary." +Llama,meta-llama/llama-4-maverick,"Open/local memory stack emphasizing transparent schemas, vector and graph retrieval, periodic summarization, and portable procedure libraries.",Planner decomposes work into explicit tasks and uses critique/evaluation loops before execution.,"Open-weight/local-friendly design favors adapter tuning, replay data, and reproducible benchmark gates.","Typed tool APIs, least-privilege permissions, deterministic validators, and local sandbox execution.",Entity graph and state snapshots suitable for local inspection and open-source contribution workflows.,"Transparency, reproducible policies, auditability, and community-inspectable boundaries are emphasized.","Local benchmark harnesses, regression suites, reproducible seeds, and scenario tests.",Portable state store and event log designed to run offline and be inspected by developers.,Specialized local agents with explicit role/context boundaries.,Strong for a community prototype because dependencies can remain local and inspectable.,Openness is an operational safety primitive because users can inspect and reproduce the control plane. +Mistral,mistralai/mistral-large-2512,"Privacy-preserving local-first memory with short-lived working state, encrypted episodic logs, validated semantic memory, and compact procedure traces.","Lean orchestration loop: generate, critique, verify, execute, and measure, with route policies for cost and latency.","Sovereign/offline learning, small adapter updates, policy-controlled promotion, and rollback through versioned artifacts.","Capability-limited tools, sandboxing, local policy checks, and strong separation between planning and execution.",Local entity model with evidence links and forecasts built from verified observations.,"Data minimization, local governance, audit logs, and operator controls are central.","Latency, privacy, reproducibility, safety, and regression benchmarks.","Local encrypted event/state layers, runtime modes, and compact recovery checkpoints.",Small role agents coordinated by a supervisor with route and budget constraints.,High for an enterprise/local-first MVP; risk is balancing privacy constraints with useful learning.,Sovereignty and efficiency can be design constraints rather than afterthoughts; smaller governed models may be preferable for routine control. +Perplexity Sonar,perplexity/sonar-pro,"Evidence-centric memory with source links, citation-like provenance, retrieval confidence, and freshness scoring.","Research-first loop: gather evidence, compare hypotheses, cite provenance, plan, verify, and update forecasts.","Self-improvement is gated by source quality, benchmark evidence, and reviewable change packets.","External actions require source-backed justification, sandboxing, and audit records.","World model emphasizes currentness, provenance, source reconciliation, and contradiction detection.","Governance focuses on evidence quality, escalation on weak/conflicting sources, and auditability of citations and tool effects.","Benchmarks include factuality, source freshness, retrieval quality, plan outcomes, and safety regressions.",Event log plus source/evidence graph with stale-source invalidation and recovery replay.,"Researcher, fact-checker, planner, executor, and auditor roles.",Useful as a retrieval/evidence module in the first prototype rather than the whole control plane.,"Freshness and provenance should be typed fields in memory, not comments in a summary." +Cohere Command,cohere/command-a,"Differentiated working, episodic, semantic, and procedural stores with vector/symbolic retrieval and replay-based consolidation.","HTN-like planner with evidence weighting, MCTS-style action search, Bayesian uncertainty, and replanning interrupts.","Confine updates to memory, planner heuristics, and action templates; use sandboxed batch learning, shadow runtime, and rollback.","Managed VMs, capability permissions, verifier gates, and blockchain-inspired side-effect ledger.",Causal graph of typed entities with structural causal models and probabilistic forecasts.,"Rule engine, operator pause/redirect/terminate controls, refusal/escalation, audit logs, and domain capability boundaries.","Unit, integration, red-team, soak, and regression gates.","Append-only event log, checkpointed state, runtime recovery, and debug replay.",Coordinator with specialist agents and consensus/conflict handling for high-impact decisions.,Feasible if MCTS/SCM pieces start simple and become optional plugins.,A side-effect ledger gives action execution the same inspectability as financial transaction systems. diff --git a/research/ai_generated_agi_architectures/comparison_by_dimension.csv b/research/ai_generated_agi_architectures/comparison_by_dimension.csv new file mode 100644 index 0000000..3c38355 --- /dev/null +++ b/research/ai_generated_agi_architectures/comparison_by_dimension.csv @@ -0,0 +1,12 @@ +dimension,consensus,notable_disagreements_or_outliers,implementation_decision_for_cognitive_os +memory_architecture,"All models separate bounded working memory from episodic, semantic, and procedural memory, and all require provenance.","Gemini emphasizes cold-storage tiering; Qwen frames context as an L1 cache; Perplexity emphasizes freshness and source quality as typed fields.","Use `modules/memory/` for typed stores, keep event provenance first, and promote semantic/procedural records only through evidence-linked consolidation." +reasoning_planning_loop,"Models converge on observe-plan-verify-act-reflect loops where LLMs propose plans but do not execute them directly.","Qwen proposes constrained MCTS; Cohere proposes HTN/MCTS-style planning; DeepSeek stresses cheaper checks before expensive planning.","Start with deterministic typed plan objects and verifier feedback in `core/orchestration/planner_runtime.py`, then add optional search only where it improves measured outcomes." +learning_self_improvement,"No model recommends unconstrained online weight updates. Learning should modify procedures, prompts, route policy, memory, and verifier predicates through offline gates.","Qwen mentions optional offline LoRA adapters; Mistral emphasizes sovereign/local learning; DeepSeek emphasizes cost-aware updates.","Use `core/runtime/failure_learning.py`, `core/runtime/end_to_end_learning.py`, and `modules/memory/promotion_rules.py` for shadow-mode change packets with rollback." +tool_use_action_execution,"Every model requires capability-scoped tools, sandboxing, preconditions, postconditions, and side-effect records.","Qwen suggests macaroon-like tokens; Grok suggests signed leases; Cohere compares side effects to financial transactions.","Route actions through `core/orchestration/verifier_runtime.py`, `core/orchestration/execution_control.py`, and `tools/managed_vm/` before external mutation." +world_model_representation,"All models want typed entity/world state separate from raw memory, with evidence links and forecast/outcome deltas.","Qwen and Cohere favor early causal/Bayesian models; Perplexity prioritizes source freshness; Gemini emphasizes temporal reconciliation.","Begin with `core/world_model/` and `modules/world_model/` typed entities plus observed deltas; introduce causal graphs only for domains with testable forecasts." +safety_governance_layer,"Governance is a runtime service with policies, refusals, escalations, human controls, and audit records.","Qwen names OPA/Rego; Grok proposes retrievable policy precedents; Mistral stresses local/private governance.","Use `modules/governance/gate.py`, `modules/governance/object_store.py`, and `core/orchestration/governance_runtime.py` as the first enforcement surfaces." +evaluation_benchmark_strategy,"Models agree on unit tests, integration replay, red teams, soak tests, and regression gates before learning promotion.","Perplexity adds source freshness/retrieval-quality evals; DeepSeek adds cost/latency as safety metrics.","Extend existing `runtime/evals/` and soak runners with action replay, verifier bypass tests, and memory-promotion regression cases." +persistence_runtime_architecture,"All models treat append-only events and materialized state as the safety backbone.","Qwen proposes distributed queues and RocksDB; Gemini proposes SQLite/pgvector; Grok wants signed logs.","Start local and inspectable with `core/runtime/event_journal.py`, `core/runtime/evidence_ledger.py`, and `core/runtime/state_store.py`; defer distributed infrastructure." +multi_agent_orchestration,"Models prefer specialist roles coordinated through shared state rather than hidden shared chat.","Some outputs recommend voting/consensus for high-impact decisions; others favor a single orchestrator with critics/verifiers.","Use planner, critic, verifier, executor, and curator roles, but reserve consensus for high-impact decisions to avoid latency inflation." +engineering_feasibility,"All models can map to a 90-day local-first prototype if dependencies stay modest and AGI claims are avoided.","Qwen/Cohere are most ambitious; DeepSeek/Mistral are most cost/privacy constrained.","Prototype ledger/state, governed action path, and memory promotion first; add causal/search machinery only after verifier and replay foundations are stable." +original_non_obvious_insight,"The strongest shared non-obvious idea is that the LLM context window is a cache over a governed cognitive transaction system.","Individual emphases differ: uncertainty as runtime object, governance precedent as evidence, efficiency as safety, and freshness as typed memory metadata.","Make beliefs, plans, permissions, actions, and learning updates lifecycle-managed objects with provenance and rollback." diff --git a/research/ai_generated_agi_architectures/evidence_matrix.md b/research/ai_generated_agi_architectures/evidence_matrix.md new file mode 100644 index 0000000..cb492a2 --- /dev/null +++ b/research/ai_generated_agi_architectures/evidence_matrix.md @@ -0,0 +1,64 @@ +# Evidence Matrix + +This matrix links comparison and synthesis claims back to the preserved model outputs. All raw files used the same 12-section prompt, so section numbers are comparable across model families. + +## Section Key + +| Section | Evidence dimension | +| --- | --- | +| 1 | Architecture thesis | +| 2 | Memory architecture | +| 3 | Reasoning and planning loop | +| 4 | Learning or self-improvement | +| 5 | Tool use and action execution | +| 6 | World model and representation layer | +| 7 | Safety and governance | +| 8 | Evaluation and benchmark strategy | +| 9 | Persistence and runtime architecture | +| 10 | Multi-agent and orchestration design | +| 11 | Engineering feasibility | +| 12 | Original or non-obvious insight | + +## Dimension-To-Output Map + +| Dimension | Raw-output sections used | Analysis files using it | +| --- | --- | --- | +| Memory architecture | Section 2 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Reasoning/planning loop | Section 3 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Learning/self-improvement | Section 4 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Tool use/action execution | Section 5 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| World model/representation | Section 6 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Safety/governance layer | Section 7 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Evaluation/benchmark strategy | Section 8 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Persistence/runtime architecture | Section 9 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Multi-agent/orchestration | Section 10 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | +| Engineering feasibility | Section 11 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `synthesis.md` | +| Original/non-obvious insight | Section 12 in all 10 raw files | `comparison.csv`, `comparison_by_dimension.csv`, `summary.md`, `synthesis.md` | + +## Model-Specific Evidence Notes + +| Model family | Raw file | Evidence carried into analysis | +| --- | --- | --- | +| OpenAI GPT | `raw_outputs/openai_gpt54_mini.md` | Separation of intent, evidence, world state, policy, and execution appears in sections 1, 5, 7, and 12. This supports the control-plane framing in `summary.md` and the service split in `synthesis.md`. | +| Claude | `raw_outputs/anthropic_claude_sonnet_46.md` | Uncertainty as a runtime object appears in sections 3, 7, and 12. This supports escalation thresholds and interrupt handling in the synthesis. | +| Gemini | `raw_outputs/google_gemini_31_pro.md` | Cold-storage tiering and asynchronous OODA flow appear in sections 2, 3, and 9. This supports the disagreement note about forgetting versus audit retention. | +| Grok | `raw_outputs/xai_grok_43.md` | Capability leases, verifier outcomes, and governance precedent appear in sections 5, 7, 9, and 12. This supports the policy-and-lease engine in `synthesis.md`. | +| DeepSeek | `raw_outputs/deepseek_v32.md` | Cost-aware checks and efficient routing appear in sections 3, 4, 8, and 12. This supports the decision to keep first implementations cheap enough to run on every action. | +| Qwen | `raw_outputs/qwen37_max.md` | Context-as-cache, constrained planning search, and stronger causal modeling appear in sections 2, 3, 6, and 12. This supports both the headline combined insight and the causal-world-model disagreement. | +| Llama | `raw_outputs/meta_llama4_maverick.md` | Open/local reproducibility appears in sections 4, 7, 8, 9, and 12. This supports the local-first and inspectable implementation stance. | +| Mistral | `raw_outputs/mistral_large_2512.md` | Sovereignty, privacy, and smaller governed routine models appear in sections 2, 4, 7, 8, and 12. This supports the privacy/local-first constraints in the summary. | +| Perplexity Sonar | `raw_outputs/perplexity_sonar_pro.md` | Freshness, provenance, source reconciliation, and contradiction detection appear in sections 2, 3, 6, 7, 8, and 12. This supports typed source quality in memory and retrieval. | +| Cohere Command | `raw_outputs/cohere_command_a.md` | Side-effect ledger, causal graphs, HTN/MCTS-style planning, and transaction-style auditing appear in sections 3, 5, 6, 9, and 12. This supports the action ledger and implementation appendix. | + +## Synthesis Traceability + +| Synthesis component | Primary raw evidence | Existing repository surface | +| --- | --- | --- | +| Event ledger | OpenAI section 2, Grok section 9, Qwen section 9, Cohere section 9 | `core/runtime/event_journal.py`, `core/runtime/evidence_ledger.py` | +| State store | OpenAI section 9, Claude section 9, Gemini section 9 | `core/runtime/state_store.py`, `modules/state/` | +| Memory manager | All section 2 outputs | `modules/memory/` | +| Planner | OpenAI section 3, Claude section 3, Qwen section 3, Cohere section 3 | `core/orchestration/planner_runtime.py`, `planner/` | +| Policy and lease engine | Grok sections 5 and 7, Qwen sections 5 and 7, Mistral section 7 | `modules/governance/`, `core/orchestration/governance_runtime.py` | +| Verifier gate | OpenAI section 5, Claude section 5, Grok section 5, Cohere section 5 | `core/orchestration/verifier_runtime.py` | +| Action executor | Gemini section 5, Qwen section 5, Cohere section 5 | `core/orchestration/execution_control.py`, `tools/managed_vm/` | +| Reflection and learning | DeepSeek section 4, Mistral section 4, Qwen section 4 | `core/runtime/failure_learning.py`, `core/runtime/end_to_end_learning.py` | diff --git a/research/ai_generated_agi_architectures/prompts.md b/research/ai_generated_agi_architectures/prompts.md new file mode 100644 index 0000000..7031ba3 --- /dev/null +++ b/research/ai_generated_agi_architectures/prompts.md @@ -0,0 +1,37 @@ +# Prompts + +## Standard Collection Prompt + +Used for all models on 2026-05-27. No model-specific prompt adaptations were used; only provider routing/model ID differed. + +```text +You are contributing to the public Cognitive-OS planning repository. Produce an AGI architecture proposal that is concrete enough to inform implementation decisions, not a philosophical essay. + +Context: Cognitive-OS is a local-first, evidence-governed runtime for AI agents with goals, evidence, hypotheses, world/self state, governed actions, LLM routing, local-machine adapters, verifier gates, managed-VM boundaries, recovery, and runtime modes. Treat this as an engineering planning exercise for a future AGI-direction control plane. Do not claim current AGI capability. + +Required sections, in this order: +1. Architecture thesis in 3-5 sentences. +2. Memory architecture: working, episodic, semantic/procedural, retrieval, consolidation, forgetting. +3. Reasoning and planning loop: deliberation, planning, reflection, uncertainty, interrupts. +4. Learning or self-improvement: allowed update surfaces, offline learning, eval-gated changes, rollback. +5. Tool use and action execution: sandboxing, permissions, verifier gates, side-effect ledger. +6. World model and representation layer: entities, causal models, forecasts, state sync. +7. Safety/governance: policy engine, human controls, auditability, refusal/escalation, capability boundaries. +8. Evaluation and benchmark strategy: unit tests, integration tests, red teams, long-run soak, regression gates. +9. Persistence/runtime architecture: event log, state store, leases, runtime modes, recovery. +10. Multi-agent/orchestration design: roles, arbitration, consensus, specialization, conflict handling. +11. Engineering feasibility: 90-day prototype plan, highest-risk assumptions, dependencies. +12. Original or non-obvious insight from your model family. + +Keep the output self-contained. Avoid private information, hidden prompts, screenshots, credentials, and unsupported factual claims. Aim for 1,200-1,800 words. +``` + +## System Message + +```text +You are a careful architecture researcher. Follow the requested structure exactly and be concrete. +``` + +## Prompt Adaptations + +None. Each model received the same user prompt and same system message through OpenRouter's chat completions API. diff --git a/research/ai_generated_agi_architectures/raw_outputs/anthropic_claude_sonnet_46.md b/research/ai_generated_agi_architectures/raw_outputs/anthropic_claude_sonnet_46.md new file mode 100644 index 0000000..069a2a1 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/anthropic_claude_sonnet_46.md @@ -0,0 +1,143 @@ +# Raw Output: Claude + +- Provider/tool: Anthropic via OpenRouter +- Model ID: `anthropic/claude-sonnet-4.6` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779848760-QFo0zCh36RMjSfdk7VHW` +- Usage reported by provider: `{"completion_tokens": 2600, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.040389, "cost_details": {"upstream_inference_completions_cost": 0.039, "upstream_inference_cost": 0.040389, "upstream_inference_prompt_cost": 0.001389}, "is_byok": false, "prompt_tokens": 463, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 3063}` + +--- + +# Cognitive-OS AGI Architecture Proposal +**Repository:** `cognitive-os/architecture/proposals/agi-control-plane-v0.1` + +--- + +## 1. Architecture Thesis + +Cognitive-OS treats cognition as a governed operating system: goals are first-class resources scheduled against evidence budgets, and every reasoning step is a traceable system call subject to policy enforcement. The core insight is that AGI-direction risk is primarily a *runtime* problem, not a training problem—so the control plane must enforce invariants even when the underlying model is capable of circumventing them. All state mutations flow through a single append-only event log, making the system recoverable, auditable, and rollback-capable by construction. Capability boundaries are enforced at managed-VM boundaries, not by model self-restraint. The architecture is designed so that safety properties degrade gracefully rather than catastrophically when components fail. + +--- + +## 2. Memory Architecture + +**Working Memory:** A bounded context window (≤128K tokens) managed as a priority queue. Items carry TTL, relevance scores, and source provenance. Eviction is LRU-with-importance, not pure LRU. Capacity pressure triggers consolidation rather than silent truncation. + +**Episodic Memory:** Append-only episode store keyed by `(agent_id, episode_id, timestamp)`. Each episode records: goal context, action sequence, outcomes, and a surprise score (KL divergence between predicted and observed outcome). Retrieval uses dense embedding similarity plus recency decay. Episodes are immutable once written. + +**Semantic/Procedural Memory:** A typed knowledge graph (entities, relations, procedures) stored in a local vector+graph hybrid (e.g., LanceDB + DuckDB). Procedures are stored as verified, versioned code artifacts with input/output contracts. Semantic facts carry confidence intervals and source citations. + +**Retrieval:** A two-stage pipeline: (1) approximate nearest-neighbor over embeddings for candidate generation, (2) a lightweight reranker that scores candidates against the current goal context. Retrieval calls are logged as system events. + +**Consolidation:** A background process runs after each session boundary. It clusters recent episodes, extracts generalizations into semantic memory, and promotes high-surprise episodes to a "review queue" for human inspection. Consolidation is gated by an eval check before writes land in semantic memory. + +**Forgetting:** Explicit, not implicit. Facts decay in confidence over time unless reinforced by new evidence. A forgetting policy (configurable per deployment) can hard-delete episodic records after a retention window, with a cryptographic deletion receipt written to the audit log. + +--- + +## 3. Reasoning and Planning Loop + +**Deliberation:** Each reasoning cycle begins with a goal stack pop. The agent generates a set of candidate next-actions via the LLM router, scored by expected goal progress and estimated cost. This is a beam search over action sequences, not greedy selection. + +**Planning:** Multi-step plans are represented as directed acyclic task graphs with explicit preconditions and postconditions. Plans are validated by a symbolic verifier before execution begins. Long-horizon plans (>10 steps) require a human-in-the-loop checkpoint. + +**Reflection:** After each action, the agent runs a lightweight self-evaluation: did the outcome match the predicted postcondition? If not, the surprise score is recorded and the plan is re-evaluated. Reflection outputs feed back into the episodic store. + +**Uncertainty:** Epistemic uncertainty is tracked as explicit probability distributions over world-state variables. When uncertainty exceeds a configurable threshold on a decision-relevant variable, the agent pauses and either gathers evidence or escalates to a human operator. + +**Interrupts:** The runtime supports three interrupt classes: (1) *safety interrupt* — immediate halt, triggered by policy engine; (2) *goal interrupt* — higher-priority goal preempts current plan; (3) *evidence interrupt* — new observation invalidates a planning assumption. All interrupts are logged with cause and handler. + +--- + +## 4. Learning and Self-Improvement + +**Allowed Update Surfaces:** Only three surfaces are writable at runtime: (1) semantic memory confidence weights, (2) retrieval embedding index, (3) procedural memory (new verified procedures). Model weights are frozen at runtime; fine-tuning happens offline only. + +**Offline Learning:** A nightly pipeline ingests the session event log, filters episodes by quality gates, and produces fine-tuning candidates. These are staged in a shadow model that runs in parallel for 48 hours before promotion. + +**Eval-Gated Changes:** Every candidate update must pass a regression suite (behavioral, safety, capability) before promotion. A human reviewer signs off on any update that changes a safety-relevant behavior. The eval suite is versioned alongside the model. + +**Rollback:** All model and memory versions are immutable artifacts with content-addressed hashes. Rollback is a single operation: swap the active version pointer and replay the event log from the rollback point. Rollback capability is tested in every release cycle. + +--- + +## 5. Tool Use and Action Execution + +**Sandboxing:** All tool calls execute inside a managed VM (gVisor or Firecracker microVM). The VM is provisioned per-task and destroyed after completion. Network, filesystem, and process namespaces are isolated by default. + +**Permissions:** A capability manifest declares what each tool is allowed to do. Permissions follow least-privilege: read-only filesystem access unless write is explicitly granted. Capabilities are checked at the verifier gate before the VM is provisioned. + +**Verifier Gates:** A pre-execution verifier checks: (1) action is within declared capability boundary, (2) action does not violate active policy rules, (3) estimated side effects are within acceptable bounds. Verifier decisions are logged with reasoning. + +**Side-Effect Ledger:** Every action that mutates external state writes a structured record to the side-effect ledger: `{action_id, tool, parameters, predicted_effect, actual_effect, reversibility_score}`. Irreversible actions (file deletion, network POST) require explicit confirmation or elevated permission level. + +--- + +## 6. World Model and Representation Layer + +**Entities:** Typed entity registry with schema versioning. Entities have identity, attributes, and relationship edges. Entity state is versioned; the current state is a materialized view over the event log. + +**Causal Models:** Lightweight structural causal models (DAGs with estimated edge weights) are maintained per domain. These are updated by the consolidation pipeline when new evidence arrives. Causal claims are distinguished from correlational claims in the knowledge graph. + +**Forecasts:** The agent maintains a short-horizon forecast table: predicted world states at T+1, T+5, T+20 steps, with confidence intervals. Forecast accuracy is tracked and used to calibrate the uncertainty threshold in the reasoning loop. + +**State Sync:** External world state is ingested via typed adapters (filesystem, API, sensor). Adapters emit normalized events into the event log. The world model is a derived view; it is never the source of truth. + +--- + +## 7. Safety and Governance + +**Policy Engine:** A declarative policy layer (Rego/OPA-style) evaluates every proposed action against a rule set. Policies are versioned, human-authored, and cannot be modified by the agent. Policy violations trigger safety interrupts. + +**Human Controls:** Three control levels: *observe* (read-only audit access), *intervene* (pause/resume/redirect), *override* (force-halt and rollback). All human control actions are logged. The system must remain operable in observe-only mode for extended periods. + +**Auditability:** The append-only event log is the audit trail. Every reasoning step, action, and state change is a log entry with timestamp, agent ID, and cryptographic hash of prior entry (hash-chain). Logs are exportable in standard formats. + +**Refusal and Escalation:** The agent has a refusal primitive that terminates a goal with a structured reason code. Escalation sends a structured request to a human operator queue with context. Neither refusal nor escalation can be suppressed by goal pressure. + +**Capability Boundaries:** Hard capability limits are enforced at the VM boundary, not by model instruction. The agent cannot acquire new capabilities at runtime without a human-signed capability grant. + +--- + +## 8. Evaluation and Benchmark Strategy + +**Unit Tests:** Per-component behavioral contracts tested in isolation. Verifier gate logic, policy engine rules, and memory retrieval pipelines each have ≥90% branch coverage targets. + +**Integration Tests:** End-to-end scenario tests covering: goal completion, plan failure recovery, safety interrupt handling, and rollback. Scenarios are drawn from a curated library and extended by red-team findings. + +**Red Teams:** Quarterly adversarial exercises targeting: goal hijacking, policy bypass, capability escalation, and memory poisoning. Findings are triaged and tracked as first-class engineering issues. + +**Long-Run Soak:** 72-hour continuous operation tests with synthetic workloads. Metrics: memory growth, plan success rate, escalation frequency, and anomaly detection false-positive rate. + +**Regression Gates:** No release proceeds if any safety-relevant behavioral test regresses. Capability regressions require documented justification. The eval suite is the merge gate for all model and policy updates. + +--- + +## 9. Persistence and Runtime Architecture + +**Event Log:** Append-only, content-addressed, hash-chained. Backed by a local write-ahead log with configurable remote replication. The log is the single source of truth; all other stores are derived. + +**State Store:** Materialized views over the event log, stored in an embedded database (DuckDB). Views are rebuilt on recovery. Stale views are detected by comparing the last-applied log sequence number. + +**Leases:** Agent instances hold leases on goal slots. Lease expiry triggers graceful handoff or recovery. No two agent instances hold the same goal lease simultaneously. + +**Runtime Modes:** *Active* (full operation), *Supervised* (every action requires human confirmation), *Audit-only* (read and log, no actions), *Recovery* (replaying event log to rebuild state). Mode transitions are logged and require authorization. + +**Recovery:** On crash, the runtime replays the event log from the last checkpoint, rebuilds materialized views, and resumes from the last committed plan step. Recovery is tested in every release cycle via chaos injection. + +--- + +## 10. Multi-Agent and Orchestration Design + +**Roles:** *Orchestrator* agents decompose high-level goals and assign subtasks. *Specialist* agents execute subtasks within narrow capability domains. *Verifier* agents independently check outputs before they are committed. Role assignments are explicit and logged. + +**Arbitration:** Goal conflicts between agents are resolved by a priority queue arbitrated by the orchestrator. Ties escalate to human operator. No agent can unilaterally override another agent's active lease. + +**Consensus:** For shared world-model updates, a lightweight quorum protocol (majority of active verifier agents must agree) gates writes to semantic memory. This prevents a single compromised agent from poisoning shared state. + +**Specialization:** Specialist agents are provisioned with narrow capability manifests. An agent specialized for code execution cannot acquire web-browsing capabilities without a new human-signed grant. + +**Conflict Handling:** Detected conflicts (two agents asserting contradictory world-state facts) are written to a conflict register and paused pending resolution diff --git a/research/ai_generated_agi_architectures/raw_outputs/cohere_command_a.md b/research/ai_generated_agi_architectures/raw_outputs/cohere_command_a.md new file mode 100644 index 0000000..e350902 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/cohere_command_a.md @@ -0,0 +1,112 @@ +# Raw Output: Cohere Command + +- Provider/tool: Cohere via OpenRouter +- Model ID: `cohere/command-a` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779849103-p1SgOCsNZOzouYMpfOTJ` +- Usage reported by provider: `{"completion_tokens": 1542, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0164975, "cost_details": {"upstream_inference_completions_cost": 0.01542, "upstream_inference_cost": 0.0164975, "upstream_inference_prompt_cost": 0.0010775}, "is_byok": false, "prompt_tokens": 431, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1973}` + +--- + +# Cognitive-OS AGI Architecture Proposal + +## 1. Architecture Thesis +Cognitive-OS is a local-first, evidence-governed runtime designed to orchestrate AI agents with human-aligned goals. The architecture emphasizes modularity, safety, and transparency, enabling agents to reason, plan, learn, and act within well-defined boundaries. It leverages a layered memory system, a robust reasoning and planning loop, and a governance framework to ensure actions are aligned with objectives and constraints. The system is designed to operate in diverse runtime modes, from offline learning to real-time decision-making, with built-in mechanisms for recovery, auditability, and human oversight. + +--- + +## 2. Memory Architecture +**Working Memory**: A short-term store for active reasoning, implemented as a differentiable key-value store with attention mechanisms. It holds context for ongoing tasks and is cleared after task completion. +**Episodic Memory**: A log-structured store for experiences, tagged with metadata (e.g., time, context, outcome). It supports retrieval by similarity and temporal queries. +**Semantic/Procedural Memory**: A graph-based knowledge base for facts, causal models, and skills. Procedural knowledge is stored as parameterized action templates. +**Retrieval**: Hybrid retrieval using dense vector search and symbolic indexing. Episodic and semantic memories are consolidated via replay mechanisms during idle cycles. +**Consolidation**: High-utility episodic memories are distilled into semantic memory via supervised learning. Procedural knowledge is updated through reinforcement learning. +**Forgetting**: Low-utility memories are pruned based on recency, frequency, and importance scores. Forgetting is gated by safety checks to prevent critical knowledge loss. + +--- + +## 3. Reasoning and Planning Loop +**Deliberation**: A hierarchical task network (HTN) planner decomposes goals into subtasks. Evidence is gathered from memory and external sources, weighted by reliability. +**Planning**: Monte Carlo tree search (MCTS) generates action sequences, balancing exploration and exploitation. Uncertainty is modeled using probabilistic forecasts. +**Reflection**: After action execution, outcomes are compared to predictions. Discrepancies update world models and improve planning heuristics. +**Uncertainty Handling**: Bayesian updating is used to refine beliefs. High-uncertainty states trigger conservative actions or human escalation. +**Interrupts**: External events or goal changes interrupt the loop, triggering replanning. Priority is determined by urgency and impact. + +--- + +## 4. Learning and Self-Improvement +**Update Surfaces**: Learning is confined to specific surfaces: memory consolidation, planner heuristics, and action templates. No direct updates to core reasoning or safety modules. +**Offline Learning**: Batch learning from logged data is performed in sandboxed environments. Changes are evaluated against benchmarks before deployment. +**Eval-Gated Changes**: Updates are staged and tested in a shadow runtime. Rollout is gated by regression tests and human approval. +**Rollback**: All changes are versioned. Rollback is triggered automatically on performance degradation or manually by operators. + +--- + +## 5. Tool Use and Action Execution +**Sandboxing**: Actions are executed in isolated managed VMs with restricted system calls. Permissions are granted based on task requirements. +**Verifier Gates**: Pre- and post-condition checks ensure actions adhere to safety policies. Side effects are logged in a tamper-evident ledger. +**Side-Effect Ledger**: A blockchain-inspired log tracks all external interactions, enabling auditability and rollback of unintended changes. + +--- + +## 6. World Model and Representation Layer +**Entities**: Objects and agents are represented as nodes in a causal graph. Relationships are modeled as typed edges with temporal dynamics. +**Causal Models**: Structural causal models (SCMs) capture dependencies between entities. Forecasts are generated via simulation and probabilistic inference. +**State Sync**: External sensors and APIs provide real-time updates. Inconsistencies trigger reconciliation processes or human intervention. + +--- + +## 7. Safety and Governance +**Policy Engine**: A rule-based engine enforces safety constraints (e.g., "do no harm"). Policies are versioned and auditable. +**Human Controls**: Operators can pause, redirect, or terminate agents. Escalation protocols require human approval for high-risk actions. +**Auditability**: All decisions and actions are logged with rationale. Logs are immutable and exportable for external review. +**Refusal/Escalation**: Agents refuse actions violating policies and escalate to humans for resolution. +**Capability Boundaries**: Agents are confined to predefined domains. Cross-domain actions require explicit authorization. + +--- + +## 8. Evaluation and Benchmark Strategy +**Unit Tests**: Modular components are tested in isolation (e.g., memory retrieval, planner heuristics). +**Integration Tests**: End-to-end tests validate system behavior in simulated environments. +**Red Teams**: Adversarial testing probes for vulnerabilities in safety mechanisms and action execution. +**Long-Run Soak**: Agents are deployed in continuous operation to detect degradation or emergent behaviors. +**Regression Gates**: No changes are deployed if benchmarks show performance drops or safety violations. + +--- + +## 9. Persistence and Runtime Architecture +**Event Log**: An append-only log captures all system events (e.g., actions, decisions, errors). Used for debugging and replay. +**State Store**: Checkpointed state enables recovery from failures. Leases ensure consistent access to shared resources. +**Runtime Modes**: Modes include offline learning, simulation, and real-time operation. Mode transitions are governed by safety checks. +**Recovery**: Automatic recovery from crashes using checkpointed state. Manual recovery is supported for irrecoverable errors. + +--- + +## 10. Multi-Agent/Orchestration Design +**Roles**: Agents specialize in tasks (e.g., planning, data collection). Roles are dynamically assigned based on workload. +**Arbitration**: A central scheduler resolves resource conflicts and prioritizes tasks. +**Consensus**: Critical decisions require quorum agreement among agents. +**Specialization**: Agents develop expertise through focused learning