diff --git a/research/ai_generated_agi_architectures/.gitattributes b/research/ai_generated_agi_architectures/.gitattributes
new file mode 100644
index 0000000..c11b805
--- /dev/null
+++ b/research/ai_generated_agi_architectures/.gitattributes
@@ -0,0 +1,2 @@
+# Preserve recorded request/raw bytes and their hashes across checkout platforms.
+* -text whitespace=cr-at-eol
diff --git a/research/ai_generated_agi_architectures/.gitignore b/research/ai_generated_agi_architectures/.gitignore
new file mode 100644
index 0000000..afaf428
--- /dev/null
+++ b/research/ai_generated_agi_architectures/.gitignore
@@ -0,0 +1,2 @@
+__pycache__/
+collection/sources/
diff --git a/research/ai_generated_agi_architectures/README.md b/research/ai_generated_agi_architectures/README.md
new file mode 100644
index 0000000..aa247e1
--- /dev/null
+++ b/research/ai_generated_agi_architectures/README.md
@@ -0,0 +1,37 @@
+# Local model architecture proposals: an auditable constraint study
+
+This packet addresses [issue #5](https://github.com/aLexzzz430/Cognitive-OS/issues/5) by collecting actual proposals from eight named open-weight instruction/chat models on one 12 GB workstation. It studies what compact local systems propose under Cognitive-OS constraints; it is **not a comparison of eight frontier services, eight independent lineages, or demonstrated AGI systems**.
+
+The useful result is a set of implementation decisions grounded in the proposals' omissions and contradictions. A section labelled “recovery” does not define recoverable file effects; a section labelled “governance” does not define authority. Several models repeat the prompt's SQLite/verifier concepts, some introduce incompatible cloud or distributed infrastructure, and TinyLlama invents empirical performance numbers. Those claims remain in the raw evidence and are explicitly rejected in the analysis.
+
+## Read the packet
+
+- [prompts.md](prompts.md): exact common prompt, serialization and generation protocol.
+- [sources.md](sources.md): pinned models, attribution, dates, licenses, local environment and edits.
+- [raw_outputs/](raw_outputs/): unedited decoded generations, including termination tokens.
+- [comparison.csv](comparison.csv): eight systems × eleven dimensions, with a verbatim quote, source line and analyst assessment in every row.
+- [summary.md](summary.md): agreements, disagreements, failures and limits of inference.
+- [synthesis.md](synthesis.md): a bounded local architecture and integration decisions tied to the current repository.
+- [collection/runs/](collection/runs/): exact requests, rendered prompts, input/output token IDs, generation settings, timestamps, hashes and runtime versions.
+- [recovery_experiment.py](recovery_experiment.py): an isolated, executable process-crash example; it does not exercise the actual Cognitive-OS runtime.
+
+## Method and evidential limits
+
+Collection uses one frozen user prompt per model, its own chat template, greedy decoding, BF16, eager attention, batch one and a 1,400-new-token cap. The requested length is at most 850 words, but equal token budgets do not imply equal word budgets. A length-capped answer is retained rather than extended or replaced. Completed model outputs are never regenerated to choose a better answer. Failed attempts are documented in [collection/incidents.md](collection/incidents.md).
+
+The sample is selected for public access, local hardware and license compatibility. Its small size, shared architecture ancestry, one prompt, one output per model and lack of blinding do not support statistical rankings or broad claims about model families. “SQLite consensus” is especially confounded: SQLite, journaling, governed tools and verifier-gated patches were explicitly supplied in the prompt. Instruction-following failures are observations for these exact runs, not universal model properties.
+
+The collection records support inspection and local reproduction, not independent proof from a model provider. Hashes establish correspondence between recorded files; they do not establish the truth of a model's assertions or prove that another device will emit identical tokens. No hidden prompts, account tokens, paid-service screenshots or model weights are included. Research analysis and scripts are separate from the raw generations attributed to the named models.
+
+## Recheck
+
+From the repository root:
+
+```bash
+python research/ai_generated_agi_architectures/verify_packet.py
+python research/ai_generated_agi_architectures/recovery_experiment.py
+python scripts/check_conos_repo_layout.py
+pytest -q tests/test_public_repo_smoke.py
+```
+
+The repository's public smoke tests require a Unix environment because existing runtime code imports `resource`. The required Linux checks are recorded separately from the isolated experiment. The packet makes no claim that all repository tests, production crash recovery or AGI capabilities have been validated. See [validation.md](validation.md) for exact completed checks and their scope.
diff --git a/research/ai_generated_agi_architectures/collection/analysis.json b/research/ai_generated_agi_architectures/collection/analysis.json
new file mode 100644
index 0000000..96f0712
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/analysis.json
@@ -0,0 +1,106 @@
+{
+  "spark": [
+    ["a 2 GB persistent state store", "Reject the GPU persistence claim: volatile VRAM is not a durable SQLite store; use disk transactions and budget model memory separately."],
+    ["a high-level planning module generates a sequence of actions", "Adapt the planner/executor separation; add explicit preconditions and verifier evidence because looping over task state does not itself detect deadlocks."],
+    ["Only approved changes are applied", "Retain the separation between proposals and authorized changes; bind approval to an immutable candidate and current policy version."],
+    ["Each patch is validated against the SQLite task state", "Adapt with actual file hashes and permission checks at execution; database state alone may be stale relative to the filesystem."],
+    ["unified, immutable world representation stored in SQLite", "Adapt as append-only observations plus a mutable projection; observed state is incomplete and cannot make arbitrary external actions reversible."],
+    ["any deviations trigger immediate halting", "Specify fail-closed behavior for concrete invariant violations; anomaly detection is not a proof of permission enforcement."],
+    ["falsifiable, deterministic tests", "Retain deterministic fault injection for recovery, but add exact crash boundaries and measurable invariants; no evaluation results were actually collected by this model."],
+    ["On crash, the runtime automatically snapshots", "Reject as a hard-crash guarantee: the process may execute no cleanup at all. Persist intent before the effect and reconcile after restart."],
+    ["managing a queue of agents", "Adapt into one bounded worker queue initially; no lock, lease or conflict protocol is specified for parallel execution."],
+    ["highly feasible within the local-first constraints", "Unsupported overall feasibility: the proposed VRAM/SQLite coupling is invalid and the 8 GB allocation ignores model and KV-cache requirements."],
+    ["all cognitive actions are traceable and reversible", "Reject universal reversibility. Traceability can be an engineering invariant for logged actions; reversibility depends on the tool and external system."]
+  ],
+  "qwen": [
+    ["SQLite for persistent storage of task states and event logs", "Retain as a modest starting point already supplied by the prompt; define transactions and distinguish journal durability from external file durability."],
+    ["heuristic-based approach to select the next action", "Useful simpler baseline; add a bounded action budget and deterministic validation. There is no specified heuristic or demonstrated reasoning capability."],
+    ["rewards for correct actions and penalties for incorrect ones", "Defer online RL until reward integrity, offline evaluation and permission invariance are specified; correctness cannot be taken from model self-report."],
+    ["simple command-line interface for tool actions", "A CLI does not ensure traceability or verification. Require typed actions, captured evidence and a trusted executor independent of model text."],
+    ["the self as a set of capabilities", "Adapt to an explicit capability registry with versioned permissions; avoid assuming the rule world represents all external state."],
+    ["simple rule-based system", "Adapt only with concrete scope checks and deny cases; no authority hierarchy or conflict resolution is defined."],
+    ["comparing its performance to a baseline system", "Underspecified: no task distribution, metrics, thresholds or actual experiment. The separate request for a falsifiable worked comparison is not fulfilled."],
+    ["a simple crash recovery mechanism", "Underspecified: no commit ordering, deduplication or file reconciliation. A database transaction alone does not cover a separate file edit."],
+    ["agents can communicate and coordinate their actions", "Defer: no communication schema, ownership or resource budget. A single executor is the lower-complexity baseline."],
+    ["Use Python for its simplicity and extensive libraries", "Python is compatible with the repository, but this is not a sizing or dependency analysis for a 12 GB workstation."],
+    ["simple, rule-based system for world and self representation", "No demonstrated novelty; keep simplicity as a baseline design preference, not an original cognitive architecture finding."]
+  ],
+  "phi": [
+    ["a compact trie, for storing SQLite task states", "Defer the trie: no key distribution, encoding or integration is defined; measure ordinary indexed SQLite before adding a second representation."],
+    ["heuristics to generate action sequences", "Adapt into a bounded planner returning typed candidates; rules and heuristics are implementable but no algorithm or quality evidence is given."],
+    ["reward signals from task completion and error rates", "Use these initially for offline reports, not automatic policy updates; distinguish verified success from a model claim."],
+    ["modular action execution framework", "Adapt to existing execution tickets and evidence records; modularity alone supplies neither authorization nor idempotency."],
+    ["nodes representing entities and edges representing relationships", "A possible later materialized graph over evidence; retain timestamps and provenance and justify its value over the existing state store."],
+    ["checks patches against a set of predefined safety rules", "Adapt with source/result hashes and current authorization checks. The output provides no rule implementation or completeness proof."],
+    ["task completion, error rates, and resource utilization", "Retain measurable outcomes; add fixed workloads and rejection thresholds. The proposed flat-file baseline comparison has no numerical pass criterion."],
+    ["a journaling system that logs changes to enable crash recovery", "The TaskState schema and worked example omit the file-write boundary and intent/result hashes; enrich them before claiming safe replay."],
+    ["using message passing for communication", "Defer distributed coordination until a bounded single-process queue is measured; delivery semantics and resource limits are missing."],
+    ["focusing on modularity and reusability", "Plausible incremental approach, but its later Implemented Components list is unsupported by this collection; proposals are not repository verification."],
+    ["a lightweight, in-memory caching layer", "Adapt only for reconstructible observations with versioned invalidation. This is a familiar optimization, not established novelty or a durability mechanism."]
+  ],
+  "smollm": [
+    ["a fast, low-latency cache and a slower, more durable storage system", "Retain the volatile/durable distinction, but sizes, eviction and authoritative data ownership are unspecified."],
+    ["perception, reasoning, and action", "Too generic for implementation: no state machine, failure bound or executor interface. Sensor processing also expands beyond the supplied file-runtime context."],
+    ["self-supervised learning", "Defer: no objective, training data, acceptance gate or permission constraint mechanism is defined."],
+    ["action abstraction", "No typed action schema or validation boundary is provided; natural-language understanding is not a substitute for executable permission checks."],
+    ["understand its own internal state and make decisions", "No representation or evidence source is specified; cannot derive a world/self data model from this assertion."],
+    ["within the constraints of the user's permissions", "Desirable outcome without an enforcement mechanism; require a trusted rule evaluator outside learned text generation."],
+    ["a technique called \"benchmarking\"", "Tautological description without metrics, baseline or falsification rule; it supplies no benchmark evidence."],
+    ["machine learning algorithms and natural language processing to persist its state", "Reject as a persistence mechanism: storage ordering and durable commits need explicit systems design, not unspecified NLP."],
+    ["a technique called \"orchestration\"", "No scheduling, messaging or conflict semantics; adds a label rather than an implementable coordination design."],
+    ["a technique called \"feasibility analysis\"", "No hardware sizing, runtime dependencies or measured constraints are provided, so feasibility is not established."],
+    ["a technique called \"insight generation\"", "No concrete insight is delivered. The length-capped output repeats its limitation and ends before its worked example."]
+  ],
+  "granite": [
+    ["in-memory SQLite database for task state and event journaling", "Reject this as the sole durable store; the output itself admits loss on crash and later promises persistent replay without specifying durable journal storage."],
+    ["generating potential patches, validating them", "Adapt the candidate/verifier loop with bounded retries; validity is not equivalent to task usefulness or a complete reasoning algorithm."],
+    ["a reward-based system to adjust its decision-making process", "Defer online updates until offline acceptance and independent outcome verification exist; no learning algorithm or permission invariance is specified."],
+    ["authorized by a centralized authority before execution", "Retain a single authority boundary for effects, with explicit capability checks and revocation semantics rather than negotiation over permission."],
+    ["using a graph-based data structure", "Optional projection over attributed observations; no schema, freshness rule or grounding test is given."],
+    ["rule-based access control, a verifier for file patches", "Adapt to existing gates and test their composition; crash recovery is operational safety and cannot replace authorization."],
+    ["consistency of the final file state", "Retain final-state consistency as an observable; replace vague more efficiently and accurately with exact hashes and fault-injection pass criteria."],
+    ["In case of a crash, the event journal records the current state", "Reject crash-time logging as a guarantee. Commit intent before effects and reconcile the filesystem before replaying pending patches."],
+    ["decentralized, negotiation-based approach", "Defer: conflicts with a simple centralized authority design unless delegation is clearly bounded; no negotiation protocol or resource cap is specified."],
+    ["incremental development and testing of individual components", "Retain incremental integration, but the claim that components are implemented is unverified and the volatile-persistence conflict must be resolved."],
+    ["a \"verifier-gated\" approach for file patches", "This was explicitly supplied by the prompt, so it is not independent novelty; reject treating unspecified risky actions as an exception to permissions."]
+  ],
+  "falcon": [
+    ["on-disk SQLite for task state persistence", "Retain disk persistence and separate transient GPU computation; the scalable pool still lacks a model/KV memory budget."],
+    ["a hierarchical reasoning engine", "Underspecified hierarchy and algorithms; implement a small bounded candidate loop before adding learning-driven planning."],
+    ["continuously refine tool usage and action execution", "Defer automatic RL: no reward trust, rollback, training budget or permission-expansion safeguard is supplied."],
+    ["a modular tool library with customizable APIs", "Adapt to a typed allowlisted interface; customizable APIs require stable contracts and independent authorization."],
+    ["sensor data and AI-generated insights", "Avoid treating generated insights as observations; provenance, freshness and confidence need explicit fields. External APIs are also unavailable in the prompt."],
+    ["fail-safe mechanisms and ethical guidelines", "Too vague to implement as governance; turn this into concrete deny rules and verifier evidence."],
+    ["task completion rates, error detection rates, and user satisfaction surveys", "Measurable categories but no workload, baseline, thresholds or actual results; add deterministic failure cases before subjective surveys."],
+    ["a checkpointing mechanism to ensure data integrity", "Checkpoint boundaries and file/SQLite atomicity are absent; the requested crash walkthrough was not produced."],
+    ["each agent specializes in specific cognitive tasks", "Potential later specialization; no bounded schedule, shared-state semantics or measured benefit over one worker."],
+    ["Leverage cloud computing resources to scale the system dynamically", "Reject for this study: paid hosted resources are explicitly unavailable and the target is one local workstation."],
+    ["integrating quantum computing elements", "Reject as unsupported scope expansion: no implementable mechanism or evidence of value within the declared hardware budget."]
+  ],
+  "danube": [
+    ["demand-paged memory management system", "A generic memory-management choice without a model/KV budget or clear CPU/GPU distinction; do not treat paging as durable task storage."],
+    ["Integrate probabilistic reasoning to handle uncertain situations", "Adapt only after defining uncertainty/evidence fields and a bounded planner interface; no inference algorithm or useful performance evidence is supplied."],
+    ["based on labeled data", "Defer training until task data, objective, budget and independent acceptance gates are specified; no permission-invariance mechanism is included."],
+    ["Python's standard library and third-party APIs", "Plausible implementation resources, but no typed interface, authorization or recovery contract. Third-party access must fit local availability constraints."],
+    ["Use a state machine to represent the current state", "Retain explicit states as a starting point, then define transitions and evidence; the proposed graph has no schema or freshness semantics."],
+    ["do not violate user permissions", "Retain as a requirement; turn the unspecified verifier and governance policies into concrete deny rules and versioned authority checks."],
+    ["Collect user feedback to evaluate the system's usability", "Useful supplementary metric, but the output supplies no objective thresholds or completed falsifiable comparison; its final experiment section is truncated."],
+    ["restoring the file from a backup", "Underspecified backup age, commit ordering and concurrent-edit handling. A backup restore can lose valid work or repeat an effect; reconcile hashes instead."],
+    ["Use communication protocols to facilitate interaction", "No actual protocol or ownership semantics; defer multiple writers until conflict and resource-budget behavior is defined."],
+    ["Use version control systems to manage changes", "Sound development practice rather than evidence of runtime feasibility; the 12 GB resource constraint remains unsized."],
+    ["Decouple the reasoning and planning loop", "Adapt to a stable candidate/executor interface; decoupling is a familiar engineering pattern and no originality has been established."]
+  ],
+  "tinyllama": [
+    ["each process can access the same amount of memory", "Reject as a GPU allocation guarantee; physical shared capacity does not imply equal safe allocations. Budget weights, KV cache and concurrent work explicitly."],
+    ["first-in, first-out (FIFO) queue", "Retain as a simple scheduler baseline; FIFO alone guarantees neither timely completion nor useful planning or safe parallel execution."],
+    ["trained on a large dataset of planning and reasoning tasks", "Unsupported training-data and implementation claim; treat RL as a proposal with no collected evidence, not a component verified in this repository."],
+    ["RESTful API", "Optional transport only; authentication, action schemas, permission checks and replay semantics are not defined."],
+    ["a simple graph representation", "No schema or grounding rules; consider only a derived visualization after structured evidence is in place."],
+    ["hardware-based cryptography and hardware-based fault tolerance", "Unsupported hardware assumptions and no tool-authorization boundary; not a usable governance specification."],
+    ["a throughput of over 100,000 tasks per second", "Reject invented empirical results: neither this throughput nor the claimed over-90-percent success rate was measured or cited. Preserve as evidence of hallucination."],
+    ["a distributed file system (DFS)", "Reject unnecessary distributed infrastructure for the single-workstation constraint; it still fails to define file-edit transaction recovery."],
+    ["a distributed coordination mechanism", "No protocol or delivery/ownership semantics; distributed complexity is unjustified without measured single-worker limitations."],
+    ["designed to be modular and extensible", "Unsubstantiated feasibility assertion; no dependencies, memory estimate or working implementation is supplied."],
+    ["allowing for easy addition of new functionality", "Generic extensibility statement repeats feasibility rather than providing a non-obvious design idea."]
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/build_comparison.py b/research/ai_generated_agi_architectures/collection/build_comparison.py
new file mode 100644
index 0000000..9c1f235
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/build_comparison.py
@@ -0,0 +1,31 @@
+"""Build a consistent comparison and locate every quoted source in raw output."""
+import csv
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+DIMENSIONS = ["memory", "reasoning_planning", "learning", "tools_actions", "world_self",
+              "safety_governance", "evaluation", "persistence_recovery", "orchestration",
+              "engineering_feasibility", "originality"]
+analysis = json.loads((ROOT / "collection/analysis.json").read_text(encoding="utf-8"))
+models = json.loads((ROOT / "collection/models.json").read_text(encoding="utf-8"))
+rows = []
+for model in models:
+    slug = model["slug"]
+    raw_path = ROOT / f"raw_outputs/{slug}.txt"
+    raw = raw_path.read_text(encoding="utf-8")
+    response = json.loads((ROOT / f"collection/runs/{slug}/response.json").read_text(encoding="utf-8"))
+    entries = analysis[slug]
+    assert len(entries) == len(DIMENSIONS), slug
+    for dimension, (quote, assessment) in zip(DIMENSIONS, entries):
+        assert quote and quote in raw, (slug, dimension, quote)
+        line = raw[:raw.index(quote)].count("\n") + 1
+        rows.append(dict(system=slug, model=model["model_id"], dimension=dimension,
+                         output_quote=quote, analysis=assessment,
+                         evidence=f"raw_outputs/{slug}.txt#L{line}",
+                         termination=response["termination"]))
+with (ROOT / "comparison.csv").open("w", newline="", encoding="utf-8") as stream:
+    writer = csv.DictWriter(stream, fieldnames=list(rows[0]))
+    writer.writeheader()
+    writer.writerows(rows)
+print(f"Wrote {len(rows)} source-checked comparison rows.")
diff --git a/research/ai_generated_agi_architectures/collection/build_sources.py b/research/ai_generated_agi_architectures/collection/build_sources.py
new file mode 100644
index 0000000..69c04e0
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/build_sources.py
@@ -0,0 +1,24 @@
+"""Render attribution directly from successful collection records."""
+import json
+from pathlib import Path
+
+root = Path(__file__).resolve().parent.parent
+models = json.loads((root / "collection/models.json").read_text())
+lines = ["# Sources and attribution", "", "All outputs were generated locally on 2026-09-09 using public Hugging Face model snapshots. Links below pin the model revision rather than a mutable model name. Provider/tool for every execution: local Hugging Face Transformers on the contributor's workstation; the named model organizations did not host these requests.", "", "| System/model | Revision | License in model metadata | UTC collection start | New tokens | Stop |", "|---|---|---|---|---:|---|"]
+for model in models:
+    response = json.loads((root / f"collection/runs/{model['slug']}/response.json").read_text())
+    license_name = "TII Falcon-LLM License 2.0" if model["slug"] == "falcon" else model["license"]
+    lines.append(f"| [{model['model_id']}]({model['source']}) | `{model['revision']}` | {license_name} | {response['started_utc']} | {response['generated_tokens']} | {response['termination']} |")
+lines.extend(["", "## Access, ancestry and licenses", "",
+    "The publishers are XHToken (Spark), Qwen/Alibaba, Microsoft (Phi), Hugging Face (SmolLM), IBM (Granite), Technology Innovation Institute/Falcon-LLM Team, TinyLlama and H2O.ai (Danube). These are eight distinct named projects/checkpoints, not eight proven independent training lineages. TinyLlama's card states it adopts the Llama2 architecture/tokenizer; Danube3 adjusts the Llama2 architecture and uses the Mistral tokenizer. Shared architecture, synthetic training material and training-data overlap limit independence. No frontier-service names are used to label local outputs.", "",
+    "License metadata and public model cards were inspected before use. Falcon is not Apache-2.0: its card links the [TII terms](https://falconllm.tii.ae/falcon-terms-and-conditions.html), including the applicable acceptable-use conditions. The Falcon-LLM Team's [release article](https://huggingface.co/blog/falcon3) is an additional attribution source. The intended use is lawful local architecture research. Model licensing is not a warranty that generated text is correct or unique. We redistribute generated proposals and small collection records, not model weights or the full downloaded model-card archive.", "",
+    "## Runtime and evidence", "",
+    "The responses record Python3.13.5, Windows, an NVIDIA GeForce RTX4070, CUDA12.8, torch2.8.0+cu128 and Transformers4.57.1, plus exact supporting package versions. Each response is the authoritative runtime record. Danube additionally required sentencepiece0.2.2 and protobuf7.36.1 before its successful invocation. The collector remains the same for all eight successful runs; its SHA-256 is recorded in every response. No model was re-run after producing a completed response.", "",
+    "`request.json` stores the exact user message, rendered chat prompt, input IDs and generation settings. `response.json` stores generated token IDs, unedited decoded text, timestamps, stop condition and hashes. `model-files.json` records the actual weight/config/tokenizer/custom-code files used locally. File hashes can establish a match to these records but cannot independently attest execution or validate claims in generated text.", "",
+    "## Edits and byte handling", "",
+    "Raw decoded outputs were not rewritten, corrected, translated, continued or deduplicated. Special tokens remain in raw_outputs; readable.txt removes tokenizer special tokens only. The collector wrote text on Windows, which can serialize newlines as CRLF. Its output_sha256 is over the decoded UTF-8 string (LF), while request_sha256 and the packet manifest are over actual file bytes. verify_packet.py checks both deliberately. Packet .gitattributes prevents Git from silently normalizing preserved evidence. Line references in comparison.csv count text lines after universal-newline decoding.", "",
+    "Analysis, comparison assessments, synthesis and scripts are separate from the eight recorded generations. The raw outputs have no human edits. The model assertions that components are implemented or benchmarks achieved have not been promoted to facts. Failed downloads/tokenizer setup and other incidents are disclosed in collection/incidents.md.", "",
+    "## Repository and external references", "",
+    "- [Original task and acceptance criteria](https://github.com/aLexzzz430/Cognitive-OS/issues/5).", "- [Inspected repository baseline](https://github.com/aLexzzz430/Cognitive-OS/tree/e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca).", "- Source paths supporting integration observations are linked in synthesis.md. Those observations are separate from model output.", "- No proprietary prompts, private API output, credentials, account screenshots or private customer data were used. Existing competing submissions were not used as sources for model outputs or the comparison.", ""])
+(root / "sources.md").write_text("\n".join(lines), encoding="utf-8")
+print("Wrote sources for", len(models), "successful model records")
diff --git a/research/ai_generated_agi_architectures/collection/collect.py b/research/ai_generated_agi_architectures/collection/collect.py
new file mode 100644
index 0000000..fdeb4ab
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/collect.py
@@ -0,0 +1,72 @@
+"""Collect one real local response; never overwrite an existing record."""
+import argparse
+from datetime import datetime, timezone
+import hashlib
+import importlib.metadata
+import json
+import os
+from pathlib import Path
+import platform
+import time
+
+os.environ['HF_HUB_OFFLINE'] = '1'
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, set_seed
+
+ROOT = Path(__file__).resolve().parent
+parser = argparse.ArgumentParser()
+parser.add_argument('--model-dir', type=Path, required=True)
+parser.add_argument('--slug', required=True)
+args = parser.parse_args()
+entry = next(m for m in json.loads((ROOT/'models.json').read_text()) if m['slug'] == args.slug)
+out = ROOT/'runs'/args.slug
+out.mkdir(parents=True, exist_ok=True)
+if (out/'response.json').exists():
+    raise SystemExit('Existing response preserved; no repeat collection.')
+prompt = (ROOT/'prompt.txt').read_text(encoding='utf-8')
+messages = [{'role':'user', 'content':prompt}]
+torch.set_num_threads(4)
+assert torch.cuda.is_available() and torch.cuda.is_bf16_supported()
+set_seed(20260909)
+started_utc = datetime.now(timezone.utc).isoformat()
+trust_code = args.slug == 'spark'
+tokenizer = AutoTokenizer.from_pretrained(args.model_dir, local_files_only=True, trust_remote_code=trust_code)
+rendered = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
+inputs = tokenizer(rendered, add_special_tokens=False, return_tensors='pt').to('cuda')
+print('LOADING', args.slug, 'prompt tokens', inputs.input_ids.shape[-1], flush=True)
+model = AutoModelForCausalLM.from_pretrained(args.model_dir, local_files_only=True, trust_remote_code=trust_code,
+    torch_dtype=torch.bfloat16, attn_implementation='eager', device_map='cuda', use_safetensors=True).eval()
+config = GenerationConfig(max_new_tokens=1400, do_sample=False, use_cache=True,
+    bos_token_id=model.generation_config.bos_token_id, eos_token_id=model.generation_config.eos_token_id,
+    pad_token_id=tokenizer.eos_token_id)
+request = dict(model=entry, messages=messages, rendered_prompt=rendered,
+    input_ids=inputs.input_ids[0].tolist(), generation_config=config.to_dict(),
+    seed=20260909, dtype='bfloat16', attention='eager', enable_thinking=False,
+    prompt_sha256=hashlib.sha256(prompt.encode()).hexdigest())
+(out/'request.json').write_text(json.dumps(request, indent=2, ensure_ascii=False)+'\n', encoding='utf-8')
+torch.cuda.synchronize()
+started = time.perf_counter()
+with torch.inference_mode():
+    result = model.generate(**inputs, generation_config=config, use_model_defaults=False)
+torch.cuda.synchronize()
+ids = result[0, inputs.input_ids.shape[-1]:].tolist()
+raw = tokenizer.decode(ids, skip_special_tokens=False)
+readable = tokenizer.decode(ids, skip_special_tokens=True)
+eos = config.eos_token_id
+eos = [eos] if isinstance(eos, int) else eos or []
+response = dict(output_ids=ids, raw_output=raw, generated_tokens=len(ids),
+    termination='eos' if ids and ids[-1] in eos else 'length',
+    seconds=time.perf_counter()-started,
+    started_utc=started_utc, completed_utc=datetime.now(timezone.utc).isoformat(),
+    model=entry, output_sha256=hashlib.sha256(raw.encode()).hexdigest(),
+    request_sha256=hashlib.sha256((out/'request.json').read_bytes()).hexdigest(),
+    collector_sha256=hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),
+    python=platform.python_version(), platform=platform.system(),
+    gpu=torch.cuda.get_device_name(), cuda=torch.version.cuda,
+    packages={p:importlib.metadata.version(p) for p in ['torch','transformers','accelerate','huggingface-hub','tokenizers','safetensors']})
+(out/'response.json').write_text(json.dumps(response, indent=2, ensure_ascii=False)+'\n', encoding='utf-8')
+raw_dir = ROOT.parent/'raw_outputs'
+raw_dir.mkdir(exist_ok=True)
+(raw_dir/f'{args.slug}.txt').write_text(raw, encoding='utf-8')
+(out/'readable.txt').write_text(readable, encoding='utf-8')
+print('COLLECTED', args.slug, len(ids), response['termination'], flush=True)
diff --git a/research/ai_generated_agi_architectures/collection/download_models.py b/research/ai_generated_agi_architectures/collection/download_models.py
new file mode 100644
index 0000000..a848ac6
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/download_models.py
@@ -0,0 +1,29 @@
+"""Download pinned public model snapshots without remote code execution."""
+import argparse
+import json
+import os
+import time
+from pathlib import Path
+
+os.environ.setdefault('HF_HUB_DISABLE_XET', '1')
+from huggingface_hub import snapshot_download
+
+ROOT = Path(__file__).resolve().parent
+parser = argparse.ArgumentParser()
+parser.add_argument('--model-root', type=Path, required=True)
+parser.add_argument('--slug', required=True)
+args = parser.parse_args()
+entry = next(m for m in json.loads((ROOT/'models.json').read_text()) if m['slug'] == args.slug)
+for attempt in range(1, 4):
+    try:
+        snapshot_download(entry['model_id'], revision=entry['revision'],
+                          local_dir=args.model_root/entry['slug'], max_workers=2,
+                          allow_patterns=['*.json', '*.jinja', '*.txt', '*.model', '*.safetensors', 'LICENSE', 'README.md'] + (['*.py'] if args.slug == 'spark' else []),
+                          token=False)
+        break
+    except Exception as exc:
+        print('DOWNLOAD_ATTEMPT_FAILED', entry['slug'], attempt, type(exc).__name__, flush=True)
+        if attempt == 3:
+            raise
+        time.sleep(5)
+print('SNAPSHOT_READY', entry['slug'], entry['revision'], flush=True)
diff --git a/research/ai_generated_agi_architectures/collection/hash_model_files.py b/research/ai_generated_agi_architectures/collection/hash_model_files.py
new file mode 100644
index 0000000..90b261d
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/hash_model_files.py
@@ -0,0 +1,23 @@
+"""Record exact local model/tokenizer/code files; omit paths outside the model."""
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--slug", required=True)
+parser.add_argument("--model-dir", type=Path, required=True)
+args = parser.parse_args()
+root = Path(__file__).resolve().parent
+model = next(m for m in json.loads((root / "models.json").read_text()) if m["slug"] == args.slug)
+records = []
+for path in sorted(args.model_dir.iterdir()):
+    if not path.is_file() or path.suffix not in (".safetensors", ".json", ".py", ".model", ".jinja"):
+        continue
+    with path.open("rb") as stream:
+        sha = hashlib.file_digest(stream, "sha256").hexdigest()
+    records.append(dict(file=path.name, bytes=path.stat().st_size, sha256=sha))
+assert any(r["file"].endswith(".safetensors") for r in records)
+out = root / "runs" / args.slug / "model-files.json"
+out.write_text(json.dumps(dict(model=model, files=records), indent=2) + "\n", encoding="utf-8")
+print(args.slug, len(records), "model files hashed", flush=True)
diff --git a/research/ai_generated_agi_architectures/collection/incidents.md b/research/ai_generated_agi_architectures/collection/incidents.md
new file mode 100644
index 0000000..1bd7afe
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/incidents.md
@@ -0,0 +1,8 @@
+# Collection incidents
+
+- Before collection, Qwen2.5-3B-Instruct was considered but its research-only license was unsuitable for this paid submission. It was replaced by Apache-2.0 Qwen2.5-1.5B-Instruct before any inference. No 3B weights or outputs were used.
+- The initial Spark invocation failed before generating an output because Transformers merged model-specific defaults into a fresh generation configuration (`top_k=-1` with sampling). The collector now sets `use_model_defaults=False`, preserving the explicitly recorded common generation settings. The failed request is retained in `runs/spark/initial-failed-request.json`. There is no response from that failed invocation.
+- Granite/Falcon downloads initially ended with interrupted HTTP bodies; TinyLlama/Danube initially had network/cache errors. Downloads were resumed at the same pinned revisions. Danube's first weight shard needed a further resume after an incomplete transfer; existing successful model outputs were preserved throughout.