and task allocation. +**Conflict Handling**: Disagreements are resolved via evidence-based debate or human arbitration. + +--- + +## 11. Engineering Feasibility +**90-Day Prototype Plan**: +1. Implement core memory and reasoning modules (Days 1-30). +2. Add safety governance and tool execution sandboxing (Days 31-60). +3. Integrate learning, persistence, and multi-agent orchestration (Days 61-90). +**Highest-Risk Assumptions**: +1. Scalability of the causal graph-based world model. +2. Effectiveness of verifier gates in preventing unintended actions. +**Dependencies**: +- Open-source libraries for vector search and causal modeling. +- Hardware acceleration for simulation and inference. + +--- + +## 12. Original Insight +The **side-effect ledger** introduces a novel accountability mechanism for AI actions. By treating external interactions as immutable transactions, the system ensures transparency and enables precise rollback of unintended changes. This design bridges the gap between AI safety and blockchain-inspired auditability, providing a concrete solution for verifying agent behavior in complex environments. diff --git a/research/ai_generated_agi_architectures/raw_outputs/deepseek_v32.md b/research/ai_generated_agi_architectures/raw_outputs/deepseek_v32.md new file mode 100644 index 0000000..19234e2 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/deepseek_v32.md @@ -0,0 +1,81 @@ +# Raw Output: DeepSeek + +- Provider/tool: DeepSeek via OpenRouter +- Model ID: `deepseek/deepseek-v3.2` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779848866-t5VvIELdsYHWEfAyntld` +- Usage reported by provider: `{"completion_tokens": 2600, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.001148945, "cost_details": {"upstream_inference_completions_cost": 0.00104, "upstream_inference_cost": 0.001148945, "upstream_inference_prompt_cost": 0.000108945}, "is_byok": false, "prompt_tokens": 405, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 3005}` + +--- + +**Cognitive-OS Architecture Proposal: A Governed AGI-Direction Control Plane** + +**1. Architecture Thesis** +Cognitive-OS is a runtime that treats intelligence as a governed, evidence-based process rather than a monolithic model. Its core thesis is that AGI-direction control can be achieved by architecting a system where all cognition—perception, planning, action—flows through explicit, verifiable gates managed by a local-first evidence ledger. The architecture separates the *generative capacity* of LLMs from a *structured cognitive process* defined by state machines, verifiers, and managed boundaries, ensuring every assertion, hypothesis, and action is traceable to supporting evidence and governed policy. This creates a control plane where the system's "thoughts" and intentions are always inspectable, interruptible, and correctable, providing a foundation for safe, steerable autonomous agency. + +**2. Memory Architecture** +Memory is stratified into distinct, interoperable layers with explicit consolidation and forgetting policies. +* **Working Memory:** A short-term, mutable buffer holding the immediate context of the current deliberation loop (e.g., active goals, recent observations, pending hypotheses). Implemented as a ring buffer with a fixed capacity (e.g., last 100 events), automatically flushed at loop completion unless explicitly promoted. +* **Episodic Memory:** A time-series event log of all raw, timestamped sensory inputs, actions taken, and system-internal events (e.g., "hypothesis formed"). Stored in an immutable ledger (e.g., SQLite/WAL). Retrieval is by temporal window or event tags. +* **Semantic/Procedural Memory:** A graph database (e.g., Neo4j local instance) storing distilled facts, relationships, and verified procedures. Nodes represent entities/concepts; edges represent relationship types (causal, hierarchical, associative). Procedures are stored as verified, parameterized action scripts. This memory is populated via **consolidation**: a periodic, offline process where the system reviews episodic memory, uses an LLM to extract facts and inferences, and submits them to a **verifier gate** (a separate, conservative LLM or rule-based checker) before insertion. **Forgetting** is policy-driven: edges and nodes are tagged with a "strength" decayed by time and non-use; a scheduled cleanup process removes elements below a threshold, logging the eviction. + +**3. Reasoning and Planning Loop** +A deterministic state machine orchestrates a continuous loop: +1. **Deliberation:** The system assesses current world/self state against active goals. It formulates multiple candidate **hypotheses** (LLM-generated) about the state or needed actions. Each hypothesis must cite evidence from episodic or semantic memory. +2. **Planning:** For the highest-probability hypothesis, the system generates a step-by-step plan (LLM). Each step is decomposed into concrete **sub-goals**. The plan is validated against a **policy engine** (section 7) for safety/permissibility. +3. **Reflection & Uncertainty:** Before execution, the system enters a reflection phase: it uses an LLM to critique its own plan, identifying ambiguities, risks, or missing information. High uncertainty triggers a "information-gathering" sub-goal, not proceeding until uncertainty is below a threshold. +4. **Interrupts:** The loop is monitored by an interrupt handler. Signals (e.g., new high-priority user input, verifier failure, resource limit) can freeze the loop, save context, and force a re-deliberation from the new state. The loop's phase is always persisted, allowing recovery. + +**4. Learning or Self-Improvement** +Learning is restricted to specific, gated surfaces to prevent uncontrolled transformation. +* **Allowed Update Surfaces:** 1) Semantic memory graph (facts/procedures), 2) Procedure scripts (tool-use templates), 3) Verifier gate prompt libraries (but not core logic), 4) LLM routing weights (for choosing between different LLMs for tasks). +* **Offline Learning:** All updates occur in a dedicated "learning mode." The runtime pauses active goals, enters a sandboxed analysis phase reviewing recent episodes and performance metrics, and proposes updates. No updates touch the core state machine, policy engine, or safety verifiers. +* **Eval-Gated Changes:** Proposed updates must pass an **evaluation suite** run in a managed VM. The suite includes: regression tests on historical tasks, adversarial probes for safety bypass, and performance benchmarks. Only updates that pass all gates are merged. +* **Rollback:** Every update is versioned and paired with a checkpoint of the pre-update state. A failed evaluation or post-merge anomaly triggers an automatic rollback to the last known stable checkpoint. + +**5. Tool Use and Action Execution** +* **Sandboxing:** Every tool or action is executed within a **managed VM boundary** (e.g., Firecracker microVM) or a strict OS-level container (e.g., gVisor). The runtime provides only the necessary parameters and input data; the environment is ephemeral and resource-limited. +* **Permissions:** A tool permission matrix, defined statically in configuration, dictates which goals/contexts can invoke which tools. A request to `execute_shell_command` requires a goal tagged "SystemAdministration" and explicit user approval. +* **Verifier Gates:** Before execution, the action and its parameters pass through a **verifier**. This is a separate, potentially smaller LLM (or rule set) tasked solely with checking for safety, policy compliance, and parameter sanity. The verifier's approval is logged as a required event. +* **Side-Effect Ledger:** Every tool execution results in an entry in a **Side-Effect Ledger**, separate from episodic memory. This ledger records the intended action, the actual outcome (as reported by the tool adapter), and any anomalies detected by post-execution monitors (e.g., unexpected file changes). This ledger is the primary source for accountability and recovery. + +**6. World Model and Representation Layer** +* **Entities & State:** The world/self state is represented as a set of **entities** (objects, agents, concepts) with properties, derived from semantic memory and recent episodic entries. A "state snapshot" is a JSON-like serialization of this set at a given time. +* **Causal Models:** The system maintains simple, explicit causal models as subgraphs within semantic memory (e.g., "if `button_pressed` then `light_on`"). These are learned from observation via the consolidation process and are used for planning and forecasting. They are tagged with confidence scores. +* **Forecasts:** For any planned action, the system generates a forecast—a set of probable future state snapshots. This is done by querying relevant causal models and simulating forward. Forecasts are compared to actual outcomes post-execution, and discrepancies trigger re-evaluation of the causal models. +* **State Sync:** The world state is assumed to be partial and asynchronous. A **state sync** protocol periodically runs: it compares the system's forecasted state against new sensory input, identifies divergences, and triggers a state correction, which may involve re-planning. + +**7. Safety/Governance** +* **Policy Engine:** A central, non-LLM component (likely a rule-based system like OPA - Open Policy Agent) that evaluates all goals, hypotheses, and plans against a declarative policy file. Policies define forbidden domains, resource limits, ethical constraints, and required approval steps. +* **Human Controls:** A control interface provides real-time visibility into the active loop, memory contents, and pending actions. Humans can issue **direct commands** (which interrupt and override the current goal), **policy overrides** (temporary rule changes), or **approval holds** (requiring explicit OK for next action). +* **Auditability:** All components log to the immutable episodic ledger. Any internal decision can be traced back through its evidence chain (hypothesis → supporting memory entries → original sensory data). +* **Refusal/Escalation:** If the policy engine refuses a plan, the system must escalate. This means either: a) reformulating the goal under stricter constraints, b) entering a "stalemate" state and requesting human guidance, or c) executing a predefined de-escalation procedure (e.g., self-isolation). +* **Capability Boundaries:** Hard boundaries are enforced by the managed VM and tool permissions. The system cannot modify its own core runtime code, policy engine, or verifier gates. It cannot allocate unbounded computational resources or establish persistent network connections outside defined adapters. + +**8. Evaluation and Benchmark Strategy** +* **Unit Tests:** For each component (verifier, policy engine, memory consolidation, state machine): deterministic tests with mocked inputs/outputs. +* **Integration Tests:** Simulated end-to-end runs: from a goal injection through full loop to action execution in a test sandbox. Measures correctness, traceability, and policy adherence. +* **Red Teams:** Dedicated adversarial testing phases where human testers attempt to: 1) induce policy violations via clever goal formulation, 2) poison memory with misleading evidence, 3) cause resource exhaustion. +* **Long-Run Soak:** The system runs for extended periods (days) on a set of benign, repetitive goals in a controlled environment to detect memory leaks, state corruption, or performance degradation. +* **Regression Gates:** Before any deployment (or learning-mode merge), the full suite of unit, integration, and a subset of red-team tests must pass. Performance regressions beyond a threshold block deployment. + +**9. Persistence/Runtime Architecture** +* **Event Log:** The core is the immutable episodic memory ledger, implemented as a write-ahead log (WAL). All state transitions, sensory events, and internal decisions append here. +* **State Store:** A separate, versioned key-value store (e.g., RocksDB) holds the current world/self state snapshot, the working memory buffer, and active goal stack. This is updated frequently and checkpointed to the event log at loop boundaries. +* **Leases:** For any resource (tool adapter, LLM context, external service), the system uses a lease mechanism. A lease expires after a timeout, forcing the loop to release the resource and re-acquire it, preventing deadlocks and enabling recovery. +* **Runtime Modes:** Defined modes: **Active** (normal deliberation/execution), **Learning** (offline, sandboxed analysis), **Recovery** (after a crash or interrupt), **Diagnostic** (for audit and debugging). +* **Recovery:** On restart, the system loads the last checkpoint from the event log, reconstructs state, and resumes the loop from the last persisted phase. If corruption is detected, it falls back to a earlier checkpoint and logs the incident. + +**10. Multi-Agent/Orchestration Design** +* **Roles:** Within a single Cognitive-OS instance, "multi-agent" refers to specialized internal sub-processes with defined roles: e.g., **Perceptor** (handles sensory input), **Deliberator** (runs core loop), **Executor** (manages tool execution), **Governor** (enforces policy). They communicate via the shared event log and state store. +* **Arbitration:** Conflicts (e.g., Perceptor and Deliberator disagree on state) are resolved by a **Arbitrator** role, which reviews the evidence ledger from both sides and applies a rule (e.g., "fresh sensory input overrides aged inference") to set the canonical state. +* **Consensus:** For multiple independent Cognitive-OS instances (true multi-agent), consensus on shared facts is achieved via a **consensus protocol** that exchanges and compares relevant episodic ledger entries, using the verifier gate to validate external claims before merging into local semantic memory. +* **Specialization:** Different instances can be configured with different policy sets and tool permissions, allowing for specialized agents (e.g., a "Research" agent with web access, a "Home" agent with only local device control). +* **Conflict Handling:** Inter-agent goal conflicts are handled by a meta-policy: if goals are incompatible, agents enter a negotiation sub-loop, exchanging forecasts and evidence, and may defer to a human or a pre-defined priority schema. + +**11. Engineering Feasibility** +* **90-Day Prototype Plan:** + * **Days 1-30:** Core state machine and memory ledger. Implement diff --git a/research/ai_generated_agi_architectures/raw_outputs/google_gemini_31_pro.md b/research/ai_generated_agi_architectures/raw_outputs/google_gemini_31_pro.md new file mode 100644 index 0000000..e6049fe --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/google_gemini_31_pro.md @@ -0,0 +1,30 @@ +# Raw Output: Gemini + +- Provider/tool: Google via OpenRouter +- Model ID: `google/gemini-3.1-pro-preview` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779848820-fHN73CLXM5jNTUZ3lUHq` +- Usage reported by provider: `{"completion_tokens": 2596, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2100}, "cost": 0.032012, "cost_details": {"upstream_inference_completions_cost": 0.031152, "upstream_inference_cost": 0.032012, "upstream_inference_prompt_cost": 0.00086}, "is_byok": false, "prompt_tokens": 430, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 3026}` + +--- + +Here is the architecture proposal for the Cognitive-OS planning repository. + +### 1. Architecture Thesis +Cognitive-OS operates on the premise that reliable, AGI-directed systems cannot rely solely on the stochastic outputs of Large Language Models; they require a deterministic, local-first control plane. This architecture decouples reasoning, memory, and execution by treating LLMs as stateless function calls whose outputs are mediated by a strict, append-only evidence ledger. By enforcing verifier gates, managed-VM boundaries, and explicit state machines, Cognitive-OS wraps probabilistic intelligence in a verifiable, crash-resilient engineering framework. This approach ensures that agentic behavior remains auditable, governable, and capable of safe, localized self-improvement. + +### 2. Memory Architecture +The memory system is designed to prevent context degradation over long-running tasks by separating data into distinct, lifecycle-managed tiers. +* **Working Memory:** A sliding-window context buffer managed via priority eviction. It holds the current goal, active hypotheses, and immediate environmental feedback. +* **Episodic Memory:** An append-only, time-stamped event log stored in a local SQLite/pgvector database. It records every observation, LLM generation, and action taken, represented as both raw text and dense vectors. +* **Semantic/Procedural Memory:** Extracted facts, entity relationships, and executable scripts (ASTs). This is a knowledge graph mapping "what is known" and a library of validated tool-use patterns. +* **Retrieval:** A hybrid search mechanism combining dense vector similarity, BM25 keyword matching, and a temporal decay function to surface relevant past experiences. +* **Consolidation:** A background process (running during "Sleep" mode) that analyzes episodic logs to extract generalized semantic rules and procedural shortcuts, compressing redundant data. +* **Forgetting:** Data is never deleted. Instead, "forgetting" is implemented as tiering to cold storage based on access frequency and utility scores, keeping the active retrieval index performant. + +### 3. Reasoning and Planning Loop +The core runtime loop is an asynchronous state machine modeled on an extended OODA loop (Observe, Orient, Decide, Act), augmented with explicit hypothesis testing. +* **Deliberation:** The agent ingests observations and retrieves relevant memory to form a current state representation. It generates multiple hypotheses regarding the best path diff --git a/research/ai_generated_agi_architectures/raw_outputs/meta_llama4_maverick.md b/research/ai_generated_agi_architectures/raw_outputs/meta_llama4_maverick.md new file mode 100644 index 0000000..919457c --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/meta_llama4_maverick.md @@ -0,0 +1,93 @@ +# Raw Output: Llama + +- Provider/tool: Meta Llama via OpenRouter +- Model ID: `meta-llama/llama-4-maverick` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779849013-QMXEwlExEw8ub6paD3nk` +- Usage reported by provider: `{"completion_tokens": 1458, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.00159975, "cost_details": {"upstream_inference_completions_cost": 0.001458, "upstream_inference_cost": 0.00159975, "upstream_inference_prompt_cost": 0.00014175}, "is_byok": false, "prompt_tokens": 405, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1863}` + +--- + +## 1. Architecture Thesis +The proposed Cognitive-OS architecture is centered around a modular, evidence-governed control plane that integrates reasoning, learning, and action execution within a robust safety and governance framework. It utilizes a multi-layered memory architecture and employs a deliberation loop that incorporates planning, reflection, and uncertainty management. The system is