+- Danube's first collection invocation failed while loading its tokenizer, before rendering a prompt or generating any tokens: the runtime lacked `sentencepiece` and `protobuf`. These dependencies were installed before retrying the same model/prompt. No failed response was invented or counted as an output.
+- The first execution of the isolated recovery experiment reached Windows temporary-directory cleanup with an open parent SQLite connection. It failed with WinError32. The script now closes that inspection connection explicitly; the subsequent complete result is preserved separately. This was an experiment harness error, not a demonstrated Cognitive-OS defect.
+- The repository layout check passed locally, but Windows public smoke-test collection failed on the existing Unix-only `resource` import. No tests were skipped to conceal this; the original Linux Python3.10/3.11 workflow was run in the public fork.
diff --git a/research/ai_generated_agi_architectures/collection/models.json b/research/ai_generated_agi_architectures/collection/models.json
new file mode 100644
index 0000000..21d32c0
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/models.json
@@ -0,0 +1,74 @@
+[
+  {
+    "slug": "spark",
+    "model_id": "XHToken/Spark-X2.5-1.7B",
+    "revision": "448e61eb392c00f2c403185c5b56d5e0665bfaab",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/XHToken/Spark-X2.5-1.7B/tree/448e61eb392c00f2c403185c5b56d5e0665bfaab"
+  },
+  {
+    "slug": "qwen",
+    "model_id": "Qwen/Qwen2.5-1.5B-Instruct",
+    "revision": "989aa7980e4cf806f80c7fef2b1adb7bc71aa306",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/989aa7980e4cf806f80c7fef2b1adb7bc71aa306"
+  },
+  {
+    "slug": "phi",
+    "model_id": "microsoft/Phi-3.5-mini-instruct",
+    "revision": "2fe192450127e6a83f7441aef6e3ca586c338b77",
+    "license": "mit",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/microsoft/Phi-3.5-mini-instruct/tree/2fe192450127e6a83f7441aef6e3ca586c338b77"
+  },
+  {
+    "slug": "smollm",
+    "model_id": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    "revision": "31b70e2e869a7173562077fd711b654946d38674",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/tree/31b70e2e869a7173562077fd711b654946d38674"
+  },
+  {
+    "slug": "granite",
+    "model_id": "ibm-granite/granite-3.3-2b-instruct",
+    "revision": "707f574c62054322f6b5b04b6d075f0a8f05e0f0",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/ibm-granite/granite-3.3-2b-instruct/tree/707f574c62054322f6b5b04b6d075f0a8f05e0f0"
+  },
+  {
+    "slug": "falcon",
+    "model_id": "tiiuae/Falcon3-1B-Instruct",
+    "revision": "28ba2251970a01dd1edc7ba7dad2eb71216ccfdf",
+    "license": "other",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/tiiuae/Falcon3-1B-Instruct/tree/28ba2251970a01dd1edc7ba7dad2eb71216ccfdf"
+  },
+  {
+    "slug": "tinyllama",
+    "model_id": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "revision": "fe8a4ea1ffedaf415f4da2f062534de366a451e6",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tree/fe8a4ea1ffedaf415f4da2f062534de366a451e6"
+  },
+  {
+    "slug": "danube",
+    "model_id": "h2oai/h2o-danube3-4b-chat",
+    "revision": "1e5c6fa6620f8bf078958069ab4581cd88e0202c",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/h2oai/h2o-danube3-4b-chat/tree/1e5c6fa6620f8bf078958069ab4581cd88e0202c"
+  }
+]
diff --git a/research/ai_generated_agi_architectures/collection/prompt.txt b/research/ai_generated_agi_architectures/collection/prompt.txt
new file mode 100644
index 0000000..824e6e9
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/prompt.txt
@@ -0,0 +1,16 @@
+Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.
+
+Write a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:
+1. Memory architecture
+2. Reasoning and planning loop
+3. Learning or self-improvement
+4. Tool use and action execution
+5. World and self representation
+6. Safety and governance
+7. Evaluation and benchmarks
+8. Persistence and runtime recovery
+9. Multi-agent orchestration
+10. Engineering feasibility
+11. Non-obvious design insight
+
+Include at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.
diff --git a/research/ai_generated_agi_architectures/collection/runs/danube/model-files.json b/research/ai_generated_agi_architectures/collection/runs/danube/model-files.json
new file mode 100644
index 0000000..cc24c5d
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/danube/model-files.json
@@ -0,0 +1,58 @@
+{
+  "model": {
+    "slug": "danube",
+    "model_id": "h2oai/h2o-danube3-4b-chat",
+    "revision": "1e5c6fa6620f8bf078958069ab4581cd88e0202c",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/h2oai/h2o-danube3-4b-chat/tree/1e5c6fa6620f8bf078958069ab4581cd88e0202c"
+  },
+  "files": [
+    {
+      "file": "config.json",
+      "bytes": 726,
+      "sha256": "706e6aff961156c19d4cde464993ef5cc23168e413b7cd97228c7db50775e903"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 132,
+      "sha256": "3370f4eae1cdb2c33f25c3d92acf6693c75e2a309c59678d871ef2a16fee784c"
+    },
+    {
+      "file": "model-00001-of-00002.safetensors",
+      "bytes": 4964598616,
+      "sha256": "7d2951a9106bb625da7fa6772421fa1945c36df37570e3d11c0b2c5dae7adecb"
+    },
+    {
+      "file": "model-00002-of-00002.safetensors",
+      "bytes": 2959105472,
+      "sha256": "1196d4b78345f6ae97166d72c7fa7b2aa93b6f74866a0f94bcf53a3a18ea4c64"
+    },
+    {
+      "file": "model.safetensors.index.json",
+      "bytes": 18005,
+      "sha256": "ff883dec052c088e59e9007d3ba47e5e86597b23dbd504ad3c9117b90b1569e6"
+    },
+    {
+      "file": "special_tokens_map.json",
+      "bytes": 484,
+      "sha256": "be39bc5918bb022620cbf11c926e1c90fd5f5a010600719bb21650acc48e5661"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 1794652,
+      "sha256": "4c03fed10572eea7d67b312d52d5975afa4b4c8d162696f242b2c08101c4835a"
+    },
+    {
+      "file": "tokenizer.model",
+      "bytes": 493443,
+      "sha256": "dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 1702,
+      "sha256": "ea1ebf0525ab9c132507d2d9629d4c8f3b01e65141ad6524eafbf6724a3d9128"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/danube/readable.txt b/research/ai_generated_agi_architectures/collection/runs/danube/readable.txt
new file mode 100644
index 0000000..55fe331
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/danube/readable.txt
@@ -0,0 +1,124 @@
+### 1. Memory Architecture
+
+**Concrete Mechanism:**
+- **Memory Allocation:** Utilize a demand-paged memory management system to efficiently allocate memory for different components.
+- **Memory Pooling:** Implement a memory pooling mechanism to reuse memory for frequently accessed components.
+
+**Limitations/Tradeoffs:**
+- **Memory Overhead:** Demand-paged memory management can introduce overhead due to page faults.
+- **Memory Leaks:** Memory pooling may lead to memory leaks if not properly managed.
+
+### 2. Reasoning and Planning Loop
+
+**Concrete Mechanism:**
+- **Rule-Based Reasoning:** Implement a rule-based reasoning system to handle simple decision-making tasks.
+- **Probabilistic Reasoning:** Integrate probabilistic reasoning to handle uncertain situations.
+
+**Limitations/Tradeoffs:**
+- **Complexity:** Rule-based systems can become complex and difficult to maintain.
+- **Scalability:** Probabilistic reasoning may not scale well for large datasets.
+
+### 3. Learning or Self-Improvement
+
+**Concrete Mechanism:**
+- **Supervised Learning:** Implement a supervised learning algorithm to improve task performance based on labeled data.
+- **Unsupervised Learning:** Use unsupervised learning techniques to discover patterns in data.
+
+**Limitations/Tradeoffs:**
+- **Data Requirements:** Supervised learning requires a large amount of labeled data.
+- **Interpretability:** Unsupervised learning may lack interpretability.
+
+### 4. Tool Use and Action Execution
+
+**Concrete Mechanism:**
+- **API Integration:** Integrate with Python's standard library and third-party APIs to execute actions.
+- **Custom Tools:** Develop custom tools for specific tasks.
+
+**Limitations/Tradeoffs:**
+- **API Dependency:** API integration can introduce dependencies and potential points of failure.
+- **Custom Tool Development:** Developing custom tools can be time-consuming and resource-intensive.
+
+### 5. World and Self Representation
+
+**Concrete Mechanism:**
+- **State Machine:** Use a state machine to represent the current state of the system.
+- **Graph Representation:** Represent the world as a graph for better understanding and manipulation.
+
+**Limitations/Tradeoffs:**
+- **Complexity:** State machines and graphs can become complex and difficult to manage.
+- **Performance:** Graph representations can be computationally expensive.
+
+### 6. Safety and Governance
+
+**Concrete Mechanism:**
+- **Verifier:** Implement a verifier to ensure that tool actions are safe and do not violate user permissions.
+- **Governance Policies:** Define governance policies to control the execution of tool actions.
+
+**Limitations/Tradeoffs:**
+- **Verification Overhead:** Verification can introduce overhead and delay in action execution.
+- **Policy Enforcement:** Enforcing governance policies can be challenging and may require additional resources.
+
+### 7. Evaluation and Benchmarks
+
+**Concrete Mechanism:**
+- **Performance Benchmarks:** Measure the performance of the system using benchmarks.
+- **User Feedback:** Collect user feedback to evaluate the system's usability and effectiveness.
+
+**Limitations/Tradeoffs:**
+- **Benchmark Accuracy:** Benchmarks may not accurately reflect real-world performance.
+- **User Subjectivity:** User feedback can be subjective and may not be representative of all users.
+
+### 8. Persistence and Runtime Recovery
+
+**Concrete Mechanism:**
+- **File Patches:** Implement file patches to handle file edits and recover from crashes.
+- **Persistent State:** Use a persistent state system to maintain the system's state across restarts.
+
+**Limitations/Tradeoffs:**
+- **File Patches:** File patches can be complex and error-prone.
+- **Persistent State:** Maintaining a persistent state can be resource-intensive.
+
+### 9. Multi-Agent Orchestration
+
+**Concrete Mechanism:**
+- **Agent Coordination:** Implement a coordination mechanism to manage multiple agents.
+- **Communication Protocols:** Use communication protocols to facilitate interaction between agents.
+
+**Limitations/Tradeoffs:**
+- **Complexity:** Multi-agent orchestration can be complex and difficult to manage.
+- **Communication Overhead:** Communication protocols can introduce overhead and delay.
+
+### 10. Engineering Feasibility
+
+**Concrete Mechanism:**
+- **Modular Design:** Design the system using a modular architecture to facilitate development and maintenance.
+- **Version Control:** Use version control systems to manage changes and collaborate on development.
+
+**Limitations/Tradeoffs:**
+- **Modularity:** Modular design can introduce complexity and make debugging difficult.
+- **Version Control:** Version control systems can be complex to manage and may introduce overhead.
+
+### 11. Non-Obvious Design Insight
+
+**Concrete Mechanism:**
+- **Decoupling:** Decouple the reasoning and planning loop from other components to improve flexibility and maintainability.
+- **Flexible Interfaces:** Design flexible interfaces to allow for easy integration of new components and features.
+
+**Limitations/Tradeoffs:**
+- **Decoupling Complexity:** Decoupling can introduce complexity and make debugging difficult.
+- **Flexible Interfaces:** Designing flexible interfaces can be challenging and may require additional effort.
+
+### Worked Example: File-Edit Task Across Crash
+
+**Concrete Mechanism:**
+- **File Editing:** Implement a file-editing task where the user edits a file.
+- **Crash Recovery:** Handle crashes by restoring the file from a backup and resuming the editing task.
+
+**Limitations/Tradeoffs:**
+- **Backup Management:** Managing backups can be complex and resource-intensive.
+- **Recovery Time:** Recovery time can be affected by the size of the file and the frequency of crashes.
+
+### Falsifiable Experiment
+
+**Concrete Mechanism:**
+- **Baseline Comparison:** Compare the proposed architecture with a simpler baseline that does not include AGI-oriented features.
diff --git a/research/ai_generated_agi_architectures/collection/runs/danube/request.json b/research/ai_generated_agi_architectures/collection/runs/danube/request.json
new file mode 100644
index 0000000..c0a3efc
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/danube/request.json
@@ -0,0 +1,407 @@
+{
+  "model": {
+    "slug": "danube",
+    "model_id": "h2oai/h2o-danube3-4b-chat",
+    "revision": "1e5c6fa6620f8bf078958069ab4581cd88e0202c",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/h2oai/h2o-danube3-4b-chat/tree/1e5c6fa6620f8bf078958069ab4581cd88e0202c"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<|prompt|>Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.</s><|answer|>",
+  "input_ids": [
+    28789,
+    28766,
+    14350,
+    447,
+    28766,
+    28767,
+    933,
+    3896,
+    396,
+    4808,
+    522,
+    14778,
+    28737,
+    28733,
+    28113,
+    13811,
+    354,
+    334,
+    3159,
+    2468,
+    28733,
+    3843,
+    28725,
+    1671,
+    20188,
+    369,
+    14778,
+    28737,
+    659,
+    750,
+    12011,
+    28723,
+    14268,
+    28747,
+    456,
+    349,
+    264,
+    1862,
+    28733,
+    4478,
+    21366,
+    16183,
+    395,
+    13208,
+    570,
+    3638,
+    1665,
+    28725,
+    396,
+    1951,
+    7024,
+    28725,
+    22175,
+    3921,
+    6768,
+    28725,
+    1429,
+    3591,
+    28733,
+    28721,
+    601,
+    1729,
+    26961,
+    28725,
+    304,
+    14456,
+    12106,
+    28723,
+    330,
+    2692,
+    771,
+    19849,
+    659,
+    28705,
+    28740,
+    28750,
+    22709,
+    28475,
+    4733,
+    28745,
+    18822,
+    5907,
+    10502,
+    2301,
+    460,
+    521,
+    13952,
+    28723,
+    15424,
+    456,
+    2758,
+    390,
+    264,
+    2621,
+    17656,
+    28725,
+    459,
+    5566,
+    302,
+    16585,
+    28723,
+    13,
+    13,
+    5238,
+    264,
+    3078,
+    864,
+    10067,
+    15297,
+    302,
+    438,
+    1080,
+    28705,
+    28783,
+    28782,
+    28734,
+    3085,
+    28725,
+    1413,
+    1167,
+    19806,
+    1474,
+    286,
+    1335,
+    742,
+    28723,
+    1263,
+    1430,
+    13573,
+    2111,
+    264,
+    12230,
+    14175,
+    304,
+    624,
+    26775,
+    442,
+    6226,
+    1769,
+    28747,
+    13,
+    28740,
+    28723,
+    19153,
+    13811,
+    13,
+    28750,
+    28723,
+    1298,
+    1412,
+    288,
+    304,
+    7394,
+    7870,
+    13,
+    28770,
+    28723,
+    17504,
+    442,
+    1008,
+    28733,
+    321,
+    729,
+    10894,
+    13,
+    28781,
+    28723,
+    12877,
+    938,
+    304,
+    2992,
+    12068,
+    13,
+    28782,
+    28723,
+    3304,
+    304,
+    1008,
+    9283,
+    13,
+    28784,
+    28723,
+    19412,
+    304,
+    2411,
+    617,
+    13,
+    28787,
+    28723,
+    24223,
+    10223,
+    304,
+    11984,
+    17181,
+    13,
+    28783,
+    28723,
+    10407,
+    10070,
+    304,
+    16183,
+    12106,
+    13,
+    28774,
+    28723,
+    18317,
+    28733,
+    14702,
+    442,
+    26844,
+    352,
+    13,
+    28740,
+    28734,
+    28723,
+    17202,
+    25953,
+    3032,
+    13,
+    28740,
+    28740,
+    28723,
+    9187,
+    28733,
+    598,
+    2123,
+    2621,
+    16164,
+    13,
+    13,
+    26438,
+    438,
+    2429,
+    624,
+    9629,
+    1178,
+    12493,
+    442,
+    4971,
+    28725,
+    264,
+    4198,
+    2757,
+    302,
+    264,
+    1729,
+    28733,
+    7128,
+    3638,
+    2673,
+    264,
+    14456,
+    28725,
+    304,
+    264,
+    27958,
+    21386,
+    6878,
+    21400,
+    574,
+    2621,
+    395,
+    264,
+    27013,
+    26605,
+    28723,
+    12443,
+    7473,
+    789,
+    13492,
+    8076,
+    28725,
+    26054,
+    28725,
+    304,
+    521,
+    22634,
+    3332,
+    4224,
+    28723,
+    2378,
+    459,
+    9182,
+    11984,
+    3325,
+    2903,
+    442,
+    5963,
+    697,
+    28723,
+    10339,
+    544,
+    9354,
+    6768,
+    2373,
+    11804,
+    2188,
+    9774,
+    28745,
+    5168,
+    1580,
+    459,
+    24435,
+    6710,
+    1395,
+    9774,
+    28723,
+    2,
+    28789,
+    28766,
+    24115,
+    28766,
+    28767
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 2,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/danube/response.json b/research/ai_generated_agi_architectures/collection/runs/danube/response.json
new file mode 100644
index 0000000..7a6b864
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/danube/response.json
@@ -0,0 +1,1434 @@
+{
+  "output_ids": [
+    27332,
+    28705,
+    28740,
+    28723,
+    19153,
+    19608,
+    482,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    9820,
+    1682,
+    10001,
+    4049,
+    16157,
+    653,
+    264,
+    5637,
+    28733,
+    28720,
+    2569,
+    4733,
+    5411,
+    1587,
+    298,
+    23463,
+    21637,
+    4733,
+    354,
+    1581,
+    8076,
+    28723,
+    13,
+    28733,
+    619,
+    9820,
+    23702,
+    288,
+    4049,
+    26256,
+    264,
+    4733,
+    6313,
+    288,
+    14175,
+    298,
+    312,
+    1730,
+    4733,
+    354,
+    11220,
+    24807,
+    8076,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    9820,
+    5235,
+    1811,
+    4049,
+    4267,
+    391,
+    28733,
+    28720,
+    2569,
+    4733,
+    5411,
+    541,
+    13097,
+    20679,
+    2940,
+    298,
+    2884,
+    9683,
+    28713,
+    28723,
+    13,
+    28733,
+    619,
+    9820,
+    1337,
+    9140,
+    4049,
+    19153,
+    6313,
+    288,
+    993,
+    1736,
+    298,
+    4733,
+    462,
+    9140,
+    513,
+    459,
+    9222,
+    6900,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28750,
+    28723,
+    1298,
+    1412,
+    288,
+    304,
+    23542,
+    7300,
+    410,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    7693,
+    28733,
+    24207,
+    1298,
+    1412,
+    288,
+    4049,
+    26256,
+    264,
+    5918,
+    28733,
+    5527,
+    24685,
+    1587,
+    298,
+    4269,
+    3588,
+    5161,
+    28733,
+    14727,
+    9796,
+    28723,
+    13,
+    28733,
+    619,
+    933,
+    28726,
+    6572,
+    3320,
+    1298,
+    1412,
+    288,
+    4049,
+    18208,
+    380,
+    2347,
+    6572,
+    3320,
+    24685,
+    298,
+    4269,
+    12518,
+    11846,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    28274,
+    472,
+    4049,
+    19712,
+    28733,
+    5527,
+    4918,
+    541,
+    2727,
+    4630,
+    304,
+    3796,
+    298,
+    9087,
+    28723,
+    13,
+    28733,
+    619,
+    12350,
+    2437,
+    4049,
+    1133,
+    28726,
+    6572,
+    3320,
+    24685,
+    993,
+    459,
+    5657,
+    1162,
+    354,
+    2475,
+    26882,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28770,
+    28723,
+    17504,
+    442,
+    8916,
+    28733,
+    2012,
+    729,
+    10894,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    15503,
+    23313,
+    17504,
+    4049,
+    26256,
+    264,
+    2195,
+    23313,
+    5168,
+    9464,
+    298,
+    4916,
+    3638,
+    4397,
+    2818,
+    356,
+    25430,
+    1178,
+    28723,
+    13,
+    28733,
+    619,
+    1733,
+    8554,
+    23313,
+    17504,
+    4049,
+    5938,
+    521,
+    8554,
+    23313,
+    5168,
+    9804,
+    298,
+    5191,
+    11533,
+    297,
+    1178,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    1333,
+    7251,
+    536,
+    1339,
+    4049,
+    6077,
+    23313,
+    5168,
+    6948,
+    264,
+    2475,
+    3558,
+    302,
+    25430,
+    1178,
+    28723,
+    13,
+    28733,
+    619,
+    3355,
+    5520,
+    2437,
+    4049,
+    935,
+    8554,
+    23313,
+    5168,
+    993,
+    5502,
+    7190,
+    2437,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28781,
+    28723,
+    12877,
+    5938,
+    304,
+    9624,
+    11663,
+    296,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    6688,
+    18208,
+    352,
+    4049,
+    18208,
+    380,
+    395,
+    21366,
+    28742,
+    28713,
+    4787,
+    7607,
+    304,
+    4008,
+    28733,
+    14999,
+    10502,
+    2301,
+    298,
+    12083,
+    6768,
+    28723,
+    13,
+    28733,
+    619,
+    7219,
+    26258,
+    4049,
+    8587,
+    2460,
+    7040,
+    354,
+    2948,
+    9796,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    6688,
+    3995,
+    416,
+    2090,
+    4049,
+    7086,
+    14678,
+    541,
+    13097,
+    24940,
+    304,
+    4628,
+    3569,
+    302,
+    7719,
+    28723,
+    13,
+    28733,
+    619,
+    7219,
+    12877,
+    10340,
+    4049,
+    8587,
+    288,
+    2460,
+    7040,
+    541,
+    347,
+    727,
+    28733,
+    17125,
+    288,
+    304,
+    3715,
+    28733,
+    501,
+    4097,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28782,
+    28723,
+    3304,
+    304,
+    8916,
+    3357,
+    15918,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    2028,
+    13253,
+    4049,
+    5938,
+    264,
+    1665,
+    5599,
+    298,
+    2904,
+    272,
+    1868,
+    1665,
+    302,
+    272,
+    1587,
+    28723,
+    13,
+    28733,
+    619,
+    8380,
+    3357,
+    15918,
+    4049,
+    17891,
+    272,
+    1526,
+    390,
+    264,
+    5246,
+    354,
+    1873,
+    6399,
+    304,
+    13313,
+    2677,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    28274,
+    472,
+    4049,
+    3885,
+    12155,
+    304,
+    23411,
+    541,
+    2727,
+    4630,
+    304,
+    3796,
+    298,
+    8594,
+    28723,
+    13,
+    28733,
+    619,
+    4712,
+    11778,
+    4049,
+    15146,
+    23384,
+    541,
+    347,
+    21728,
+    578,
+    9212,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28784,
+    28723,
+    19412,
+    304,
+    6696,
+    617,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    5760,
+    3591,
+    4049,
+    26256,
+    264,
+    1429,
+    3591,
+    298,
+    5407,
+    369,
+    3921,
+    6768,
+    460,
+    5023,
+    304,
+    511,
+    459,
+    4107,
+    380,
+    2188,
+    9774,
+    28723,
+    13,
+    28733,
+    619,
+    28777,
+    5794,
+    617,
+    2538,
+    22966,
+    4049,
+    27350,
+    2411,
+    617,
+    10086,
+    298,
+    2602,
+    272,
+    12068,
+    302,
+    3921,
+    6768,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    5760,
+    2500,
+    5235,
+    1811,
+    4049,
+    2927,
+    2500,
+    541,
+    13097,
+    20679,
+    304,
+    8579,
+    297,
+    2992,
+    12068,
+    28723,
+    13,
+    28733,
+    619,
+    7755,
+    1618,
+    5712,
+    466,
+    4049,
+    1618,
+    1392,
+    4669,
+    2411,
+    617,
+    10086,
+    541,
+    347,
+    14361,
+    304,
+    993,
+    2699,
+    4870,
+    5823,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28787,
+    28723,
+    24223,
+    10223,
+    304,
+    4121,
+    338,
+    17181,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    4712,
+    11778,
+    4121,
+    338,
+    17181,
+    4049,
+    2597,
+    3275,
+    272,
+    4397,
+    302,
+    272,
+    1587,
+    1413,
+    11984,
+    17181,
+    28723,
+    13,
+    28733,
+    619,
+    730,
+    4615,
+    286,
+    1435,
+    4049,
+    26272,
+    2188,
+    12139,
+    298,
+    15627,
+    272,
+    1587,
+    28742,
+    28713,
+    592,
+    2437,
+    304,
+    23798,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    18113,
+    338,
+    3325,
+    4035,
+    324,
+    2426,
+    4049,
+    4121,
+    338,
+    17181,
+    993,
+    459,
+    24329,
+    7967,
+    1353,
+    28733,
+    9471,
+    4397,
+    28723,
+    13,
+    28733,
+    619,
+    730,
+    25558,
+    2574,
+    4049,
+    1247,
+    12139,
+    541,
+    347,
+    3817,
+    495,
+    304,
+    993,
+    459,
+    347,
+    15312,
+    302,
+    544,
+    5443,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28783,
+    28723,
+    10407,
+    10070,
+    304,
+    14046,
+    3523,
+    9580,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    2171,
+    4186,
+    1927,
+    4049,
+    26256,
+    1729,
+    26961,
+    298,
+    4269,
+    1729,
+    1382,
+    1046,
+    304,
+    9443,
+    477,
+    1439,
+    14650,
+    28723,
+    13,
+    28733,
+    619,
+    13603,
+    8441,
+    3885,
+    4049,
+    5938,
+    264,
+    24777,
+    1665,
+    1587,
+    298,
+    9087,
+    272,
+    1587,
+    28742,
+    28713,
+    1665,
+    2673,
+    1846,
+    8060,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    2171,
+    4186,
+    1927,
+    4049,
+    6184,
+    26961,
+    541,
+    347,
+    4630,
+    304,
+    2118,
+    28733,
+    783,
+    538,
+    28723,
+    13,
+    28733,
+    619,
+    13603,
+    8441,
+    3885,
+    4049,
+    351,
+    1690,
+    1781,
+    264,
+    24777,
+    1665,
+    541,
+    347,
+    3715,
+    28733,
+    501,
+    4097,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28774,
+    28723,
+    18317,
+    28733,
+    14829,
+    1667,
+    26844,
+    352,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    14829,
+    23184,
+    2235,
+    4049,
+    26256,
+    264,
+    6618,
+    2235,
+    14175,
+    298,
+    8594,
+    5166,
+    11767,
+    28723,
+    13,
+    28733,
+    619,
+    3912,
+    370,
+    3696,
+    28564,
+    28713,
+    4049,
+    5938,
+    8520,
+    10189,
+    28713,
+    298,
+    25729,
+    11186,
+    1444,
+    11767,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    28274,
+    472,
+    4049,
+    18317,
+    28733,
+    14702,
+    442,
+    26844,
+    352,
+    541,
+    347,
+    4630,
+    304,
+    3796,
+    298,
+    8594,
+    28723,
+    13,
+    28733,
+    619,
+    3912,
+    370,
+    3696,
+    5235,
+    1811,
+    4049,
+    5795,
+    3696,
+    10189,
+    28713,
+    541,
+    13097,
+    20679,
+    304,
+    8579,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28740,
+    28734,
+    28723,
+    17202,
+    4615,
+    293,
+    3032,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    1701,
+    1098,
+    8648,
+    4049,
+    8648,
+    272,
+    1587,
+    1413,
+    264,
+    968,
+    1098,
+    13811,
+    298,
+    25729,
+    4099,
+    304,
+    12364,
+    28723,
+    13,
+    28733,
+    619,
+    4841,
+    10336,
+    4049,
+    5938,
+    2751,
+    2602,
+    4918,
+    298,
+    8594,
+    4435,
+    304,
+    8248,
+    380,
+    356,
+    4099,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    1701,
+    1098,
+    472,
+    4049,
+    3813,
+    1098,
+    2621,
+    541,
+    13097,
+    17599,
+    304,
+    1038,
+    10183,
+    3080,
+    3796,
+    28723,
+    13,
+    28733,
+    619,
+    4841,
+    10336,
+    4049,
+    7155,
+    2602,
+    4918,
+    541,
+    347,
+    4630,
+    298,
+    8594,
+    304,
+    993,
+    13097,
+    20679,
+    28723,
+    13,
+    13,
+    27332,
+    28705,
+    28740,
+    28740,
+    28723,
+    9187,
+    28733,
+    1445,
+    2123,
+    8648,
+    9513,
+    454,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    6039,
+    280,
+    8182,
+    4049,
+    6712,
+    280,
+    792,
+    272,
+    24685,
+    304,
+    7394,
+    7870,
+    477,
+    799,
+    8076,
+    298,
+    4916,
+    22407,
+    304,
+    9087,
+    2437,
+    28723,
+    13,
+    28733,
+    619,
+    28765,
+    1913,
+    1070,
+    4287,
+    9288,
+    4049,
+    8648,
+    17574,
+    791,
+    9288,
+    298,
+    1914,
+    354,
+    3411,
+    14678,
+    302,
+    633,
+    8076,
+    304,
+    4190,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    6039,
+    280,
+    8182,
+    26967,
+    472,
+    4049,
+    6712,
+    280,
+    8182,
+    541,
+    13097,
+    17599,
+    304,
+    1038,
+    10183,
+    3080,
+    3796,
+    28723,
+    13,
+    28733,
+    619,
+    28765,
+    1913,
+    1070,
+    4287,
+    9288,
+    4049,
+    8648,
+    288,
+    17574,
+    791,
+    9288,
+    541,
+    347,
+    14361,
+    304,
+    993,
+    2699,
+    4870,
+    4261,
+    28723,
+    13,
+    13,
+    27332,
+    5066,
+    286,
+    16693,
+    28747,
+    6184,
+    28733,
+    8470,
+    10290,
+    4868,
+    1556,
+    4603,
+    1029,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    2171,
+    2690,
+    4328,
+    4049,
+    26256,
+    264,
+    1729,
+    28733,
+    286,
+    4328,
+    3638,
+    970,
+    272,
+    2188,
+    1382,
+    1046,
+    264,
+    1729,
+    28723,
+    13,
+    28733,
+    619,
+    18789,
+    1029,
+    3523,
+    9580,
+    4049,
+    16736,
+    1439,
+    14650,
+    486,
+    1846,
+    4590,
+    272,
+    1729,
+    477,
+    264,
+    17503,
+    304,
+    605,
+    21542,
+    272,
+    19617,
+    3638,
+    28723,
+    13,
+    13,
+    348,
+    13063,
+    697,
+    28748,
+    1470,
+    770,
+    14567,
+    4049,
+    13,
+    28733,
+    619,
+    27449,
+    9466,
+    4049,
+    2213,
+    4054,
+    852,
+    9023,
+    541,
+    347,
+    4630,
+    304,
+    3715,
+    28733,
+    501,
+    4097,
+    28723,
+    13,
+    28733,
+    619,
+    3755,
+    9580,
+    5329,
+    4049,
+    3523,
+    9580,
+    727,
+    541,
+    347,
+    11180,
+    486,
+    272,
+    1669,
+    302,
+    272,
+    1729,
+    304,
+    272,
+    11010,
+    302,
+    1439,
+    14650,
+    28723,
+    13,
+    13,
+    27332,
+    401,
+    973,
+    21386,
+    11135,
+    2487,
+    13,
+    13,
+    348,
+    856,
+    7916,
+    27164,
+    1443,
+    4049,
+    13,
+    28733,
+    619,
+    10836,
+    4661,
+    21963,
+    2350,
+    4049,
+    3880,
+    492,
+    272,
+    8278,
+    13811,
+    395,
+    264,
+    27013,
+    26605,
+    369,
+    1235,
+    459,
+    3024,
+    14778,
+    28737,
+    28733,
+    28113,
+    4190,
+    28723,
+    13
+  ],
+  "raw_output": "### 1. Memory Architecture\n\n**Concrete Mechanism:**\n- **Memory Allocation:** Utilize a demand-paged memory management system to efficiently allocate memory for different components.\n- **Memory Pooling:** Implement a memory pooling mechanism to reuse memory for frequently accessed components.\n\n**Limitations/Tradeoffs:**\n- **Memory Overhead:** Demand-paged memory management can introduce overhead due to page faults.\n- **Memory Leaks:** Memory pooling may lead to memory leaks if not properly managed.\n\n### 2. Reasoning and Planning Loop\n\n**Concrete Mechanism:**\n- **Rule-Based Reasoning:** Implement a rule-based reasoning system to handle simple decision-making tasks.\n- **Probabilistic Reasoning:** Integrate probabilistic reasoning to handle uncertain situations.\n\n**Limitations/Tradeoffs:**\n- **Complexity:** Rule-based systems can become complex and difficult to maintain.\n- **Scalability:** Probabilistic reasoning may not scale well for large datasets.\n\n### 3. Learning or Self-Improvement\n\n**Concrete Mechanism:**\n- **Supervised Learning:** Implement a supervised learning algorithm to improve task performance based on labeled data.\n- **Unsupervised Learning:** Use unsupervised learning techniques to discover patterns in data.\n\n**Limitations/Tradeoffs:**\n- **Data Requirements:** Supervised learning requires a large amount of labeled data.\n- **Interpretability:** Unsupervised learning may lack interpretability.\n\n### 4. Tool Use and Action Execution\n\n**Concrete Mechanism:**\n- **API Integration:** Integrate with Python's standard library and third-party APIs to execute actions.\n- **Custom Tools:** Develop custom tools for specific tasks.\n\n**Limitations/Tradeoffs:**\n- **API Dependency:** API integration can introduce dependencies and potential points of failure.\n- **Custom Tool Development:** Developing custom tools can be time-consuming and resource-intensive.\n\n### 5. World and Self Representation\n\n**Concrete Mechanism:**\n- **State Machine:** Use a state machine to represent the current state of the system.\n- **Graph Representation:** Represent the world as a graph for better understanding and manipulation.\n\n**Limitations/Tradeoffs:**\n- **Complexity:** State machines and graphs can become complex and difficult to manage.\n- **Performance:** Graph representations can be computationally expensive.\n\n### 6. Safety and Governance\n\n**Concrete Mechanism:**\n- **Verifier:** Implement a verifier to ensure that tool actions are safe and do not violate user permissions.\n- **Governance Policies:** Define governance policies to control the execution of tool actions.\n\n**Limitations/Tradeoffs:**\n- **Verification Overhead:** Verification can introduce overhead and delay in action execution.\n- **Policy Enforcement:** Enforcing governance policies can be challenging and may require additional resources.\n\n### 7. Evaluation and Benchmarks\n\n**Concrete Mechanism:**\n- **Performance Benchmarks:** Measure the performance of the system using benchmarks.\n- **User Feedback:** Collect user feedback to evaluate the system's usability and effectiveness.\n\n**Limitations/Tradeoffs:**\n- **Benchmark Accuracy:** Benchmarks may not accurately reflect real-world performance.\n- **User Subjectivity:** User feedback can be subjective and may not be representative of all users.\n\n### 8. Persistence and Runtime Recovery\n\n**Concrete Mechanism:**\n- **File Patches:** Implement file patches to handle file edits and recover from crashes.\n- **Persistent State:** Use a persistent state system to maintain the system's state across restarts.\n\n**Limitations/Tradeoffs:**\n- **File Patches:** File patches can be complex and error-prone.\n- **Persistent State:** Maintaining a persistent state can be resource-intensive.\n\n### 9. Multi-Agent Orchestration\n\n**Concrete Mechanism:**\n- **Agent Coordination:** Implement a coordination mechanism to manage multiple agents.\n- **Communication Protocols:** Use communication protocols to facilitate interaction between agents.\n\n**Limitations/Tradeoffs:**\n- **Complexity:** Multi-agent orchestration can be complex and difficult to manage.\n- **Communication Overhead:** Communication protocols can introduce overhead and delay.\n\n### 10. Engineering Feasibility\n\n**Concrete Mechanism:**\n- **Modular Design:** Design the system using a modular architecture to facilitate development and maintenance.\n- **Version Control:** Use version control systems to manage changes and collaborate on development.\n\n**Limitations/Tradeoffs:**\n- **Modularity:** Modular design can introduce complexity and make debugging difficult.\n- **Version Control:** Version control systems can be complex to manage and may introduce overhead.\n\n### 11. Non-Obvious Design Insight\n\n**Concrete Mechanism:**\n- **Decoupling:** Decouple the reasoning and planning loop from other components to improve flexibility and maintainability.\n- **Flexible Interfaces:** Design flexible interfaces to allow for easy integration of new components and features.\n\n**Limitations/Tradeoffs:**\n- **Decoupling Complexity:** Decoupling can introduce complexity and make debugging difficult.\n- **Flexible Interfaces:** Designing flexible interfaces can be challenging and may require additional effort.\n\n### Worked Example: File-Edit Task Across Crash\n\n**Concrete Mechanism:**\n- **File Editing:** Implement a file-editing task where the user edits a file.\n- **Crash Recovery:** Handle crashes by restoring the file from a backup and resuming the editing task.\n\n**Limitations/Tradeoffs:**\n- **Backup Management:** Managing backups can be complex and resource-intensive.\n- **Recovery Time:** Recovery time can be affected by the size of the file and the frequency of crashes.\n\n### Falsifiable Experiment\n\n**Concrete Mechanism:**\n- **Baseline Comparison:** Compare the proposed architecture with a simpler baseline that does not include AGI-oriented features.\n",
+  "generated_tokens": 1400,
+  "termination": "length",
+  "seconds": 45.891886299999896,
+  "started_utc": "2026-09-09T11:09:30.948093+00:00",
+  "completed_utc": "2026-09-09T11:10:25.913262+00:00",
+  "model": {
+    "slug": "danube",
+    "model_id": "h2oai/h2o-danube3-4b-chat",
+    "revision": "1e5c6fa6620f8bf078958069ab4581cd88e0202c",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/h2oai/h2o-danube3-4b-chat/tree/1e5c6fa6620f8bf078958069ab4581cd88e0202c"
+  },
+  "output_sha256": "d05428144444892c9a460ca8e14790810eee265babc29226224cd5e5b429df8c",
+  "request_sha256": "74676f83407410afb9d4a0956f323a636b8ada3391eed55f8e37127fc32ad14e",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/falcon/model-files.json b/research/ai_generated_agi_architectures/collection/runs/falcon/model-files.json
new file mode 100644
index 0000000..1196800
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/falcon/model-files.json
@@ -0,0 +1,43 @@
+{
+  "model": {
+    "slug": "falcon",
+    "model_id": "tiiuae/Falcon3-1B-Instruct",
+    "revision": "28ba2251970a01dd1edc7ba7dad2eb71216ccfdf",
+    "license": "other",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/tiiuae/Falcon3-1B-Instruct/tree/28ba2251970a01dd1edc7ba7dad2eb71216ccfdf"
+  },
+  "files": [
+    {
+      "file": "config.json",
+      "bytes": 656,
+      "sha256": "8a037915db0c54dfbed9a734868bacb684ab1dea7bcbb3964e2fc637da113b3b"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 113,
+      "sha256": "615d05e2d3d650048a367a891cf42aaac3bd1be6964ca17089f8020b2058258e"
+    },
+    {
+      "file": "model.safetensors",
+      "bytes": 3338836632,
+      "sha256": "3f551b3271b550bfcf7c65282cdc8b3627c6ba8887e6dfa7492809f7b16cb087"
+    },
+    {
+      "file": "special_tokens_map.json",
+      "bytes": 826,
+      "sha256": "1d14eedf90af206dde753809a93b13ff59c5bbc424f3e54c02bca1a26b982f37"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 9780754,
+      "sha256": "96c1b62711ea59cb36cf4ab46cc551fc64b2168ac0a288448026088fb9ec7f4a"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 364623,
+      "sha256": "2e6f9ee711000ac383a007148159f3b9d8b23bf32efc593f1156c6ba65cddaef"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/falcon/readable.txt b/research/ai_generated_agi_architectures/collection/runs/falcon/readable.txt
new file mode 100644
index 0000000..521a74d
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/falcon/readable.txt
@@ -0,0 +1,72 @@
+**Proposal for Cognitive-OS Architecture**
+
+**1. Memory Architecture**
+
+- **Mechanism**: Utilize a hybrid memory architecture combining on-disk SQLite for task state persistence and a scalable GPU memory pool for real-time processing.
+- **Limitation**: Balancing between fast access to task data and efficient GPU utilization can be challenging, potentially leading to performance bottlenecks during intensive computations.
+
+**2. Reasoning and Planning Loop**
+
+- **Mechanism**: Implement a hierarchical reasoning engine using a combination of rule-based logic and machine learning algorithms to enhance decision-making and planning capabilities.
+- **Limitation**: The complexity of reasoning tasks may require significant computational resources, potentially limiting the system's responsiveness in dynamic environments.
+
+**3. Learning or Self-Improvement**
+
+- **Mechanism**: Employ reinforcement learning techniques to continuously refine tool usage and action execution based on feedback from task outcomes.
+- **Limitation**: The learning process must be robust and adaptable to diverse task requirements, which may necessitate extensive data collection and model retraining.
+
+**4. Tool Use and Action Execution**
+
+- **Mechanism**: Integrate a modular tool library with customizable APIs, allowing for seamless integration and extension of existing tools.
+- **Limitation**: The diversity of tool functionalities may require a comprehensive library, which could be resource-intensive to develop and maintain.
+
+**5. World and Self Representation**
+
+- **Mechanism**: Develop a comprehensive world model using a combination of sensor data and AI-generated insights, ensuring accurate perception and understanding of the environment.
+- **Limitation**: The accuracy and reliability of world representation depend on the quality and quantity of data, which may be limited by external APIs.
+
+**6. Safety and Governance**
+
+- **Mechanism**: Implement a safety framework using a combination of fail-safe mechanisms and ethical guidelines, ensuring that actions are aligned with safety and ethical standards.
+- **Limitation**: Balancing safety with operational efficiency can be challenging, potentially leading to trade-offs in real-time decision-making.
+
+**7. Evaluation and Benchmarks**
+
+- **Mechanism**: Use a suite of performance metrics and benchmarks, including task completion rates, error detection rates, and user satisfaction surveys, to evaluate the system's effectiveness.
+- **Limitation**: The evaluation process must be transparent and standardized to ensure fair comparisons with other systems.
+
+**8. Persistence and Runtime Recovery**
+
+- **Mechanism**: Employ a robust file-based persistence strategy with a checkpointing mechanism to ensure data integrity during runtime.
+- **Limitation**: The complexity of file patching and recovery processes may introduce additional overhead, impacting system performance.
+
+**9. Multi-agent Orchestration**
+
+- **Mechanism**: Design a multi-agent architecture where each agent specializes in specific cognitive tasks, facilitating efficient resource allocation and task delegation.
+- **Limitation**: The complexity of managing interactions between multiple agents can introduce coordination challenges, requiring sophisticated communication protocols.
+
+**10. Engineering Feasibility**
+
+- **Mechanism**: Leverage cloud computing resources to scale the system dynamically, ensuring that it can handle varying workloads efficiently.
+- **Limitation**: The cost of cloud services may be prohibitive, especially for a system with high computational demands.
+
+**11. Non-obvious Design Insight**
+
+- **Mechanism**: Explore the potential of integrating quantum computing elements to enhance computational speed and efficiency, although this is a research frontier with significant technical challenges.
+- **Limitation**: Quantum computing introduces new complexities and requires substantial theoretical and practical advancements.
+
+**Implementation Details**
+
+- **Memory Architecture**: Utilize a combination of on-disk SQLite for task persistence and a GPU memory pool for real-time processing.
+- **Reasoning and Planning Loop**: Employ a hierarchical reasoning engine with reinforcement learning for enhanced decision-making.
+- **Learning or Self-Improvement**: Implement a reinforcement learning framework to continuously refine tool usage and action execution.
+- **Tool Use and Action Execution**: Develop a modular tool library with customizable APIs for seamless integration and extension.
+- **World and Self Representation**: Integrate a comprehensive world model using sensor data and AI insights, ensuring accurate perception and understanding.
+- **Safety and Governance**: Implement a safety framework with fail-safe mechanisms and ethical guidelines, ensuring alignment with safety and ethical standards.
+- **Evaluation and Benchmarks**: Use performance metrics and benchmarks to evaluate system effectiveness, with transparency and standardization in evaluation processes.
+- **Persistence and Runtime Recovery**: Employ a file-based persistence strategy with checkpointing for data integrity and a robust file patching mechanism.
+- **Multi-agent Orchestration**: Design a multi-agent architecture with specialized agents for task specialization, ensuring efficient resource allocation.
+- **Engineering Feasibility**: Leverage cloud computing resources for scalable system management, addressing potential cost constraints.
+- **Non-obvious Design Insight**: Explore quantum computing integration for enhanced computational speed, acknowledging the significant technical challenges.
+
+This proposal outlines a comprehensive architecture for Cognitive-OS, focusing on memory, reasoning, learning, tool use, safety, and multi-agent orchestration. Each component is designed with a balance of practical implementation and research-oriented considerations, aiming to address the unique constraints of the local-first Python runtime environment.
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/collection/runs/falcon/request.json b/research/ai_generated_agi_architectures/collection/runs/falcon/request.json
new file mode 100644
index 0000000..f9c7809
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/falcon/request.json
@@ -0,0 +1,381 @@
+{
+  "model": {
+    "slug": "falcon",
+    "model_id": "tiiuae/Falcon3-1B-Instruct",
+    "revision": "28ba2251970a01dd1edc7ba7dad2eb71216ccfdf",
+    "license": "other",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/tiiuae/Falcon3-1B-Instruct/tree/28ba2251970a01dd1edc7ba7dad2eb71216ccfdf"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<|user|>\nPropose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n\n<|assistant|>\n",
+  "input_ids": [
+    2051,
+    2115,
+    5517,
+    100846,
+    12,
+    3345,
+    5084,
+    2296,
+    5187,
+    2513,
+    15467,
+    2064,
+    2036,
+    19741,
+    11584,
+    2346,
+    44429,
+    2036,
+    4930,
+    2035,
+    3392,
+    17248,
+    2361,
+    15467,
+    2064,
+    2536,
+    2698,
+    11186,
+    2037,
+    18769,
+    2049,
+    2442,
+    2340,
+    2265,
+    3721,
+    2036,
+    8186,
+    18206,
+    13590,
+    2368,
+    70732,
+    6962,
+    3526,
+    2035,
+    2296,
+    3713,
+    7696,
+    2035,
+    14297,
+    4366,
+    7492,
+    2035,
+    117108,
+    2036,
+    2094,
+    2559,
+    3581,
+    22971,
+    2035,
+    2305,
+    13137,
+    9303,
+    2037,
+    2356,
+    4276,
+    55340,
+    2536,
+    2226,
+    2040,
+    2041,
+    17302,
+    30535,
+    6688,
+    2050,
+    12821,
+    6320,
+    27176,
+    2402,
+    25820,
+    2037,
+    12756,
+    2442,
+    5093,
+    2382,
+    2265,
+    3248,
+    28546,
+    2035,
+    2453,
+    6210,
+    2306,
+    11346,
+    2037,
+    12,
+    12,
+    7643,
+    2265,
+    32588,
+    7584,
+    11480,
+    2306,
+    2426,
+    2838,
+    2226,
+    2047,
+    2044,
+    2039,
+    4876,
+    2035,
+    3311,
+    2867,
+    26203,
+    33669,
+    51152,
+    2037,
+    3228,
+    3174,
+    12445,
+    3638,
+    2265,
+    11681,
+    13232,
+    2305,
+    2596,
+    18364,
+    2408,
+    109288,
+    2049,
+    12,
+    2040,
+    2037,
+    20148,
+    11584,
+    12,
+    2041,
+    2037,
+    95126,
+    2305,
+    6314,
+    11322,
+    12,
+    2042,
+    2037,
+    13064,
+    2408,
+    3418,
+    2036,
+    72548,
+    12,
+    2043,
+    2037,
+    16082,
+    2793,
+    2305,
+    4656,
+    13620,
+    12,
+    2044,
+    2037,
+    4941,
+    2305,
+    3418,
+    10819,
+    12,
+    2045,
+    2037,
+    12475,
+    2305,
+    19111,
+    12,
+    2046,
+    2037,
+    28812,
+    2305,
+    39933,
+    12,
+    2047,
+    2037,
+    94606,
+    2305,
+    13590,
+    9303,
+    12,
+    2048,
+    2037,
+    17284,
+    2036,
+    16793,
+    87355,
+    12,
+    2040,
+    2039,
+    2037,
+    12404,
+    40096,
+    12,
+    2040,
+    2040,
+    2037,
+    10133,
+    2036,
+    105281,
+    3248,
+    12019,
+    12,
+    12,
+    29832,
+    2426,
+    4131,
+    2596,
+    11956,
+    3128,
+    13402,
+    2408,
+    5606,
+    2035,
+    2265,
+    5447,
+    4101,
+    2306,
+    2265,
+    3581,
+    2036,
+    14245,
+    6962,
+    4189,
+    2265,
+    13137,
+    2035,
+    2305,
+    2265,
+    27583,
+    18576,
+    8628,
+    19626,
+    2456,
+    3248,
+    2368,
+    2265,
+    21653,
+    23782,
+    2037,
+    16707,
+    9413,
+    2733,
+    11474,
+    8368,
+    2035,
+    16675,
+    2035,
+    2305,
+    53067,
+    4139,
+    4562,
+    2037,
+    4300,
+    2453,
+    9892,
+    21480,
+    4341,
+    2408,
+    34386,
+    2037,
+    9586,
+    2504,
+    9201,
+    7492,
+    3659,
+    13221,
+    4392,
+    9624,
+    2050,
+    5309,
+    3336,
+    2453,
+    33637,
+    6750,
+    3096,
+    9624,
+    2037,
+    12,
+    12,
+    2051,
+    2115,
+    91961,
+    100846,
+    12
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 11,
+    "bos_token_id": 11,
+    "eos_token_id": 11,
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/falcon/response.json b/research/ai_generated_agi_architectures/collection/runs/falcon/response.json
new file mode 100644
index 0000000..6b9b97b
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/falcon/response.json
@@ -0,0 +1,1149 @@
+{
+  "output_ids": [
+    2608,
+    55848,
+    2346,
+    44429,
+    2036,
+    4930,
+    24341,
+    2608,
+    12,
+    12,
+    2608,
+    2040,
+    2037,
+    20148,
+    24341,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    75756,
+    2265,
+    15717,
+    6688,
+    11584,
+    20151,
+    2354,
+    2036,
+    32440,
+    70732,
+    2346,
+    6962,
+    3526,
+    35630,
+    2305,
+    2265,
+    35475,
+    30535,
+    6688,
+    6949,
+    2346,
+    3191,
+    2036,
+    3650,
+    8126,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    77611,
+    3287,
+    5139,
+    3623,
+    2302,
+    6962,
+    3128,
+    2305,
+    7615,
+    30535,
+    28462,
+    2469,
+    2351,
+    10271,
+    2035,
+    11044,
+    5678,
+    2302,
+    4754,
+    78583,
+    3438,
+    20929,
+    76009,
+    2037,
+    12,
+    12,
+    2608,
+    2041,
+    2037,
+    95126,
+    2305,
+    15989,
+    28252,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    34471,
+    2265,
+    52651,
+    23644,
+    5103,
+    3311,
+    2265,
+    8104,
+    2306,
+    7145,
+    2036,
+    5224,
+    12729,
+    2305,
+    6330,
+    5309,
+    20736,
+    2302,
+    8376,
+    5332,
+    2036,
+    11421,
+    2305,
+    6314,
+    11346,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    17120,
+    2306,
+    23644,
+    9430,
+    2753,
+    3839,
+    4919,
+    36453,
+    5616,
+    2035,
+    11044,
+    21821,
+    2278,
+    3161,
+    2030,
+    2106,
+    51606,
+    2303,
+    10341,
+    13432,
+    2037,
+    12,
+    12,
+    2608,
+    2042,
+    2037,
+    13064,
+    2408,
+    12212,
+    2036,
+    31189,
+    18010,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    15029,
+    42993,
+    5309,
+    8107,
+    2302,
+    18307,
+    35293,
+    4366,
+    9076,
+    2305,
+    4656,
+    13620,
+    4047,
+    2354,
+    9091,
+    2463,
+    6962,
+    11998,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    5309,
+    3269,
+    3336,
+    2351,
+    14271,
+    2305,
+    44290,
+    2302,
+    10411,
+    6962,
+    6375,
+    2035,
+    2632,
+    2753,
+    80908,
+    9857,
+    3128,
+    5649,
+    2305,
+    4637,
+    116842,
+    2037,
+    12,
+    12,
+    2608,
+    2043,
+    2037,
+    16082,
+    6156,
+    2305,
+    11653,
+    48364,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    96489,
+    2265,
+    27762,
+    4366,
+    7551,
+    2368,
+    31530,
+    27176,
+    2035,
+    8215,
+    2346,
+    23263,
+    11954,
+    2305,
+    9267,
+    2306,
+    6487,
+    5882,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    12852,
+    2306,
+    4366,
+    31846,
+    2753,
+    3839,
+    2265,
+    9503,
+    7551,
+    2035,
+    2632,
+    2891,
+    2351,
+    6257,
+    2036,
+    46624,
+    2302,
+    3198,
+    2305,
+    5307,
+    2037,
+    12,
+    12,
+    2608,
+    2044,
+    2037,
+    4941,
+    2305,
+    12212,
+    62345,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    6874,
+    2265,
+    9503,
+    3139,
+    4637,
+    3311,
+    2265,
+    8104,
+    2306,
+    14348,
+    3128,
+    2305,
+    13638,
+    2036,
+    16704,
+    13100,
+    2035,
+    12137,
+    9389,
+    17125,
+    2305,
+    6368,
+    2306,
+    2278,
+    4524,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    12480,
+    2305,
+    17767,
+    2306,
+    3139,
+    10819,
+    6843,
+    2354,
+    2278,
+    4103,
+    2305,
+    14255,
+    2306,
+    3128,
+    2035,
+    2632,
+    2753,
+    2351,
+    6036,
+    2465,
+    9201,
+    27176,
+    2037,
+    12,
+    12,
+    2608,
+    2045,
+    2037,
+    12475,
+    2305,
+    40717,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    34471,
+    2265,
+    5841,
+    11170,
+    3311,
+    2265,
+    8104,
+    2306,
+    4457,
+    2036,
+    4737,
+    16332,
+    2305,
+    18214,
+    10927,
+    2035,
+    12137,
+    2361,
+    7492,
+    2402,
+    21432,
+    2368,
+    5841,
+    2305,
+    18214,
+    7467,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    77611,
+    5841,
+    2368,
+    14800,
+    9943,
+    2469,
+    2351,
+    10271,
+    2035,
+    11044,
+    5678,
+    2302,
+    6543,
+    2036,
+    13871,
+    2303,
+    3191,
+    2036,
+    3650,
+    5332,
+    2036,
+    11421,
+    2037,
+    12,
+    12,
+    2608,
+    2046,
+    2037,
+    28812,
+    2305,
+    124659,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    6156,
+    2265,
+    12141,
+    2306,
+    4754,
+    15533,
+    2305,
+    39933,
+    2035,
+    3496,
+    6962,
+    11981,
+    6603,
+    2035,
+    3272,
+    14670,
+    6603,
+    2035,
+    2305,
+    4392,
+    12682,
+    19205,
+    2035,
+    2302,
+    13141,
+    2278,
+    3161,
+    2030,
+    2106,
+    15439,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    12027,
+    3269,
+    3336,
+    2351,
+    14172,
+    2305,
+    30322,
+    2302,
+    5009,
+    5855,
+    26357,
+    2368,
+    2645,
+    5119,
+    2037,
+    12,
+    12,
+    2608,
+    2047,
+    2037,
+    94606,
+    2305,
+    25426,
+    21927,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    15029,
+    2265,
+    14271,
+    3581,
+    2036,
+    5224,
+    35630,
+    6753,
+    2368,
+    2265,
+    41508,
+    2292,
+    13232,
+    2302,
+    5009,
+    3128,
+    13945,
+    3438,
+    13590,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    17120,
+    2306,
+    3581,
+    74261,
+    2305,
+    9303,
+    8234,
+    2753,
+    13002,
+    5111,
+    21100,
+    2035,
+    36547,
+    3161,
+    4754,
+    2037,
+    12,
+    12,
+    2608,
+    2048,
+    2037,
+    17284,
+    2036,
+    16793,
+    87516,
+    2359,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    7260,
+    2265,
+    7573,
+    2036,
+    16793,
+    11584,
+    2931,
+    3174,
+    8528,
+    23126,
+    2303,
+    3913,
+    17813,
+    9430,
+    2035,
+    33923,
+    7615,
+    6257,
+    22310,
+    2305,
+    6962,
+    30792,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    17120,
+    2306,
+    11024,
+    14998,
+    3287,
+    5439,
+    10148,
+    2469,
+    13002,
+    22048,
+    7428,
+    2035,
+    15775,
+    15798,
+    7361,
+    20893,
+    2037,
+    12,
+    12,
+    2608,
+    2040,
+    2039,
+    2037,
+    12404,
+    7160,
+    112205,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    84627,
+    8502,
+    16809,
+    5616,
+    2302,
+    7593,
+    2278,
+    3161,
+    37296,
+    2035,
+    12137,
+    2361,
+    2370,
+    2469,
+    6431,
+    19672,
+    54126,
+    15762,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    2427,
+    3604,
+    2306,
+    8502,
+    3718,
+    2753,
+    2351,
+    86826,
+    2035,
+    4788,
+    2346,
+    2265,
+    3161,
+    2368,
+    3056,
+    36453,
+    12342,
+    2037,
+    12,
+    12,
+    2608,
+    2040,
+    2040,
+    2037,
+    10133,
+    2036,
+    105281,
+    7260,
+    41356,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    99527,
+    28536,
+    26662,
+    2278,
+    5105,
+    2306,
+    28137,
+    26062,
+    16809,
+    6632,
+    2302,
+    8376,
+    36453,
+    5735,
+    2305,
+    9943,
+    2035,
+    6701,
+    2442,
+    2340,
+    2265,
+    4139,
+    39466,
+    2368,
+    4919,
+    7584,
+    7428,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    12021,
+    6014,
+    28536,
+    43619,
+    16809,
+    23861,
+    2616,
+    41272,
+    2305,
+    6836,
+    12036,
+    22330,
+    2305,
+    9460,
+    39113,
+    2037,
+    12,
+    12,
+    2608,
+    47540,
+    16788,
+    2608,
+    12,
+    12,
+    2036,
+    2226,
+    2608,
+    17535,
+    24341,
+    28536,
+    75756,
+    2265,
+    8104,
+    2306,
+    2354,
+    2036,
+    32440,
+    70732,
+    2346,
+    6962,
+    35630,
+    2305,
+    2265,
+    30535,
+    6688,
+    6949,
+    2346,
+    3191,
+    2036,
+    3650,
+    8126,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    22730,
+    2292,
+    2305,
+    15989,
+    28252,
+    28536,
+    15029,
+    2265,
+    52651,
+    23644,
+    5103,
+    2368,
+    42993,
+    5309,
+    2346,
+    14323,
+    5332,
+    2036,
+    11421,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    27456,
+    2408,
+    12212,
+    2036,
+    31189,
+    18010,
+    28536,
+    34471,
+    2265,
+    42993,
+    5309,
+    11170,
+    2302,
+    18307,
+    35293,
+    4366,
+    9076,
+    2305,
+    4656,
+    13620,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    19472,
+    6156,
+    2305,
+    11653,
+    48364,
+    28536,
+    6874,
+    2265,
+    27762,
+    4366,
+    7551,
+    2368,
+    31530,
+    27176,
+    2346,
+    23263,
+    11954,
+    2305,
+    9267,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    15026,
+    2305,
+    12212,
+    62345,
+    28536,
+    96489,
+    2265,
+    9503,
+    3139,
+    4637,
+    3311,
+    14348,
+    3128,
+    2305,
+    13638,
+    13100,
+    2035,
+    12137,
+    9389,
+    17125,
+    2305,
+    6368,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    40747,
+    2305,
+    40717,
+    28536,
+    34471,
+    2265,
+    5841,
+    11170,
+    2368,
+    4457,
+    2036,
+    4737,
+    16332,
+    2305,
+    18214,
+    10927,
+    2035,
+    12137,
+    19589,
+    2368,
+    5841,
+    2305,
+    18214,
+    7467,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    50038,
+    2305,
+    124659,
+    28536,
+    6156,
+    4754,
+    15533,
+    2305,
+    39933,
+    2302,
+    13141,
+    3161,
+    15439,
+    2035,
+    2368,
+    19173,
+    2305,
+    68745,
+    2303,
+    12027,
+    8234,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    65158,
+    2305,
+    25426,
+    21927,
+    28536,
+    15029,
+    2265,
+    3581,
+    2036,
+    5224,
+    35630,
+    6753,
+    2368,
+    41508,
+    2292,
+    2346,
+    3128,
+    13945,
+    2305,
+    2265,
+    14271,
+    3581,
+    74261,
+    13232,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    18218,
+    2036,
+    16793,
+    87516,
+    2359,
+    28536,
+    7260,
+    2265,
+    7573,
+    2036,
+    16793,
+    11584,
+    2368,
+    15816,
+    10148,
+    2346,
+    6962,
+    47272,
+    2035,
+    12137,
+    7615,
+    6257,
+    22310,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    67393,
+    7160,
+    112205,
+    28536,
+    84627,
+    8502,
+    16809,
+    5616,
+    2346,
+    35475,
+    3161,
+    4810,
+    2035,
+    16923,
+    5105,
+    3604,
+    20247,
+    2037,
+    12,
+    2036,
+    2226,
+    2608,
+    11676,
+    2036,
+    105281,
+    7260,
+    41356,
+    28536,
+    26662,
+    26062,
+    16809,
+    11954,
+    2346,
+    14323,
+    36453,
+    5735,
+    2035,
+    37793,
+    2278,
+    4919,
+    7584,
+    7428,
+    2037,
+    12,
+    12,
+    3277,
+    11480,
+    30208,
+    2265,
+    9503,
+    11584,
+    2346,
+    44429,
+    2036,
+    4930,
+    2035,
+    13069,
+    2354,
+    6688,
+    2035,
+    23644,
+    2035,
+    5309,
+    2035,
+    4366,
+    2793,
+    2035,
+    5841,
+    2035,
+    2305,
+    7573,
+    2036,
+    16793,
+    87355,
+    2037,
+    7375,
+    8989,
+    2340,
+    5167,
+    2368,
+    2265,
+    7465,
+    2306,
+    9460,
+    8697,
+    2305,
+    4139,
+    2036,
+    19741,
+    22600,
+    2035,
+    24998,
+    2302,
+    4218,
+    2278,
+    4948,
+    20247,
+    2306,
+    2278,
+    3721,
+    2036,
+    8186,
+    18206,
+    13590,
+    4524,
+    2037,
+    11
+  ],
+  "raw_output": "**Proposal for Cognitive-OS Architecture**\n\n**1. Memory Architecture**\n\n- **Mechanism**: Utilize a hybrid memory architecture combining on-disk SQLite for task state persistence and a scalable GPU memory pool for real-time processing.\n- **Limitation**: Balancing between fast access to task data and efficient GPU utilization can be challenging, potentially leading to performance bottlenecks during intensive computations.\n\n**2. Reasoning and Planning Loop**\n\n- **Mechanism**: Implement a hierarchical reasoning engine using a combination of rule-based logic and machine learning algorithms to enhance decision-making and planning capabilities.\n- **Limitation**: The complexity of reasoning tasks may require significant computational resources, potentially limiting the system's responsiveness in dynamic environments.\n\n**3. Learning or Self-Improvement**\n\n- **Mechanism**: Employ reinforcement learning techniques to continuously refine tool usage and action execution based on feedback from task outcomes.\n- **Limitation**: The learning process must be robust and adaptable to diverse task requirements, which may necessitate extensive data collection and model retraining.\n\n**4. Tool Use and Action Execution**\n\n- **Mechanism**: Integrate a modular tool library with customizable APIs, allowing for seamless integration and extension of existing tools.\n- **Limitation**: The diversity of tool functionalities may require a comprehensive library, which could be resource-intensive to develop and maintain.\n\n**5. World and Self Representation**\n\n- **Mechanism**: Develop a comprehensive world model using a combination of sensor data and AI-generated insights, ensuring accurate perception and understanding of the environment.\n- **Limitation**: The accuracy and reliability of world representation depend on the quality and quantity of data, which may be limited by external APIs.\n\n**6. Safety and Governance**\n\n- **Mechanism**: Implement a safety framework using a combination of fail-safe mechanisms and ethical guidelines, ensuring that actions are aligned with safety and ethical standards.\n- **Limitation**: Balancing safety with operational efficiency can be challenging, potentially leading to trade-offs in real-time decision-making.\n\n**7. Evaluation and Benchmarks**\n\n- **Mechanism**: Use a suite of performance metrics and benchmarks, including task completion rates, error detection rates, and user satisfaction surveys, to evaluate the system's effectiveness.\n- **Limitation**: The evaluation process must be transparent and standardized to ensure fair comparisons with other systems.\n\n**8. Persistence and Runtime Recovery**\n\n- **Mechanism**: Employ a robust file-based persistence strategy with a checkpointing mechanism to ensure data integrity during runtime.\n- **Limitation**: The complexity of file patching and recovery processes may introduce additional overhead, impacting system performance.\n\n**9. Multi-agent Orchestration**\n\n- **Mechanism**: Design a multi-agent architecture where each agent specializes in specific cognitive tasks, facilitating efficient resource allocation and task delegation.\n- **Limitation**: The complexity of managing interactions between multiple agents can introduce coordination challenges, requiring sophisticated communication protocols.\n\n**10. Engineering Feasibility**\n\n- **Mechanism**: Leverage cloud computing resources to scale the system dynamically, ensuring that it can handle varying workloads efficiently.\n- **Limitation**: The cost of cloud services may be prohibitive, especially for a system with high computational demands.\n\n**11. Non-obvious Design Insight**\n\n- **Mechanism**: Explore the potential of integrating quantum computing elements to enhance computational speed and efficiency, although this is a research frontier with significant technical challenges.\n- **Limitation**: Quantum computing introduces new complexities and requires substantial theoretical and practical advancements.\n\n**Implementation Details**\n\n- **Memory Architecture**: Utilize a combination of on-disk SQLite for task persistence and a GPU memory pool for real-time processing.\n- **Reasoning and Planning Loop**: Employ a hierarchical reasoning engine with reinforcement learning for enhanced decision-making.\n- **Learning or Self-Improvement**: Implement a reinforcement learning framework to continuously refine tool usage and action execution.\n- **Tool Use and Action Execution**: Develop a modular tool library with customizable APIs for seamless integration and extension.\n- **World and Self Representation**: Integrate a comprehensive world model using sensor data and AI insights, ensuring accurate perception and understanding.\n- **Safety and Governance**: Implement a safety framework with fail-safe mechanisms and ethical guidelines, ensuring alignment with safety and ethical standards.\n- **Evaluation and Benchmarks**: Use performance metrics and benchmarks to evaluate system effectiveness, with transparency and standardization in evaluation processes.\n- **Persistence and Runtime Recovery**: Employ a file-based persistence strategy with checkpointing for data integrity and a robust file patching mechanism.\n- **Multi-agent Orchestration**: Design a multi-agent architecture with specialized agents for task specialization, ensuring efficient resource allocation.\n- **Engineering Feasibility**: Leverage cloud computing resources for scalable system management, addressing potential cost constraints.\n- **Non-obvious Design Insight**: Explore quantum computing integration for enhanced computational speed, acknowledging the significant technical challenges.\n\nThis proposal outlines a comprehensive architecture for Cognitive-OS, focusing on memory, reasoning, learning, tool use, safety, and multi-agent orchestration. Each component is designed with a balance of practical implementation and research-oriented considerations, aiming to address the unique constraints of the local-first Python runtime environment.<|endoftext|>",
+  "generated_tokens": 1115,
+  "termination": "eos",
+  "seconds": 17.41935840000224,
+  "started_utc": "2026-09-09T10:50:29.570799+00:00",
+  "completed_utc": "2026-09-09T10:50:48.641702+00:00",
+  "model": {
+    "slug": "falcon",
+    "model_id": "tiiuae/Falcon3-1B-Instruct",
+    "revision": "28ba2251970a01dd1edc7ba7dad2eb71216ccfdf",
+    "license": "other",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/tiiuae/Falcon3-1B-Instruct/tree/28ba2251970a01dd1edc7ba7dad2eb71216ccfdf"
+  },
+  "output_sha256": "40699258adb8d28e7a674306833ea986efbc9f827ddd5e5484987d4789c66be4",
+  "request_sha256": "6e8d9064f1f4d3afbe287d31f341c60b930f12cfb352515e1bd993010ad26956",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/granite/model-files.json b/research/ai_generated_agi_architectures/collection/runs/granite/model-files.json
new file mode 100644
index 0000000..d92365d
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/granite/model-files.json
@@ -0,0 +1,63 @@
+{
+  "model": {
+    "slug": "granite",
+    "model_id": "ibm-granite/granite-3.3-2b-instruct",
+    "revision": "707f574c62054322f6b5b04b6d075f0a8f05e0f0",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/ibm-granite/granite-3.3-2b-instruct/tree/707f574c62054322f6b5b04b6d075f0a8f05e0f0"
+  },
+  "files": [
+    {
+      "file": "added_tokens.json",
+      "bytes": 207,
+      "sha256": "bb33d55934aa82d29cc62f3d19cdbc60f315763f6ccee21bdfd8b3bde2f33d3b"
+    },
+    {
+      "file": "config.json",
+      "bytes": 787,
+      "sha256": "9202d328d8368958aab7dad89a8e6aa35c250b3a2eedd2d8850ee0bcceca65f6"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 132,
+      "sha256": "9c95e80167f08fbeb1feba239e0749507c25d18ffab43ffa10887821eed21c38"
+    },
+    {
+      "file": "model-00001-of-00002.safetensors",
+      "bytes": 4999999840,
+      "sha256": "12880d33c0ad4726af5cf8c07406905f9b496253c58ee46f52be8bde8ccf2254"
+    },
+    {
+      "file": "model-00002-of-00002.safetensors",
+      "bytes": 67121712,
+      "sha256": "a8757c5bf7627933e7fddbd9bab0533491b4dc0962820e0617f356ca1a379ffa"
+    },
+    {
+      "file": "model.safetensors.index.json",
+      "bytes": 29835,
+      "sha256": "32ea3f438335d51c8e630a83898ceb23bdffd34291a0eade07474211928fa243"
+    },
+    {
+      "file": "special_tokens_map.json",
+      "bytes": 801,
+      "sha256": "21ce694081bb9ae1bd4bc64549e72e0799ebb74705e6b650e3585d85b71ebdc1"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 3476578,
+      "sha256": "91168e938f05796aa6dcca7e485e4b30ab52785320c7a6391ecef86e6c84681e"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 9930,
+      "sha256": "f65a6a5a911424c85f157c40cfbdf06e025814c755480ba2e998d7fba1178664"
+    },
+    {
+      "file": "vocab.json",
+      "bytes": 776995,
+      "sha256": "80ab859339a2525fdfbda14bc39df02dffb824aefdaf86426217bbb146d17e01"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/granite/readable.txt b/research/ai_generated_agi_architectures/collection/runs/granite/readable.txt
new file mode 100644
index 0000000..48ffcfe
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/granite/readable.txt
@@ -0,0 +1,75 @@
+1. **Memory Architecture**
+
+   Mechanism: Cognitive-OS employs a hybrid memory model, utilizing GPU memory for computationally intensive tasks and a compact, in-memory SQLite database for task state and event journaling. This approach leverages the GPU's parallel processing capabilities for complex computations while maintaining a lightweight, persistent record of task states.