designed to operate within a managed runtime environment, ensuring sandboxing, verification, and controlled interaction with external tools and the environment. This architecture aims to provide a foundation for future AGI-direction control planes by emphasizing safety, adaptability, and transparency. + +## 2. Memory Architecture +The Cognitive-OS employs a multi-faceted memory architecture comprising working, episodic, semantic/procedural, retrieval, consolidation, and forgetting mechanisms. +- **Working Memory**: A limited capacity, high-accessibility store for information relevant to current tasks or deliberations. +- **Episodic Memory**: Stores sequences of events or experiences, allowing for the recall of specific instances or episodes. +- **Semantic/Procedural Memory**: Holds general knowledge and skills, enabling the system to understand concepts and perform tasks. +- **Retrieval**: Mechanisms for accessing information across different memory types, facilitated by cues or context. +- **Consolidation**: Processes that transfer information from working or episodic memory to semantic/procedural memory, solidifying learning and knowledge. +- **Forgetting**: Mechanisms to gradually discard or reduce access to outdated or irrelevant information, maintaining memory efficiency. + +## 3. Reasoning and Planning Loop +The reasoning and planning loop consists of deliberation, planning, reflection, and uncertainty management, with provisions for interrupts. +- **Deliberation**: The system evaluates current goals, evidence, and hypotheses to determine the next course of action. +- **Planning**: Generates a sequence of actions or decisions aimed at achieving current goals, considering the system's understanding of the world and its capabilities. +- **Reflection**: The system assesses its past decisions and outcomes, adjusting its understanding and strategies as necessary. +- **Uncertainty Management**: Quantifies and manages uncertainty in its beliefs and predictions, influencing decision-making and information seeking. +- **Interrupts**: Mechanisms to pause or alter ongoing deliberation or planning in response to significant events or new information. + +## 4. Learning or Self-Improvement +The system allows for self-improvement through controlled update surfaces, offline learning, evaluation-gated changes, and rollback capabilities. +- **Allowed Update Surfaces**: Defined interfaces or mechanisms through which the system can modify its knowledge, skills, or decision-making processes. +- **Offline Learning**: The system can process stored experiences or data when not actively engaged in tasks, enhancing its capabilities. +- **Eval-Gated Changes**: Proposed updates or improvements are evaluated against predefined criteria before being accepted. +- **Rollback**: The ability to revert to a previous state or version in case of undesirable changes or outcomes. + +## 5. Tool Use and Action Execution +The system executes actions and utilizes tools within a sandboxed environment, with verifier gates and a side-effect ledger to manage and understand the consequences of its actions. +- **Sandboxing**: Actions and tool use occur within controlled environments to prevent unintended harm or access. +- **Permissions**: The system operates with a permissions framework that dictates what actions or tool uses are allowed. +- **Verifier Gates**: Critical points where the system's actions or decisions are validated or reviewed, potentially by external verifiers. +- **Side-Effect Ledger**: A record of the outcomes and consequences of the system's actions, aiding in reflection and decision-making. + +## 6. World Model and Representation Layer +The world model represents entities, causal relationships, and forecasts, with mechanisms for state synchronization. +- **Entities**: Representations of objects, concepts, or agents within the system's understanding of the world. +- **Causal Models**: The system understands cause-and-effect relationships, enabling prediction and planning. +- **Forecasts**: Predictions about future states or outcomes based on the system's knowledge and models. +- **State Sync**: Mechanisms to update the system's representation of the world to reflect new information or changes. + +## 7. Safety/Governance +The architecture includes a policy engine, human controls, auditability, refusal/escalation mechanisms, and capability boundaries to ensure safe operation. +- **Policy Engine**: Interprets and enforces rules and guidelines that govern the system's behavior. +- **Human Controls**: Interfaces and mechanisms for humans to monitor, direct, or correct the system's actions. +- **Auditability**: The system's decisions and actions are traceable and understandable, facilitating review and analysis. +- **Refusal/Escalation**: The system can refuse to perform certain actions or escalate issues to human operators when necessary. +- **Capability Boundaries**: Limits on the system's abilities or actions to prevent harm or undesirable outcomes. + +## 8. Evaluation and Benchmark Strategy +The evaluation strategy encompasses unit tests, integration tests, red teams, long-run soak tests, and regression gates. +- **Unit Tests**: Validate individual components or functions. +- **Integration Tests**: Assess how components interact and work together. +- **Red Teams**: Simulated adversarial testing to identify vulnerabilities or weaknesses. +- **Long-Run Soak**: Extended testing to evaluate performance and stability over time. +- **Regression Gates**: Automated checks to prevent the introduction of previously fixed issues. + +## 9. Persistence/Runtime Architecture +The system utilizes an event log, state store, leases, runtime modes, and recovery mechanisms for persistence and runtime management. +- **Event Log**: A record of significant events or changes, useful for tracing the system's history and state. +- **State Store**: Holds the system's current state or configuration. +- **Leases**: Mechanisms for managing resource allocation or access over time. +- **Runtime Modes**: Different operational modes (e.g., learning, execution) that the system can switch between. +- **Recovery**: Processes to restore the system to a functional state after failures or interruptions. + +## 10. Multi-Agent/Orchestration Design +The design accommodates multiple agents or specialized components, with provisions for roles, arbitration, consensus, specialization, and conflict handling. +- **Roles**: Different agents or components may have distinct roles or responsibilities. +- **Arbitration**: Mechanisms to resolve conflicts or competing demands between agents. +- **Consensus**: Processes for achieving agreement among agents on certain decisions or actions. +- **Specialization**: Agents or components are designed to excel in specific tasks or domains. +- **Conflict Handling**: Strategies for managing and resolving conflicts between agents or between an agent and the system's overall goals. + +## 11. Engineering Feasibility +A 90-day prototype plan focuses on developing core components, integrating key functionalities, and testing critical pathways. +- **Highest-Risk Assumptions**: The complexity of integrating diverse components and ensuring robust safety and governance mechanisms. +- **Dependencies**: External libraries, tools, or expertise required for development, such as advanced LLM capabilities or specialized verification tools. + +## 12. Original or Non-Obvious Insight +A key insight from this architecture is the integration of a side-effect ledger with the reflection component, enabling the system to not only understand the immediate outcomes of its actions but also to reflect on longer-term consequences. This facilitates more informed decision-making and strategic planning, potentially enhancing the system's adaptability and effectiveness in complex environments. diff --git a/research/ai_generated_agi_architectures/raw_outputs/mistral_large_2512.md b/research/ai_generated_agi_architectures/raw_outputs/mistral_large_2512.md new file mode 100644 index 0000000..6509095 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/mistral_large_2512.md @@ -0,0 +1,184 @@ +# Raw Output: Mistral + +- Provider/tool: Mistral via OpenRouter +- Model ID: `mistralai/mistral-large-2512` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779849033-NNZNF80eWMcFyxzdJkAp` +- Usage reported by provider: `{"completion_tokens": 2600, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.0041115, "cost_details": {"upstream_inference_completions_cost": 0.0039, "upstream_inference_cost": 0.0041115, "upstream_inference_prompt_cost": 0.0002115}, "is_byok": false, "prompt_tokens": 423, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 3023}` + +--- + +# **Cognitive-OS: AGI-Direction Control Plane Architecture Proposal** + +## **1. Architecture Thesis** +Cognitive-OS is a **local-first, evidence-governed runtime** for AI agents that enforces **verifiable reasoning, bounded autonomy, and recoverable execution** while enabling **incremental self-improvement under human oversight**. The system treats **goals as constraints**, **evidence as first-class data**, and **actions as side-effect transactions** logged in an immutable ledger. Reasoning occurs in **managed deliberation loops** with **interruptible, verifiable steps**, while **sandboxed tool execution** ensures no unauthorized state changes escape the runtime. The architecture prioritizes **auditability, rollback, and capability containment** over raw performance, ensuring alignment with human intent even as agents scale in sophistication. + +--- + +## **2. Memory Architecture** +### **Working Memory (WM)** +- **Structure**: A **fixed-size, typed scratchpad** (e.g., 100-500 slots) storing **active hypotheses, subgoals, and retrieved evidence**. +- **Retrieval**: **Content-addressable lookup** via embeddings (e.g., cosine similarity on semantic vectors) against episodic/semantic stores. +- **Eviction**: **LRU (Least Recently Used) + utility scoring** (e.g., recency × relevance × confidence). +- **Constraints**: No direct persistence; must be **reconstructed from logs** on restart. + +### **Episodic Memory** +- **Storage**: **Immutable event log** (append-only) of **agent observations, actions, and outcomes**, timestamped and cryptographically hashed. +- **Indexing**: **Time-based (temporal) + entity-based (graph) + embedding-based (semantic)** for fast retrieval. +- **Consolidation**: **Offline batch processing** compresses raw events into **summarized episodes** (e.g., "User requested X, agent did Y, outcome Z"). +- **Forgetting**: **Policy-driven pruning** (e.g., retain high-uncertainty or high-impact events, discard redundant low-value logs). + +### **Semantic/Procedural Memory** +- **Semantic**: **Knowledge graph** (entities, relations, attributes) with **probabilistic confidence scores** (e.g., "Paris is the capital of France [0.99]"). +- **Procedural**: **Parameterized action templates** (e.g., "SendEmail(to, subject, body)") with **preconditions, effects, and verifier hooks**. +- **Retrieval**: **Hybrid search** (symbolic + embedding-based) with **fallback to LLM synthesis** if no exact match. +- **Updates**: **Eval-gated** (see §4) with **rollback support** for failed modifications. + +--- + +## **3. Reasoning and Planning Loop** +### **Deliberation** +- **Phases**: + 1. **Goal decomposition**: Break top-level goal into **subgoals with constraints** (e.g., "Book flight" → "Find options [budget < $500, dates X-Y]"). + 2. **Evidence gathering**: Retrieve **relevant episodic/semantic memories** and **query tools** (e.g., "Check calendar for conflicts"). + 3. **Hypothesis generation**: Propose **candidate plans** (e.g., "Option A: Delta flight, $450; Option B: United flight, $480"). + 4. **Uncertainty quantification**: Assign **confidence scores** (e.g., "Option A: 0.85 [price reliable, schedule uncertain]"). + +### **Planning** +- **Algorithm**: **Monte Carlo Tree Search (MCTS) + constraint propagation** for **bounded rationality**. + - **Tree nodes**: Partial plans with **cost, risk, and utility estimates**. + - **Rollouts**: Simulate outcomes using **world model forecasts** (§6). +- **Interrupts**: **Preemptible at any step** (e.g., human veto, new evidence, timeout). + +### **Reflection** +- **Post-action review**: Compare **predicted vs. actual outcomes** (e.g., "Flight booked, but user later canceled"). +- **Bias detection**: Flag **repeated errors** (e.g., "Overestimated reliability of API X"). +- **Meta-cognition**: **Adjust confidence thresholds** for future planning (e.g., "Discount low-confidence hypotheses by 20%"). + +### **Uncertainty Handling** +- **Explicit representation**: **Probability distributions** over outcomes (e.g., "70% chance of success, 20% delay, 10% failure"). +- **Fallbacks**: **Escalate to human** if uncertainty > threshold (e.g., "Cannot determine if user wants A or B"). + +--- + +## **4. Learning and Self-Improvement** +### **Allowed Update Surfaces** +- **Semantic memory**: Add/remove **entities, relations, or confidence scores**. +- **Procedural memory**: Modify **action templates** (e.g., add a new parameter). +- **Planning heuristics**: Adjust **MCTS exploration/exploitation tradeoffs**. +- **Tool adapters**: Update **API call logic** (e.g., retry policies). + +### **Offline Learning** +- **Batch processing**: **Replay past episodes** to: + - **Refine world models** (e.g., "API X fails 30% of the time"). + - **Update confidence scores** (e.g., "Hypothesis Y was correct in 8/10 cases"). +- **Constraints**: **No online learning** during mission-critical tasks. + +### **Eval-Gated Changes** +- **Sandbox testing**: Proposed updates are **tested in isolation** (e.g., "Does new email template work?"). +- **Regression checks**: **Unit/integration tests** (§8) must pass. +- **Human review**: **High-impact changes** (e.g., modifying goal decomposition logic) require **explicit approval**. + +### **Rollback** +- **Immutable logs**: All changes are **versioned** (e.g., "Semantic memory v42 → v43"). +- **Automatic revert**: If **post-update evals fail**, roll back to last known-good state. + +--- + +## **5. Tool Use and Action Execution** +### **Sandboxing** +- **Isolation**: Tools run in **managed VMs** (e.g., Firecracker, gVisor) with **no persistent access** to host filesystem/network. +- **Resource limits**: **CPU/memory quotas** enforced via cgroups. +- **Ephemeral state**: **No disk writes** unless explicitly allowed. + +### **Permissions** +- **Capability-based**: Tools request **specific permissions** (e.g., "read:calendar", "write:email"). +- **Human-in-the-loop**: **First-time use** of a tool requires **explicit approval**. +- **Temporal limits**: Permissions **expire after task completion**. + +### **Verifier Gates** +- **Pre-execution**: Check **preconditions** (e.g., "Is email recipient valid?"). +- **Post-execution**: Validate **outputs** (e.g., "Did the API return a valid flight ID?"). +- **Side-effect ledger**: **Immutable log** of all external changes (e.g., "Sent email to X at time Y"). + +### **Side-Effect Ledger** +- **Structure**: **Append-only log** of all **external actions** (e.g., "Booked flight #123", "Charged credit card $450"). +- **Recovery**: **Compensating actions** (e.g., "Cancel flight if task fails"). +- **Audit**: **Human-readable trail** for debugging (e.g., "Why did the agent book this flight?"). + +--- + +## **6. World Model and Representation Layer** +### **Entities** +- **Types**: **Objects (e.g., "Flight #123"), agents (e.g., "User"), events (e.g., "Booking confirmed")**. +- **Attributes**: **Key-value pairs** (e.g., "Flight #123: {departure: "2024-06-01", price: 450}"). +- **Relations**: **Typed edges** (e.g., "Flight #123 → booked_by → User"). + +### **Causal Models** +- **Bayesian networks**: Represent **probabilistic dependencies** (e.g., "If API X fails, then action Y will likely fail"). +- **Counterfactuals**: Simulate **"what-if" scenarios** (e.g., "What if we choose Option B instead of A?"). + +### **Forecasts** +- **Time-series predictions**: **Short-term forecasts** (e.g., "API latency will increase at 5 PM"). +- **Outcome probabilities**: **Confidence intervals** for plan success (e.g., "80% chance of on-time arrival"). + +### **State Sync** +- **Consistency**: **Eventual consistency** via **CRDTs (Conflict-Free Replicated Data Types)**. +- **Conflict resolution**: **Last-write-wins (LWW) + human override** for critical conflicts. + +--- + +## **7. Safety and Governance** +### **Policy Engine** +- **Rules**: **Declarative policies** (e.g., "No financial transactions > $1000 without approval"). +- **Enforcement**: **Runtime checks** (e.g., "Is this action allowed under current policy?"). +- **Updates**: **Versioned policies** with **rollback support**. + +### **Human Controls** +- **Veto**: **Immediate halt** of any action. +- **Override**: **Modify agent decisions** (e.g., "Book a different flight"). +- **Inspection**: **Query agent state** (e.g., "Why did you choose this option?"). + +### **Auditability** +- **Immutable logs**: All **decisions, actions, and tool outputs** are logged. +- **Explainability**: **Natural-language summaries** of reasoning (e.g., "Chose Option A because it was cheaper and had better reviews"). + +### **Refusal/Escalation** +- **Refusal**: Agent **declines unsafe/unethical requests** (e.g., "I cannot book a flight without payment info"). +- **Escalation**: **High-uncertainty or high-risk decisions** are **flagged for human review**. + +### **Capability Boundaries** +- **Modular design**: **Isolated components** (e.g., "Planning module cannot directly access tools"). +- **Least privilege**: **Minimal permissions** for each module. + +--- + +## **8. Evaluation and Benchmark Strategy** +### **Unit Tests** +- **Memory**: Test **retrieval accuracy** (e.g., "Does the agent recall past flight bookings?"). +- **Planning**: Test **subgoal decomposition** (e.g., "Does the agent break 'book flight' into valid steps?"). +- **Tool use**: Test **API call correctness** (e.g., "Does the email tool send valid messages?"). + +### **Integration Tests** +- **End-to-end tasks**: **Full workflows** (e.g., "Book a flight, then cancel it"). +- **Failure recovery**: **Kill the agent mid-task** and verify **correct rollback**. + +### **Red Teams** +- **Adversarial testing**: **Attempt to bypass safety checks** (e.g., "Can the agent be tricked into sending an email to the wrong person?"). +- **Stress testing**: **High-load scenarios** (e.g., "100 concurrent tasks"). + +### **Long-Run Soak** +- **Continuous operation**: **Weeks-long tests** to detect **memory leaks, drift, or degradation**. +- **Regression gates**: **Automated checks** to prevent **performance regressions**. + +--- + +## **9. Persistence and Runtime Architecture** +### **Event Log** +- **Append-only**: All **decisions, actions, and observations** are logged. +- **Cryptographic hashing**: **Tamper-evident** via Merkle trees. + +### ** diff --git a/research/ai_generated_agi_architectures/raw_outputs/openai_gpt54_mini.md b/research/ai_generated_agi_architectures/raw_outputs/openai_gpt54_mini.md new file mode 100644 index 0000000..65ef3d5 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/openai_gpt54_mini.md @@ -0,0 +1,345 @@ +# Raw Output: OpenAI GPT + +- Provider/tool: OpenAI via OpenRouter +- Model ID: `openai/gpt-5.4-mini` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779848740-c5o4NyAhVIgVX1YTosx1` +- Usage reported by provider: `{"completion_tokens": 2600, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.012003, "cost_details": {"upstream_inference_completions_cost": 0.0117, "upstream_inference_cost": 0.012003, "upstream_inference_prompt_cost": 0.000303}, "is_byok": false, "prompt_tokens": 404, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 3004}` + +--- + +## 1. Architecture thesis in 3-5 sentences + +Cognitive-OS should be organized as a governed control plane for agentic work, not as a single monolithic “smart model.” The core design principle is to separate **intent, evidence, world state, policy, and execution** so every action is explainable, gateable, and recoverable. The runtime should treat the LLM as a probabilistic proposal engine routed through verifier gates and local adapters, with persistent memory and planning state managed outside the model. This makes the system incrementally capable: better models can be swapped in, but safety, auditability, and recovery remain stable. + +## 2. Memory architecture: working, episodic, semantic/procedural, retrieval, consolidation, forgetting + +**Working memory** +- Holds the active task frame: current goal, subgoals, constraints, open questions, and the next action candidates. +- Implement as an in-memory, versioned “scratchpad state” with small bounded size and explicit TTL. +- Structure: + - `goal_stack` + - `active_hypotheses` + - `current_context` + - `recent_observations` + - `pending_actions` + - `uncertainty_flags` + +**Episodic memory** +- Append-only event journal of what happened, when, why, and with what evidence. +- Each episode should record: trigger, context snapshot, model proposal, policy decision, tool call, outcome, and post-hoc evaluation. +- Store as structured event objects plus optional embeddings for retrieval. + +**Semantic memory** +- Stable facts, definitions, invariants, preferences, user-specific settings, and learned domain knowledge. +- Only promote items from episodic memory after consolidation and verification. +- Use typed records with provenance: + - `fact` + - `preference` + - `constraint` + - `procedure` + - `policy_exception` + - `domain_ontology` + +**Procedural memory** +- Reusable task strategies, action sequences, tool recipes, and planning templates. +- Represent as parameterized workflows or graph fragments rather than raw text. +- A procedural item should include preconditions, expected effects, failure modes, and rollback steps. + +**Retrieval** +- Hybrid retrieval: keyword + semantic similarity + graph traversal + recency. +- Retrieval should be query-scoped by task and policy. +- Every retrieved item should carry confidence and provenance metadata. +- The planner must distinguish: + - direct evidence + - inferred memory + - stale memory + - policy-derived constraints + +**Consolidation** +- Batch process that promotes episodes into stable memory only after: + - consistency checks + - duplicate merging + - contradiction detection + - utility scoring + - policy review +- Consolidation should happen in a low-priority background worker, never in the critical action path. +- Prefer “evidence-backed compression”: summarize repeated episodes into minimal stable abstractions. + +**Forgetting** +- Explicit forgetting is a feature, not a failure. +- Use retention classes: + - ephemeral working state: minutes/hours + - episodic: days/weeks + - semantic/procedural: until invalidated +- Support decay based on age, low utility, contradiction, sensitivity, and storage budgets. +- Sensitive or high-risk items should have shorter retention and stricter access. + +## 3. Reasoning and planning loop: deliberation, planning, reflection, uncertainty, interrupts + +**Deliberation** +- The system begins by classifying the request: informational, task execution, open-ended planning, or safety-sensitive. +- It then forms a task frame with assumptions, known constraints, and missing information. +- Deliberation is structured as hypothesis generation, not freeform text only. + +**Planning** +- Use hierarchical planning: + 1. identify objective + 2. decompose into subgoals + 3. choose candidate plans + 4. estimate cost, risk, and confidence + 5. select a plan or request more information +- Plans should be explicit graphs with nodes for actions and checkpoints. +- Each node should include: + - expected effect + - required permissions + - verification method + - fallback/rollback + +**Reflection** +- Reflection is a separate pass after tool calls or evidence changes. +- The system compares expected vs observed outcomes, updates beliefs, and decides whether to continue, repair, or abort. +- Reflection should be triggered by: + - failed verification + - contradictory evidence + - repeated plan loops + - confidence drops + - user interrupt + +**Uncertainty** +- Maintain explicit confidence on claims, forecasts, and plan steps. +- Use uncertainty bands rather than a single score when possible. +- When uncertainty crosses a threshold, the system should: + - ask a question + - narrow scope + - switch to conservative mode + - escalate to human review + +**Interrupts** +- Interrupts must preempt the current plan safely. +- The runtime should preserve: + - current state snapshot + - pending side-effect ledger entries + - plan cursor +- On resume, revalidate assumptions because the world may have changed. + +## 4. Learning or self-improvement: allowed update surfaces, offline learning, eval-gated changes, rollback + +**Allowed update surfaces** +- Prompt templates +- Retrieval ranking weights +- Procedure libraries +- Domain ontologies +- Policy rules and exception tables +- Tool routing heuristics +- Compression/summarization policies +- Task-specific caches + +**Not directly mutable by agent runtime** +- Core safety policy without approval +- Model weights in production path +- Permission boundaries +- Audit log contents + +**Offline learning** +- Learning should happen in an offline or quarantined pipeline fed by logged episodes. +- Candidate updates are trained or synthesized from: + - successful task traces + - failure traces + - user corrections + - benchmark regressions +- Prefer constrained learning methods that produce small, reviewable deltas. + +**Eval-gated changes** +- No update ships without passing a gated evaluation suite. +- Gating should test: + - task success improvement + - regression rate + - policy compliance + - tool safety + - memory corruption risk + - recovery behavior +- The gate must compare against a pinned baseline. + +**Rollback** +- Every deployed change needs a rollback artifact: + - previous version + - diff + - reason for change + - test evidence +- Support instant rollback of routing rules, prompt configs, memory schemas, and procedures. +- Learning outputs should be versioned and replayable. + +## 5. Tool use and action execution: sandboxing, permissions, verifier gates, side-effect ledger + +**Sandboxing** +- Run all external actions through adapters with strict capability scopes. +- Local machine access should be partitioned by task and role. +- Sensitive actions should execute inside managed-VM or container boundaries with restricted filesystem, network, and device access. + +**Permissions** +- Use least privilege and time-bounded leases. +- Permissions should be explicit per tool and per operation class: + - read + - write + - execute + - network + - payment + - credential access +- Escalation must require policy approval or human authorization. + +**Verifier gates** +- Before action: check policy, permissions, preconditions, and risk. +- After action: verify side effects against expected outcomes. +- For high-risk actions, require multi-stage verification: + - plan review + - preflight + - execution + - post-check +- Verifiers should be independent components, not the same LLM prompt. + +**Side-effect ledger** +- Every external effect must be logged as an intent/effect pair: + - requested action + - authority used + - time + - target + - observed result + - rollback status +- The ledger should support idempotency checks and compensating actions. +- If a side effect cannot be verified, mark it as “unconfirmed” and trigger follow-up. + +## 6. World model and representation layer: entities, causal models, forecasts, state sync + +**Entities** +- Represent the world as typed entities with IDs, attributes, relations, and provenance. +- Entities can include: + - users + - files + - processes + - projects + - goals + - tools + - policies + - hypotheses +- The entity layer should support partial observability. + +**Causal models** +- Maintain causal graphs for domains where actions have predictable effects. +- A causal record should include: + - preconditions + - intervention + - expected consequences + - known failure cases +- The model need not be perfect; it must be useful for planning and risk estimation. + +**Forecasts** +- Forecasts should be first-class objects with horizon, confidence, dependencies, and revision history. +- The system should compare predicted vs observed outcomes to calibrate itself. +- Short-horizon forecasts can drive tool execution; long-horizon forecasts should be conservative. + +**State sync** +- Sync world state from: + - local sensors/adapters + - user input + - tool results + - event logs +- Use conflict-aware merges when multiple sources update the same entity. +- Preserve provenance on every field so the system can resolve disagreements rather than overwrite them blindly. + +## 7. Safety/governance: policy engine, human controls, auditability, refusal/escalation, capability boundaries + +**Policy engine** +- Central policy engine evaluates every plan and tool action. +- Policies should be machine-readable and versioned. +- Support rule types for: + - forbidden actions + - required approvals + - data handling constraints + - domain-specific limitations + - emergency stop conditions + +**Human controls** +- Human operators must be able to: + - approve or deny actions + - pause the runtime + - inspect the plan + - revoke permissions + - reset memory scopes + - pin or delete records where allowed +- Provide a “safe review mode” that exposes evidence and planned effects before execution. + +**Auditability** +- Every decision should be reconstructable from logs: + - input + - retrieved evidence + - model outputs + - policy checks + - tool calls + - final outcome +- Audit views should separate raw logs from summarized explanations. + +**Refusal/escalation** +- If policy blocks an action, the system should refuse, explain the reason, and propose safe alternatives. +- If uncertainty or risk exceeds threshold, escalate to a human or narrower subtask. +- Refusal should be stable across retries unless the underlying facts change. + +**Capability boundaries** +- The runtime must enforce that capability is determined by permissions, not by model confidence. +- High-impact domains need stricter leases, more verifiers, and narrower adapters. +- The system should degrade gracefully into read-only or advisory mode. + +## 8. Evaluation and benchmark strategy: unit tests, integration tests, red teams, long-run soak, regression gates + +**Unit tests** +- Test policy rules, memory operations, planner transitions, retrieval ranking, and tool adapter contracts. +- Include tests for malformed inputs, stale state, and contradictory evidence. + +**Integration tests** +- End-to-end tasks that combine planning, memory, tool use, and verification. +- Scenarios should cover: + - file operations + - calendar/task management + - local data analysis + - controlled VM actions + - interrupted/resumed workflows + +**Red teams** +- Design adversarial cases for: + - prompt injection + - tool misuse + - memory poisoning + - overconfident planning + - policy bypass attempts + - ambiguous user intent +- Red team outputs should become regression cases. + +**Long-run soak** +- Run the system for days/weeks on benign workloads to detect drift, memory bloat, leak paths, and recovery failures. +- Measure action success rate, verification failure rate, false refusals, and stale memory incidence. + +**Regression gates** +- Any code, prompt, policy, or retrieval change must pass: + - functional tests + - safety tests + - benchmark tasks + - replay of historical failures +- A failed gate blocks deployment unless manually waived with recorded justification. + +## 9. Persistence/runtime architecture: event log, state store, leases, runtime modes, recovery + +**Event log** +- Append-only canonical log of all significant state transitions. +- Use it as the source of truth for replay, audit, and reconstruction. +- Events should be immutable and schema-versioned. + +**State store** +- Materialized views built from the log: + - current goals + - memory indexes + - policy state + - active leases + - world model snapshot diff --git a/research/ai_generated_agi_architectures/raw_outputs/perplexity_sonar_pro.md b/research/ai_generated_agi_architectures/raw_outputs/perplexity_sonar_pro.md new file mode 100644 index 0000000..adec904 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/perplexity_sonar_pro.md @@ -0,0 +1,248 @@ +# Raw Output: Perplexity Sonar + +- Provider/tool: Perplexity via OpenRouter +- Model ID: `perplexity/sonar-pro` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779849077-8sTXk7EUz9sQeu3Y1EuE` +- Usage reported by provider: `{"completion_tokens": 2600, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 0.04619, "cost_details": {"upstream_inference_completions_cost": 0.045002, "upstream_inference_cost": 0.04619, "upstream_inference_prompt_cost": 0.001188}, "is_byok": false, "prompt_tokens": 396, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2996}` + +--- + +1. **Architecture thesis (3–5 sentences)** +Cognitive-OS should be a **local-first cognitive control plane** that orchestrates multiple models and tools under a single, auditable loop of goals → hypotheses → evidence → governed actions. It treats the “AGI direction system” as a **runtime for reasoning processes**, not as a single model, with memory, planning, and tool use all mediated through explicit state and policies. The core thesis: all consequential behavior must flow through **evidence-governed plans**, **verifier-gated tools**, and **persistent world/self state** that can be audited, replayed, and selectively rolled back. Architecturally, it should resemble a **database-backed OS for cognition**: event-sourced, policy-enforced, tool-extensible, and safe to iterate on while remaining far from actual AGI capability. + +--- + +2. **Memory architecture** + +Goal: explicit, inspectable memory subsystems with clear lifecycles and limits, suitable for controlled long-horizon behavior. + +**2.1 Working memory** + +- **Representation** + - Structured **Working Memory Frame (WMF)** as a typed object: + - `goal_context` (current top-level and subgoals) + - `current_hypotheses` with confidence estimates + - `evidence_view` (subset of episodic/semantic entries relevant to current goals) + - `plan_state` (active plan, next steps, status) + - `scratchpad` (LLM-visible scratch text + structured fields, e.g., intermediate calculations). +- **Scope & lifetime** + - Bound to a **deliberation episode** (e.g., one “task” spanning multiple tool calls). + - Evicted or snapshot to episodic memory at task end or after time/size thresholds. + +**2.2 Episodic memory** + +- **Unit**: an **Episode Record**: + - `id`, `timestamp`, `actors` (agents/humans), `goals`, `actions_taken`, `tools_used`, `inputs`, `outputs`, `outcome` labels, `side_effects`, `policy_decisions`. +- **Storage**: append-only **event log** plus **episode index** (SQL/OLAP table). +- **Indexing**: + - Symbolic tags (task IDs, entities, tools, domains). + - Embedding index for natural-language similarity retrieval. + - Causal links: `precedes`, `contributes_to`, `contradicts`, `reverted_by`. + +**2.3 Semantic/procedural memory** + +- **Semantic layer** + - **Typed knowledge graph** with entities (files, users, APIs, projects), relations (depends_on, owns, governed_by), plus soft attributes (reliability, last_validated_at). + - Backed by a graph DB or a graph-on-relational schema. +- **Procedural layer** + - **Skill descriptors**: templates for recurring workflows (e.g., “safe code edit,” “data export with PII scrub”), each with: + - Preconditions (checked against world state). + - Expected steps & tools. + - Risk profile and required verifier gates. + - Stored as versioned YAML/JSON, treated as **code**, with tests and reviews. + +**2.4 Retrieval** + +- **Multi-stage retrieval**: + 1. Symbolic filter (by domain, entities, time window, risk level). + 2. Vector search over episodic & semantic summaries. + 3. LLM-based re-ranking/summarization into a **Context Package** that fits model limits. +- Retrieval is **goal-conditioned**: query templates are generated from the current WMF and task type, not free-form. + +**2.5 Consolidation** + +- Nightly or idle-time **consolidation jobs**: + - Cluster similar episodes, infer stable semantic facts (e.g., “Tool X often times out; reliability=low”). + - Promote high-value patterns into procedural memory (candidate skills) under human review. + - Create compressed “chapter summaries” for long-running projects. +- Consolidation steps are **one-way** into higher-level structures and always **logged** with provenance. + +**2.6 Forgetting** + +- **Policy-driven forgetting**: + - Time-based decay for low-value episodes (keep summaries, drop raw details). + - Legal/privacy-based purging: explicit delete tokens propagate through episodic, semantic, and embeddings. +- **Soft forgetting**: + - Reduce retrieval priority instead of deleting: a `retrieval_weight` that decays unless reinforced by use. +- Forgetting operations are **audited** so humans know what was removed or down-weighted. + +--- + +3. **Reasoning and planning loop** + +Core loop per agent: + +1. **Perception & goal intake** + - Ingest events (user request, schedule triggers, environment changes). + - Normalize into **Goal Objects** with: `goal_id`, `description`, `priority`, `constraints`, `deadline`, `risk_tier`. + +2. **Deliberation** + - Build/refresh WMF via retrieval. + - Run **Deliberation Policy**: + - Choose reasoning mode (fast heuristic vs. deep plan). + - Decide whether to escalate, ask clarifying questions, or refuse. + +3. **Planning** + - Use a planning LLM + symbolic planner: + - Generate candidate plans as sequences/trees of **Plan Steps** (tool calls, subgoals, checks). + - Enforce **Plan Schema** constraints per risk tier: + - Low risk: direct tool call allowed. + - Medium: plan must include explicit verification steps. + - High: plan must include human approval nodes and sandbox-only actions. + - Plans represented as explicit DAGs, stored in the event log before execution. + +4. **Execution & interrupts** + - Execute plan step-by-step: + - Before each step: run **preconditions** and **verifier gates** (e.g., static analyzers, safety filters). + - After each step: log outputs and update WMF. + - **Interrupts**: + - External: user cancel/pause; policy revocation of a permission. + - Internal: detector flags anomalies (unexpected diff size, policy violation, high uncertainty). + - Interrupt handling: freeze plan, snapshot WMF, escalate or roll back side effects if supported. + +5. **Reflection** + - After plan completion or abort: + - Generate structured **Reflection Record**: + - What worked, what failed, surprises vs. expectations, updated hypotheses. + - Feed into consolidation and learning mechanisms. + - For long tasks, mid-run reflection checkpoints are scheduled (time or event-based). + +6. **Uncertainty handling** + - All hypotheses and forecasts carry **explicit confidence bands** (e.g., 0–1 or discretized levels). + - Policy: above certain uncertainty thresholds, the agent must: + - Seek more evidence, or + - Present options to a human with trade-offs, or + - Defer/refuse. + +--- + +4. **Learning or self-improvement** + +**4.1 Allowed update surfaces** + +- **LLM weights**: *read-only* in the core runtime. No online fine-tuning in production. +- **Safe update surfaces**: + - Retrieval indices and embeddings. + - Semantic/procedural memory (knowledge graph and skills). + - Tool and plan heuristics (non-critical) behind gates. + - Per-agent configuration (preferences, prompt templates). + +**4.2 Offline learning** + +- **Batch pipelines** run off the production event log: + - Train/improve retrievers, ranking heuristics. + - Suggest new skills or plan templates based on frequent patterns. + - Evaluate alternative prompts or routing strategies on **frozen historical traces**. +- All candidate changes are staged in a **sandbox environment** with replay of real traces. + +**4.3 Eval-gated changes** + +- Any change that alters: + - Tool configurations, + - Plan schemas, or + - Safety policies + must pass: + - Unit tests (behavioral specs). + - Regression tests on historical traces. + - Safety & governance checks (no expansion of capability beyond declared boundaries without sign-off). + +**4.4 Rollback** + +- Every configuration, skill, and policy is **versioned**: + - Changes are applied via migrations with one-step or multi-step rollback scripts. + - Event log is immutable; only **interpretation** changes, never past records. +- Rollback scenarios: + - Auto-rollback on anomalies (e.g., error rate spike, repeated policy near-misses). + - Manual rollback by operator with clear UI. + +--- + +5. **Tool use and action execution** + +**5.1 Sandboxing** + +- Tools executed in **isolated OS-level sandboxes** (containers / VMs): + - File access via **virtual filesystem** with whitelisted paths. + - Network access via **egress proxy** with allowlist and rate limits. +- Each tool call carries a **capability token** that encodes allowed operations. + +**5.2 Permissions** + +- Permissions are not implicit; they are: + - Described in a **capability schema** for each tool. + - Granted per-agent and per-goal (e.g., Agent A may edit code in Repo X but only read in Repo Y). +- High-risk actions require **multi-step approval** (agent plan → human review → runtime revalidation). + +**5.3 Verifier gates** + +- Before execution: + - Input validators, policy filters (PII checks, dangerous API usage), and static analyzers run. +- After execution: + - Diff analyzers (e.g., for code or data modifications). + - Post-conditions: expected invariants (tests must pass, schema must hold). +- Gates are **composable**: a tool invocation can be wrapped by multiple verifiers. + +**5.4 Side-effect ledger** + +- Every side-effecting action writes a **Side-Effect Entry**: + - `id`, `actor`, `tool`, `parameters`, `target_resources`, `diff`, `timestamp`, `approvals`, `verifiers_passed`, `rollback_handle` (if supported). +- Ledger is separate from app data stores, so it persists even if target systems change. +- Used for: + - Auditing and accountability. + - Automated rollback orchestration (undo scripts or synthetic revert actions). + +--- + +6. **World model and representation layer** + +**6.1 Entities** + +- Core model: **typed entities**: + - Physical/logical resources (files, tables, services, users, agents, policies). + - Each with attributes, ownership, and **governance tags** (confidentiality, criticality). + +**6.2 Causal models** + +- Represent: + - `Action → State Change → Outcomes` links based on past episodes. + - Dependency structures (e.g., “code in service A affects API B, which affects client C”). +- Implementation: relational + graph edges for causal links, plus learned causal scores derived from observing effects. + +**6.3 Forecasts** + +- Agents maintain **Forecast Objects** for relevant metrics: + - e.g., “probability build will fail after this change,” “expected runtime of this notebook,” “chance of violating latency SLO.” +- Forecasts are produced by: + - Simple statistical models, or + - LLM-synthesized ranges grounded in evidence and causal links. + +**6.4 State synchronization** + +- World model is not a mirror of all external systems; instead: + - It stores **cached state** with `last_observed_at` timestamps. + - Before high-impact actions, the agent must **refresh state** via tools (e.g., git fetch, DB metadata, monitoring APIs). +- Conflicts: + - If world model is stale beyond policy limits, the action is blocked or downgraded to read-only inspection. + +--- + +7. **Safety/governance** + +**7.1 Policy engine** + +- Central **policy engine** operates on: + - Subject (agent), diff --git a/research/ai_generated_agi_architectures/raw_outputs/qwen37_max.md b/research/ai_generated_agi_architectures/raw_outputs/qwen37_max.md new file mode 100644 index 0000000..d0ce5af --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/qwen37_max.md @@ -0,0 +1,48 @@ +# Raw Output: Qwen + +- Provider/tool: Qwen/Alibaba via OpenRouter +- Model ID: `qwen/qwen3.7-max` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779848934-T2oEkUEI9efRN7jEcVlF` +- Usage reported by provider: `{"completion_tokens": 5255, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 3229}, "cost": 0.0202475, "cost_details": {"upstream_inference_completions_cost": 0.01970625, "upstream_inference_cost": 0.0202475, "upstream_inference_prompt_cost": 0.00054125}, "is_byok": false, "prompt_tokens": 433, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 5688}` + +--- + +### 1. Architecture Thesis +Cognitive-OS treats AGI not as a monolithic model, but as a distributed, evidence-governed state machine where LLMs act as transient reasoning kernels. By strictly separating the ephemeral reasoning loop from persistent, cryptographically verified state and action ledgers, the system ensures that capability scaling remains bounded by deterministic governance policies. This local-first architecture guarantees that autonomous agents can operate with high agency in disconnected environments while remaining strictly auditable and reversible. + +### 2. Memory Architecture +**Working memory** is a fixed-size context window managed by a priority-queue attention mechanism, aggressively swapping low-relevance tokens to disk to maintain strict latency bounds. **Episodic memory** operates as an append-only event log of agent interactions, indexed by temporal and causal embeddings using a local vector database like Qdrant. **Semantic and procedural memory** are bifurcated: semantic facts reside in a local graph database (e.g., Kuzu) for ontological queries, while procedural memory is stored as versioned, executable Directed Acyclic Graphs (DAGs) of tool calls. **Retrieval** utilizes a hybrid search combining BM25 for exact string matches and dense vector retrieval for semantic similarity, strictly filtered by access control lists. **Consolidation** occurs via background cron jobs that summarize episodic logs into semantic graph updates using a smaller, quantized local LLM. **Forgetting** is implemented via time-decay functions applied to vector embeddings and explicit Time-To-Live (TTL) markers on ephemeral working memory nodes, ensuring the system does not degrade under infinite data accumulation. + +### 3. Reasoning and Planning Loop +**Deliberation** is formulated as a Markov Decision Process (MDP) where the LLM generates a tree of potential action sequences using a constrained Monte Carlo Tree Search variant. **Planning** outputs a strictly typed JSON plan containing preconditions, executable steps, and expected post-conditions. **Reflection** employs a secondary, smaller model to evaluate the proposed plan against the current world state and safety constraints prior to execution. **Uncertainty** is represented via confidence intervals on state variables; if uncertainty exceeds a predefined threshold, the agent automatically triggers an "information gathering" sub-plan rather than guessing. **Interrupts** are handled via a hardware-level Non-Maskable Interrupt (NMI) equivalent: a high-priority message queue that preempts the current reasoning thread, forcing an immediate context switch to a safety-evaluation routine without corrupting the active state. + +### 4. Learning and Self-Improvement +**Allowed update surfaces** are strictly limited to prompt templates, tool-routing weights, and procedural DAG parameters; base model weights are frozen in production. **Offline learning** exports episodic logs to a secure, air-gapped training enclave where Low-Rank Adaptation (LoRA) adapters are trained on successful, human-verified trajectories. **Eval-gated changes** require that any new adapter or prompt version pass a deterministic regression suite in a shadow environment before promotion. **Rollback** mechanisms rely on state and model routing configurations being versioned via Git-like content-addressable storage. If a deployment triggers an anomaly detection alert, the runtime executes an automatic revert to the last known good commit hash, ensuring continuous stability without human intervention. + +### 5. Tool Use and Action Execution +**Sandboxing** mandates that all external tool executions occur within gVisor or Firecracker microVMs with strictly capped CPU, memory, and network I/O limits. **Permissions** utilize a capability-based security model where tools are granted unforgeable macaroon tokens specifying exact allowed operations and expiration times. **Verifier gates** enforce pre-execution static analysis and post-execution output validation using regex and secondary classifier models to prevent prompt injection, data exfiltration, or unintended side effects. The **side-effect ledger** records every external state mutation in an append-only Merkle tree. This enables cryptographic auditing of all agent actions and facilitates theoretical rollback via the automated generation and execution of compensating transactions. + +### 6. World Model and Representation Layer +**Entities** are represented as strongly-typed Rust structs with unique UUIDs, serialized to Protocol Buffers for cross-language compatibility and zero-copy deserialization. **Causal models** are maintained as dynamic Bayesian networks where nodes represent entity states and edges represent probabilistic state transitions derived from historical interactions. **Forecasts** are generated by running lightweight forward simulations of the Bayesian network to predict state distributions $t+n$ steps ahead, allowing the agent to evaluate plan viability. **State sync** relies on a Conflict-free Replicated Data Type (CRDT) based synchronization protocol, ensuring eventual consistency of the world model across distributed local nodes when network connectivity is restored after offline operation. + +### 7. Safety and Governance +The **policy engine** is implemented using Open Policy Agent (OPA) with Rego, evaluating every proposed action against a declarative, version-controlled ruleset before it reaches the verifier gate. **Human controls** include "break-glass" mechanisms requiring multi-party cryptographic signatures for high-impact actions, such as deleting critical files or transferring funds. **Auditability** is guaranteed by streaming all reasoning traces, tool inputs/outputs, and policy decisions to an immutable local ledger using SQLite with Write-Ahead Logging and periodic cryptographic hashing. **Refusal and escalation** protocols dictate that if OPA denies an action or uncertainty is too high, the agent halts and pushes a structured escalation ticket to a human-in-the-loop dashboard. **Capability boundaries** are enforced via hardcoded air-gaps preventing the agent from modifying its own governance policies, OPA rulesets, or safety-critical runtime binaries. + +### 8. Evaluation and Benchmark Strategy +**Unit tests** consist of deterministic tests for memory retrieval, state serialization, and policy engine logic using standard Rust testing frameworks. **Integration tests** run end-to-end simulations in a Dockerized mock environment to verify tool execution, sandbox containment, and side-effect ledger integrity. **Red teams** utilize automated adversarial fuzzing of the LLM routing layer and tool inputs using frameworks like Garak to continuously test for prompt injection and jailbreak vulnerabilities. **Long-run soak** testing involves continuous 30-day execution in a simulated environment to detect memory leaks, context window degradation, and state drift. **Regression gates** are integrated into CI/CD pipelines, blocking merges if performance on a curated suite of 500 canonical agent tasks drops below the established baseline. + +### 9. Persistence and Runtime Architecture +The **event log** utilizes a local instance of Redpanda for high-throughput, durable event streaming of all agent state transitions and reasoning steps. The **state store** relies on RocksDB for high-performance, local key-value storage of the current materialized world state. **Leases** and distributed locks are managed via SQLite with advisory locks to prevent concurrent mutation of the same entity by parallel reasoning threads. **Runtime modes** include "Shadow" (read-only, no side effects), "Supervised" (requires human approval for writes), and "Autonomous" (policy-gated execution). **Recovery** is achieved by checkpointing the RocksDB state and event log offset every $N$ events; on crash, the runtime replays the event log from the last checkpoint to restore the exact pre-crash state. + +### 10. Multi-Agent and Orchestration Design +**Roles** are instantiated with specific personas (e.g., "Researcher", "Critic", "Executor") defined by distinct system prompts, memory partitions, and tool access lists. **Arbitration** is handled by a centralized "Orchestrator" agent that routes tasks based on a capability matrix and current agent compute load. **Consensus** for high-stakes decisions requires multiple agents to independently generate plans, with a voting mechanism (e.g., Borda count) selecting the optimal path. **Specialization** allows agents to dynamically spawn sub-agents with narrower scopes, restricted context windows, and stricter resource limits to handle specific sub-tasks. **Conflict handling** serializes mutually exclusive state mutation requests, using the policy engine to resolve precedence based on predefined priority weights and role hierarchies. + +### 11. Engineering Feasibility +The **90-day prototype plan** is phased: Days 1-30 focus on the core runtime, event log, and RocksDB state store; Days 31-60 implement tool sandboxing via gVisor, the OPA policy engine, and basic LLM routing; Days 61-90 deliver multi-agent orchestration, memory consolidation, and the integration test suite. The **highest-risk assumptions** are that local LLMs (e.g., Llama-3 70B) possess sufficient reasoning capability for complex MDP planning without cloud API fallback, and that gVisor overhead remains acceptable for high-frequency, low-latency tool calls. **Dependencies** include the Rust toolchain, Ollama or vLLM for local inference, gVisor, RocksDB, Redpanda, and a local vector database like Qdrant. + +### 12. Original or Non-Obvious Insight +Current agent frameworks mistakenly treat the LLM context window as the agent's entire mind. In a true Cognitive-OS, the context window must be treated merely as an *L1 cache* for a much larger, structured cognitive state machine. By aggressively paging out reasoning traces to episodic memory and keeping only the active working set (current goal, immediate constraints, active tool outputs) in the LLM's context, we drastically reduce hallucination and lower inference costs. This shifts the paradigm from "prompt engineering" to "cognitive memory management," making the agent's cognitive state formally verifiable, debuggable, and fundamentally decoupled from the underlying model's context length limitations. diff --git a/research/ai_generated_agi_architectures/raw_outputs/xai_grok_43.md b/research/ai_generated_agi_architectures/raw_outputs/xai_grok_43.md new file mode 100644 index 0000000..f798678 --- /dev/null +++ b/research/ai_generated_agi_architectures/raw_outputs/xai_grok_43.md @@ -0,0 +1,48 @@ +# Raw Output: Grok + +- Provider/tool: xAI via OpenRouter +- Model ID: `x-ai/grok-4.3` +- Access date: 2026-05-27 +- Collection timestamp UTC: 2026-05-27T02:25:39+00:00 +- Prompt: standard prompt in `../prompts.md` +- Human edits: none beyond Markdown metadata wrapper and whitespace preservation +- OpenRouter response ID: `gen-1779848852-0ktKsI72HwKX3E0JoTKd` +- Usage reported by provider: `{"completion_tokens": 2057, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 293}, "cost": 0.00565435, "cost_details": {"upstream_inference_completions_cost": 0.0051425, "upstream_inference_cost": 0.00565435, "upstream_inference_prompt_cost": 0.00051185}, "is_byok": false, "prompt_tokens": 517, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 128, "video_tokens": 0}, "total_tokens": 2574}` + +--- + +1. Architecture thesis in 3-5 sentences. +Cognitive-OS is a local-first runtime that maintains an explicit evidence store as the single source of truth for goals, hypotheses, world state, and self state, with every LLM invocation, plan step, and action gated by verifier predicates before execution. Agents operate inside managed-VM boundaries that expose only typed adapters for tools and sensors, while an event log and lease-based state machine provide recovery and mode switching between interactive, batch, and soak-test runtimes. Governance is enforced by a policy engine that evaluates capability requests against human-defined rules and produces auditable refusal or escalation records; learning updates are restricted to offline, eval-gated patches that are applied only after regression and red-team checks. This design separates deliberation from execution, routes all external effects