+
+   Limitation: The reliance on GPU memory imposes a trade-off between computational power and data persistence. In case of a crash, the in-memory SQLite database may be lost, requiring a recovery mechanism.
+
+2. **Reasoning and Planning Loop**
+
+   Mechanism: Cognitive-OS utilizes a verifier-gated file patch system for reasoning and planning. The system maintains a verifier component that checks the validity of proposed file patches against a predefined set of rules and constraints. The planning loop involves generating potential patches, validating them, and executing the most promising ones.
+
+   Limitation: The verifier's complexity may introduce latency, potentially slowing down the reasoning and planning process.
+
+3. **Learning or Self-Improvement**
+
+   Mechanism: Cognitive-OS incorporates a reinforcement learning module that learns from user interactions and task outcomes. The module uses a reward-based system to adjust its decision-making process, improving over time without explicit programming.
+
+   Limitation: The learning process may require extensive user interaction and feedback, which could be time-consuming and resource-intensive.
+
+4. **Tool Use and Action Execution**
+
+   Mechanism: Cognitive-OS uses a gated action execution system, where each tool action is verified and authorized by a centralized authority before execution. This ensures that actions adhere to predefined rules and constraints.
+
+   Limitation: The gatekeeping mechanism may introduce overhead, potentially slowing down action execution.
+
+5. **World and Self Representation**
+
+   Mechanism: Cognitive-OS maintains a compact, in-memory representation of the world and self, using a graph-based data structure. This allows for efficient querying and reasoning about the system's state and potential actions.
+
+   Limitation: The compact representation may limit the system's ability to capture complex, nuanced relationships in the world.
+
+6. **Safety and Governance**
+
+   Mechanism: Cognitive-OS employs a multi-layered safety and governance system, including a rule-based access control, a verifier for file patches, and a crash recovery mechanism. These components work together to ensure safe and controlled system behavior.
+
+   Limitation: The system's safety relies on the correct implementation and configuration of these governance mechanisms, which could be challenging to guarantee.
+
+7. **Evaluation and Benchmarks**
+
+   Mechanism: Cognitive-OS uses a falsifiable experiment comparing its performance on a file-edit task across a crash scenario against a simpler baseline. The experiment involves simulating a crash during a file-edit task, observing the system's recovery and continued functionality.
+
+   Limitation: The experiment's validity depends on the accuracy of the simulated crash and the robustness of the recovery mechanism.
+
+8. **Persistence and Runtime Recovery**
+
+   Mechanism: Cognitive-OS utilizes a crash-tolerant event journal to persist task states and event logs. In case of a crash, the system recovers by replaying the event journal, restoring the task state to the last known consistent point.
+
+   Limitation: The recovery process may introduce a delay, potentially affecting the system's responsiveness during a crash.
+
+9. **Multi-agent Orchestration**
+
+   Mechanism: Cognitive-OS proposes a decentralized, negotiation-based approach for multi-agent orchestration. Agents communicate and negotiate to coordinate tasks, leveraging a shared world representation and a negotiation protocol.
+
+   Limitation: The negotiation process may introduce complexity and potential communication overhead, especially in large-scale multi-agent systems.
+
+10. **Engineering Feasibility**
+
+    Mechanism: Cognitive-OS is designed with a modular architecture, allowing for incremental development and testing of individual components. The system's components are implemented in Python, leveraging existing libraries and frameworks.
+
+    Limitation: The modular design may introduce complexity in managing component interactions and ensuring overall system coherence.
+
+11. **Non-obvious Design Insight**
+
+    Mechanism: Cognitive-OS employs a "verifier-gated" approach for file patches, allowing for fine-grained control over action execution. This design insight enables the system to balance safety and flexibility, allowing for both controlled, verified actions and opportunistic, potentially risky ones.
+
+    Limitation: The effectiveness of this design insight relies on the correct implementation and tuning of the verifier component, which could be challenging.
+
+**Worked Example: File-Edit Task Across a Crash**
+
+Consider a user editing a file (F) in Cognitive-OS. The system maintains the file's state in the SQLite database and an in-memory graph representation. During the edit, the user initiates a patch (P) to modify the file. The verifier component checks P against predefined rules, and if valid, it is executed, updating the file and the in-memory graph.
+
+In case of a crash, the event journal records the current state (file state, graph state, and patch P). Upon recovery, the system replays the event journal, restoring the file state to the last known consistent point and applying any pending patches.
+
+**Falsifiable Experiment: File-Edit Task Across a Crash**
+
+Design an experiment where Cognitive-OS and a simpler baseline (e.g., a file editor without crash recovery) perform a file-edit task. Simulate a crash during the task for both systems. Measure the time taken to recover and continue the task, as well as the consistency of the final file state. The falsifiable hypothesis is that Cognitive-OS will recover and continue the task more efficiently and accurately than the simpler baseline.
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/collection/runs/granite/request.json b/research/ai_generated_agi_architectures/collection/runs/granite/request.json
new file mode 100644
index 0000000..2d3d53c
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/granite/request.json
@@ -0,0 +1,449 @@
+{
+  "model": {
+    "slug": "granite",
+    "model_id": "ibm-granite/granite-3.3-2b-instruct",
+    "revision": "707f574c62054322f6b5b04b6d075f0a8f05e0f0",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/ibm-granite/granite-3.3-2b-instruct/tree/707f574c62054322f6b5b04b6d075f0a8f05e0f0"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<|start_of_role|>system<|end_of_role|>Knowledge Cutoff Date: April 2024.\nToday's Date: September 09, 2026.\nYou are Granite, developed by IBM. You are a helpful AI assistant.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>",
+  "input_ids": [
+    49152,
+    2946,
+    49153,
+    39558,
+    390,
+    17071,
+    2821,
+    44,
+    30468,
+    225,
+    36,
+    34,
+    36,
+    38,
+    32,
+    203,
+    23669,
+    1182,
+    2821,
+    44,
+    33841,
+    225,
+    34,
+    43,
+    30,
+    225,
+    36,
+    34,
+    36,
+    40,
+    32,
+    203,
+    4282,
+    884,
+    8080,
+    278,
+    659,
+    30,
+    18909,
+    810,
+    25697,
+    32,
+    2448,
+    884,
+    312,
+    17247,
+    19551,
+    47330,
+    32,
+    0,
+    203,
+    49152,
+    496,
+    49153,
+    590,
+    3605,
+    600,
+    2054,
+    444,
+    399,
+    14819,
+    31,
+    42368,
+    15488,
+    436,
+    390,
+    29778,
+    31,
+    2636,
+    30,
+    2876,
+    12559,
+    299,
+    688,
+    399,
+    14819,
+    1401,
+    2583,
+    34016,
+    32,
+    7049,
+    44,
+    458,
+    438,
+    312,
+    2196,
+    31,
+    2232,
+    4865,
+    6091,
+    623,
+    24864,
+    2899,
+    1603,
+    30,
+    600,
+    1692,
+    16366,
+    30,
+    17547,
+    4559,
+    6885,
+    30,
+    48519,
+    31,
+    89,
+    733,
+    803,
+    26975,
+    30,
+    461,
+    14865,
+    25376,
+    32,
+    399,
+    3982,
+    1389,
+    13548,
+    1401,
+    225,
+    35,
+    36,
+    21124,
+    13949,
+    4117,
+    45,
+    25966,
+    30206,
+    13417,
+    884,
+    31731,
+    32,
+    394,
+    4827,
+    458,
+    1619,
+    619,
+    312,
+    6735,
+    13118,
+    30,
+    646,
+    28028,
+    432,
+    18804,
+    32,
+    203,
+    203,
+    2538,
+    312,
+    30836,
+    22131,
+    18918,
+    432,
+    821,
+    4630,
+    225,
+    42,
+    39,
+    34,
+    8153,
+    30,
+    1471,
+    3282,
+    16696,
+    2465,
+    1451,
+    318,
+    5759,
+    2052,
+    32,
+    2616,
+    2504,
+    19325,
+    7624,
+    312,
+    26322,
+    17016,
+    461,
+    1591,
+    12702,
+    556,
+    16719,
+    1719,
+    44,
+    203,
+    35,
+    32,
+    11348,
+    15488,
+    203,
+    36,
+    32,
+    26448,
+    299,
+    461,
+    26116,
+    5258,
+    203,
+    37,
+    32,
+    14292,
+    556,
+    630,
+    31,
+    28577,
+    14648,
+    203,
+    38,
+    32,
+    10018,
+    793,
+    461,
+    2125,
+    6063,
+    203,
+    39,
+    32,
+    10896,
+    461,
+    630,
+    10293,
+    203,
+    40,
+    32,
+    358,
+    21732,
+    461,
+    23714,
+    723,
+    203,
+    41,
+    32,
+    32428,
+    461,
+    37673,
+    203,
+    42,
+    32,
+    46752,
+    461,
+    6091,
+    25376,
+    203,
+    43,
+    32,
+    10473,
+    31,
+    4867,
+    45867,
+    31687,
+    203,
+    35,
+    34,
+    32,
+    26585,
+    5246,
+    302,
+    3453,
+    203,
+    35,
+    35,
+    32,
+    12495,
+    31,
+    906,
+    3040,
+    6735,
+    44757,
+    203,
+    203,
+    8509,
+    821,
+    6225,
+    1591,
+    8007,
+    706,
+    5365,
+    556,
+    2568,
+    30,
+    312,
+    13096,
+    2280,
+    432,
+    312,
+    803,
+    31,
+    3176,
+    2899,
+    10160,
+    312,
+    14865,
+    30,
+    461,
+    312,
+    296,
+    3528,
+    15621,
+    9624,
+    31374,
+    1370,
+    6735,
+    623,
+    312,
+    29952,
+    22340,
+    32,
+    475,
+    7978,
+    103,
+    1708,
+    7586,
+    6339,
+    30,
+    48546,
+    30,
+    461,
+    707,
+    15655,
+    13234,
+    10017,
+    32,
+    3278,
+    646,
+    37350,
+    17521,
+    3276,
+    556,
+    29692,
+    993,
+    32,
+    19114,
+    1169,
+    6594,
+    6885,
+    4797,
+    17103,
+    1256,
+    4227,
+    45,
+    9608,
+    2298,
+    646,
+    45889,
+    11764,
+    5521,
+    4227,
+    32,
+    203,
+    0,
+    203,
+    49152,
+    17594,
+    49153
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 0,
+    "bos_token_id": 0,
+    "eos_token_id": 0,
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/granite/response.json b/research/ai_generated_agi_architectures/collection/runs/granite/response.json
new file mode 100644
index 0000000..758f15f
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/granite/response.json
@@ -0,0 +1,1309 @@
+{
+  "output_ids": [
+    35,
+    32,
+    1115,
+    5017,
+    29321,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    12480,
+    315,
+    1269,
+    312,
+    41984,
+    4117,
+    1542,
+    30,
+    4527,
+    6183,
+    13949,
+    4117,
+    436,
+    19578,
+    1247,
+    629,
+    18540,
+    8253,
+    461,
+    312,
+    15319,
+    30,
+    328,
+    31,
+    5566,
+    24864,
+    3500,
+    436,
+    2899,
+    1603,
+    461,
+    1692,
+    16366,
+    299,
+    32,
+    1348,
+    10078,
+    42101,
+    101,
+    322,
+    13949,
+    1182,
+    11935,
+    8202,
+    18804,
+    436,
+    8640,
+    17989,
+    993,
+    2218,
+    38812,
+    312,
+    42700,
+    30,
+    21308,
+    3627,
+    432,
+    2899,
+    10066,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    316,
+    4339,
+    544,
+    13949,
+    4117,
+    1360,
+    97,
+    1464,
+    312,
+    16719,
+    31,
+    1719,
+    3733,
+    42877,
+    7169,
+    461,
+    706,
+    33068,
+    32,
+    701,
+    1135,
+    432,
+    312,
+    14865,
+    30,
+    322,
+    328,
+    31,
+    5566,
+    24864,
+    3500,
+    1631,
+    526,
+    15058,
+    30,
+    33783,
+    312,
+    25376,
+    17016,
+    32,
+    203,
+    203,
+    36,
+    32,
+    1115,
+    10453,
+    299,
+    461,
+    4061,
+    10137,
+    17526,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    4527,
+    5052,
+    312,
+    48519,
+    31,
+    89,
+    733,
+    803,
+    7841,
+    2664,
+    436,
+    5966,
+    299,
+    461,
+    26116,
+    32,
+    886,
+    2664,
+    48628,
+    312,
+    48519,
+    2577,
+    688,
+    9371,
+    322,
+    31887,
+    432,
+    17934,
+    803,
+    26975,
+    8187,
+    312,
+    32681,
+    739,
+    432,
+    7019,
+    461,
+    13383,
+    32,
+    886,
+    26116,
+    5258,
+    37383,
+    17335,
+    13792,
+    26975,
+    30,
+    39987,
+    3026,
+    30,
+    461,
+    16924,
+    322,
+    4630,
+    10678,
+    12466,
+    11668,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    48519,
+    1182,
+    22992,
+    1631,
+    19979,
+    26731,
+    30,
+    22035,
+    12899,
+    299,
+    2835,
+    322,
+    5966,
+    299,
+    461,
+    26116,
+    2164,
+    32,
+    203,
+    203,
+    37,
+    32,
+    1115,
+    20903,
+    556,
+    6609,
+    31,
+    37035,
+    14648,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    26286,
+    1196,
+    312,
+    38070,
+    36132,
+    9608,
+    2297,
+    688,
+    999,
+    282,
+    2425,
+    645,
+    1256,
+    28653,
+    461,
+    2899,
+    46197,
+    32,
+    886,
+    2297,
+    5251,
+    312,
+    17836,
+    31,
+    7873,
+    2664,
+    372,
+    10891,
+    2819,
+    17200,
+    31,
+    36195,
+    2164,
+    30,
+    44652,
+    2288,
+    1133,
+    2876,
+    8007,
+    16031,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    9608,
+    2164,
+    1631,
+    1205,
+    1447,
+    18540,
+    1256,
+    15994,
+    461,
+    10998,
+    30,
+    1510,
+    3065,
+    526,
+    1133,
+    31,
+    39270,
+    299,
+    461,
+    2869,
+    31,
+    410,
+    18540,
+    32,
+    203,
+    203,
+    38,
+    32,
+    1115,
+    3653,
+    4417,
+    461,
+    5245,
+    16254,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    5251,
+    312,
+    485,
+    733,
+    2125,
+    6063,
+    2664,
+    30,
+    2154,
+    2504,
+    4559,
+    2125,
+    438,
+    20442,
+    461,
+    23779,
+    810,
+    312,
+    19109,
+    1191,
+    27756,
+    2670,
+    6063,
+    32,
+    1348,
+    26091,
+    688,
+    6885,
+    1544,
+    7020,
+    372,
+    32681,
+    7019,
+    461,
+    13383,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    21910,
+    10504,
+    299,
+    17016,
+    1631,
+    19979,
+    28359,
+    30,
+    22035,
+    12899,
+    299,
+    2835,
+    2125,
+    6063,
+    32,
+    203,
+    203,
+    39,
+    32,
+    1115,
+    6708,
+    461,
+    6609,
+    44961,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    48628,
+    312,
+    15319,
+    30,
+    328,
+    31,
+    5566,
+    10293,
+    432,
+    322,
+    5788,
+    461,
+    630,
+    30,
+    1471,
+    312,
+    3996,
+    31,
+    7873,
+    706,
+    5193,
+    32,
+    1348,
+    6972,
+    436,
+    17457,
+    38654,
+    461,
+    5966,
+    299,
+    2625,
+    322,
+    2664,
+    1182,
+    1603,
+    461,
+    13792,
+    6885,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    15319,
+    10293,
+    1631,
+    2723,
+    322,
+    2664,
+    1182,
+    13609,
+    372,
+    12847,
+    8640,
+    30,
+    17428,
+    6087,
+    25041,
+    328,
+    322,
+    5788,
+    32,
+    203,
+    203,
+    40,
+    32,
+    1115,
+    44798,
+    461,
+    570,
+    38082,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    12480,
+    315,
+    1269,
+    312,
+    6959,
+    31,
+    1823,
+    318,
+    26160,
+    461,
+    23714,
+    723,
+    2664,
+    30,
+    6237,
+    312,
+    5750,
+    31,
+    7873,
+    2857,
+    3498,
+    30,
+    312,
+    48519,
+    436,
+    803,
+    26975,
+    30,
+    461,
+    312,
+    14865,
+    25376,
+    17016,
+    32,
+    8204,
+    6339,
+    1389,
+    10752,
+    372,
+    7279,
+    10061,
+    461,
+    28355,
+    2664,
+    5853,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    2664,
+    1182,
+    26160,
+    35691,
+    544,
+    322,
+    3809,
+    4736,
+    461,
+    3488,
+    432,
+    3282,
+    23714,
+    723,
+    39772,
+    30,
+    1510,
+    3065,
+    526,
+    663,
+    464,
+    819,
+    299,
+    372,
+    28764,
+    32,
+    203,
+    203,
+    41,
+    32,
+    1115,
+    19637,
+    461,
+    551,
+    4778,
+    5507,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    5251,
+    312,
+    296,
+    3528,
+    15621,
+    9624,
+    31374,
+    2819,
+    7871,
+    544,
+    312,
+    803,
+    31,
+    3176,
+    2899,
+    10160,
+    312,
+    14865,
+    13174,
+    8187,
+    312,
+    29952,
+    22340,
+    32,
+    886,
+    9624,
+    37383,
+    3760,
+    24793,
+    312,
+    14865,
+    5929,
+    312,
+    803,
+    31,
+    3176,
+    2899,
+    30,
+    1522,
+    21408,
+    322,
+    2664,
+    1182,
+    25376,
+    461,
+    45253,
+    8904,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    9624,
+    1182,
+    31887,
+    12428,
+    544,
+    322,
+    14222,
+    432,
+    322,
+    37968,
+    14865,
+    461,
+    322,
+    28819,
+    4321,
+    432,
+    322,
+    25376,
+    17016,
+    32,
+    203,
+    203,
+    42,
+    32,
+    1115,
+    20421,
+    461,
+    7310,
+    35559,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    4527,
+    5052,
+    312,
+    14865,
+    31,
+    102,
+    11520,
+    692,
+    1692,
+    16366,
+    372,
+    23637,
+    2899,
+    10066,
+    461,
+    1692,
+    8821,
+    32,
+    701,
+    1135,
+    432,
+    312,
+    14865,
+    30,
+    322,
+    2664,
+    21618,
+    101,
+    810,
+    28617,
+    299,
+    322,
+    1692,
+    16366,
+    30,
+    6109,
+    8696,
+    322,
+    2899,
+    1603,
+    372,
+    322,
+    2401,
+    8967,
+    14488,
+    2260,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    25376,
+    2164,
+    1631,
+    19979,
+    312,
+    7808,
+    30,
+    22035,
+    13328,
+    299,
+    322,
+    2664,
+    1182,
+    12554,
+    28597,
+    5929,
+    312,
+    14865,
+    32,
+    203,
+    203,
+    43,
+    32,
+    1115,
+    6221,
+    31,
+    4867,
+    546,
+    30588,
+    31687,
+    326,
+    3017,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    534,
+    11598,
+    312,
+    409,
+    15785,
+    1191,
+    30,
+    7528,
+    34752,
+    31,
+    7873,
+    10078,
+    436,
+    6959,
+    31,
+    4867,
+    45867,
+    31687,
+    32,
+    8170,
+    10704,
+    25420,
+    461,
+    7528,
+    48120,
+    372,
+    12651,
+    8253,
+    30,
+    48093,
+    4879,
+    312,
+    7176,
+    5788,
+    10293,
+    461,
+    312,
+    7528,
+    34752,
+    7079,
+    32,
+    3017,
+    16106,
+    367,
+    44,
+    886,
+    7528,
+    34752,
+    2164,
+    1631,
+    19979,
+    22992,
+    461,
+    13792,
+    14854,
+    28359,
+    30,
+    17587,
+    328,
+    7622,
+    31,
+    3804,
+    6959,
+    31,
+    4867,
+    10737,
+    32,
+    203,
+    203,
+    35,
+    34,
+    32,
+    1115,
+    3711,
+    8637,
+    15311,
+    302,
+    3453,
+    326,
+    446,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    438,
+    15764,
+    623,
+    312,
+    46226,
+    15488,
+    30,
+    18081,
+    436,
+    33530,
+    8226,
+    461,
+    6325,
+    432,
+    10090,
+    6339,
+    32,
+    886,
+    2664,
+    1182,
+    6339,
+    884,
+    7586,
+    328,
+    4865,
+    30,
+    48093,
+    4879,
+    4905,
+    11323,
+    461,
+    29879,
+    32,
+    446,
+    16106,
+    367,
+    44,
+    886,
+    46226,
+    6735,
+    1631,
+    19979,
+    22992,
+    328,
+    26934,
+    2577,
+    28653,
+    461,
+    42056,
+    18869,
+    2664,
+    2098,
+    2734,
+    942,
+    32,
+    203,
+    203,
+    35,
+    35,
+    32,
+    1115,
+    3910,
+    31,
+    906,
+    3040,
+    11755,
+    18751,
+    694,
+    326,
+    446,
+    3110,
+    32818,
+    44,
+    390,
+    29778,
+    31,
+    2636,
+    12480,
+    315,
+    1269,
+    312,
+    313,
+    38170,
+    31,
+    89,
+    733,
+    20,
+    10078,
+    436,
+    803,
+    26975,
+    30,
+    18081,
+    436,
+    8394,
+    31,
+    771,
+    8835,
+    3498,
+    2288,
+    2125,
+    6063,
+    32,
+    1348,
+    6735,
+    44757,
+    19841,
+    322,
+    2664,
+    372,
+    14025,
+    26160,
+    461,
+    41459,
+    30,
+    18081,
+    436,
+    4335,
+    28355,
+    30,
+    20442,
+    6885,
+    461,
+    23885,
+    311,
+    5401,
+    30,
+    22035,
+    17179,
+    7643,
+    11668,
+    32,
+    446,
+    16106,
+    367,
+    44,
+    886,
+    5744,
+    28597,
+    432,
+    458,
+    6735,
+    44757,
+    35691,
+    544,
+    322,
+    3809,
+    4736,
+    461,
+    44551,
+    432,
+    322,
+    48519,
+    2577,
+    30,
+    1510,
+    3065,
+    526,
+    663,
+    464,
+    819,
+    299,
+    32,
+    203,
+    203,
+    326,
+    2986,
+    318,
+    5938,
+    44,
+    2032,
+    31,
+    3579,
+    4187,
+    4404,
+    3503,
+    312,
+    41235,
+    326,
+    203,
+    203,
+    37466,
+    312,
+    1256,
+    16459,
+    312,
+    803,
+    308,
+    56,
+    27,
+    328,
+    390,
+    29778,
+    31,
+    2636,
+    32,
+    886,
+    2664,
+    48628,
+    322,
+    803,
+    1182,
+    1603,
+    328,
+    322,
+    24864,
+    3500,
+    461,
+    600,
+    328,
+    31,
+    5566,
+    3996,
+    10293,
+    32,
+    35225,
+    322,
+    4553,
+    30,
+    322,
+    1256,
+    26441,
+    1196,
+    312,
+    7841,
+    308,
+    66,
+    27,
+    372,
+    5351,
+    322,
+    803,
+    32,
+    886,
+    48519,
+    2577,
+    9371,
+    447,
+    8187,
+    32681,
+    7019,
+    30,
+    461,
+    415,
+    2080,
+    30,
+    561,
+    438,
+    10768,
+    30,
+    13284,
+    322,
+    803,
+    461,
+    322,
+    328,
+    31,
+    5566,
+    3996,
+    32,
+    203,
+    203,
+    383,
+    1135,
+    432,
+    312,
+    14865,
+    30,
+    322,
+    1692,
+    16366,
+    9212,
+    322,
+    1550,
+    1603,
+    308,
+    761,
+    1603,
+    30,
+    3996,
+    1603,
+    30,
+    461,
+    7841,
+    447,
+    547,
+    2772,
+    267,
+    25376,
+    30,
+    322,
+    2664,
+    28617,
+    101,
+    322,
+    1692,
+    16366,
+    30,
+    6109,
+    8696,
+    322,
+    803,
+    1603,
+    372,
+    322,
+    2401,
+    8967,
+    14488,
+    2260,
+    461,
+    23242,
+    1346,
+    11350,
+    26975,
+    32,
+    203,
+    203,
+    326,
+    56,
+    3528,
+    15621,
+    21400,
+    44,
+    2032,
+    31,
+    3579,
+    4187,
+    4404,
+    3503,
+    312,
+    41235,
+    326,
+    203,
+    203,
+    8899,
+    600,
+    9624,
+    2154,
+    390,
+    29778,
+    31,
+    2636,
+    461,
+    312,
+    29952,
+    22340,
+    308,
+    87,
+    32,
+    89,
+    4317,
+    312,
+    803,
+    7012,
+    2876,
+    14865,
+    25376,
+    27,
+    4436,
+    312,
+    803,
+    31,
+    3176,
+    2899,
+    32,
+    9296,
+    6197,
+    312,
+    14865,
+    5929,
+    322,
+    2899,
+    436,
+    4335,
+    10737,
+    32,
+    22623,
+    322,
+    1133,
+    11508,
+    372,
+    21618,
+    461,
+    4036,
+    322,
+    2899,
+    30,
+    619,
+    4487,
+    619,
+    322,
+    25429,
+    432,
+    322,
+    1158,
+    803,
+    1603,
+    32,
+    886,
+    296,
+    3528,
+    15621,
+    40989,
+    438,
+    688,
+    390,
+    29778,
+    31,
+    2636,
+    1098,
+    21618,
+    461,
+    4036,
+    322,
+    2899,
+    1829,
+    42990,
+    461,
+    21726,
+    12225,
+    2784,
+    322,
+    29952,
+    22340,
+    32,
+    0
+  ],
+  "raw_output": "1. **Memory Architecture**\n\n   Mechanism: Cognitive-OS employs a hybrid memory model, utilizing GPU memory for computationally intensive tasks and a compact, in-memory SQLite database for task state and event journaling. This approach leverages the GPU's parallel processing capabilities for complex computations while maintaining a lightweight, persistent record of task states.\n\n   Limitation: The reliance on GPU memory imposes a trade-off between computational power and data persistence. In case of a crash, the in-memory SQLite database may be lost, requiring a recovery mechanism.\n\n2. **Reasoning and Planning Loop**\n\n   Mechanism: Cognitive-OS utilizes a verifier-gated file patch system for reasoning and planning. The system maintains a verifier component that checks the validity of proposed file patches against a predefined set of rules and constraints. The planning loop involves generating potential patches, validating them, and executing the most promising ones.\n\n   Limitation: The verifier's complexity may introduce latency, potentially slowing down the reasoning and planning process.\n\n3. **Learning or Self-Improvement**\n\n   Mechanism: Cognitive-OS incorporates a reinforcement learning module that learns from user interactions and task outcomes. The module uses a reward-based system to adjust its decision-making process, improving over time without explicit programming.\n\n   Limitation: The learning process may require extensive user interaction and feedback, which could be time-consuming and resource-intensive.\n\n4. **Tool Use and Action Execution**\n\n   Mechanism: Cognitive-OS uses a gated action execution system, where each tool action is verified and authorized by a centralized authority before execution. This ensures that actions adhere to predefined rules and constraints.\n\n   Limitation: The gatekeeping mechanism may introduce overhead, potentially slowing down action execution.\n\n5. **World and Self Representation**\n\n   Mechanism: Cognitive-OS maintains a compact, in-memory representation of the world and self, using a graph-based data structure. This allows for efficient querying and reasoning about the system's state and potential actions.\n\n   Limitation: The compact representation may limit the system's ability to capture complex, nuanced relationships in the world.\n\n6. **Safety and Governance**\n\n   Mechanism: Cognitive-OS employs a multi-layered safety and governance system, including a rule-based access control, a verifier for file patches, and a crash recovery mechanism. These components work together to ensure safe and controlled system behavior.\n\n   Limitation: The system's safety relies on the correct implementation and configuration of these governance mechanisms, which could be challenging to guarantee.\n\n7. **Evaluation and Benchmarks**\n\n   Mechanism: Cognitive-OS uses a falsifiable experiment comparing its performance on a file-edit task across a crash scenario against a simpler baseline. The experiment involves simulating a crash during a file-edit task, observing the system's recovery and continued functionality.\n\n   Limitation: The experiment's validity depends on the accuracy of the simulated crash and the robustness of the recovery mechanism.\n\n8. **Persistence and Runtime Recovery**\n\n   Mechanism: Cognitive-OS utilizes a crash-tolerant event journal to persist task states and event logs. In case of a crash, the system recovers by replaying the event journal, restoring the task state to the last known consistent point.\n\n   Limitation: The recovery process may introduce a delay, potentially affecting the system's responsiveness during a crash.\n\n9. **Multi-agent Orchestration**\n\n   Mechanism: Cognitive-OS proposes a decentralized, negotiation-based approach for multi-agent orchestration. Agents communicate and negotiate to coordinate tasks, leveraging a shared world representation and a negotiation protocol.\n\n   Limitation: The negotiation process may introduce complexity and potential communication overhead, especially in large-scale multi-agent systems.\n\n10. **Engineering Feasibility**\n\n    Mechanism: Cognitive-OS is designed with a modular architecture, allowing for incremental development and testing of individual components. The system's components are implemented in Python, leveraging existing libraries and frameworks.\n\n    Limitation: The modular design may introduce complexity in managing component interactions and ensuring overall system coherence.\n\n11. **Non-obvious Design Insight**\n\n    Mechanism: Cognitive-OS employs a \"verifier-gated\" approach for file patches, allowing for fine-grained control over action execution. This design insight enables the system to balance safety and flexibility, allowing for both controlled, verified actions and opportunistic, potentially risky ones.\n\n    Limitation: The effectiveness of this design insight relies on the correct implementation and tuning of the verifier component, which could be challenging.\n\n**Worked Example: File-Edit Task Across a Crash**\n\nConsider a user editing a file (F) in Cognitive-OS. The system maintains the file's state in the SQLite database and an in-memory graph representation. During the edit, the user initiates a patch (P) to modify the file. The verifier component checks P against predefined rules, and if valid, it is executed, updating the file and the in-memory graph.\n\nIn case of a crash, the event journal records the current state (file state, graph state, and patch P). Upon recovery, the system replays the event journal, restoring the file state to the last known consistent point and applying any pending patches.\n\n**Falsifiable Experiment: File-Edit Task Across a Crash**\n\nDesign an experiment where Cognitive-OS and a simpler baseline (e.g., a file editor without crash recovery) perform a file-edit task. Simulate a crash during the task for both systems. Measure the time taken to recover and continue the task, as well as the consistency of the final file state. The falsifiable hypothesis is that Cognitive-OS will recover and continue the task more efficiently and accurately than the simpler baseline.<|end_of_text|>",
+  "generated_tokens": 1275,
+  "termination": "eos",
+  "seconds": 37.74324210000486,
+  "started_utc": "2026-09-09T10:49:45.157700+00:00",
+  "completed_utc": "2026-09-09T10:50:25.482846+00:00",
+  "model": {
+    "slug": "granite",
+    "model_id": "ibm-granite/granite-3.3-2b-instruct",
+    "revision": "707f574c62054322f6b5b04b6d075f0a8f05e0f0",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/ibm-granite/granite-3.3-2b-instruct/tree/707f574c62054322f6b5b04b6d075f0a8f05e0f0"
+  },
+  "output_sha256": "8b92a9679629ead92c95440920dbc2c4c3b09ea287d96be3f773a65c99bf8ced",
+  "request_sha256": "1cc70599897235e97710c15f80c3d360c8867996f64858ca936ff782a58b7d4e",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/phi/model-files.json b/research/ai_generated_agi_architectures/collection/runs/phi/model-files.json
new file mode 100644
index 0000000..47138cd
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/phi/model-files.json
@@ -0,0 +1,63 @@
+{
+  "model": {
+    "slug": "phi",
+    "model_id": "microsoft/Phi-3.5-mini-instruct",
+    "revision": "2fe192450127e6a83f7441aef6e3ca586c338b77",
+    "license": "mit",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/microsoft/Phi-3.5-mini-instruct/tree/2fe192450127e6a83f7441aef6e3ca586c338b77"
+  },
+  "files": [
+    {
+      "file": "added_tokens.json",
+      "bytes": 306,
+      "sha256": "f8e5a880c6c563a126d9efacb654e105fc4b66f21e5a540f04436e9487a955fb"
+    },
+    {
+      "file": "config.json",
+      "bytes": 3451,
+      "sha256": "224de4f6a15b9d2a89695ec04b7f7ab2dd93a008a506979925e5a88cb5804974"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 195,
+      "sha256": "71ff75d883319b53e16cbb8c7b61363a5138e3d342fc79dd96c2b1cb208d8ff8"
+    },
+    {
+      "file": "model-00001-of-00002.safetensors",
+      "bytes": 4972489328,
+      "sha256": "c5214cdb995ed3dd716add8d9efbfe016b76bb2f1c4c1e6c1c6a95497d7a8837"
+    },
+    {
+      "file": "model-00002-of-00002.safetensors",
+      "bytes": 2669692552,
+      "sha256": "41246eed2b75b66526339c5d32d6f7acdefe0bd24180f97c74303f4656877344"
+    },
+    {
+      "file": "model.safetensors.index.json",
+      "bytes": 16331,
+      "sha256": "6eec78ee2a442445d9d342a7cd9a763da40a49bd91d8611b7db5e9a29c90a428"
+    },
+    {
+      "file": "special_tokens_map.json",
+      "bytes": 665,
+      "sha256": "4e87d5e13d218af26da10a072490ebc08ccadddaef10ab0ec52b33204a49e918"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 1844408,
+      "sha256": "9e2ae3d66819f163cdcfedba5078f3a3af8118c2712491ed60912f77886bda6f"
+    },
+    {
+      "file": "tokenizer.model",
+      "bytes": 499723,
+      "sha256": "9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 3984,
+      "sha256": "92a6e3f894075fda19f47d8755c6ce65b2a974716307d13ae5d79196011ebb44"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/phi/readable.txt b/research/ai_generated_agi_architectures/collection/runs/phi/readable.txt
new file mode 100644
index 0000000..0d68202
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/phi/readable.txt
@@ -0,0 +1,84 @@
+**Technical Proposal for Cognitive-OS AGI-Oriented Architecture**
+
+**1. Memory Architecture**
+*Mechanism:* Utilize a memory-efficient data structure, such as a compact trie, for storing SQLite task states and event journals.
+*Limitation:* The compact trie may have slower access times compared to a hash table, impacting performance for large datasets.
+
+**2. Reasoning and Planning Loop**
+*Mechanism:* Implement a lightweight rule-based system for reasoning, with a planning module that uses heuristics to generate action sequences.
+*Limitation:* Rule-based systems may lack the flexibility to handle complex, novel situations without extensive rule engineering.
+
+**3. Learning or Self-Improvement**
+*Mechanism:* Integrate a reinforcement learning module that operates on a simplified environment model, using reward signals from task completion and error rates.
+*Limitation:* The lack of a rich environment and limited GPU memory constrains the complexity of the model, potentially hindering learning.
+
+**4. Tool Use and Action Execution**
+*Mechanism:* Develop a modular action execution framework that interfaces with the SQLite database and event journal, allowing for governed tool actions.
+*Limitation:* The modularity may introduce overhead, and the governance mechanism could limit the system's ability to adapt to new tools or actions.
+
+**5. World and Self Representation**
+*Mechanism:* Use a compact graph-based representation of the world, with nodes representing entities and edges representing relationships.
+*Limitation:* The graph abstraction may not capture the full complexity of the world, leading to oversimplification.
+
+**6. Safety and Governance**
+*Mechanism:* Implement a verifier-gated file patch system that checks patches against a set of predefined safety rules before execution.
+*Limitation:* The verification process may slow down file operations, and the set of safety rules may not cover all possible unsafe scenarios.
+
+**7. Evaluation and Benchmarks**
+*Mechanism:* Establish a suite of benchmarks that measure the system's performance in task completion, error rates, and resource utilization.
+*Limitation:* Benchmarks may not fully capture the system's ability to handle real-world, unpredictable scenarios.
+
+**8. Persistence and Runtime Recovery**
+*Mechanism:* Use SQLite for persistent storage, with a journaling system that logs changes to enable crash recovery.
+*Limitation:* SQLite's write-ahead logging may not be sufficient for high-throughput or concurrent access scenarios.
+
+**9. Multi-agent Orchestration**
+*Mechanism:* Design a coordination layer that manages interactions between different components, using message passing for communication.
+*Limitation:* The message-passing system may introduce latency and complexity in coordinating distributed components.
+
+**10. Engineering Feasibility**
+*Mechanism:* Leverage existing Python libraries and frameworks to build the system, focusing on modularity and reusability.
+*Limitation:* The reliance on external libraries may limit the system's customizability and increase the dependency on third-party code.
+
+**11. Non-obvious Design Insight**
+*Mechanism:* Implement a lightweight, in-memory caching layer to reduce database access and improve performance.
+*Limitation:* The caching layer may introduce consistency issues and require careful invalidation strategies.
+
+**Data Schema Example: Task State**
+```sql
+CREATE TABLE TaskState (
+    TaskID INTEGER PRIMARY KEY,
+    State TEXT NOT NULL,
+    Timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+);
+```
+
+**Worked Example: File-Edit Task Across a Crash**
+1. User initiates a file-edit task, triggering an event in the journal.
+2. The system updates the task state in the SQLite database.
+3. A crash occurs, but the event journal and task state are preserved.
+4. Upon recovery, the system reconstructs the task state from the journal and database, resuming the task.
+
+**Falsifiable Experiment**
+Compare the proposed architecture with a simpler baseline that uses a flat file system for task state storage and lacks a journaling system. Measure task completion time, error rates, and resource utilization during normal operation and under simulated crash scenarios.
+
+**Implemented Components:**
+- SQLite database
+- Event journal
+- Rule-based reasoning system
+- Reinforcement learning module
+- Graph-based world representation
+- Verifier-gated file patch system
+
+**Proposed Components:**
+- Modular action execution framework
+- Coordination layer for multi-agent orchestration
+- In-memory caching layer
+
+**Unresolved Research Questions:**
+- How to optimize the compact trie for large datasets
+- Methods for expanding the rule-based system's flexibility
+- Techniques for enhancing the reinforcement learning module's complexity within memory constraints
+- Strategies for improving the efficiency of the message-passing system
+
+This proposal outlines an AGI-oriented architecture for Cognitive-OS, addressing key components and tradeoffs within the given constraints. The design aims to balance performance, safety, and learning capabilities while acknowledging the limitations imposed by the local-first Python runtime and SQLite task state management.
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/collection/runs/phi/request.json b/research/ai_generated_agi_architectures/collection/runs/phi/request.json
new file mode 100644
index 0000000..edcff07
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/phi/request.json
@@ -0,0 +1,412 @@
+{
+  "model": {
+    "slug": "phi",
+    "model_id": "microsoft/Phi-3.5-mini-instruct",
+    "revision": "2fe192450127e6a83f7441aef6e3ca586c338b77",
+    "license": "mit",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/microsoft/Phi-3.5-mini-instruct/tree/2fe192450127e6a83f7441aef6e3ca586c338b77"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<|user|>\nPropose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n<|end|>\n<|assistant|>\n",
+  "input_ids": [
+    32010,
+    1019,
+    4220,
+    385,
+    2334,
+    519,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    363,
+    315,
+    3811,
+    3321,
+    29899,
+    3267,
+    29892,
+    1728,
+    5995,
+    292,
+    393,
+    16369,
+    29902,
+    756,
+    1063,
+    14363,
+    29889,
+    15228,
+    29901,
+    445,
+    338,
+    263,
+    1887,
+    29899,
+    4102,
+    5132,
+    10073,
+    411,
+    23299,
+    3414,
+    2106,
+    29892,
+    385,
+    1741,
+    8955,
+    29892,
+    4095,
+    287,
+    5780,
+    8820,
+    29892,
+    1147,
+    3709,
+    29899,
+    29887,
+    630,
+    934,
+    13261,
+    267,
+    29892,
+    322,
+    8095,
+    24205,
+    29889,
+    319,
+    2323,
+    664,
+    19569,
+    756,
+    29871,
+    29896,
+    29906,
+    19289,
+    22796,
+    3370,
+    29936,
+    17791,
+    12530,
+    23649,
+    526,
+    443,
+    16515,
+    29889,
+    6479,
+    271,
+    445,
+    3030,
+    408,
+    263,
+    2874,
+    7276,
+    29892,
+    451,
+    10757,
+    310,
+    27108,
+    29889,
+    13,
+    13,
+    6113,
+    263,
+    3022,
+    895,
+    16905,
+    24963,
+    310,
+    472,
+    1556,
+    29871,
+    29947,
+    29945,
+    29900,
+    3838,
+    29892,
+    773,
+    1438,
+    28121,
+    1353,
+    287,
+    2343,
+    886,
+    29889,
+    1152,
+    1269,
+    28435,
+    2367,
+    263,
+    18387,
+    13336,
+    322,
+    697,
+    29485,
+    470,
+    11302,
+    2696,
+    29901,
+    13,
+    29896,
+    29889,
+    18914,
+    11258,
+    13,
+    29906,
+    29889,
+    830,
+    1658,
+    292,
+    322,
+    18987,
+    2425,
+    13,
+    29941,
+    29889,
+    29257,
+    470,
+    1583,
+    29899,
+    326,
+    16123,
+    882,
+    13,
+    29946,
+    29889,
+    21704,
+    671,
+    322,
+    3158,
+    8225,
+    13,
+    29945,
+    29889,
+    2787,
+    322,
+    1583,
+    8954,
+    13,
+    29953,
+    29889,
+    14795,
+    3305,
+    322,
+    4095,
+    749,
+    13,
+    29955,
+    29889,
+    382,
+    4387,
+    362,
+    322,
+    23513,
+    29879,
+    13,
+    29947,
+    29889,
+    9034,
+    11416,
+    322,
+    10073,
+    24205,
+    13,
+    29929,
+    29889,
+    14974,
+    29899,
+    14748,
+    22624,
+    16444,
+    362,
+    13,
+    29896,
+    29900,
+    29889,
+    22557,
+    28326,
+    4127,
+    13,
+    29896,
+    29896,
+    29889,
+    10050,
+    29899,
+    711,
+    2366,
+    2874,
+    25483,
+    13,
+    13,
+    29419,
+    472,
+    3203,
+    697,
+    6261,
+    848,
+    10938,
+    470,
+    5067,
+    29892,
+    263,
+    3796,
+    1342,
+    310,
+    263,
+    934,
+    29899,
+    5628,
+    3414,
+    4822,
+    263,
+    8095,
+    29892,
+    322,
+    263,
+    285,
+    1338,
+    28677,
+    7639,
+    17420,
+    596,
+    2874,
+    411,
+    263,
+    13682,
+    2362,
+    5570,
+    29889,
+    6652,
+    6202,
+    728,
+    8762,
+    7117,
+    29892,
+    9551,
+    1338,
+    29892,
+    322,
+    443,
+    9778,
+    1490,
+    5925,
+    5155,
+    29889,
+    1938,
+    451,
+    11817,
+    23513,
+    2582,
+    470,
+    7537,
+    800,
+    29889,
+    19152,
+    599,
+    7029,
+    8820,
+    2629,
+    8052,
+    1404,
+    11239,
+    29936,
+    6509,
+    1818,
+    451,
+    4047,
+    2705,
+    7985,
+    1906,
+    11239,
+    29889,
+    13,
+    32007,
+    32001
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 32000,
+    "bos_token_id": 1,
+    "eos_token_id": [
+      32007,
+      32001,
+      32000
+    ],
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/phi/response.json b/research/ai_generated_agi_architectures/collection/runs/phi/response.json
new file mode 100644
index 0000000..720991f
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/phi/response.json
@@ -0,0 +1,1278 @@
+{
+  "output_ids": [
+    3579,
+    7141,
+    3049,
+    936,
+    1019,
+    1066,
+    284,
+    363,
+    315,
+    3811,
+    3321,
+    29899,
+    3267,
+    16369,
+    29902,
+    29899,
+    15988,
+    14927,
+    28333,
+    1068,
+    13,
+    13,
+    1068,
+    29896,
+    29889,
+    18914,
+    28333,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    22310,
+    675,
+    263,
+    3370,
+    29899,
+    8462,
+    848,
+    3829,
+    29892,
+    1316,
+    408,
+    263,
+    11071,
+    534,
+    347,
+    29892,
+    363,
+    15446,
+    23299,
+    3414,
+    5922,
+    322,
+    1741,
+    21824,
+    1338,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    11071,
+    534,
+    347,
+    1122,
+    505,
+    20312,
+    2130,
+    3064,
+    9401,
+    304,
+    263,
+    6608,
+    1591,
+    29892,
+    10879,
+    292,
+    4180,
+    363,
+    2919,
+    20035,
+    29889,
+    13,
+    13,
+    1068,
+    29906,
+    29889,
+    830,
+    1658,
+    292,
+    322,
+    1858,
+    9450,
+    21493,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    1954,
+    2037,
+    263,
+    3578,
+    7915,
+    5751,
+    29899,
+    6707,
+    1788,
+    363,
+    24481,
+    29892,
+    411,
+    263,
+    18987,
+    3883,
+    393,
+    3913,
+    540,
+    332,
+    6765,
+    304,
+    5706,
+    3158,
+    15602,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    27308,
+    29899,
+    6707,
+    6757,
+    1122,
+    10225,
+    278,
+    8525,
+    4127,
+    304,
+    4386,
+    4280,
+    29892,
+    9554,
+    18845,
+    1728,
+    20607,
+    5751,
+    21639,
+    29889,
+    13,
+    13,
+    1068,
+    29941,
+    29889,
+    29257,
+    470,
+    21782,
+    29899,
+    1888,
+    16123,
+    882,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    17100,
+    403,
+    263,
+    15561,
+    1454,
+    13561,
+    6509,
+    3883,
+    393,
+    1751,
+    1078,
+    373,
+    263,
+    20875,
+    5177,
+    1904,
+    29892,
+    773,
+    20751,
+    18470,
+    515,
+    3414,
+    13285,
+    322,
+    1059,
+    19257,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    10225,
+    310,
+    263,
+    8261,
+    5177,
+    322,
+    9078,
+    22796,
+    3370,
+    1040,
+    336,
+    1144,
+    278,
+    13644,
+    310,
+    278,
+    1904,
+    29892,
+    19998,
+    298,
+    513,
+    3241,
+    6509,
+    29889,
+    13,
+    13,
+    1068,
+    29946,
+    29889,
+    21704,
+    4803,
+    322,
+    9123,
+    11080,
+    918,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    10682,
+    263,
+    878,
+    1070,
+    3158,
+    8225,
+    6890,
+    393,
+    19510,
+    411,
+    278,
+    23299,
+    2566,
+    322,
+    1741,
+    8955,
+    29892,
+    14372,
+    363,
+    4095,
+    287,
+    5780,
+    8820,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    878,
+    1070,
+    537,
+    1122,
+    14944,
+    18702,
+    29892,
+    322,
+    278,
+    4095,
+    749,
+    13336,
+    1033,
+    4046,
+    278,
+    1788,
+    29915,
+    29879,
+    11509,
+    304,
+    7744,
+    304,
+    716,
+    8492,
+    470,
+    8820,
+    29889,
+    13,
+    13,
+    1068,
+    29945,
+    29889,
+    2787,
+    322,
+    21782,
+    16314,
+    362,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    4803,
+    263,
+    11071,
+    3983,
+    29899,
+    6707,
+    8954,
+    310,
+    278,
+    3186,
+    29892,
+    411,
+    7573,
+    15783,
+    16212,
+    322,
+    12770,
+    15783,
+    21702,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    3983,
+    27086,
+    428,
+    1122,
+    451,
+    10446,
+    278,
+    2989,
+    13644,
+    310,
+    278,
+    3186,
+    29892,
+    8236,
+    304,
+    288,
+    874,
+    13699,
+    2450,
+    29889,
+    13,
+    13,
+    1068,
+    29953,
+    29889,
+    14795,
+    3305,
+    322,
+    6769,
+    749,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    1954,
+    2037,
+    263,
+    1147,
+    3709,
+    29899,
+    29887,
+    630,
+    934,
+    13261,
+    1788,
+    393,
+    12747,
+    13261,
+    267,
+    2750,
+    263,
+    731,
+    310,
+    758,
+    12119,
+    15332,
+    6865,
+    1434,
+    8225,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    1147,
+    2450,
+    1889,
+    1122,
+    5232,
+    1623,
+    934,
+    6931,
+    29892,
+    322,
+    278,
+    731,
+    310,
+    15332,
+    6865,
+    1122,
+    451,
+    4612,
+    599,
+    1950,
+    25110,
+    21846,
+    29889,
+    13,
+    13,
+    1068,
+    29955,
+    29889,
+    382,
+    4387,
+    362,
+    322,
+    4111,
+    16580,
+    29879,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    2661,
+    370,
+    1674,
+    263,
+    9460,
+    310,
+    23513,
+    29879,
+    393,
+    5645,
+    278,
+    1788,
+    29915,
+    29879,
+    4180,
+    297,
+    3414,
+    13285,
+    29892,
+    1059,
+    19257,
+    29892,
+    322,
+    6503,
+    3667,
+    2133,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    4111,
+    16580,
+    29879,
+    1122,
+    451,
+    8072,
+    10446,
+    278,
+    1788,
+    29915,
+    29879,
+    11509,
+    304,
+    4386,
+    1855,
+    29899,
+    11526,
+    29892,
+    443,
+    27711,
+    519,
+    21846,
+    29889,
+    13,
+    13,
+    1068,
+    29947,
+    29889,
+    9034,
+    11416,
+    322,
+    24875,
+    3599,
+    22205,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    4803,
+    23299,
+    363,
+    28152,
+    8635,
+    29892,
+    411,
+    263,
+    8955,
+    292,
+    1788,
+    393,
+    10748,
+    3620,
+    304,
+    9025,
+    8095,
+    24205,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    23299,
+    29915,
+    29879,
+    2436,
+    29899,
+    29874,
+    2813,
+    12183,
+    1122,
+    451,
+    367,
+    8002,
+    363,
+    1880,
+    29899,
+    20678,
+    649,
+    470,
+    21984,
+    2130,
+    21846,
+    29889,
+    13,
+    13,
+    1068,
+    29929,
+    29889,
+    14974,
+    29899,
+    14748,
+    1394,
+    305,
+    16444,
+    362,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    12037,
+    263,
+    29311,
+    3381,
+    7546,
+    393,
+    767,
+    1179,
+    22060,
+    1546,
+    1422,
+    7117,
+    29892,
+    773,
+    2643,
+    6819,
+    363,
+    12084,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    2643,
+    29899,
+    3364,
+    292,
+    1788,
+    1122,
+    14944,
+    23316,
+    1270,
+    322,
+    13644,
+    297,
+    6615,
+    1218,
+    13235,
+    7117,
+    29889,
+    13,
+    13,
+    1068,
+    29896,
+    29900,
+    29889,
+    22557,
+    5169,
+    294,
+    4127,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    951,
+    19698,
+    5923,
+    5132,
+    9562,
+    322,
+    29143,
+    304,
+    2048,
+    278,
+    1788,
+    29892,
+    12789,
+    4746,
+    373,
+    878,
+    1070,
+    537,
+    322,
+    337,
+    375,
+    3097,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    12536,
+    749,
+    373,
+    7029,
+    9562,
+    1122,
+    4046,
+    278,
+    1788,
+    29915,
+    29879,
+    2888,
+    466,
+    3097,
+    322,
+    7910,
+    278,
+    10609,
+    373,
+    4654,
+    29899,
+    22633,
+    775,
+    29889,
+    13,
+    13,
+    1068,
+    29896,
+    29896,
+    29889,
+    10050,
+    29899,
+    711,
+    2366,
+    12037,
+    13377,
+    523,
+    1068,
+    13,
+    29930,
+    6816,
+    5083,
+    1608,
+    29901,
+    29930,
+    1954,
+    2037,
+    263,
+    3578,
+    7915,
+    29892,
+    297,
+    29899,
+    14834,
+    22488,
+    7546,
+    304,
+    10032,
+    2566,
+    2130,
+    322,
+    11157,
+    4180,
+    29889,
+    13,
+    29930,
+    29931,
+    326,
+    7018,
+    29901,
+    29930,
+    450,
+    22488,
+    7546,
+    1122,
+    14944,
+    5718,
+    3819,
+    5626,
+    322,
+    1996,
+    16010,
+    8340,
+    362,
+    16650,
+    583,
+    29889,
+    13,
+    13,
+    1068,
+    1469,
+    1102,
+    2603,
+    8741,
+    29901,
+    9330,
+    4306,
+    1068,
+    13,
+    28956,
+    2850,
+    13,
+    27045,
+    10911,
+    9330,
+    2792,
+    313,
+    13,
+    1678,
+    9330,
+    1367,
+    2672,
+    4330,
+    17070,
+    29778,
+    14636,
+    29892,
+    13,
+    1678,
+    4306,
+    323,
+    12194,
+    6058,
+    4265,
+    29892,
+    13,
+    1678,
+    7870,
+    7416,
+    27640,
+    2544,
+    8890,
+    22236,
+    315,
+    4574,
+    29450,
+    29918,
+    15307,
+    1254,
+    19297,
+    13,
+    416,
+    13,
+    28956,
+    13,
+    13,
+    1068,
+    5531,
+    287,
+    8741,
+    29901,
+    3497,
+    29899,
+    6103,
+    9330,
+    7255,
+    2124,
+    263,
+    6781,
+    1161,
+    1068,
+    13,
+    29896,
+    29889,
+    4911,
+    14511,
+    1078,
+    263,
+    934,
+    29899,
+    5628,
+    3414,
+    29892,
+    7135,
+    292,
+    385,
+    1741,
+    297,
+    278,
+    8955,
+    29889,
+    13,
+    29906,
+    29889,
+    450,
+    1788,
+    11217,
+    278,
+    3414,
+    2106,
+    297,
+    278,
+    23299,
+    2566,
+    29889,
+    13,
+    29941,
+    29889,
+    319,
+    8095,
+    10008,
+    29892,
+    541,
+    278,
+    1741,
+    8955,
+    322,
+    3414,
+    2106,
+    526,
+    21634,
+    29889,
+    13,
+    29946,
+    29889,
+    19956,
+    24205,
+    29892,
+    278,
+    1788,
+    337,
+    11433,
+    29879,
+    278,
+    3414,
+    2106,
+    515,
+    278,
+    8955,
+    322,
+    2566,
+    29892,
+    620,
+    9929,
+    278,
+    3414,
+    29889,
+    13,
+    13,
+    1068,
+    29943,
+    1338,
+    28677,
+    1222,
+    15362,
+    1068,
+    13,
+    6843,
+    598,
+    278,
+    7972,
+    11258,
+    411,
+    263,
+    13682,
+    2362,
+    5570,
+    393,
+    3913,
+    263,
+    12151,
+    934,
+    1788,
+    363,
+    3414,
+    2106,
+    8635,
+    322,
+    425,
+    4684,
+    263,
+    8955,
+    292,
+    1788,
+    29889,
+    2191,
+    3745,
+    3414,
+    13285,
+    931,
+    29892,
+    1059,
+    19257,
+    29892,
+    322,
+    6503,
+    3667,
+    2133,
+    2645,
+    4226,
+    5858,
+    322,
+    1090,
+    1027,
+    7964,
+    8095,
+    21846,
+    29889,
+    13,
+    13,
+    1068,
+    1888,
+    2037,
+    287,
+    422,
+    9340,
+    29901,
+    1068,
+    13,
+    29899,
+    23299,
+    2566,
+    13,
+    29899,
+    6864,
+    8955,
+    13,
+    29899,
+    27308,
+    29899,
+    6707,
+    24481,
+    1788,
+    13,
+    29899,
+    18334,
+    1454,
+    13561,
+    6509,
+    3883,
+    13,
+    29899,
+    12367,
+    29899,
+    6707,
+    3186,
+    8954,
+    13,
+    29899,
+    1798,
+    3709,
+    29899,
+    29887,
+    630,
+    934,
+    13261,
+    1788,
+    13,
+    13,
+    1068,
+    1184,
+    4752,
+    422,
+    9340,
+    29901,
+    1068,
+    13,
+    29899,
+    3382,
+    1070,
+    3158,
+    8225,
+    6890,
+    13,
+    29899,
+    3189,
+    536,
+    3381,
+    7546,
+    363,
+    2473,
+    29899,
+    14748,
+    22624,
+    16444,
+    362,
+    13,
+    29899,
+    512,
+    29899,
+    14834,
+    22488,
+    7546,
+    13,
+    13,
+    1068,
+    2525,
+    9778,
+    1490,
+    10550,
+    894,
+    29879,
+    29901,
+    1068,
+    13,
+    29899,
+    1128,
+    304,
+    24656,
+    278,
+    11071,
+    534,
+    347,
+    363,
+    2919,
+    20035,
+    13,
+    29899,
+    8108,
+    29879,
+    363,
+    7985,
+    292,
+    278,
+    5751,
+    29899,
+    6707,
+    1788,
+    29915,
+    29879,
+    8525,
+    4127,
+    13,
+    29899,
+    1920,
+    6387,
+    1912,
+    363,
+    427,
+    5403,
+    3277,
+    278,
+    15561,
+    1454,
+    13561,
+    6509,
+    3883,
+    29915,
+    29879,
+    13644,
+    2629,
+    3370,
+    11938,
+    13,
+    29899,
+    3767,
+    1845,
+    583,
+    363,
+    4857,
+    1747,
+    278,
+    19201,
+    310,
+    278,
+    2643,
+    29899,
+    3364,
+    292,
+    1788,
+    13,
+    13,
+    4013,
+    24963,
+    714,
+    9012,
+    385,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    363,
+    315,
+    3811,
+    3321,
+    29899,
+    3267,
+    29892,
+    3211,
+    292,
+    1820,
+    7117,
+    322,
+    11302,
+    22450,
+    2629,
+    278,
+    2183,
+    11938,
+    29889,
+    450,
+    2874,
+    263,
+    9893,
+    304,
+    17346,
+    4180,
+    29892,
+    15332,
+    29892,
+    322,
+    6509,
+    27108,
+    1550,
+    24084,
+    3460,
+    278,
+    27028,
+    527,
+    4752,
+    491,
+    278,
+    1887,
+    29899,
+    4102,
+    5132,
+    10073,
+    322,
+    23299,
+    3414,
+    2106,
+    10643,
+    29889,
+    32007
+  ],
+  "raw_output": "**Technical Proposal for Cognitive-OS AGI-Oriented Architecture**\n\n**1. Memory Architecture**\n*Mechanism:* Utilize a memory-efficient data structure, such as a compact trie, for storing SQLite task states and event journals.\n*Limitation:* The compact trie may have slower access times compared to a hash table, impacting performance for large datasets.\n\n**2. Reasoning and Planning Loop**\n*Mechanism:* Implement a lightweight rule-based system for reasoning, with a planning module that uses heuristics to generate action sequences.\n*Limitation:* Rule-based systems may lack the flexibility to handle complex, novel situations without extensive rule engineering.\n\n**3. Learning or Self-Improvement**\n*Mechanism:* Integrate a reinforcement learning module that operates on a simplified environment model, using reward signals from task completion and error rates.\n*Limitation:* The lack of a rich environment and limited GPU memory constrains the complexity of the model, potentially hindering learning.\n\n**4. Tool Use and Action Execution**\n*Mechanism:* Develop a modular action execution framework that interfaces with the SQLite database and event journal, allowing for governed tool actions.\n*Limitation:* The modularity may introduce overhead, and the governance mechanism could limit the system's ability to adapt to new tools or actions.\n\n**5. World and Self Representation**\n*Mechanism:* Use a compact graph-based representation of the world, with nodes representing entities and edges representing relationships.\n*Limitation:* The graph abstraction may not capture the full complexity of the world, leading to oversimplification.\n\n**6. Safety and Governance**\n*Mechanism:* Implement a verifier-gated file patch system that checks patches against a set of predefined safety rules before execution.\n*Limitation:* The verification process may slow down file operations, and the set of safety rules may not cover all possible unsafe scenarios.\n\n**7. Evaluation and Benchmarks**\n*Mechanism:* Establish a suite of benchmarks that measure the system's performance in task completion, error rates, and resource utilization.\n*Limitation:* Benchmarks may not fully capture the system's ability to handle real-world, unpredictable scenarios.\n\n**8. Persistence and Runtime Recovery**\n*Mechanism:* Use SQLite for persistent storage, with a journaling system that logs changes to enable crash recovery.\n*Limitation:* SQLite's write-ahead logging may not be sufficient for high-throughput or concurrent access scenarios.\n\n**9. Multi-agent Orchestration**\n*Mechanism:* Design a coordination layer that manages interactions between different components, using message passing for communication.\n*Limitation:* The message-passing system may introduce latency and complexity in coordinating distributed components.\n\n**10. Engineering Feasibility**\n*Mechanism:* Leverage existing Python libraries and frameworks to build the system, focusing on modularity and reusability.\n*Limitation:* The reliance on external libraries may limit the system's customizability and increase the dependency on third-party code.\n\n**11. Non-obvious Design Insight**\n*Mechanism:* Implement a lightweight, in-memory caching layer to reduce database access and improve performance.\n*Limitation:* The caching layer may introduce consistency issues and require careful invalidation strategies.\n\n**Data Schema Example: Task State**\n```sql\nCREATE TABLE TaskState (\n    TaskID INTEGER PRIMARY KEY,\n    State TEXT NOT NULL,\n    Timestamp DATETIME DEFAULT CURRENT_TIMESTAMP\n);\n```\n\n**Worked Example: File-Edit Task Across a Crash**\n1. User initiates a file-edit task, triggering an event in the journal.\n2. The system updates the task state in the SQLite database.\n3. A crash occurs, but the event journal and task state are preserved.\n4. Upon recovery, the system reconstructs the task state from the journal and database, resuming the task.\n\n**Falsifiable Experiment**\nCompare the proposed architecture with a simpler baseline that uses a flat file system for task state storage and lacks a journaling system. Measure task completion time, error rates, and resource utilization during normal operation and under simulated crash scenarios.\n\n**Implemented Components:**\n- SQLite database\n- Event journal\n- Rule-based reasoning system\n- Reinforcement learning module\n- Graph-based world representation\n- Verifier-gated file patch system\n\n**Proposed Components:**\n- Modular action execution framework\n- Coordination layer for multi-agent orchestration\n- In-memory caching layer\n\n**Unresolved Research Questions:**\n- How to optimize the compact trie for large datasets\n- Methods for expanding the rule-based system's flexibility\n- Techniques for enhancing the reinforcement learning module's complexity within memory constraints\n- Strategies for improving the efficiency of the message-passing system\n\nThis proposal outlines an AGI-oriented architecture for Cognitive-OS, addressing key components and tradeoffs within the given constraints. The design aims to balance performance, safety, and learning capabilities while acknowledging the limitations imposed by the local-first Python runtime and SQLite task state management.<|end|>",
+  "generated_tokens": 1244,
+  "termination": "eos",
+  "seconds": 42.53926969999884,
+  "started_utc": "2026-09-09T10:24:23.351118+00:00",
+  "completed_utc": "2026-09-09T10:25:11.007414+00:00",
+  "model": {
+    "slug": "phi",
+    "model_id": "microsoft/Phi-3.5-mini-instruct",
+    "revision": "2fe192450127e6a83f7441aef6e3ca586c338b77",
+    "license": "mit",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/microsoft/Phi-3.5-mini-instruct/tree/2fe192450127e6a83f7441aef6e3ca586c338b77"
+  },
+  "output_sha256": "9b414a37ad465bfe74c7a788bcc001ed0f755108dfba13f1b855707e08e55e34",
+  "request_sha256": "473ae4f47940736507181d3a6570b865cac4b1df12003fe5c7af0699e21af55d",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/qwen/model-files.json b/research/ai_generated_agi_architectures/collection/runs/qwen/model-files.json
new file mode 100644
index 0000000..99a3291
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/qwen/model-files.json
@@ -0,0 +1,43 @@
+{
+  "model": {
+    "slug": "qwen",
+    "model_id": "Qwen/Qwen2.5-1.5B-Instruct",
+    "revision": "989aa7980e4cf806f80c7fef2b1adb7bc71aa306",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/989aa7980e4cf806f80c7fef2b1adb7bc71aa306"
+  },
+  "files": [
+    {
+      "file": "config.json",
+      "bytes": 660,
+      "sha256": "98d2ff8cc47488d08a2b0b3acf4eb99ef210779b42bd48605f6b8e36acdbf670"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 242,
+      "sha256": "e558847a8b4402616f1273797b015104dc266fe4b520056fca88823ba8f8ebe6"
+    },
+    {
+      "file": "model.safetensors",
+      "bytes": 3087467144,
+      "sha256": "dd924a11b4c220f385b51ffa522daea7c9f3d850e31b162bb5661df483c6d3ee"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 7031645,
+      "sha256": "c0382117ea329cdf097041132f6d735924b697924d6f6fc3945713e96ce87539"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 7305,
+      "sha256": "5b5d4f65d0acd3b2d56a35b56d374a36cbc1c8fa5cf3b3febbbfabf22f359583"
+    },
+    {
+      "file": "vocab.json",
+      "bytes": 2776833,
+      "sha256": "ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/qwen/readable.txt b/research/ai_generated_agi_architectures/collection/runs/qwen/readable.txt
new file mode 100644
index 0000000..c7e866d
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/qwen/readable.txt
@@ -0,0 +1,49 @@
+### Technical Proposal for Cognitive-OS
+
+#### 1. Memory Architecture
+- **Mechanism:** Utilize SQLite for persistent storage of task states and event logs. This ensures data integrity and quick access.