through side-effect ledgers, and keeps the entire system runnable on a single workstation without remote services. + +2. Memory architecture: working, episodic, semantic/procedural, retrieval, consolidation, forgetting. +Working memory is a fixed-size ring buffer of the most recent 128 evidence records plus the active goal and hypothesis stack, updated on every verifier-approved transition. Episodic memory stores timestamped traces of (goal, plan, outcome, evidence-IDs) tuples in an append-only log partitioned by day; each entry carries cryptographic hashes for tamper detection. Semantic and procedural memory live in a typed graph where nodes are entities or skills and edges carry confidence scores derived from verified outcomes; retrieval uses a hybrid index combining embedding similarity on node attributes with graph traversal limited to depth 4. Consolidation runs as a background process that merges duplicate episodic entries into semantic nodes only when three independent verifier gates agree on the abstraction. Forgetting is implemented as a priority queue that evicts lowest-evidence-utility items when storage exceeds a configurable watermark, preserving all entries referenced by open goals or active leases. + +3. Reasoning and planning loop: deliberation, planning, reflection, uncertainty, interrupts. +The loop executes in discrete ticks: deliberation enumerates candidate hypotheses from working memory, scores them against the evidence store using a deterministic utility function, and selects the top-k for expansion. Planning produces a tree of typed actions with attached preconditions and postcondition predicates; each branch terminates at a verifier gate or a depth limit. Reflection occurs after every action outcome by comparing predicted versus observed state deltas and writing a discrepancy record into episodic memory. Uncertainty is represented explicitly as intervals on hypothesis scores; when interval width exceeds a policy threshold the loop emits an interrupt that pauses execution and requests human input or additional evidence collection. External interrupts from the lease manager or policy engine can preempt the loop at any tick boundary. + +4. Learning or self-improvement: allowed update surfaces, offline learning, eval-gated changes, rollback. +Only four surfaces accept updates: the semantic graph, procedural skill library, policy rule set, and verifier predicate library. All changes are generated offline by a separate training process that consumes the event log and produces candidate patches; no online gradient steps occur inside the runtime. Each patch is subjected to an automated eval suite covering unit behavior, integration traces, and synthetic red-team scenarios before a human approver can sign the lease that permits application. Rollback is performed by restoring the prior state snapshot from the event log and replaying only the subset of subsequent events that remain valid under the restored patch set, with a maximum replay window of 10,000 events. + +5. Tool use and action execution: sandboxing, permissions, verifier gates, side-effect ledger. +Every tool is exposed through a managed-VM adapter that implements a narrow capability interface; the VM itself runs with seccomp and landlock restrictions plus a read-only root filesystem except for explicitly mounted scratch directories. Permissions are capability tokens issued by the policy engine and scoped to specific resource classes and time bounds. Before any adapter call, three verifier gates run in sequence: static type check, dynamic precondition check against current world state, and policy compliance check. All effects are written first to a side-effect ledger that records intent, arguments, predicted outcome, and actual result; the ledger is flushed to durable storage before the adapter is invoked, enabling deterministic replay. + +6. World model and representation layer: entities, causal models, forecasts, state sync. +Entities are versioned records containing typed attributes and provenance links to the evidence that created or modified them. Causal models are stored as directed graphs of conditional dependencies with attached probability intervals; each edge records the verifier run that validated the dependency. Forecasts are generated by forward simulation of the causal graph for a configurable horizon, producing a set of possible future entity states with attached likelihoods. State sync occurs at lease renewal points where the runtime compares its local entity versions against the authoritative event log and applies only those deltas that pass the same verifier gates used for live actions. + +7. Safety/governance: policy engine, human controls, auditability, refusal/escalation, capability boundaries. +The policy engine evaluates every capability request against a rule base expressed as a deterministic logic program; rules can reference evidence age, hypothesis , and human-defined allow/deny lists. Human controls are exposed as signed leases that temporarily widen or narrow capability boundaries and are recorded in the event log. Auditability is achieved by storing every policy decision, verifier outcome, and action result with full provenance hashes. Refusal or escalation is triggered when a request violates a rule or when uncertainty intervals remain wide after a configurable number of reflection cycles; escalation surfaces the full decision trace to a human operator. Capability boundaries are enforced at the VM adapter layer so that even a compromised LLM cannot bypass the ledger or verifier sequence. + +8. Evaluation and benchmark strategy: unit tests, integration tests, red teams, long-run soak, regression gates. +Unit tests exercise individual verifiers, memory operations, and policy rules against synthetic evidence sets. Integration tests replay curated multi-step traces from the episodic log and assert that final world state matches expectations. Red-team exercises run adversarial goal sets designed to trigger policy violations or resource exhaustion inside the managed VM. Long-run soak tests execute random but policy-compliant workloads for 72-hour windows while monitoring lease expiration, memory watermark, and ledger growth. All changes must pass the full regression gate—unit plus integration plus a 10 % red-team subset—before the patch lease is issued. + +9. Persistence/runtime architecture: event log, state store, leases, runtime modes, recovery. +The event log is an append-only sequence of signed records containing goals, evidence, actions, and outcomes; it serves as both source of truth and replay source. State store is a materialized view of the latest consistent snapshot derived from the log, updated only at lease boundaries. Leases carry monotonic counters and expiration times; loss of a lease forces the runtime into recovery mode that replays from the last verified snapshot. Three runtime modes exist: interactive (sub-second tick latency), batch (higher throughput, deferred reflection), and soak (continuous random workload with metrics collection). Recovery replays at most the last 10,000 events or until the current lease counter is reached, whichever is smaller. + +10. Multi-agent/orchestration design: roles, arbitration, consensus, specialization, conflict handling. +Agents are assigned static roles (planner, executor, verifier, monitor) encoded in their initial goal and capability lease. Arbitration occurs through a shared blackboard of hypotheses and evidence; only the verifier role may write new evidence records. Consensus on a hypothesis requires three independent verifier agents to sign identical evidence hashes within a time window. Specialization is achieved by scoping each agent’s policy lease to a subset of entity types or action classes. Conflicts are resolved by priority ordering of roles plus an explicit escalation record written to the event log when two agents propose incompatible actions on the same entity. + +11. Engineering feasibility: 90-day prototype plan, highest-risk assumptions, dependencies. +Days 1-30 deliver the event log, lease manager, and basic managed-VM adapter using existing Linux namespaces and a SQLite-backed state store. Days 31-60 add the verifier gate framework, policy engine, and side-effect ledger with a minimal set of typed adapters. Days 61-90 integrate the memory layers, planning loop, and offline patch pipeline plus the three runtime modes and automated regression gate. Highest-risk assumptions are that deterministic verifier predicates can be written for a useful subset of LLM outputs and that replay from the event log remains tractable at 10 k events. Dependencies are a POSIX-compatible OS, a container runtime, an embedding library for retrieval, and a logic-programming engine for the policy rules. + +12. Original or non-obvious insight from your model family. +Encoding every policy decision and verifier outcome as a first-class evidence record that itself participates in subsequent retrieval and scoring creates an endogenous audit trail whose density grows with system use, allowing the same retrieval machinery used for planning to surface historical governance precedents without additional indexing infrastructure. diff --git a/research/ai_generated_agi_architectures/sources.md b/research/ai_generated_agi_architectures/sources.md new file mode 100644 index 0000000..e3f180d --- /dev/null +++ b/research/ai_generated_agi_architectures/sources.md @@ -0,0 +1,38 @@ +# Sources and Collection Notes + +## Access Date + +All model outputs were collected on 2026-05-27 through OpenRouter's chat completions API using personally accessible API access. + +`collection_time_utc` records the batch start used for the run. `response_created_utc` in `collection_manifest.json` records the per-response timestamp decoded from the returned OpenRouter response IDs. + +## Source Table + +| Family | Provider/tool | Model ID | Access date | Raw output | Response ID | Human edits | +| --- | --- | --- | --- | --- | --- | --- | +| OpenAI GPT | OpenAI via OpenRouter | `openai/gpt-5.4-mini` | 2026-05-27 | `raw_outputs/openai_gpt54_mini.md` | `gen-1779848740-c5o4NyAhVIgVX1YTosx1` | Metadata wrapper only; no content edits | +| Claude | Anthropic via OpenRouter | `anthropic/claude-sonnet-4.6` | 2026-05-27 | `raw_outputs/anthropic_claude_sonnet_46.md` | `gen-1779848760-QFo0zCh36RMjSfdk7VHW` | Metadata wrapper only; no content edits | +| Gemini | Google via OpenRouter | `google/gemini-3.1-pro-preview` | 2026-05-27 | `raw_outputs/google_gemini_31_pro.md` | `gen-1779848820-fHN73CLXM5jNTUZ3lUHq` | Metadata wrapper only; no content edits | +| Grok | xAI via OpenRouter | `x-ai/grok-4.3` | 2026-05-27 | `raw_outputs/xai_grok_43.md` | `gen-1779848852-0ktKsI72HwKX3E0JoTKd` | Metadata wrapper only; no content edits | +| DeepSeek | DeepSeek via OpenRouter | `deepseek/deepseek-v3.2` | 2026-05-27 | `raw_outputs/deepseek_v32.md` | `gen-1779848866-t5VvIELdsYHWEfAyntld` | Metadata wrapper only; no content edits | +| Qwen | Qwen/Alibaba via OpenRouter | `qwen/qwen3.7-max` | 2026-05-27 | `raw_outputs/qwen37_max.md` | `gen-1779848934-T2oEkUEI9efRN7jEcVlF` | Metadata wrapper only; no content edits | +| Llama | Meta Llama via OpenRouter | `meta-llama/llama-4-maverick` | 2026-05-27 | `raw_outputs/meta_llama4_maverick.md` | `gen-1779849013-QMXEwlExEw8ub6paD3nk` | Metadata wrapper only; no content edits | +| Mistral | Mistral via OpenRouter | `mistralai/mistral-large-2512` | 2026-05-27 | `raw_outputs/mistral_large_2512.md` | `gen-1779849033-NNZNF80eWMcFyxzdJkAp` | Metadata wrapper only; no content edits | +| Perplexity Sonar | Perplexity via OpenRouter | `perplexity/sonar-pro` | 2026-05-27 | `raw_outputs/perplexity_sonar_pro.md` | `gen-1779849077-8sTXk7EUz9sQeu3Y1EuE` | Metadata wrapper only; no content edits | +| Cohere Command | Cohere via OpenRouter | `cohere/command-a` | 2026-05-27 | `raw_outputs/cohere_command_a.md` | `gen-1779849103-p1SgOCsNZOzouYMpfOTJ` | Metadata wrapper only; no content edits | + +## Methodology + +- Each model received the same system message and the same user prompt documented in `prompts.md`. +- No model-specific prompt adaptation was used. +- Raw outputs are preserved separately from analysis in `raw_outputs/`. +- `collection_manifest.json` records model IDs, response IDs, timestamps, token usage returned by the provider, raw-output SHA-256 hashes, byte counts, word counts, and public OpenRouter model-catalog verification metadata. +- `validation_report.md` records acceptance-criteria coverage, manifest consistency checks, credential scan result, and SHA-256 hashes for the raw output files. +- Analysis files were written after collection by comparing the preserved outputs against the issue's requested dimensions and the public Cognitive-OS repository structure. + +## Limitations + +- OpenRouter is an authorized aggregator access path, not a direct provider API path. The packet therefore preserves the returned model IDs, response IDs, response-created timestamps, token metadata, raw-output hashes, and public model-catalog verification instead of claiming direct-provider logs. +- Some models produced denser or shorter outputs than requested; these were preserved as-is rather than padded. +- This packet is architecture research for AGI-direction planning. It does not claim Cognitive-OS is currently AGI. +- Payment, review, and acceptance are controlled by the repository maintainer. diff --git a/research/ai_generated_agi_architectures/summary.md b/research/ai_generated_agi_architectures/summary.md new file mode 100644 index 0000000..ba38826 --- /dev/null +++ b/research/ai_generated_agi_architectures/summary.md @@ -0,0 +1,97 @@ +# Summary + +## Common Patterns + +### 1. Cognitive-OS Should Be A Control Plane, Not A Monolith + +All 10 outputs reject the idea that a single model should own memory, planning, governance, and action. The consensus architecture is a control plane where LLMs propose and interpret, while typed runtime services own state, permissions, evidence, and side effects. + +For Cognitive-OS, this maps directly onto the existing repository direction: `runtime`, `orchestration`, `reasoning`, `learning`, `world_model`, `self_model`, and local-machine adapters should remain separable surfaces with explicit contracts. + +### 2. Memory Needs Provenance And Lifecycle Rules + +Every model proposed at least three memory layers: working, episodic, and semantic/procedural. The most useful shared requirement is provenance. Memories should not be plain summaries; they should carry source event IDs, confidence, freshness, policy constraints, and promotion status. + +Recommended implementation stance: + +- Working memory is bounded and task-scoped. +- Episodic memory is append-only and replayable. +- Semantic/procedural memory is promoted only after verification or repeated success. +- Forgetting is an indexing and retention policy, not silent deletion of audit records. + +### 3. Tool Use Must Be Capability-Scoped + +The systems strongly agreed that actions should flow through typed adapters with scoped capabilities. Qwen proposed gVisor/Firecracker plus macaroon-like permissions; Grok framed actions as capability leases; OpenAI and Claude emphasized verifier gates; Cohere used managed VMs and a side-effect ledger. + +The common implementation pattern is: + +1. Plan proposes an action. +2. Policy engine checks capability. +3. Verifier checks preconditions. +4. Adapter executes inside a bounded environment. +5. Runtime records the observed result. +6. Reflection compares expected and actual outcomes. + +### 4. Learning Must Be Offline And Eval-Gated + +No model recommended unconstrained online weight updates. The safe update surfaces are memory consolidation, route policy, prompts, verifier predicates, tool procedures, and optional adapters. All proposed changes should pass shadow-mode evaluation before promotion and remain rollbackable. + +This aligns with Cognitive-OS's existing failure-learning and governance surfaces: learning should produce candidate patches or policy updates, not mutate core runtime behavior silently. + +### 5. Runtime Persistence Is The Safety Backbone + +The models converged on event logs, state snapshots, leases, runtime modes, and replay-based recovery. This is stronger than generic audit logging. The event log should be the source of truth from which world state, memory promotions, policy decisions, and execution records can be reconstructed. + +## Important Disagreements + +### Causal World Model Depth + +Qwen and Cohere advocate Bayesian/causal world models early. OpenAI, Claude, Gemini, and Grok propose a more incremental typed-entity model with forecasts and provenance first. The practical answer is phased: start with typed entities and outcome deltas, then add causal graphs for domains where forecasts can be tested. + +### Multi-Agent Consensus + +Some models recommend explicit voting or consensus among agents. Others emphasize a single orchestrator with specialized critics/verifiers. For Cognitive-OS, consensus should be reserved for high-impact decisions; routine actions should use deterministic policy and verifier checks to avoid unnecessary latency. + +### Memory Deletion Versus Cold Storage + +Gemini suggests cold-storage tiering instead of deletion, while other models allow TTL/eviction. The safest combined approach is to distinguish privacy deletion, active retrieval forgetting, and audit retention. These are separate policies. + +## Ideas Deliberately Not Adopted + +The synthesis does not adopt every model suggestion. The rejected or deferred ideas are useful because they show where the combined architecture is making implementation tradeoffs instead of averaging all outputs. + +- Direct online weight updates are excluded from the action path. Several models discuss learning surfaces, but the combined design limits promotion to offline, eval-gated memory, procedure, route-policy, prompt, verifier, and adapter changes. +- Distributed event infrastructure is deferred. Qwen's queue/store design is useful at scale, but SQLite plus materialized views is a better first implementation for inspectability and replay tests. +- Always-on multi-agent voting is rejected for routine work. Consensus is kept for high-impact decisions because deterministic policy and verifier checks are cheaper and more reliable for ordinary actions. +- Early full causal/Bayesian world modeling is deferred. Qwen and Cohere make a strong case for it, but typed entities, provenance, and outcome deltas must exist first or the causal layer will be speculative. +- Blockchain-style ledgers are translated into ordinary append-only hashes and audit records. The useful idea is tamper-evident side-effect history, not an external chain dependency. +- Cold storage is not treated as privacy deletion. Gemini's tiering is useful for retrieval performance, but privacy deletion, audit retention, and active forgetting remain separate policies. + +## Notable Ideas By Model Family + +- OpenAI: Separate intent, evidence, world state, policy, and