+- **Limitation:** SQLite's transactional nature may introduce overhead, potentially slowing down operations.
+
+#### 2. Reasoning and Planning Loop
+- **Mechanism:** Implement a simple planner that uses a heuristic-based approach to select the next action. This reduces complexity but may require frequent updates.
+- **Limitation:** Heuristics may not always lead to optimal decisions, leading to suboptimal performance.
+
+#### 3. Learning or Self-Improvement
+- **Mechanism:** Incorporate a basic reinforcement learning component to learn from user interactions. This could involve rewards for correct actions and penalties for incorrect ones.
+- **Limitation:** Learning rate and reward system need tuning to ensure stable performance.
+
+#### 4. Tool Use and Action Execution
+- **Mechanism:** Use a simple command-line interface for tool actions. This ensures that all actions are traceable and verifiable.
+- **Limitation:** User input may be limited, potentially restricting the scope of tasks that can be performed.
+
+#### 5. World and Self Representation
+- **Mechanism:** Represent the world as a set of rules and the self as a set of capabilities. This allows for a simple, yet effective, representation of the environment and the agent.
+- **Limitation:** The world model may not capture complex interactions, leading to potential misrepresentations.
+
+#### 6. Safety and Governance
+- **Mechanism:** Implement a simple verification system to ensure that actions do not violate the rules of the world. This is done through a simple rule-based system.
+- **Limitation:** Verification may not be foolproof, leading to potential violations.
+
+#### 7. Evaluation and Benchmarks
+- **Mechanism:** Use a simple benchmarking tool to evaluate the performance of Cognitive-OS. This could involve comparing its performance to a baseline system.
+- **Limitation:** Benchmarks may not fully capture the complexity of real-world tasks, leading to potential underestimation of performance.
+
+#### 8. Persistence and Runtime Recovery
+- **Mechanism:** Use SQLite for persistent storage and a simple crash recovery mechanism. This ensures that the system can recover from crashes.
+- **Limitation:** SQLite's transactional nature may introduce overhead, potentially slowing down operations.
+
+#### 9. Multi-Agent Orchestration
+- **Mechanism:** Implement a simple multi-agent system where agents can communicate and coordinate their actions. This allows for more complex tasks to be performed.
+- **Limitation:** Communication and coordination may be limited, leading to potential inefficiencies.
+
+#### 10. Engineering Feasibility
+- **Mechanism:** Use Python for its simplicity and extensive libraries, which allows for easy implementation of the proposed architecture.
+- **Limitation:** Python's memory usage may be a concern, especially for large-scale systems.
+
+#### 11. Non-Obvious Design Insight
+- **Mechanism:** Use a simple, rule-based system for world and self representation, which allows for easy implementation and understanding.
+- **Limitation:** This simplicity may limit the complexity of the system, leading to potential limitations in the system's capabilities.
+
+---
+
+This proposal outlines a basic implementation of Cognitive-OS, focusing on the key components and limitations of the system. The design is intended to be a starting point for further development and refinement.
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/collection/runs/qwen/request.json b/research/ai_generated_agi_architectures/collection/runs/qwen/request.json
new file mode 100644
index 0000000..0a11e38
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/qwen/request.json
@@ -0,0 +1,395 @@
+{
+  "model": {
+    "slug": "qwen",
+    "model_id": "Qwen/Qwen2.5-1.5B-Instruct",
+    "revision": "989aa7980e4cf806f80c7fef2b1adb7bc71aa306",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/989aa7980e4cf806f80c7fef2b1adb7bc71aa306"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n<|im_start|>user\nPropose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n<|im_end|>\n<|im_start|>assistant\n",
+  "input_ids": [
+    151644,
+    8948,
+    198,
+    2610,
+    525,
+    1207,
+    16948,
+    11,
+    3465,
+    553,
+    54364,
+    14817,
+    13,
+    1446,
+    525,
+    264,
+    10950,
+    17847,
+    13,
+    151645,
+    198,
+    151644,
+    872,
+    198,
+    2008,
+    960,
+    458,
+    4211,
+    480,
+    15082,
+    40,
+    35085,
+    17646,
+    369,
+    72135,
+    12,
+    3126,
+    11,
+    2041,
+    20409,
+    429,
+    15082,
+    40,
+    702,
+    1012,
+    16994,
+    13,
+    9608,
+    25,
+    419,
+    374,
+    264,
+    2205,
+    36943,
+    13027,
+    15592,
+    448,
+    28334,
+    3383,
+    1584,
+    11,
+    458,
+    1538,
+    8322,
+    11,
+    26702,
+    5392,
+    6168,
+    11,
+    88737,
+    2371,
+    657,
+    1034,
+    28660,
+    11,
+    323,
+    9920,
+    13351,
+    13,
+    362,
+    3175,
+    95891,
+    702,
+    220,
+    16,
+    17,
+    18865,
+    22670,
+    4938,
+    26,
+    21009,
+    7171,
+    33356,
+    525,
+    34987,
+    13,
+    18877,
+    419,
+    2266,
+    438,
+    264,
+    2884,
+    21568,
+    11,
+    537,
+    5904,
+    315,
+    16928,
+    382,
+    7985,
+    264,
+    63594,
+    10916,
+    13734,
+    315,
+    518,
+    1429,
+    220,
+    23,
+    20,
+    15,
+    4244,
+    11,
+    1667,
+    1493,
+    44214,
+    48826,
+    69452,
+    13,
+    1752,
+    1817,
+    14496,
+    2968,
+    264,
+    14175,
+    16953,
+    323,
+    825,
+    20278,
+    476,
+    6559,
+    1847,
+    510,
+    16,
+    13,
+    13850,
+    17646,
+    198,
+    17,
+    13,
+    26759,
+    287,
+    323,
+    9115,
+    6337,
+    198,
+    18,
+    13,
+    20909,
+    476,
+    656,
+    36925,
+    776,
+    7830,
+    198,
+    19,
+    13,
+    13474,
+    990,
+    323,
+    1917,
+    11320,
+    198,
+    20,
+    13,
+    4337,
+    323,
+    656,
+    13042,
+    198,
+    21,
+    13,
+    18702,
+    323,
+    34086,
+    198,
+    22,
+    13,
+    39288,
+    323,
+    62019,
+    198,
+    23,
+    13,
+    64236,
+    323,
+    15592,
+    13351,
+    198,
+    24,
+    13,
+    17439,
+    41935,
+    69884,
+    2165,
+    198,
+    16,
+    15,
+    13,
+    16595,
+    68443,
+    198,
+    16,
+    16,
+    13,
+    11581,
+    92449,
+    2469,
+    2884,
+    20017,
+    271,
+    22283,
+    518,
+    3245,
+    825,
+    11464,
+    821,
+    10802,
+    476,
+    3749,
+    11,
+    264,
+    6439,
+    3110,
+    315,
+    264,
+    1034,
+    22142,
+    3383,
+    3941,
+    264,
+    9920,
+    11,
+    323,
+    264,
+    31932,
+    22619,
+    9342,
+    26297,
+    697,
+    2884,
+    448,
+    264,
+    34288,
+    25869,
+    13,
+    422,
+    88647,
+    11537,
+    6813,
+    11,
+    24258,
+    11,
+    323,
+    80161,
+    3412,
+    4755,
+    13,
+    3155,
+    537,
+    17023,
+    28431,
+    3059,
+    476,
+    51846,
+    13,
+    13655,
+    678,
+    9250,
+    6168,
+    2878,
+    14275,
+    1196,
+    8541,
+    26,
+    6832,
+    1969,
+    537,
+    49394,
+    9225,
+    1846,
+    8541,
+    624,
+    151645,
+    198,
+    151644,
+    77091,
+    198
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 151645,
+    "bos_token_id": 151643,
+    "eos_token_id": [
+      151645,
+      151643
+    ],
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/qwen/response.json b/research/ai_generated_agi_architectures/collection/runs/qwen/response.json
new file mode 100644
index 0000000..254efdc
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/qwen/response.json
@@ -0,0 +1,738 @@
+{
+  "output_ids": [
+    14374,
+    26668,
+    65890,
+    369,
+    72135,
+    12,
+    3126,
+    271,
+    820,
+    220,
+    16,
+    13,
+    13850,
+    37843,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    10167,
+    551,
+    28334,
+    369,
+    24999,
+    5819,
+    315,
+    3383,
+    5302,
+    323,
+    1538,
+    18422,
+    13,
+    1096,
+    25351,
+    821,
+    16614,
+    323,
+    3974,
+    2615,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    28334,
+    594,
+    7745,
+    278,
+    6993,
+    1231,
+    19131,
+    31015,
+    11,
+    13581,
+    47308,
+    1495,
+    7525,
+    382,
+    820,
+    220,
+    17,
+    13,
+    26759,
+    287,
+    323,
+    27680,
+    21359,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    31075,
+    264,
+    4285,
+    49711,
+    429,
+    5711,
+    264,
+    66609,
+    5980,
+    5486,
+    311,
+    3293,
+    279,
+    1790,
+    1917,
+    13,
+    1096,
+    25271,
+    23094,
+    714,
+    1231,
+    1373,
+    20757,
+    8837,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    1260,
+    324,
+    5589,
+    1231,
+    537,
+    2677,
+    2990,
+    311,
+    22823,
+    11181,
+    11,
+    6388,
+    311,
+    1186,
+    2912,
+    2861,
+    5068,
+    382,
+    820,
+    220,
+    18,
+    13,
+    20909,
+    476,
+    10115,
+    12,
+    47842,
+    7830,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    53704,
+    349,
+    264,
+    6770,
+    71278,
+    6832,
+    3692,
+    311,
+    3960,
+    504,
+    1196,
+    21880,
+    13,
+    1096,
+    1410,
+    21058,
+    21160,
+    369,
+    4396,
+    6168,
+    323,
+    29986,
+    369,
+    15114,
+    6174,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    20909,
+    4379,
+    323,
+    11313,
+    1849,
+    1184,
+    41338,
+    311,
+    5978,
+    15175,
+    5068,
+    382,
+    820,
+    220,
+    19,
+    13,
+    13474,
+    5443,
+    323,
+    5586,
+    30928,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    5443,
+    264,
+    4285,
+    3210,
+    8447,
+    3749,
+    369,
+    5392,
+    6168,
+    13,
+    1096,
+    25351,
+    429,
+    678,
+    6168,
+    525,
+    11655,
+    480,
+    323,
+    2739,
+    22619,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    2657,
+    1946,
+    1231,
+    387,
+    7199,
+    11,
+    13581,
+    64851,
+    279,
+    6891,
+    315,
+    9079,
+    429,
+    646,
+    387,
+    10660,
+    382,
+    820,
+    220,
+    20,
+    13,
+    4337,
+    323,
+    10115,
+    78046,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    21950,
+    279,
+    1879,
+    438,
+    264,
+    738,
+    315,
+    5601,
+    323,
+    279,
+    656,
+    438,
+    264,
+    738,
+    315,
+    16928,
+    13,
+    1096,
+    6147,
+    369,
+    264,
+    4285,
+    11,
+    3602,
+    7373,
+    11,
+    13042,
+    315,
+    279,
+    4573,
+    323,
+    279,
+    8315,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    576,
+    1879,
+    1614,
+    1231,
+    537,
+    12322,
+    6351,
+    21880,
+    11,
+    6388,
+    311,
+    4650,
+    5786,
+    35269,
+    804,
+    382,
+    820,
+    220,
+    21,
+    13,
+    18702,
+    323,
+    80589,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    31075,
+    264,
+    4285,
+    22901,
+    1849,
+    311,
+    5978,
+    429,
+    6168,
+    653,
+    537,
+    40487,
+    279,
+    5601,
+    315,
+    279,
+    1879,
+    13,
+    1096,
+    374,
+    2814,
+    1526,
+    264,
+    4285,
+    5912,
+    5980,
+    1849,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    55473,
+    1231,
+    537,
+    387,
+    23977,
+    15780,
+    11,
+    6388,
+    311,
+    4650,
+    26557,
+    382,
+    820,
+    220,
+    22,
+    13,
+    39288,
+    323,
+    35258,
+    15544,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    5443,
+    264,
+    4285,
+    28431,
+    287,
+    5392,
+    311,
+    15442,
+    279,
+    5068,
+    315,
+    72135,
+    12,
+    3126,
+    13,
+    1096,
+    1410,
+    21058,
+    26297,
+    1181,
+    5068,
+    311,
+    264,
+    25869,
+    1849,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    35258,
+    15544,
+    1231,
+    537,
+    7225,
+    12322,
+    279,
+    23094,
+    315,
+    1931,
+    30084,
+    9079,
+    11,
+    6388,
+    311,
+    4650,
+    1212,
+    477,
+    5465,
+    315,
+    5068,
+    382,
+    820,
+    220,
+    23,
+    13,
+    64236,
+    323,
+    10954,
+    33911,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    5443,
+    28334,
+    369,
+    24999,
+    5819,
+    323,
+    264,
+    4285,
+    9920,
+    13351,
+    16953,
+    13,
+    1096,
+    25351,
+    429,
+    279,
+    1849,
+    646,
+    11731,
+    504,
+    36137,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    28334,
+    594,
+    7745,
+    278,
+    6993,
+    1231,
+    19131,
+    31015,
+    11,
+    13581,
+    47308,
+    1495,
+    7525,
+    382,
+    820,
+    220,
+    24,
+    13,
+    17439,
+    45118,
+    2521,
+    26320,
+    2165,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    31075,
+    264,
+    4285,
+    7299,
+    41935,
+    1849,
+    1380,
+    13009,
+    646,
+    19032,
+    323,
+    16184,
+    862,
+    6168,
+    13,
+    1096,
+    6147,
+    369,
+    803,
+    6351,
+    9079,
+    311,
+    387,
+    10660,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    30866,
+    323,
+    37693,
+    1231,
+    387,
+    7199,
+    11,
+    6388,
+    311,
+    4650,
+    89225,
+    42590,
+    382,
+    820,
+    220,
+    16,
+    15,
+    13,
+    16595,
+    3841,
+    300,
+    3147,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    5443,
+    13027,
+    369,
+    1181,
+    38975,
+    323,
+    16376,
+    20186,
+    11,
+    892,
+    6147,
+    369,
+    4135,
+    8129,
+    315,
+    279,
+    10981,
+    17646,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    13027,
+    594,
+    4938,
+    10431,
+    1231,
+    387,
+    264,
+    4643,
+    11,
+    5310,
+    369,
+    3460,
+    12934,
+    5942,
+    382,
+    820,
+    220,
+    16,
+    16,
+    13,
+    11581,
+    12,
+    4121,
+    2469,
+    6982,
+    70971,
+    198,
+    12,
+    3070,
+    57067,
+    67813,
+    66963,
+    5443,
+    264,
+    4285,
+    11,
+    5912,
+    5980,
+    1849,
+    369,
+    1879,
+    323,
+    656,
+    13042,
+    11,
+    892,
+    6147,
+    369,
+    4135,
+    8129,
+    323,
+    8660,
+    624,
+    12,
+    3070,
+    16527,
+    367,
+    66963,
+    1096,
+    38975,
+    1231,
+    3930,
+    279,
+    23094,
+    315,
+    279,
+    1849,
+    11,
+    6388,
+    311,
+    4650,
+    9481,
+    304,
+    279,
+    1849,
+    594,
+    16928,
+    382,
+    44364,
+    1986,
+    13734,
+    49629,
+    264,
+    6770,
+    8129,
+    315,
+    72135,
+    12,
+    3126,
+    11,
+    21080,
+    389,
+    279,
+    1376,
+    6813,
+    323,
+    9481,
+    315,
+    279,
+    1849,
+    13,
+    576,
+    2884,
+    374,
+    10602,
+    311,
+    387,
+    264,
+    5916,
+    1459,
+    369,
+    4623,
+    4401,
+    323,
+    72913,
+    13,
+    151645
+  ],
+  "raw_output": "### Technical Proposal for Cognitive-OS\n\n#### 1. Memory Architecture\n- **Mechanism:** Utilize SQLite for persistent storage of task states and event logs. This ensures data integrity and quick access.\n- **Limitation:** SQLite's transactional nature may introduce overhead, potentially slowing down operations.\n\n#### 2. Reasoning and Planning Loop\n- **Mechanism:** Implement a simple planner that uses a heuristic-based approach to select the next action. This reduces complexity but may require frequent updates.\n- **Limitation:** Heuristics may not always lead to optimal decisions, leading to suboptimal performance.\n\n#### 3. Learning or Self-Improvement\n- **Mechanism:** Incorporate a basic reinforcement learning component to learn from user interactions. This could involve rewards for correct actions and penalties for incorrect ones.\n- **Limitation:** Learning rate and reward system need tuning to ensure stable performance.\n\n#### 4. Tool Use and Action Execution\n- **Mechanism:** Use a simple command-line interface for tool actions. This ensures that all actions are traceable and verifiable.\n- **Limitation:** User input may be limited, potentially restricting the scope of tasks that can be performed.\n\n#### 5. World and Self Representation\n- **Mechanism:** Represent the world as a set of rules and the self as a set of capabilities. This allows for a simple, yet effective, representation of the environment and the agent.\n- **Limitation:** The world model may not capture complex interactions, leading to potential misrepresentations.\n\n#### 6. Safety and Governance\n- **Mechanism:** Implement a simple verification system to ensure that actions do not violate the rules of the world. This is done through a simple rule-based system.\n- **Limitation:** Verification may not be foolproof, leading to potential violations.\n\n#### 7. Evaluation and Benchmarks\n- **Mechanism:** Use a simple benchmarking tool to evaluate the performance of Cognitive-OS. This could involve comparing its performance to a baseline system.\n- **Limitation:** Benchmarks may not fully capture the complexity of real-world tasks, leading to potential underestimation of performance.\n\n#### 8. Persistence and Runtime Recovery\n- **Mechanism:** Use SQLite for persistent storage and a simple crash recovery mechanism. This ensures that the system can recover from crashes.\n- **Limitation:** SQLite's transactional nature may introduce overhead, potentially slowing down operations.\n\n#### 9. Multi-Agent Orchestration\n- **Mechanism:** Implement a simple multi-agent system where agents can communicate and coordinate their actions. This allows for more complex tasks to be performed.\n- **Limitation:** Communication and coordination may be limited, leading to potential inefficiencies.\n\n#### 10. Engineering Feasibility\n- **Mechanism:** Use Python for its simplicity and extensive libraries, which allows for easy implementation of the proposed architecture.\n- **Limitation:** Python's memory usage may be a concern, especially for large-scale systems.\n\n#### 11. Non-Obvious Design Insight\n- **Mechanism:** Use a simple, rule-based system for world and self representation, which allows for easy implementation and understanding.\n- **Limitation:** This simplicity may limit the complexity of the system, leading to potential limitations in the system's capabilities.\n\n---\n\nThis proposal outlines a basic implementation of Cognitive-OS, focusing on the key components and limitations of the system. The design is intended to be a starting point for further development and refinement.<|im_end|>",
+  "generated_tokens": 704,
+  "termination": "eos",
+  "seconds": 20.158238899995922,
+  "started_utc": "2026-09-09T10:20:35.944982+00:00",
+  "completed_utc": "2026-09-09T10:20:58.262417+00:00",
+  "model": {
+    "slug": "qwen",
+    "model_id": "Qwen/Qwen2.5-1.5B-Instruct",
+    "revision": "989aa7980e4cf806f80c7fef2b1adb7bc71aa306",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/989aa7980e4cf806f80c7fef2b1adb7bc71aa306"
+  },
+  "output_sha256": "b29a63710a909c5b54386505d72c0c5e546aef55b37f28876b2db46aa83e4720",
+  "request_sha256": "86997251d0ddae4a81e0db3bdce9dd0ac78f521864715fabaf8243709fd0cffc",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/smollm/model-files.json b/research/ai_generated_agi_architectures/collection/runs/smollm/model-files.json
new file mode 100644
index 0000000..9ba4e62
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/smollm/model-files.json
@@ -0,0 +1,68 @@
+{
+  "model": {
+    "slug": "smollm",
+    "model_id": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    "revision": "31b70e2e869a7173562077fd711b654946d38674",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/tree/31b70e2e869a7173562077fd711b654946d38674"
+  },
+  "files": [
+    {
+      "file": "all_results.json",
+      "bytes": 785,
+      "sha256": "1908cf6a24c3d69aff2ab1d2a9c0366dc882a1fe7f91e0555f8e9df6612fac39"
+    },
+    {
+      "file": "config.json",
+      "bytes": 908,
+      "sha256": "994f50b16abb4ae00880baefe03c10260b5bd608d2bf586f7056ca05a534feea"
+    },
+    {
+      "file": "eval_results.json",
+      "bytes": 587,
+      "sha256": "fb82804b504f34c680f766dcb2b22da23cc7f93cd37f23186900559c8ee33109"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 132,
+      "sha256": "87b916edaaab66b3899b9d0dd0752727dff6666686da0504d89ae0a6e055a013"
+    },
+    {
+      "file": "model.safetensors",
+      "bytes": 3422777952,
+      "sha256": "f55217be716b6a997b97b9d8d7eb6fad02e00858f5010ec24f64603c3a98a0e8"
+    },
+    {
+      "file": "special_tokens_map.json",
+      "bytes": 655,
+      "sha256": "2b7379f3ae813529281a5c602bc5a11c1d4e0a99107aaa597fe936c1e813ca52"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 2104556,
+      "sha256": "9ca9acddb6525a194ec8ac7a87f24fbba7232a9a15ffa1af0c1224fcd888e47c"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 3764,
+      "sha256": "4ec77d44f62efeb38d7e044a1db318f6a939438425312dfa333b8382dbad98df"
+    },
+    {
+      "file": "train_results.json",
+      "bytes": 232,
+      "sha256": "0a1575a24263963c53c634f6f40a3e8127e69f05c97039461f590e4ee2a91324"
+    },
+    {
+      "file": "trainer_state.json",
+      "bytes": 86369,
+      "sha256": "e33c4af76d28551752fc40189bff99ea69dba5234b1bf4857e4db136ecdd04a1"
+    },
+    {
+      "file": "vocab.json",
+      "bytes": 800662,
+      "sha256": "82b84012e3add4d01d12ba14442026e49b8cbbaead1f79ecf3d919784f82dc79"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/smollm/readable.txt b/research/ai_generated_agi_architectures/collection/runs/smollm/readable.txt
new file mode 100644
index 0000000..3e1b20d
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/smollm/readable.txt
@@ -0,0 +1,69 @@
+1. Memory Architecture:
+
+The memory architecture of Cognitive-OS is designed to be highly scalable and efficient, with a focus on reducing memory footprint while maintaining performance. The system uses a combination of memory hierarchies, including a fast, low-latency cache and a slower, more durable storage system. The cache is used to store frequently accessed data, while the storage system is used to store less frequently accessed data. The system also uses a technique called "memory pooling" to reduce memory usage by reusing memory blocks that are no longer in use.
+
+Limitation: The memory architecture may not be suitable for systems with extremely large amounts of data, as it may not be able to handle the high memory usage required for such systems.
+
+2. Reasoning and Planning Loop:
+
+The reasoning and planning loop of Cognitive-OS is designed to be highly flexible and adaptable, allowing the system to learn and improve over time. The loop consists of three main components: perception, reasoning, and action. The perception component uses machine learning algorithms to interpret sensor data and identify patterns. The reasoning component uses logical rules and machine learning algorithms to reason about the data and make decisions. The action component uses the results of the reasoning component to take actions in the world.
+
+Limitation: The reasoning and planning loop may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+3. Learning or Self-Improvement:
+
+The learning or self-improvement component of Cognitive-OS is designed to allow the system to learn from its experiences and improve over time. The system uses a combination of machine learning algorithms and reinforcement learning to learn from its experiences. The system also uses a technique called "self-supervised learning" to learn about itself and improve its performance.
+
+Limitation: The learning or self-improvement component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+4. Tool Use and Action Execution:
+
+The tool use and action execution component of Cognitive-OS is designed to allow the system to use tools and execute actions in the world. The system uses a combination of machine learning algorithms and natural language processing to understand the commands and actions that are given to it. The system also uses a technique called "action abstraction" to simplify complex actions and make them easier to execute.
+
+Limitation: The tool use and action execution component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+5. World and Self Representation:
+
+The world and self representation component of Cognitive-OS is designed to allow the system to understand its environment and itself. The system uses a combination of machine learning algorithms and natural language processing to understand the world and itself. The system also uses a technique called "self-representation" to understand its own internal state and make decisions.
+
+Limitation: The world and self representation component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+6. Safety and Governance:
+
+The safety and governance component of Cognitive-OS is designed to ensure that the system operates safely and securely. The system uses a combination of machine learning algorithms and natural language processing to detect and prevent potential safety hazards. The system also uses a technique called "governance" to ensure that the system operates within the constraints of the user's permissions.
+
+Limitation: The safety and governance component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+7. Evaluation and Benchmarks:
+
+The evaluation and benchmarks component of Cognitive-OS is designed to measure the system's performance and compare it to other systems. The system uses a combination of machine learning algorithms and natural language processing to evaluate its performance and compare it to other systems. The system also uses a technique called "benchmarking" to measure its performance and identify areas for improvement.
+
+Limitation: The evaluation and benchmarks component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+8. Persistence and Runtime Recovery:
+
+The persistence and runtime recovery component of Cognitive-OS is designed to ensure that the system can persist its state and recover from crashes. The system uses a combination of machine learning algorithms and natural language processing to persist its state and recover from crashes. The system also uses a technique called "runtime recovery" to recover from crashes and resume normal operation.
+
+Limitation: The persistence and runtime recovery component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+9. Multi-Agent Orchestration:
+
+The multi-agent orchestration component of Cognitive-OS is designed to allow multiple agents to work together to achieve a common goal. The system uses a combination of machine learning algorithms and natural language processing to coordinate the actions of multiple agents. The system also uses a technique called "orchestration" to manage the interactions between multiple agents.
+
+Limitation: The multi-agent orchestration component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+10. Engineering Feasibility:
+
+The engineering feasibility component of Cognitive-OS is designed to evaluate the feasibility of the system's design. The system uses a combination of machine learning algorithms and natural language processing to evaluate the feasibility of the system's design. The system also uses a technique called "feasibility analysis" to identify potential challenges and limitations.
+
+Limitation: The engineering feasibility component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+11. Non-obvious Design Insight:
+
+The non-obvious design insight component of Cognitive-OS is designed to identify potential design improvements that may not be immediately obvious. The system uses a combination of machine learning algorithms and natural language processing to identify potential design improvements. The system also uses a technique called "insight generation" to generate potential design improvements.
+
+Limitation: The non-obvious design insight component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+Worked Example:
+
+Here is a worked example of a file-edit task across a crash:
diff --git a/research/ai_generated_agi_architectures/collection/runs/smollm/request.json b/research/ai_generated_agi_architectures/collection/runs/smollm/request.json
new file mode 100644
index 0000000..ca8b87b
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/smollm/request.json
@@ -0,0 +1,406 @@
+{
+  "model": {
+    "slug": "smollm",
+    "model_id": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    "revision": "31b70e2e869a7173562077fd711b654946d38674",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/tree/31b70e2e869a7173562077fd711b654946d38674"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n<|im_start|>user\nPropose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n<|im_end|>\n<|im_start|>assistant\n",
+  "input_ids": [
+    1,
+    9690,
+    198,
+    2683,
+    359,
+    253,
+    5356,
+    5646,
+    11173,
+    3365,
+    3511,
+    308,
+    34519,
+    28,
+    7018,
+    411,
+    407,
+    19712,
+    8182,
+    2,
+    198,
+    1,
+    4093,
+    198,
+    27355,
+    602,
+    354,
+    2666,
+    515,
+    330,
+    18480,
+    29,
+    14617,
+    6277,
+    327,
+    18062,
+    29,
+    4531,
+    28,
+    1355,
+    15819,
+    338,
+    330,
+    18480,
+    553,
+    719,
+    6551,
+    30,
+    12577,
+    42,
+    451,
+    314,
+    253,
+    1679,
+    29,
+    7298,
+    5905,
+    29709,
+    351,
+    15142,
+    663,
+    3856,
+    1215,
+    28,
+    354,
+    2121,
+    3844,
+    28,
+    16935,
+    1763,
+    3643,
+    28,
+    2303,
+    6433,
+    29,
+    24317,
+    2301,
+    14681,
+    28,
+    284,
+    14388,
+    5888,
+    30,
+    330,
+    2244,
+    746,
+    19727,
+    553,
+    216,
+    33,
+    34,
+    30861,
+    29593,
+    3500,
+    43,
+    17475,
+    5940,
+    31175,
+    359,
+    27692,
+    30,
+    6045,
+    451,
+    2468,
+    347,
+    253,
+    1157,
+    24653,
+    28,
+    441,
+    2364,
+    282,
+    7596,
+    30,
+    198,
+    198,
+    19161,
+    253,
+    19484,
+    5289,
+    10317,
+    282,
+    418,
+    768,
+    216,
+    40,
+    37,
+    32,
+    1924,
+    28,
+    1015,
+    623,
+    20654,
+    22266,
+    31183,
+    30,
+    1068,
+    971,
+    14927,
+    1928,
+    253,
+    7257,
+    6958,
+    284,
+    582,
+    18928,
+    355,
+    3735,
+    3430,
+    42,
+    198,
+    33,
+    30,
+    16128,
+    6277,
+    198,
+    34,
+    30,
+    39853,
+    284,
+    3946,
+    8762,
+    198,
+    35,
+    30,
+    5663,
+    355,
+    639,
+    29,
+    40491,
+    198,
+    36,
+    30,
+    15985,
+    722,
+    284,
+    2353,
+    10295,
+    198,
+    37,
+    30,
+    2260,
+    284,
+    639,
+    6133,
+    198,
+    38,
+    30,
+    8498,
+    284,
+    10378,
+    198,
+    39,
+    30,
+    17734,
+    284,
+    34701,
+    198,
+    40,
+    30,
+    6272,
+    19193,
+    284,
+    29709,
+    5888,
+    198,
+    41,
+    30,
+    17139,
+    29,
+    15159,
+    30598,
+    309,
+    198,
+    33,
+    32,
+    30,
+    8574,
+    24956,
+    198,
+    33,
+    33,
+    30,
+    7000,
+    29,
+    675,
+    1944,
+    1157,
+    6808,
+    198,
+    198,
+    788,
+    8886,
+    418,
+    2124,
+    582,
+    7719,
+    940,
+    17808,
+    355,
+    8334,
+    28,
+    253,
+    4147,
+    1183,
+    282,
+    253,
+    2301,
+    29,
+    16367,
+    3856,
+    1699,
+    253,
+    14388,
+    28,
+    284,
+    253,
+    23979,
+    15739,
+    4308,
+    10348,
+    469,
+    1157,
+    351,
+    253,
+    14272,
+    15046,
+    30,
+    33774,
+    621,
+    7513,
+    3439,
+    28,
+    14915,
+    28,
+    284,
+    34021,
+    1151,
+    2029,
+    30,
+    3315,
+    441,
+    4938,
+    26211,
+    1844,
+    355,
+    16040,
+    30,
+    5545,
+    511,
+    5004,
+    3643,
+    1127,
+    8275,
+    2914,
+    16998,
+    43,
+    1380,
+    1251,
+    441,
+    31249,
+    3876,
+    967,
+    16998,
+    30,
+    198,
+    2,
+    198,
+    1,
+    520,
+    9531,
+    198
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 2,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/smollm/response.json b/research/ai_generated_agi_architectures/collection/runs/smollm/response.json
new file mode 100644
index 0000000..34e2290
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/smollm/response.json
@@ -0,0 +1,1434 @@
+{
+  "output_ids": [
+    33,
+    30,
+    16128,
+    17063,
+    42,
+    198,
+    198,
+    504,
+    3500,
+    6277,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    325,
+    3452,
+    32639,
+    284,
+    3758,
+    28,
+    351,
+    253,
+    1593,
+    335,
+    4345,
+    3500,
+    11662,
+    979,
+    4719,
+    2851,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3500,
+    24134,
+    28,
+    1285,
+    253,
+    3538,
+    28,
+    1642,
+    29,
+    9362,
+    1254,
+    13842,
+    284,
+    253,
+    14020,
+    28,
+    540,
+    16770,
+    4759,
+    817,
+    30,
+    378,
+    13842,
+    314,
+    804,
+    288,
+    4249,
+    4540,
+    13399,
+    940,
+    28,
+    979,
+    260,
+    4759,
+    817,
+    314,
+    804,
+    288,
+    4249,
+    1181,
+    4540,
+    13399,
+    940,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    18210,
+    34352,
+    18,
+    288,
+    2369,
+    3500,
+    6486,
+    411,
+    42673,
+    3500,
+    6703,
+    338,
+    359,
+    787,
+    2848,
+    281,
+    722,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    3500,
+    6277,
+    654,
+    441,
+    325,
+    5712,
+    327,
+    1734,
+    351,
+    4843,
+    1507,
+    5109,
+    282,
+    940,
+    28,
+    347,
+    357,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    260,
+    904,
+    3500,
+    6486,
+    2309,
+    327,
+    715,
+    1734,
+    30,
+    1116,
+    34,
+    30,
+    39853,
+    284,
+    13408,
+    31198,
+    42,
+    198,
+    198,
+    504,
+    10115,
+    284,
+    3946,
+    8762,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    325,
+    3452,
+    8686,
+    284,
+    26239,
+    28,
+    3910,
+    260,
+    817,
+    288,
+    835,
+    284,
+    1947,
+    690,
+    655,
+    30,
+    378,
+    8762,
+    5956,
+    282,
+    1296,
+    1085,
+    3439,
+    42,
+    8302,
+    28,
+    10115,
+    28,
+    284,
+    2353,
+    30,
+    378,
+    8302,
+    5077,
+    2925,
+    3746,
+    1380,
+    7688,
+    288,
+    4481,
+    8764,
+    940,
+    284,
+    2669,
+    3077,
+    30,
+    378,
+    10115,
+    5077,
+    2925,
+    9619,
+    3372,
+    284,
+    3746,
+    1380,
+    7688,
+    288,
+    2166,
+    563,
+    260,
+    940,
+    284,
+    919,
+    3884,
+    30,
+    378,
+    2353,
+    5077,
+    2925,
+    260,
+    1844,
+    282,
+    260,
+    10115,
+    5077,
+    288,
+    1188,
+    3643,
+    281,
+    260,
+    905,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    10115,
+    284,
+    3946,
+    8762,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    35,
+    30,
+    5663,
+    355,
+    9506,
+    29,
+    22531,
+    14325,
+    42,
+    198,
+    198,
+    504,
+    1380,
+    355,
+    639,
+    29,
+    40491,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    1167,
+    260,
+    817,
+    288,
+    835,
+    429,
+    624,
+    2647,
+    284,
+    1947,
+    690,
+    655,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    19387,
+    1380,
+    288,
+    835,
+    429,
+    624,
+    2647,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    771,
+    29,
+    34719,
+    1380,
+    18,
+    288,
+    835,
+    563,
+    2581,
+    284,
+    1947,
+    624,
+    2851,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    1380,
+    355,
+    639,
+    29,
+    40491,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    36,
+    30,
+    15985,
+    4076,
+    284,
+    10008,
+    47234,
+    42,
+    198,
+    198,
+    504,
+    1763,
+    722,
+    284,
+    2353,
+    10295,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    1167,
+    260,
+    817,
+    288,
+    722,
+    2549,
+    284,
+    13504,
+    3643,
+    281,
+    260,
+    905,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    1044,
+    260,
+    10237,
+    284,
+    3643,
+    338,
+    359,
+    1836,
+    288,
+    357,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    4667,
+    27156,
+    18,
+    288,
+    21000,
+    1784,
+    3643,
+    284,
+    919,
+    601,
+    4285,
+    288,
+    13504,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    1763,
+    722,
+    284,
+    2353,
+    10295,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    37,
+    30,
+    2260,
+    284,
+    9506,
+    28427,
+    42,
+    198,
+    198,
+    504,
+    905,
+    284,
+    639,
+    6133,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    1167,
+    260,
+    817,
+    288,
+    1044,
+    624,
+    1357,
+    284,
+    2581,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    1044,
+    260,
+    905,
+    284,
+    2581,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    771,
+    29,
+    27956,
+    18,
+    288,
+    1044,
+    624,
+    1038,
+    4611,
+    1215,
+    284,
+    919,
+    3884,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    905,
+    284,
+    639,
+    6133,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    38,
+    30,
+    8498,
+    284,
+    29452,
+    42,
+    198,
+    198,
+    504,
+    2950,
+    284,
+    10378,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    2381,
+    338,
+    260,
+    817,
+    13662,
+    8342,
+    284,
+    19942,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    4087,
+    284,
+    1746,
+    1566,
+    2950,
+    10796,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    12402,
+    524,
+    18,
+    288,
+    2381,
+    338,
+    260,
+    817,
+    13662,
+    1127,
+    260,
+    10195,
+    282,
+    260,
+    2914,
+    506,
+    16998,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    2950,
+    284,
+    10378,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    39,
+    30,
+    17734,
+    284,
+    40374,
+    11838,
+    42,
+    198,
+    198,
+    504,
+    6523,
+    284,
+    34701,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    2621,
+    260,
+    817,
+    506,
+    2851,
+    284,
+    6811,
+    357,
+    288,
+    550,
+    1734,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    6611,
+    624,
+    2851,
+    284,
+    6811,
+    357,
+    288,
+    550,
+    1734,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    27901,
+    36671,
+    18,
+    288,
+    2621,
+    624,
+    2851,
+    284,
+    2669,
+    1721,
+    327,
+    6281,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    6523,
+    284,
+    34701,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    40,
+    30,
+    6272,
+    19193,
+    284,
+    28793,
+    19057,
+    42,
+    198,
+    198,
+    504,
+    17526,
+    284,
+    29709,
+    5888,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    2381,
+    338,
+    260,
+    817,
+    416,
+    5795,
+    624,
+    1215,
+    284,
+    8516,
+    429,
+    22571,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    5795,
+    624,
+    1215,
+    284,
+    8516,
+    429,
+    22571,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    36964,
+    5888,
+    18,
+    288,
+    8516,
+    429,
+    22571,
+    284,
+    19563,
+    2955,
+    4953,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    17526,
+    284,
+    29709,
+    5888,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    41,
+    30,
+    17139,
+    29,
+    32150,
+    38377,
+    1321,
+    309,
+    42,
+    198,
+    198,
+    504,
+    4785,
+    29,
+    15159,
+    30598,
+    309,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    1167,
+    2701,
+    7531,
+    288,
+    746,
+    1592,
+    288,
+    3025,
+    253,
+    1273,
+    3491,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    13033,
+    260,
+    3643,
+    282,
+    2701,
+    7531,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    270,
+    2916,
+    1321,
+    309,
+    18,
+    288,
+    4511,
+    260,
+    5759,
+    826,
+    2701,
+    7531,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    4785,
+    29,
+    15159,
+    30598,
+    309,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    33,
+    32,
+    30,
+    8574,
+    6173,
+    292,
+    1968,
+    42,
+    198,
+    198,
+    504,
+    4665,
+    24956,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    6611,
+    260,
+    24956,
+    282,
+    260,
+    817,
+    506,
+    1157,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    6611,
+    260,
+    24956,
+    282,
+    260,
+    817,
+    506,
+    1157,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    4565,
+    292,
+    1968,
+    2318,
+    18,
+    288,
+    2669,
+    1566,
+    2529,
+    284,
+    7250,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    4665,
+    24956,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    1116,
+    33,
+    33,
+    30,
+    7000,
+    29,
+    675,
+    1944,
+    5843,
+    47537,
+    42,
+    198,
+    198,
+    504,
+    1603,
+    29,
+    675,
+    1944,
+    1157,
+    6808,
+    5077,
+    282,
+    18062,
+    29,
+    4531,
+    314,
+    2724,
+    288,
+    2669,
+    1566,
+    1157,
+    8209,
+    338,
+    654,
+    441,
+    325,
+    4654,
+    5452,
+    30,
+    378,
+    817,
+    2925,
+    253,
+    4925,
+    282,
+    3746,
+    1380,
+    7688,
+    284,
+    1782,
+    1789,
+    4604,
+    288,
+    2669,
+    1566,
+    1157,
+    8209,
+    30,
+    378,
+    817,
+    597,
+    2925,
+    253,
+    4476,
+    1217,
+    476,
+    927,
+    445,
+    4686,
+    18,
+    288,
+    5051,
+    1566,
+    1157,
+    8209,
+    30,
+    198,
+    198,
+    21324,
+    2511,
+    42,
+    378,
+    1603,
+    29,
+    675,
+    1944,
+    1157,
+    6808,
+    5077,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    5605,
+    1784,
+    28,
+    6639,
+    4978,
+    28,
+    347,
+    357,
+    12563,
+    335,
+    3746,
+    1380,
+    7688,
+    338,
+    654,
+    441,
+    325,
+    1730,
+    288,
+    2930,
+    3112,
+    2001,
+    288,
+    4340,
+    1920,
+    30,
+    198,
+    198,
+    16923,
+    277,
+    12066,
+    42,
+    198,
+    198,
+    4590,
+    314,
+    253,
+    4147,
+    1183,
+    282,
+    253,
+    2301,
+    29,
+    16367,
+    3856,
+    1699,
+    253,
+    14388,
+    42,
+    198
+  ],
+  "raw_output": "1. Memory Architecture:\n\nThe memory architecture of Cognitive-OS is designed to be highly scalable and efficient, with a focus on reducing memory footprint while maintaining performance. The system uses a combination of memory hierarchies, including a fast, low-latency cache and a slower, more durable storage system. The cache is used to store frequently accessed data, while the storage system is used to store less frequently accessed data. The system also uses a technique called \"memory pooling\" to reduce memory usage by reusing memory blocks that are no longer in use.\n\nLimitation: The memory architecture may not be suitable for systems with extremely large amounts of data, as it may not be able to handle the high memory usage required for such systems.\n\n2. Reasoning and Planning Loop:\n\nThe reasoning and planning loop of Cognitive-OS is designed to be highly flexible and adaptable, allowing the system to learn and improve over time. The loop consists of three main components: perception, reasoning, and action. The perception component uses machine learning algorithms to interpret sensor data and identify patterns. The reasoning component uses logical rules and machine learning algorithms to reason about the data and make decisions. The action component uses the results of the reasoning component to take actions in the world.\n\nLimitation: The reasoning and planning loop may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n3. Learning or Self-Improvement:\n\nThe learning or self-improvement component of Cognitive-OS is designed to allow the system to learn from its experiences and improve over time. The system uses a combination of machine learning algorithms and reinforcement learning to learn from its experiences. The system also uses a technique called \"self-supervised learning\" to learn about itself and improve its performance.\n\nLimitation: The learning or self-improvement component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n4. Tool Use and Action Execution:\n\nThe tool use and action execution component of Cognitive-OS is designed to allow the system to use tools and execute actions in the world. The system uses a combination of machine learning algorithms and natural language processing to understand the commands and actions that are given to it. The system also uses a technique called \"action abstraction\" to simplify complex actions and make them easier to execute.\n\nLimitation: The tool use and action execution component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n5. World and Self Representation:\n\nThe world and self representation component of Cognitive-OS is designed to allow the system to understand its environment and itself. The system uses a combination of machine learning algorithms and natural language processing to understand the world and itself. The system also uses a technique called \"self-representation\" to understand its own internal state and make decisions.\n\nLimitation: The world and self representation component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n6. Safety and Governance:\n\nThe safety and governance component of Cognitive-OS is designed to ensure that the system operates safely and securely. The system uses a combination of machine learning algorithms and natural language processing to detect and prevent potential safety hazards. The system also uses a technique called \"governance\" to ensure that the system operates within the constraints of the user's permissions.\n\nLimitation: The safety and governance component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n7. Evaluation and Benchmarks:\n\nThe evaluation and benchmarks component of Cognitive-OS is designed to measure the system's performance and compare it to other systems. The system uses a combination of machine learning algorithms and natural language processing to evaluate its performance and compare it to other systems. The system also uses a technique called \"benchmarking\" to measure its performance and identify areas for improvement.\n\nLimitation: The evaluation and benchmarks component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n8. Persistence and Runtime Recovery:\n\nThe persistence and runtime recovery component of Cognitive-OS is designed to ensure that the system can persist its state and recover from crashes. The system uses a combination of machine learning algorithms and natural language processing to persist its state and recover from crashes. The system also uses a technique called \"runtime recovery\" to recover from crashes and resume normal operation.\n\nLimitation: The persistence and runtime recovery component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n9. Multi-Agent Orchestration:\n\nThe multi-agent orchestration component of Cognitive-OS is designed to allow multiple agents to work together to achieve a common goal. The system uses a combination of machine learning algorithms and natural language processing to coordinate the actions of multiple agents. The system also uses a technique called \"orchestration\" to manage the interactions between multiple agents.\n\nLimitation: The multi-agent orchestration component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n10. Engineering Feasibility:\n\nThe engineering feasibility component of Cognitive-OS is designed to evaluate the feasibility of the system's design. The system uses a combination of machine learning algorithms and natural language processing to evaluate the feasibility of the system's design. The system also uses a technique called \"feasibility analysis\" to identify potential challenges and limitations.\n\nLimitation: The engineering feasibility component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\n11. Non-obvious Design Insight:\n\nThe non-obvious design insight component of Cognitive-OS is designed to identify potential design improvements that may not be immediately obvious. The system uses a combination of machine learning algorithms and natural language processing to identify potential design improvements. The system also uses a technique called \"insight generation\" to generate potential design improvements.\n\nLimitation: The non-obvious design insight component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.\n\nWorked Example:\n\nHere is a worked example of a file-edit task across a crash:\n",
+  "generated_tokens": 1400,
+  "termination": "length",
+  "seconds": 35.31872889999795,
+  "started_utc": "2026-09-09T10:25:21.982553+00:00",
+  "completed_utc": "2026-09-09T10:25:59.241124+00:00",
+  "model": {
+    "slug": "smollm",
+    "model_id": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
+    "revision": "31b70e2e869a7173562077fd711b654946d38674",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/tree/31b70e2e869a7173562077fd711b654946d38674"
+  },
+  "output_sha256": "da68c99167bfd10d22e1b47a80fc796e9d62be32e47d4f11ffb53ecfb55b5d76",
+  "request_sha256": "af9d5a871defeefa147f310e2840a6543670c46b7bbb43107c13692dcc53b5ad",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/spark/initial-failed-request.json b/research/ai_generated_agi_architectures/collection/runs/spark/initial-failed-request.json
new file mode 100644
index 0000000..7157f1b
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/spark/initial-failed-request.json
@@ -0,0 +1,393 @@
+{
+  "model": {
+    "slug": "spark",
+    "model_id": "XHToken/Spark-X2.5-1.7B",
+    "revision": "448e61eb392c00f2c403185c5b56d5e0665bfaab",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/XHToken/Spark-X2.5-1.7B/tree/448e61eb392c00f2c403185c5b56d5e0665bfaab"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<｜start▁of▁sentence｜><|System|>\nyou are a helpful assistant.<｜end▁of▁sentence｜><｜start▁of▁sentence｜><|User|>Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n<｜end▁of▁sentence｜><｜start▁of▁sentence｜><|Bot|></think>",
+  "input_ids": [
+    0,
+    130972,
+    198,
+    18178,
+    599,
+    259,
+    18639,
+    31627,
+    27,
+    1,
+    0,
+    130973,
+    2256,
+    5708,
+    300,
+    7106,
+    839,
+    31793,
+    54,
+    48716,
+    22895,
+    396,
+    423,
+    74601,
+    26,
+    5088,
+    25,
+    3497,
+    53930,
+    455,
+    31793,
+    54,
+    1044,
+    1360,
+    17091,
+    27,
+    31986,
+    39,
+    659,
+    390,
+    259,
+    3643,
+    77885,
+    37796,
+    24211,
+    478,
+    25638,
+    926,
+    8925,
+    3519,
+    25,
+    300,
+    4209,
+    18630,
+    25,
+    69779,
+    10156,
+    13282,
+    25,
+    1377,
+    14364,
+    6623,
+    861,
+    3849,
+    53452,
+    25,
+    317,
+    33896,
+    17332,
+    27,
+    401,
+    5133,
+    1505,
+    46155,
+    1044,
+    220,
+    30,
+    31,
+    37156,
+    69625,
+    9399,
+    40,
+    40533,
+    12641,
+    91190,
+    599,
+    87636,
+    27,
+    53734,
+    659,
+    5554,
+    463,
+    259,
+    3219,
+    38302,
+    25,
+    702,
+    8590,
+    314,
+    25733,
+    27,
+    198,
+    198,
+    20485,
+    259,
+    106629,
+    15531,
+    27489,
+    314,
+    511,
+    2026,
+    220,
+    37,
+    34,
+    29,
+    6073,
+    25,
+    2648,
+    1883,
+    58375,
+    97258,
+    2810,
+    1266,
+    27,
+    2842,
+    2440,
+    28060,
+    4336,
+    259,
+    21586,
+    15206,
+    317,
+    1135,
+    43468,
+    553,
+    13296,
+    5000,
+    39,
+    198,
+    30,
+    27,
+    42487,
+    22895,
+    198,
+    31,
+    27,
+    69833,
+    295,
+    317,
+    11887,
+    13611,
+    198,
+    32,
+    27,
+    32722,
+    553,
+    1413,
+    36228,
+    39174,
+    631,
+    198,
+    33,
+    27,
+    28501,
+    1730,
+    317,
+    6031,
+    24302,
+    198,
+    34,
+    27,
+    8734,
+    317,
+    1413,
+    19214,
+    198,
+    35,
+    27,
+    32969,
+    317,
+    45552,
+    198,
+    36,
+    27,
+    63668,
+    317,
+    119398,
+    198,
+    37,
+    27,
+    22404,
+    26123,
+    317,
+    24211,
+    17332,
+    198,
+    38,
+    27,
+    22839,
+    113437,
+    81400,
+    405,
+    198,
+    30,
+    29,
+    27,
+    28834,
+    75420,
+    198,
+    30,
+    30,
+    27,
+    21096,
+    56582,
+    3977,
+    3219,
+    26946,
+    198,
+    198,
+    92996,
+    511,
+    5086,
+    1135,
+    21392,
+    1870,
+    28290,
+    553,
+    9213,
+    25,
+    259,
+    9979,
+    4465,
+    314,
+    259,
+    3849,
+    26,
+    20710,
+    8925,
+    5288,
+    259,
+    33896,
+    25,
+    317,
+    259,
+    42933,
+    109274,
+    8304,
+    31872,
+    876,
+    3219,
+    478,
+    259,
+    52622,
+    29096,
+    27,
+    13893,
+    11884,
+    1223,
+    20763,
+    10857,
+    25,
+    48574,
+    25,
+    317,
+    604,
+    92856,
+    4248,
+    7349,
+    27,
+    6992,
+    702,
+    22555,
+    51395,
+    3728,
+    553,
+    125383,
+    27,
+    28104,
+    847,
+    13834,
+    13282,
+    3813,
+    29852,
+    3754,
+    55333,
+    40,
+    7820,
+    3144,
+    702,
+    66858,
+    13079,
+    2705,
+    55333,
+    27,
+    198,
+    1,
+    0,
+    130976,
+    4
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 1,
+    "bos_token_id": 0,
+    "eos_token_id": 1,
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/spark/model-files.json b/research/ai_generated_agi_architectures/collection/runs/spark/model-files.json
new file mode 100644
index 0000000..58b98c4
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/spark/model-files.json
@@ -0,0 +1,73 @@
+{
+  "model": {
+    "slug": "spark",
+    "model_id": "XHToken/Spark-X2.5-1.7B",
+    "revision": "448e61eb392c00f2c403185c5b56d5e0665bfaab",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/XHToken/Spark-X2.5-1.7B/tree/448e61eb392c00f2c403185c5b56d5e0665bfaab"
+  },
+  "files": [
+    {
+      "file": "chat_template.jinja",
+      "bytes": 4648,
+      "sha256": "705345721970da40e6aa56db548a46ecd1d8d94a5418808fa180ac19ee77a9ba"
+    },
+    {
+      "file": "config.json",
+      "bytes": 1838,
+      "sha256": "16aa5d3619d0d893e7157f2499675430844eb7b383c80ecc1b3e8e8928a75d52"
+    },
+    {
+      "file": "configuration_spark.py",
+      "bytes": 4329,
+      "sha256": "02218597240490f490659b184052db940b08163ff9c3af7b7e59323f6f963722"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 281,
+      "sha256": "804a5276a9d237600688267dd2bf6f9d4129ec21faf417f870d5d52b32ad9e6c"
+    },
+    {
+      "file": "model-00001-of-00002.safetensors",
+      "bytes": 1997108320,
+      "sha256": "b1629719b3f6de5de922abb3c90e45927550dd6f790f43030dc812cafce1af87"
+    },
+    {
+      "file": "model-00002-of-00002.safetensors",
+      "bytes": 1418232176,
+      "sha256": "3537675bfb657cb6d1f70bb1fab9b83fcb79ce5693f2557992548eb6d2bf13a0"
+    },
+    {
+      "file": "model.safetensors.index.json",
+      "bytes": 18805,
+      "sha256": "cc2b212985f5d0469bf926903e4bd7ee81856687baa0c6c6b55ff200d4cdc63f"
+    },
+    {
+      "file": "modeling_spark.py",
+      "bytes": 19418,
+      "sha256": "9cf0d1ad2b54b9f7088792779ddd8b5cb4d4fe63bfea054da7f2dd16362bcaf4"
+    },
+    {
+      "file": "special_tokens_map.json",
+      "bytes": 156,
+      "sha256": "48a257e3f5f20a004e7c30482a70ae19ae7d9cf713144f2a1394ba20af8c3cb5"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 10115786,
+      "sha256": "710cce15cf3565674c499f9413997c6e8101f2bdd96245cff8f0311fb501248c"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 4797,
+      "sha256": "0729b0aeb1b88e000687b51c72d5aece759233bb4f6fb353eb497676e04d43b0"
+    },
+    {
+      "file": "vocab.json",
+      "bytes": 3032795,
+      "sha256": "9f42cb7e7af5afe27cae2337d272d60b5659e10143ef4bb952f642b847916fe1"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/spark/readable.txt b/research/ai_generated_agi_architectures/collection/runs/spark/readable.txt
new file mode 100644
index 0000000..142ab37
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/spark/readable.txt
@@ -0,0 +1,55 @@
+1. Memory architecture
+**Mechanism:** A unified 12 GB GPU memory pool is partitioned into a fixed 8 GB working set for active reasoning, a 2 GB persistent state store, and a 2 GB volatile cache. All memory access is pinned to a single memory-mapped SQLite database, ensuring atomic writes and preventing fragmentation.
+**Limitation/Tradeoff:** The fixed 8 GB working set creates a hard memory ceiling, meaning the runtime cannot scale to larger tasks or concurrent agents without reallocating memory, which introduces latency and potential data loss if the system crashes.
+
+2. Reasoning and planning loop
+**Mechanism:** A deterministic, loop-based reasoning engine processes tasks using a hybrid approach: a high-level planning module generates a sequence of actions, followed by a low-level execution module that performs the actual work. The loop continuously evaluates the plan against the SQLite task state to detect deadlocks or errors.
+**Limitation/Tradeoff:** The rigid loop structure limits the system's flexibility, preventing the runtime from dynamically adapting to novel or highly complex problem-solving scenarios without rewriting the entire planning logic.
+
+3. Learning or self-improvement
+**Mechanism:** A constrained, permission-bound learning subsystem operates as a sandboxed module. It utilizes the SQLite task state to store proposed modifications, which are then verified against the current safety and governance rules. Only approved changes are applied to the working memory.
+**Limitation/Tradeoff:** The strict sandboxing prevents the runtime from silently expanding permissions or learning new capabilities, ensuring that any self-improvement strictly adheres to the current user-defined boundaries and safety constraints.
+
+4. Tool use and action execution
+**Mechanism:** A governed tool execution layer translates high-level actions into SQLite-backed, verifier-gated file patches. Each patch is validated against the SQLite task state before execution, ensuring that only permitted file modifications are applied.
+**Limitation/Tradeoff:** The strict verification process introduces a delay in execution, meaning file edits may take longer than the original task, which can impact the overall responsiveness of the local runtime.
+
+5. World and self representation
+**Mechanism:** The runtime maintains a unified, immutable world representation stored in SQLite. This includes the current state of the task, the history of executed actions, and the current state of the system. The self representation is derived from this state, ensuring that the system's internal state is fully traceable and recoverable.
+**Limitation/Tradeoff:** The reliance on a single SQLite database for both world and self representation creates a single point of failure. If the database is corrupted or lost, the runtime loses its ability to reconstruct the system state, making recovery difficult.
+
+6. Safety and governance
+**Mechanism:** A verifier-gated governance layer enforces strict rules before any file patches or tool actions are executed. The system continuously monitors the state for anomalies, and any deviations trigger immediate halting of the execution process.
+**Limitation/Tradeoff:** The continuous monitoring introduces overhead, meaning the system must constantly evaluate its state, which can slow down critical operations and introduce unnecessary computational load on the GPU memory.
+
+7. Evaluation and benchmarks
+**Mechanism:** A dedicated evaluation module runs a series of falsifiable, deterministic tests against the SQLite task state. These tests measure the system's ability to correctly execute tasks, detect errors, and maintain state integrity.
+**Limitation/Tradeoff:** The reliance on deterministic tests means the system cannot evaluate complex, emergent, or highly unpredictable behaviors, limiting the evaluation of the system's true cognitive capabilities.
+
+8. Persistence and runtime recovery
+**Mechanism:** The SQLite database acts as the primary persistence layer. On crash, the runtime automatically snapshots the current task state and the system state. On startup, it restores the snapshot, allowing the system to resume from the exact point of failure.
+**Limitation/Tradeoff:** The snapshotting process itself consumes memory and time, meaning the runtime must allocate additional storage during recovery, which can introduce overhead and potential resource contention during the critical startup phase.
+
+9. Multi-agent orchestration
+**Mechanism:** The runtime supports multi-agent orchestration by managing a queue of agents. Agents are executed sequentially or in parallel based on the SQLite task state, with the system continuously monitoring the state to manage resource allocation and prevent conflicts.
+**Limitation/Tradeoff:** The concurrent execution of multiple agents introduces complex coordination overhead, which can lead to resource contention and synchronization delays, potentially degrading the overall performance of the system.
+
+10. Engineering feasibility
+**Mechanism:** The architecture is highly feasible within the local-first constraints. The SQLite-backed design ensures data integrity and atomicity, while the GPU memory partitioning provides a clear, bounded resource allocation strategy.
+**Limitation/Tradeoff:** The reliance on local SQLite databases and GPU memory partitioning introduces a high degree of coupling between the runtime and the underlying storage and hardware, making the system difficult to scale or adapt to future hardware changes.