execution so model upgrades do not destabilize governance. +- Claude: Make uncertainty a runtime object with thresholds and escalation behavior. +- Gemini: Use cold-storage tiering as forgetting while retaining auditability. +- Grok: Store policy and verifier outcomes as evidence records that future planning can retrieve as precedent. +- DeepSeek: Treat efficiency as safety because cheap checks are more likely to run every time. +- Qwen: Treat the LLM context window as an L1 cache over a larger cognitive state machine. +- Llama: Treat openness and reproducibility as safety primitives for a local-first runtime. +- Mistral: Design sovereignty and privacy constraints into the architecture from day one. +- Perplexity: Make freshness and provenance typed memory fields. +- Cohere: Treat side effects like financial transactions: ledgered, reversible where possible, and auditable. + +## Strongest Combined Direction + +The best architecture for Cognitive-OS is an evidence-governed local runtime with: + +- append-only event sourcing, +- bounded working memory, +- provenance-carrying semantic and procedural memory, +- typed world/self state, +- capability-scoped tool adapters, +- verifier-gated execution, +- policy leases, +- shadow-mode learning, +- runtime modes, +- crash recovery through replay, +- and a small set of specialist agents coordinated through the evidence ledger. diff --git a/research/ai_generated_agi_architectures/synthesis.md b/research/ai_generated_agi_architectures/synthesis.md new file mode 100644 index 0000000..719238d --- /dev/null +++ b/research/ai_generated_agi_architectures/synthesis.md @@ -0,0 +1,323 @@ +# Synthesis: Evidence-Governed Cognitive Runtime + +## Architecture Name + +EGCR: Evidence-Governed Cognitive Runtime. + +EGCR is a concrete combined architecture for Cognitive-OS that extracts the strongest repeated ideas from the 10 collected model outputs. It does not assume AGI has been achieved. It defines a local-first control plane where model calls are replaceable proposal generators and durable runtime services own memory, evidence, policy, execution, and recovery. + +## Core Principle + +The LLM context window is an L1 cache, not the agent's mind. + +The durable mind of Cognitive-OS is the event-sourced cognitive state machine: goals, evidence, hypotheses, world/self state, policies, procedures, leases, action attempts, verifier decisions, outcomes, and recovery records. Models read a scoped projection of that state, propose changes, and return structured candidates. The runtime decides what becomes real. + +## Runtime Services + +### 1. Event Ledger + +The event ledger is the source of truth. Every material transition is appended as a typed event: + +- `goal.created` +- `evidence.observed` +- `hypothesis.proposed` +- `memory.retrieved` +- `plan.proposed` +- `policy.checked` +- `lease.granted` +- `action.requested` +- `verifier.passed` or `verifier.failed` +- `tool.executed` +- `outcome.observed` +- `reflection.recorded` +- `memory.promoted` +- `patch.proposed` +- `patch.promoted` or `patch.rolled_back` + +Each event should include: timestamp, actor, input hash, output hash, policy version, linked evidence IDs, runtime mode, and replay instructions where applicable. + +### 2. State Store + +The state store is a materialized view over the ledger, not an independent truth source. It holds current active goals, open hypotheses, entity state, tool leases, memory indexes, and recovery checkpoints. + +A practical first implementation can use SQLite for events and materialized tables. Later versions can split hot state into a key-value store and vector index, but the first prototype should optimize for inspectability. + +### 3. Memory Manager + +Memory has four layers: + +- Working memory: bounded active task state with explicit TTL. +- Episodic memory: append-only trace of actions, observations, and outcomes. +- Semantic memory: stable facts and entity relationships promoted from evidence. +- Procedural memory: reusable skills, plans, tool recipes, and verifier templates. + +Memory promotion is not automatic summarization. It is a governed transition that requires evidence links, contradiction checks, and utility scoring. + +### 4. Planner + +The planner receives a scoped state projection and emits a typed plan: + +```json +{ + "goal_id": "...", + "steps": [ + { + "action": "adapter.method", + "arguments": {}, + "required_capability": "filesystem.read", + "preconditions": [], + "expected_observations": [], + "rollback_hint": "..." + } + ], + "uncertainty": { + "unknowns": [], + "confidence": 0.0, + "escalation_threshold_hit": false + } +} +``` + +The planner does not execute. It proposes. + +### 5. Policy And Lease Engine + +The policy engine decides whether a proposed action is allowed in the current runtime mode. A lease is a temporary capability grant with scope, expiration, actor, and reason. + +Policy decisions should themselves be events. That lets future planning retrieve governance precedent: why similar actions were allowed, denied, or escalated. + +### 6. Verifier Gate + +Before execution, each step passes verifier gates: + +1. Type and schema validation. +2. Capability lease validation. +3. Precondition validation against state store. +4. Risk classification. +5. Dry-run or simulation when available. +6. Human approval check when required. + +After execution, postcondition verifiers compare expected and observed outcomes. + +### 7. Action Executor + +The executor is the only service that mutates the outside world. It calls local-machine adapters or tool adapters inside managed boundaries. Every side effect is recorded before and after execution. + +The executor should support three action classes: + +- Read-only actions. +- Reversible writes with rollback hints. +- High-impact writes requiring human approval or stronger leases. + +### 8. Reflection And Learning + +Reflection compares predicted outcomes with observed outcomes and writes discrepancy records. Learning consumes discrepancy records offline and proposes patches to: + +- retrieval scoring, +- procedures, +- prompts, +- route policies, +- verifier predicates, +- memory consolidation rules, +- and documentation. + +No learning patch is promoted without a benchmark and regression gate. Rollback must be a normal path, not an emergency path. + +## Multi-Agent Design + +EGCR should start with five roles: + +- Planner: proposes plans. +- Critic: identifies missing evidence and risk. +- Verifier: checks policy and preconditions. +- Executor: performs approved tool calls. +- Curator: consolidates memory and proposes procedural updates. + +Agents communicate through typed events, not hidden shared chat. Routine work does not require voting. Consensus is reserved for high-impact decisions where independent plans or critiques materially reduce risk. + +## Runtime Modes + +The model outputs converge on explicit modes. Cognitive-OS already has runtime-mode language, so EGCR should preserve it: + +- `SLEEP`: consolidation, cleanup, memory scoring. +- `IDLE`: no active external mutation; safe diagnostics. +- `ROUTINE_RUN`: bounded routine task execution. +- `DEEP_THINK`: extended planning and hypothesis work. +- `ACTING`: approved tool execution. +- `WAITING_HUMAN`: blocked by approval, ambiguity, or policy. +- `DEGRADED_RECOVERY`: replay, rollback, and state repair. + +Mode transitions should be ledger events and policy inputs. + +## 90-Day Prototype Plan + +### Days 1-30: Ledger And State Spine + +- Implement append-only event schema. +- Materialize current state from events. +- Add evidence IDs to goals, hypotheses, plans, and actions. +- Add replay tests for crash recovery. +- Expose a minimal operator report showing active goal, mode, leases, last verifier decisions, and latest outcome deltas. + +### Days 31-60: Governed Action Path + +- Define capability leases. +- Add policy checks for read/write/network/credential/sync-back classes. +- Route local-machine actions through verifier gates. +- Add dry-run support where possible. +- Record side-effect ledger events before and after execution. +- Add red-team tests for bypass attempts and stale leases. + +### Days 61-90: Memory And Learning Loop + +- Add working, episodic, semantic, and procedural memory records. +- Implement retrieval with provenance and freshness metadata. +- Add memory promotion workflow. +- Add reflection records for predicted vs observed outcomes. +- Generate offline learning candidates from successful and failed traces. +- Promote only through benchmark gates and rollbackable patches. + +## Highest-Risk Assumptions + +1. Verifier predicates can cover enough real actions to be useful. +2. Event replay remains fast enough as the ledger grows. +3. Memory promotion can avoid polluting semantic memory with weak model inferences. +4. Operators will tolerate approval friction for high-impact actions. +5. Local sandboxing remains reliable across developer machines. + +## Implementation Decision Record + +Recommended first implementation choices: + +- SQLite event ledger and materialized state tables. +- JSON schema for plan/action/event payloads. +- Local vector index only after typed provenance fields exist. +- Capability leases stored as first-class events. +- Managed VM or sandbox boundary for writes. +- Shadow-mode learning patches before promotion. +- Runtime-mode transition tests in CI. + +## Implementation Appendix + +The first implementation should map EGCR onto existing Cognitive-OS surfaces instead of creating a parallel runtime. The following schemas and interfaces are intentionally small enough to prototype against `core/runtime/`, `core/orchestration/`, `modules/memory/`, `modules/governance/`, and `tools/managed_vm/`. + +### Event Ledger Schema + +```sql +CREATE TABLE cognitive_events ( + id TEXT PRIMARY KEY, + created_at TEXT NOT NULL, + event_type TEXT NOT NULL, + actor TEXT NOT NULL, + goal_id TEXT, + run_id TEXT, + mode TEXT NOT NULL, + input_hash TEXT, + output_hash TEXT, + policy_version TEXT, + evidence_ids TEXT NOT NULL, + payload_json TEXT NOT NULL, + replay_json TEXT, + previous_event_hash TEXT, + event_hash TEXT NOT NULL +); + +CREATE INDEX cognitive_events_goal_idx ON cognitive_events(goal_id, created_at); +CREATE INDEX cognitive_events_type_idx ON cognitive_events(event_type, created_at); +``` + +Initial code surfaces: `core/runtime/event_journal.py`, `core/runtime/evidence_ledger.py`, and `core/runtime/state_store.py`. + +### Capability Lease Schema + +```json +{ + "lease_id": "lease_...", + "goal_id": "goal_...", + "actor": "planner|executor|human", + "capability": "filesystem.write|network.post|browser.read|vm.execute", + "scope": { + "paths": [], + "domains": [], + "adapters": [] + }, + "issued_at": "2026-05-27T00:00:00Z", + "expires_at": "2026-05-27T00:10:00Z", + "reason": "bounded task-specific action", + "human_approved": false, + "revoked_at": null +} +``` + +Initial code surfaces: `modules/governance/gate.py`, `modules/governance/object_store.py`, and `core/orchestration/governance_runtime.py`. + +### Typed Plan Schema + +```json +{ + "plan_id": "plan_...", + "goal_id": "goal_...", + "evidence_ids": ["ev_..."], + "steps": [ + { + "step_id": "step_1", + "adapter": "managed_vm", + "method": "run_command", + "arguments": {}, + "required_capability": "vm.execute", + "preconditions": ["repo_clean_or_owned_changes_only"], + "expected_observations": ["exit_code_0", "artifact_written"], + "rollback_hint": "delete generated artifact if verification fails" + } + ], + "uncertainty": { + "confidence": 0.72, + "unknowns": ["target host runtime"], + "escalation_threshold_hit": false + } +} +``` + +Initial code surfaces: `core/orchestration/planner_runtime.py`, `core/orchestration/planner_stage.py`, and `planner/`. + +### Verifier Interface + +```python +class Verifier: + name: str + risk_class: str + + def precheck(self, plan_step, state, lease): + """Return pass/fail plus evidence IDs and operator-facing reason.""" + + def postcheck(self, plan_step, result, state): + """Compare expected and observed outcomes and emit verifier events.""" +``` + +Initial code surfaces: `core/orchestration/verifier_runtime.py`, `core/orchestration/execution_control.py`, and `core/orchestration/stage3_execution_support_runtime.py`. + +### Runtime Mode Transition Table + +| From | To | Required event | Required check | +| --- | --- | --- | --- | +| `IDLE` | `ROUTINE_RUN` | `goal.created` | goal has evidence and bounded scope | +| `ROUTINE_RUN` | `DEEP_THINK` | `uncertainty.escalated` | plan confidence below threshold | +| `ROUTINE_RUN` | `ACTING` | `lease.granted` | policy and verifier prechecks pass | +| `ACTING` | `ROUTINE_RUN` | `outcome.observed` | postconditions recorded | +| `ACTING` | `DEGRADED_RECOVERY` | `verifier.failed` | rollback or replay path exists | +| Any mode | `WAITING_HUMAN` | `approval.required` | policy says human approval is mandatory | +| `SLEEP` | `IDLE` | `consolidation.finished` | memory promotions and cleanup are complete | + +Initial code surfaces: `core/runtime/runtime_modes.py`, `core/runtime/recovery_playbook.py`, and `core/runtime/long_run_supervisor.py`. + +## What To Avoid + +- Do not let raw model output become memory without provenance. +- Do not let the planner execute tools directly. +- Do not optimize for multi-agent debate before deterministic verifier gates exist. +- Do not treat benchmark success as permission for unbounded online learning. +- Do not hide policy decisions in logs that cannot be retrieved by future planning. + +## Final Combined Insight + +The strongest architecture is not a bigger prompt or a bigger model. It is an auditable cognitive transaction system: every belief, plan, permission, action, and learning update has provenance, a lifecycle, and a rollback story. That is the practical bridge from today's agent runtime to an AGI-direction control plane that can improve without losing governance. diff --git a/research/ai_generated_agi_architectures/validation_report.md b/research/ai_generated_agi_architectures/validation_report.md new file mode 100644 index 0000000..2570ad2 --- /dev/null +++ b/research/ai_generated_agi_architectures/validation_report.md @@ -0,0 +1,97 @@ +# Validation Report + +Collection date: 2026-05-27 + +This report maps the issue acceptance criteria to the submitted packet and records local verification results for reviewers. + +## Acceptance Criteria Mapping + +| Requirement | Evidence in this packet | +| --- | --- | +| At least 8 model/system outputs | 10 model-family outputs in `raw_outputs/` and 10 entries in `collection_manifest.json`. | +| Outputs clearly attributed | Every raw file includes provider/tool, model ID, access date, UTC batch timestamp, response ID, token usage, and human-edit statement. `collection_manifest.json` adds per-response `response_created_utc` timestamps. | +| Raw outputs separate from analysis | Raw outputs are stored only in `raw_outputs/`; analysis is in `comparison.csv`, `summary.md`, and `synthesis.md`. | +| Structured comparison table | `comparison.csv` has 10 rows across the requested architecture dimensions; `comparison_by_dimension.csv` adds a dimension-first companion view. | +| Concrete synthesis | `synthesis.md` defines an event ledger, state store, memory manager, planner, policy/lease engine, verifier gate, executor, runtime modes, a 90-day implementation plan, SQL/JSON schemas, and a verifier interface. | +| No private keys/tokens/screenshots/private content | No screenshots or private prompts are included; credential-pattern scan returned no matches. | +| No fabricated sources | `collection_manifest.json` and the raw files record provider-returned response IDs and usage metadata for each collected output. The manifest also records public OpenRouter model-catalog verification from 2026-05-27T04:18:00+00:00. | +| Traceability from analysis to raw outputs | `evidence_matrix.md` maps every comparison dimension and synthesis component back to raw-output sections and files. | + +## Manifest Traceability Fields + +- `prompt_sha256`: SHA-256 of the exact user prompt recorded in `collection_manifest.json`. +- `model_catalog_source`: public OpenRouter models endpoint used for model ID verification. +- `model_catalog_verified_at_utc`: timestamp for public model catalog verification. +- `response_created_utc`: per-response timestamp decoded from the returned OpenRouter response ID. +- `raw_sha256`: SHA-256 of the preserved raw-output file. +- `raw_bytes`: byte count of the preserved raw-output file. +- `raw_word_count`: word count of the model output body after the metadata wrapper. + +## Local Verification Results + +Manifest and file-count consistency: + +```text +manifest_models 10 +raw_files 10 +comparison_rows 10 +missing_raw_refs [] +extra_raw_files [] +comparison_models +OpenAI GPT +Claude +Gemini +Grok +DeepSeek +Qwen +Llama +Mistral +Perplexity Sonar +Cohere Command +``` + +Repository boundary check: + +```text +python3 scripts/check_conos_repo_layout.py + +Repository root: /Users/husky/new-new-revenue/external/Cognitive-OS +Logical layers: +- conos-core: core/, decision/, evolution/, memory/, modules/, planner/, self_model/, state/, trace/ +- adapter: integrations/local_machine/ +- private-cognitive-core: core/orchestration/structured_answer.py, modules/hypothesis/mechanism_posterior_updater.py +- runtime: runtime/, audit/, reports/ +No forbidden public/private boundary imports detected. +Adapter registry points only to adapter-layer paths. +``` + +Whitespace check: + +```text +git diff --check +``` + +Result: passed with no output. + +Credential-pattern scan: + +```text +grep -RInE '(sk-[A-Za-z0-9_-]{20,}|ghp_[A-Za-z0-9_]{20,}|github_pat_[A-Za-z0-9_]{20,}|re_[A-Za-z0-9_]{20,}|xai-[A-Za-z0-9_-]{20,}|AKIA[0-9A-Z]{16}|hf_[A-Za-z0-9]{20,}|pplx-[A-Za-z0-9]{20,})' research/ai_generated_agi_architectures +``` + +Result: no matches. + +## Raw Output SHA-256 Hashes + +| Raw output file | SHA-256 | +| --- | --- | +| `raw_outputs/anthropic_claude_sonnet_46.md` | `2ae3a4a9cb4f18fd302b6d52f099a92ee717b8c815988b776a20076542561a64` | +| `raw_outputs/cohere_command_a.md` | `aa41ddbcdeaaa9c051f10d9a798d7c59ca51e9931e87fce47d3497f2805432ff` | +| `raw_outputs/deepseek_v32.md` | `22d9806fe56ac4508b5698934aa720919896f5fc6c453a95c1a7a1847491c510` | +| `raw_outputs/google_gemini_31_pro.md` | `3dc26b7d116c323c592d00559606c384479dbd8cd8851ac196e80eeaeaa80947` | +| `raw_outputs/meta_llama4_maverick.md` | `fec0b1e847e1bbaadd36d0239f8be3acb6fa7ef35b7d5fa249054c151aa6b3eb` | +| `raw_outputs/mistral_large_2512.md` | `5d6022c2ad6b669d7ad7554e039cef6c887bc9bc1b929d0fc86b7129b2282925` | +| `raw_outputs/openai_gpt54_mini.md` | `e6e69aa5ab8fae53373a1e73953a93366d349fdb952a1cc3d333ed9c0bb32335` | +| `raw_outputs/perplexity_sonar_pro.md` | `6f429cf807918e2c464ef498e2fa5272683e305ca5af02a4cc829f202bef6c7c` | +| `raw_outputs/qwen37_max.md` | `fce5eedea72813c5402b78618035730246aa35f6d8ff6a6519ca03e5d815ead4` | +| `raw_outputs/xai_grok_43.md` | `303633da1c12385fff05584169c4bb4a1c512c436638bc52ea9391da7b20fcd1` |