+
+11. Non-obvious design insight
+**Mechanism:** The core insight is that the system's cognitive capability is bounded by its strict, verifiable state representation. By treating the world and self as a single, immutable SQLite database, the system ensures that all cognitive actions are traceable and reversible. This design prevents the runtime from accumulating unbounded cognitive state, thereby establishing a clear boundary for the system's cognitive capabilities.
+
+**Worked Example: File-Edit Task Across a Crash**
+**Task:** Edit a local file to add a new configuration block.
+**Execution:** The runtime initiates the task, writes the initial state to SQLite. The system executes the edit, updating the SQLite task state. Upon detecting a crash, the runtime snapshots the SQLite database. On restart, the runtime restores the snapshot, re-executes the edit, and verifies the new state.
+**Result:** The system successfully recovers the exact state of the file, ensuring the edit is applied correctly without losing any prior context or data.
+
+**Falsifiable Experiment:**
+**Baseline:** A simpler, non-cognitive runtime that only executes basic file operations without state tracking or recovery.
+**Experiment:** Run the file-edit task against the proposed architecture versus the baseline.
+**Hypothesis:** The proposed architecture will successfully recover the file state after a crash, while the baseline will lose the file state, demonstrating the system's superior durability and recovery capabilities.
+
+**Distinction of Components:**
+*   **Implemented Components:** The 12 GB GPU memory pool, the SQLite-backed
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/collection/runs/spark/request.json b/research/ai_generated_agi_architectures/collection/runs/spark/request.json
new file mode 100644
index 0000000..7157f1b
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/spark/request.json
@@ -0,0 +1,393 @@
+{
+  "model": {
+    "slug": "spark",
+    "model_id": "XHToken/Spark-X2.5-1.7B",
+    "revision": "448e61eb392c00f2c403185c5b56d5e0665bfaab",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/XHToken/Spark-X2.5-1.7B/tree/448e61eb392c00f2c403185c5b56d5e0665bfaab"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<｜start▁of▁sentence｜><|System|>\nyou are a helpful assistant.<｜end▁of▁sentence｜><｜start▁of▁sentence｜><|User|>Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n<｜end▁of▁sentence｜><｜start▁of▁sentence｜><|Bot|></think>",
+  "input_ids": [
+    0,
+    130972,
+    198,
+    18178,
+    599,
+    259,
+    18639,
+    31627,
+    27,
+    1,
+    0,
+    130973,
+    2256,
+    5708,
+    300,
+    7106,
+    839,
+    31793,
+    54,
+    48716,
+    22895,
+    396,
+    423,
+    74601,
+    26,
+    5088,
+    25,
+    3497,
+    53930,
+    455,
+    31793,
+    54,
+    1044,
+    1360,
+    17091,
+    27,
+    31986,
+    39,
+    659,
+    390,
+    259,
+    3643,
+    77885,
+    37796,
+    24211,
+    478,
+    25638,
+    926,
+    8925,
+    3519,
+    25,
+    300,
+    4209,
+    18630,
+    25,
+    69779,
+    10156,
+    13282,
+    25,
+    1377,
+    14364,
+    6623,
+    861,
+    3849,
+    53452,
+    25,
+    317,
+    33896,
+    17332,
+    27,
+    401,
+    5133,
+    1505,
+    46155,
+    1044,
+    220,
+    30,
+    31,
+    37156,
+    69625,
+    9399,
+    40,
+    40533,
+    12641,
+    91190,
+    599,
+    87636,
+    27,
+    53734,
+    659,
+    5554,
+    463,
+    259,
+    3219,
+    38302,
+    25,
+    702,
+    8590,
+    314,
+    25733,
+    27,
+    198,
+    198,
+    20485,
+    259,
+    106629,
+    15531,
+    27489,
+    314,
+    511,
+    2026,
+    220,
+    37,
+    34,
+    29,
+    6073,
+    25,
+    2648,
+    1883,
+    58375,
+    97258,
+    2810,
+    1266,
+    27,
+    2842,
+    2440,
+    28060,
+    4336,
+    259,
+    21586,
+    15206,
+    317,
+    1135,
+    43468,
+    553,
+    13296,
+    5000,
+    39,
+    198,
+    30,
+    27,
+    42487,
+    22895,
+    198,
+    31,
+    27,
+    69833,
+    295,
+    317,
+    11887,
+    13611,
+    198,
+    32,
+    27,
+    32722,
+    553,
+    1413,
+    36228,
+    39174,
+    631,
+    198,
+    33,
+    27,
+    28501,
+    1730,
+    317,
+    6031,
+    24302,
+    198,
+    34,
+    27,
+    8734,
+    317,
+    1413,
+    19214,
+    198,
+    35,
+    27,
+    32969,
+    317,
+    45552,
+    198,
+    36,
+    27,
+    63668,
+    317,
+    119398,
+    198,
+    37,
+    27,
+    22404,
+    26123,
+    317,
+    24211,
+    17332,
+    198,
+    38,
+    27,
+    22839,
+    113437,
+    81400,
+    405,
+    198,
+    30,
+    29,
+    27,
+    28834,
+    75420,
+    198,
+    30,
+    30,
+    27,
+    21096,
+    56582,
+    3977,
+    3219,
+    26946,
+    198,
+    198,
+    92996,
+    511,
+    5086,
+    1135,
+    21392,
+    1870,
+    28290,
+    553,
+    9213,
+    25,
+    259,
+    9979,
+    4465,
+    314,
+    259,
+    3849,
+    26,
+    20710,
+    8925,
+    5288,
+    259,
+    33896,
+    25,
+    317,
+    259,
+    42933,
+    109274,
+    8304,
+    31872,
+    876,
+    3219,
+    478,
+    259,
+    52622,
+    29096,
+    27,
+    13893,
+    11884,
+    1223,
+    20763,
+    10857,
+    25,
+    48574,
+    25,
+    317,
+    604,
+    92856,
+    4248,
+    7349,
+    27,
+    6992,
+    702,
+    22555,
+    51395,
+    3728,
+    553,
+    125383,
+    27,
+    28104,
+    847,
+    13834,
+    13282,
+    3813,
+    29852,
+    3754,
+    55333,
+    40,
+    7820,
+    3144,
+    702,
+    66858,
+    13079,
+    2705,
+    55333,
+    27,
+    198,
+    1,
+    0,
+    130976,
+    4
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 1,
+    "bos_token_id": 0,
+    "eos_token_id": 1,
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/spark/response.json b/research/ai_generated_agi_architectures/collection/runs/spark/response.json
new file mode 100644
index 0000000..1216943
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/spark/response.json
@@ -0,0 +1,1434 @@
+{
+  "output_ids": [
+    30,
+    27,
+    42487,
+    22895,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    401,
+    74610,
+    220,
+    30,
+    31,
+    37156,
+    69625,
+    9399,
+    11420,
+    390,
+    31024,
+    290,
+    1464,
+    259,
+    12486,
+    220,
+    37,
+    37156,
+    5181,
+    1593,
+    396,
+    8900,
+    47570,
+    25,
+    259,
+    220,
+    31,
+    37156,
+    42177,
+    3519,
+    7550,
+    25,
+    317,
+    259,
+    220,
+    31,
+    37156,
+    61454,
+    19562,
+    27,
+    3801,
+    9399,
+    4561,
+    390,
+    96438,
+    312,
+    259,
+    5133,
+    9399,
+    3030,
+    9203,
+    25638,
+    926,
+    11245,
+    25,
+    26564,
+    37875,
+    29650,
+    317,
+    40279,
+    99733,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    12486,
+    220,
+    37,
+    37156,
+    5181,
+    1593,
+    18756,
+    259,
+    4026,
+    9399,
+    35807,
+    25,
+    10738,
+    275,
+    24211,
+    6847,
+    10371,
+    312,
+    10165,
+    15862,
+    553,
+    51300,
+    17399,
+    3497,
+    333,
+    27176,
+    1791,
+    9399,
+    25,
+    1120,
+    57365,
+    67840,
+    317,
+    6484,
+    1870,
+    6627,
+    690,
+    275,
+    2298,
+    92622,
+    27,
+    198,
+    198,
+    31,
+    27,
+    69833,
+    295,
+    317,
+    11887,
+    13611,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    401,
+    90351,
+    25,
+    13611,
+    8061,
+    47570,
+    8067,
+    10984,
+    15862,
+    2648,
+    259,
+    25497,
+    6355,
+    39,
+    259,
+    2349,
+    19825,
+    11887,
+    14461,
+    43549,
+    259,
+    12875,
+    314,
+    13282,
+    25,
+    9312,
+    666,
+    259,
+    4338,
+    19825,
+    24302,
+    14461,
+    455,
+    46603,
+    275,
+    9708,
+    1505,
+    27,
+    614,
+    13611,
+    38064,
+    130441,
+    275,
+    3593,
+    4124,
+    275,
+    25638,
+    926,
+    8925,
+    3519,
+    312,
+    18248,
+    9495,
+    116153,
+    553,
+    10775,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    42940,
+    13611,
+    6982,
+    20833,
+    275,
+    2298,
+    1045,
+    29786,
+    25,
+    40279,
+    275,
+    24211,
+    692,
+    80748,
+    110049,
+    312,
+    13730,
+    553,
+    9727,
+    6554,
+    4747,
+    122948,
+    35524,
+    3497,
+    333,
+    48807,
+    275,
+    6940,
+    11887,
+    23561,
+    27,
+    198,
+    198,
+    32,
+    27,
+    32722,
+    553,
+    1413,
+    36228,
+    39174,
+    631,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    401,
+    70556,
+    25,
+    24294,
+    76169,
+    7820,
+    127483,
+    43301,
+    463,
+    259,
+    11631,
+    50737,
+    14461,
+    27,
+    1429,
+    86861,
+    275,
+    25638,
+    926,
+    8925,
+    3519,
+    312,
+    7550,
+    10580,
+    42265,
+    25,
+    1120,
+    599,
+    1959,
+    41360,
+    4124,
+    275,
+    3345,
+    10360,
+    317,
+    45552,
+    11154,
+    27,
+    16143,
+    20906,
+    6132,
+    599,
+    10188,
+    312,
+    275,
+    5181,
+    9399,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    18740,
+    11631,
+    6376,
+    295,
+    47323,
+    275,
+    24211,
+    692,
+    66858,
+    39433,
+    55333,
+    553,
+    7820,
+    1151,
+    25733,
+    25,
+    26564,
+    455,
+    1347,
+    1413,
+    36228,
+    39174,
+    631,
+    41458,
+    39943,
+    278,
+    312,
+    275,
+    3345,
+    3754,
+    62416,
+    29583,
+    317,
+    10360,
+    26863,
+    27,
+    198,
+    198,
+    33,
+    27,
+    28501,
+    1730,
+    317,
+    6031,
+    24302,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    401,
+    69779,
+    10156,
+    24302,
+    9466,
+    91037,
+    2349,
+    19825,
+    13282,
+    1464,
+    25638,
+    926,
+    2846,
+    25394,
+    25,
+    1377,
+    14364,
+    6623,
+    861,
+    3849,
+    53452,
+    27,
+    13113,
+    27299,
+    390,
+    56439,
+    4124,
+    275,
+    25638,
+    926,
+    8925,
+    3519,
+    2471,
+    24302,
+    25,
+    26564,
+    455,
+    1699,
+    37077,
+    3849,
+    42265,
+    599,
+    10188,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    18740,
+    43568,
+    2617,
+    57365,
+    259,
+    14804,
+    304,
+    24302,
+    25,
+    10738,
+    3849,
+    1810,
+    1756,
+    1564,
+    2813,
+    7670,
+    1489,
+    275,
+    7675,
+    8925,
+    25,
+    1120,
+    764,
+    7124,
+    275,
+    10594,
+    114932,
+    314,
+    275,
+    3643,
+    24211,
+    27,
+    198,
+    198,
+    34,
+    27,
+    8734,
+    317,
+    1413,
+    19214,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    614,
+    24211,
+    54919,
+    259,
+    74610,
+    25,
+    762,
+    90202,
+    3179,
+    19214,
+    17933,
+    304,
+    25638,
+    926,
+    27,
+    1713,
+    8934,
+    275,
+    3345,
+    3519,
+    314,
+    275,
+    8925,
+    25,
+    275,
+    6961,
+    314,
+    33763,
+    13282,
+    25,
+    317,
+    275,
+    3345,
+    3519,
+    314,
+    275,
+    2298,
+    27,
+    614,
+    1413,
+    19214,
+    390,
+    18482,
+    692,
+    659,
+    3519,
+    25,
+    26564,
+    455,
+    275,
+    2298,
+    1045,
+    11026,
+    3519,
+    390,
+    10672,
+    21188,
+    839,
+    317,
+    17665,
+    839,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    91257,
+    410,
+    259,
+    5133,
+    25638,
+    926,
+    11245,
+    396,
+    2748,
+    3179,
+    317,
+    1413,
+    19214,
+    18756,
+    259,
+    5133,
+    2746,
+    314,
+    13223,
+    27,
+    2625,
+    275,
+    11245,
+    390,
+    2348,
+    26547,
+    553,
+    8980,
+    25,
+    275,
+    24211,
+    72210,
+    1777,
+    8482,
+    312,
+    127764,
+    275,
+    2298,
+    3519,
+    25,
+    4937,
+    17332,
+    7277,
+    27,
+    198,
+    198,
+    35,
+    27,
+    32969,
+    317,
+    45552,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    401,
+    1377,
+    14364,
+    6623,
+    861,
+    45552,
+    9466,
+    648,
+    97862,
+    18740,
+    11154,
+    2471,
+    1347,
+    3849,
+    53452,
+    553,
+    10156,
+    13282,
+    599,
+    33763,
+    27,
+    614,
+    2298,
+    38064,
+    72903,
+    275,
+    3519,
+    396,
+    92082,
+    25,
+    317,
+    1347,
+    72791,
+    18345,
+    22110,
+    3275,
+    1629,
+    314,
+    275,
+    24302,
+    2617,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    18776,
+    18473,
+    57365,
+    44640,
+    25,
+    10738,
+    275,
+    2298,
+    3144,
+    23314,
+    19132,
+    1777,
+    3519,
+    25,
+    1120,
+    764,
+    8273,
+    2321,
+    11192,
+    13127,
+    317,
+    25698,
+    43392,
+    43202,
+    6279,
+    410,
+    275,
+    69625,
+    9399,
+    27,
+    198,
+    198,
+    36,
+    27,
+    63668,
+    317,
+    119398,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    401,
+    18553,
+    16231,
+    14461,
+    15679,
+    259,
+    7221,
+    314,
+    42933,
+    109274,
+    25,
+    90351,
+    10960,
+    4124,
+    275,
+    25638,
+    926,
+    8925,
+    3519,
+    27,
+    4793,
+    10960,
+    6834,
+    275,
+    2298,
+    1045,
+    8482,
+    312,
+    25120,
+    26292,
+    15862,
+    25,
+    18248,
+    10775,
+    25,
+    317,
+    14275,
+    3519,
+    32335,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    91257,
+    410,
+    90351,
+    10960,
+    5156,
+    275,
+    2298,
+    6847,
+    19132,
+    6554,
+    25,
+    15424,
+    327,
+    25,
+    553,
+    9727,
+    87806,
+    31390,
+    25,
+    43185,
+    275,
+    16231,
+    314,
+    275,
+    2298,
+    1045,
+    2917,
+    25958,
+    25733,
+    27,
+    198,
+    198,
+    37,
+    27,
+    22404,
+    26123,
+    317,
+    24211,
+    17332,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    614,
+    25638,
+    926,
+    11245,
+    22815,
+    463,
+    275,
+    9640,
+    72402,
+    9466,
+    27,
+    3740,
+    33896,
+    25,
+    275,
+    24211,
+    19456,
+    25757,
+    70447,
+    275,
+    3345,
+    8925,
+    3519,
+    317,
+    275,
+    2298,
+    3519,
+    27,
+    3740,
+    50789,
+    25,
+    488,
+    3866,
+    6086,
+    275,
+    50351,
+    25,
+    16536,
+    275,
+    2298,
+    312,
+    38275,
+    692,
+    275,
+    8013,
+    2746,
+    314,
+    13223,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    50351,
+    1629,
+    2617,
+    8222,
+    7046,
+    278,
+    9399,
+    317,
+    1279,
+    25,
+    10738,
+    275,
+    24211,
+    3144,
+    71367,
+    8121,
+    10904,
+    3357,
+    17332,
+    25,
+    1120,
+    764,
+    25698,
+    44640,
+    317,
+    6484,
+    11211,
+    102709,
+    3357,
+    275,
+    11192,
+    50789,
+    9515,
+    27,
+    198,
+    198,
+    38,
+    27,
+    22839,
+    113437,
+    81400,
+    405,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    614,
+    24211,
+    20815,
+    4846,
+    113437,
+    81400,
+    405,
+    666,
+    26524,
+    259,
+    20907,
+    314,
+    17399,
+    27,
+    99743,
+    599,
+    33763,
+    8044,
+    11454,
+    553,
+    304,
+    19405,
+    4464,
+    410,
+    275,
+    25638,
+    926,
+    8925,
+    3519,
+    25,
+    478,
+    275,
+    2298,
+    38064,
+    18473,
+    275,
+    3519,
+    312,
+    14734,
+    11211,
+    42008,
+    317,
+    9678,
+    46275,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    51300,
+    24302,
+    314,
+    8052,
+    17399,
+    57365,
+    6554,
+    42862,
+    44640,
+    25,
+    1120,
+    764,
+    3277,
+    312,
+    11211,
+    102709,
+    317,
+    93463,
+    52813,
+    25,
+    21460,
+    26895,
+    295,
+    275,
+    10594,
+    6154,
+    314,
+    275,
+    2298,
+    27,
+    198,
+    198,
+    30,
+    29,
+    27,
+    28834,
+    75420,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    614,
+    22895,
+    390,
+    9727,
+    46662,
+    3813,
+    275,
+    3643,
+    77885,
+    26863,
+    27,
+    614,
+    25638,
+    926,
+    2846,
+    25394,
+    3219,
+    32003,
+    1870,
+    32335,
+    317,
+    29177,
+    13357,
+    25,
+    2539,
+    275,
+    69625,
+    9399,
+    128385,
+    7478,
+    259,
+    6102,
+    25,
+    47297,
+    11211,
+    42008,
+    11518,
+    27,
+    198,
+    1033,
+    49903,
+    8960,
+    28,
+    80912,
+    5000,
+    16551,
+    614,
+    91257,
+    410,
+    3643,
+    25638,
+    926,
+    47094,
+    317,
+    69625,
+    9399,
+    128385,
+    57365,
+    259,
+    2349,
+    10350,
+    314,
+    34672,
+    2274,
+    275,
+    24211,
+    317,
+    275,
+    21579,
+    10904,
+    317,
+    24149,
+    25,
+    4937,
+    275,
+    2298,
+    7277,
+    312,
+    10371,
+    553,
+    19136,
+    312,
+    6075,
+    24149,
+    6132,
+    27,
+    198,
+    198,
+    30,
+    30,
+    27,
+    21096,
+    56582,
+    3977,
+    3219,
+    26946,
+    198,
+    1033,
+    58,
+    6359,
+    2892,
+    16551,
+    614,
+    10545,
+    26946,
+    390,
+    455,
+    275,
+    2298,
+    1045,
+    25958,
+    32596,
+    390,
+    47297,
+    666,
+    1777,
+    18740,
+    25,
+    1377,
+    109274,
+    3519,
+    19214,
+    27,
+    6816,
+    35695,
+    275,
+    3179,
+    317,
+    1413,
+    463,
+    259,
+    5133,
+    25,
+    762,
+    90202,
+    25638,
+    926,
+    11245,
+    25,
+    275,
+    2298,
+    32003,
+    455,
+    847,
+    25958,
+    13282,
+    599,
+    21188,
+    839,
+    317,
+    87739,
+    27,
+    1713,
+    3219,
+    47323,
+    275,
+    24211,
+    692,
+    19484,
+    23013,
+    604,
+    105644,
+    25958,
+    3519,
+    25,
+    28757,
+    40799,
+    259,
+    6102,
+    20835,
+    396,
+    275,
+    2298,
+    1045,
+    25958,
+    25733,
+    27,
+    198,
+    198,
+    1033,
+    14142,
+    290,
+    35470,
+    39,
+    10680,
+    26,
+    19046,
+    28727,
+    113207,
+    259,
+    12876,
+    1571,
+    1033,
+    198,
+    1033,
+    15165,
+    16551,
+    46902,
+    259,
+    3643,
+    3849,
+    312,
+    1613,
+    259,
+    1151,
+    14915,
+    6243,
+    27,
+    198,
+    1033,
+    46210,
+    16551,
+    614,
+    24211,
+    12116,
+    1607,
+    275,
+    8925,
+    25,
+    29650,
+    275,
+    6262,
+    3519,
+    312,
+    25638,
+    926,
+    27,
+    614,
+    2298,
+    100382,
+    275,
+    23556,
+    25,
+    50635,
+    275,
+    25638,
+    926,
+    8925,
+    3519,
+    27,
+    50927,
+    64258,
+    259,
+    33896,
+    25,
+    275,
+    24211,
+    25757,
+    70447,
+    275,
+    25638,
+    926,
+    11245,
+    27,
+    3740,
+    57329,
+    25,
+    275,
+    24211,
+    3866,
+    6086,
+    275,
+    50351,
+    25,
+    333,
+    20013,
+    363,
+    4316,
+    275,
+    23556,
+    25,
+    317,
+    1377,
+    23797,
+    275,
+    1151,
+    3519,
+    27,
+    198,
+    1033,
+    5705,
+    16551,
+    614,
+    2298,
+    17136,
+    1631,
+    25249,
+    275,
+    8013,
+    3519,
+    314,
+    275,
+    3849,
+    25,
+    26564,
+    275,
+    23556,
+    390,
+    10188,
+    25120,
+    3497,
+    24225,
+    1347,
+    7508,
+    5554,
+    553,
+    1870,
+    27,
+    198,
+    198,
+    1033,
+    51,
+    1854,
+    109274,
+    77084,
+    16551,
+    198,
+    1033,
+    51932,
+    9160,
+    16551,
+    401,
+    52622,
+    25,
+    3217,
+    3093,
+    74601,
+    24211,
+    455,
+    1699,
+    100382,
+    10746,
+    3849,
+    13127,
+    3497,
+    3519,
+    25995,
+    553,
+    17332,
+    27,
+    198,
+    1033,
+    126495,
+    16551,
+    15699,
+    275,
+    3849,
+    26,
+    20710,
+    8925,
+    4124,
+    275,
+    10580,
+    22895,
+    23268,
+    275,
+    29096,
+    27,
+    198,
+    1033,
+    123584,
+    129332,
+    16551,
+    614,
+    10580,
+    22895,
+    969,
+    17136,
+    17665,
+    275,
+    3849,
+    3519,
+    2078,
+    259,
+    33896,
+    25,
+    2539,
+    275,
+    29096,
+    969,
+    15571,
+    275,
+    3849,
+    3519,
+    25,
+    57014,
+    275,
+    2298,
+    1045,
+    20816,
+    60740,
+    317,
+    17332,
+    25733,
+    27,
+    198,
+    198,
+    1033,
+    28572,
+    25245,
+    314,
+    121757,
+    16551,
+    198,
+    23,
+    256,
+    7307,
+    102158,
+    121757,
+    16551,
+    614,
+    220,
+    30,
+    31,
+    37156,
+    69625,
+    9399,
+    11420,
+    25,
+    275,
+    25638,
+    926,
+    2846,
+    25394
+  ],
+  "raw_output": "1. Memory architecture\n**Mechanism:** A unified 12 GB GPU memory pool is partitioned into a fixed 8 GB working set for active reasoning, a 2 GB persistent state store, and a 2 GB volatile cache. All memory access is pinned to a single memory-mapped SQLite database, ensuring atomic writes and preventing fragmentation.\n**Limitation/Tradeoff:** The fixed 8 GB working set creates a hard memory ceiling, meaning the runtime cannot scale to larger tasks or concurrent agents without reallocating memory, which introduces latency and potential data loss if the system crashes.\n\n2. Reasoning and planning loop\n**Mechanism:** A deterministic, loop-based reasoning engine processes tasks using a hybrid approach: a high-level planning module generates a sequence of actions, followed by a low-level execution module that performs the actual work. The loop continuously evaluates the plan against the SQLite task state to detect deadlocks or errors.\n**Limitation/Tradeoff:** The rigid loop structure limits the system's flexibility, preventing the runtime from dynamically adapting to novel or highly complex problem-solving scenarios without rewriting the entire planning logic.\n\n3. Learning or self-improvement\n**Mechanism:** A constrained, permission-bound learning subsystem operates as a sandboxed module. It utilizes the SQLite task state to store proposed modifications, which are then verified against the current safety and governance rules. Only approved changes are applied to the working memory.\n**Limitation/Tradeoff:** The strict sandboxing prevents the runtime from silently expanding permissions or learning new capabilities, ensuring that any self-improvement strictly adheres to the current user-defined boundaries and safety constraints.\n\n4. Tool use and action execution\n**Mechanism:** A governed tool execution layer translates high-level actions into SQLite-backed, verifier-gated file patches. Each patch is validated against the SQLite task state before execution, ensuring that only permitted file modifications are applied.\n**Limitation/Tradeoff:** The strict verification process introduces a delay in execution, meaning file edits may take longer than the original task, which can impact the overall responsiveness of the local runtime.\n\n5. World and self representation\n**Mechanism:** The runtime maintains a unified, immutable world representation stored in SQLite. This includes the current state of the task, the history of executed actions, and the current state of the system. The self representation is derived from this state, ensuring that the system's internal state is fully traceable and recoverable.\n**Limitation/Tradeoff:** The reliance on a single SQLite database for both world and self representation creates a single point of failure. If the database is corrupted or lost, the runtime loses its ability to reconstruct the system state, making recovery difficult.\n\n6. Safety and governance\n**Mechanism:** A verifier-gated governance layer enforces strict rules before any file patches or tool actions are executed. The system continuously monitors the state for anomalies, and any deviations trigger immediate halting of the execution process.\n**Limitation/Tradeoff:** The continuous monitoring introduces overhead, meaning the system must constantly evaluate its state, which can slow down critical operations and introduce unnecessary computational load on the GPU memory.\n\n7. Evaluation and benchmarks\n**Mechanism:** A dedicated evaluation module runs a series of falsifiable, deterministic tests against the SQLite task state. These tests measure the system's ability to correctly execute tasks, detect errors, and maintain state integrity.\n**Limitation/Tradeoff:** The reliance on deterministic tests means the system cannot evaluate complex, emergent, or highly unpredictable behaviors, limiting the evaluation of the system's true cognitive capabilities.\n\n8. Persistence and runtime recovery\n**Mechanism:** The SQLite database acts as the primary persistence layer. On crash, the runtime automatically snapshots the current task state and the system state. On startup, it restores the snapshot, allowing the system to resume from the exact point of failure.\n**Limitation/Tradeoff:** The snapshotting process itself consumes memory and time, meaning the runtime must allocate additional storage during recovery, which can introduce overhead and potential resource contention during the critical startup phase.\n\n9. Multi-agent orchestration\n**Mechanism:** The runtime supports multi-agent orchestration by managing a queue of agents. Agents are executed sequentially or in parallel based on the SQLite task state, with the system continuously monitoring the state to manage resource allocation and prevent conflicts.\n**Limitation/Tradeoff:** The concurrent execution of multiple agents introduces complex coordination overhead, which can lead to resource contention and synchronization delays, potentially degrading the overall performance of the system.\n\n10. Engineering feasibility\n**Mechanism:** The architecture is highly feasible within the local-first constraints. The SQLite-backed design ensures data integrity and atomicity, while the GPU memory partitioning provides a clear, bounded resource allocation strategy.\n**Limitation/Tradeoff:** The reliance on local SQLite databases and GPU memory partitioning introduces a high degree of coupling between the runtime and the underlying storage and hardware, making the system difficult to scale or adapt to future hardware changes.\n\n11. Non-obvious design insight\n**Mechanism:** The core insight is that the system's cognitive capability is bounded by its strict, verifiable state representation. By treating the world and self as a single, immutable SQLite database, the system ensures that all cognitive actions are traceable and reversible. This design prevents the runtime from accumulating unbounded cognitive state, thereby establishing a clear boundary for the system's cognitive capabilities.\n\n**Worked Example: File-Edit Task Across a Crash**\n**Task:** Edit a local file to add a new configuration block.\n**Execution:** The runtime initiates the task, writes the initial state to SQLite. The system executes the edit, updating the SQLite task state. Upon detecting a crash, the runtime snapshots the SQLite database. On restart, the runtime restores the snapshot, re-executes the edit, and verifies the new state.\n**Result:** The system successfully recovers the exact state of the file, ensuring the edit is applied correctly without losing any prior context or data.\n\n**Falsifiable Experiment:**\n**Baseline:** A simpler, non-cognitive runtime that only executes basic file operations without state tracking or recovery.\n**Experiment:** Run the file-edit task against the proposed architecture versus the baseline.\n**Hypothesis:** The proposed architecture will successfully recover the file state after a crash, while the baseline will lose the file state, demonstrating the system's superior durability and recovery capabilities.\n\n**Distinction of Components:**\n*   **Implemented Components:** The 12 GB GPU memory pool, the SQLite-backed",
+  "generated_tokens": 1400,
+  "termination": "length",
+  "seconds": 46.12879409999732,
+  "started_utc": "2026-09-09T10:16:24.294704+00:00",
+  "completed_utc": "2026-09-09T10:17:12.684060+00:00",
+  "model": {
+    "slug": "spark",
+    "model_id": "XHToken/Spark-X2.5-1.7B",
+    "revision": "448e61eb392c00f2c403185c5b56d5e0665bfaab",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/XHToken/Spark-X2.5-1.7B/tree/448e61eb392c00f2c403185c5b56d5e0665bfaab"
+  },
+  "output_sha256": "e02b44dff5186e1220411d87227b56d49ec231f63a207b772885efac8ad0169e",
+  "request_sha256": "4b6cb5522707568479c2a68b951d9daa7c3b54ca3dca642cdbc33b12e8af12ab",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/tinyllama/model-files.json b/research/ai_generated_agi_architectures/collection/runs/tinyllama/model-files.json
new file mode 100644
index 0000000..856744b
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/tinyllama/model-files.json
@@ -0,0 +1,53 @@
+{
+  "model": {
+    "slug": "tinyllama",
+    "model_id": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "revision": "fe8a4ea1ffedaf415f4da2f062534de366a451e6",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tree/fe8a4ea1ffedaf415f4da2f062534de366a451e6"
+  },
+  "files": [
+    {
+      "file": "config.json",
+      "bytes": 608,
+      "sha256": "486bedda3a6988332e60d9638a09ca4b260d34ebcf1b19e22cf3b140b63d8fe9"
+    },
+    {
+      "file": "eval_results.json",
+      "bytes": 566,
+      "sha256": "637daaebe7aa8af0e678f9e79ec33fa29f8635090d45ec813f0e7474320d7351"
+    },
+    {
+      "file": "generation_config.json",
+      "bytes": 124,
+      "sha256": "18046d04f5bd8b4998095ecabdd17a1bf0053d9acdccead4a05be4a3575f3c5c"
+    },
+    {
+      "file": "model.safetensors",
+      "bytes": 2200119864,
+      "sha256": "6e6001da2106d4757498752a021df6c2bdc332c650aae4bae6b0c004dcf14933"
+    },
+    {
+      "file": "special_tokens_map.json",
+      "bytes": 551,
+      "sha256": "82d96d7a9e6ced037f12394b7ea6a5b02e6ca87e0d11edaa8d60d9be857ce7db"
+    },
+    {
+      "file": "tokenizer.json",
+      "bytes": 1842767,
+      "sha256": "bcd04f0eadf90287bd26e1a183ac487d8a141b09b06aecb7725bbdd343640f2e"
+    },
+    {
+      "file": "tokenizer.model",
+      "bytes": 499723,
+      "sha256": "9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347"
+    },
+    {
+      "file": "tokenizer_config.json",
+      "bytes": 1289,
+      "sha256": "7b41ba7d0eb91e77914ca3dafde559ea3e19878769b7e68409e89bed5222e77a"
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/tinyllama/readable.txt b/research/ai_generated_agi_architectures/collection/runs/tinyllama/readable.txt
new file mode 100644
index 0000000..0ef713c
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/tinyllama/readable.txt
@@ -0,0 +1,51 @@
+Introduction
+
+Cognitive-OS is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. The system is designed to be implementable and scalable, with a focus on AGI-oriented architecture. This proposal aims to propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved.
+
+Memory Architecture
+
+Cognitive-OS uses a memory architecture that is designed to be efficient and scalable. The system uses a single workstation with 12 GB of GPU memory, which is shared across all processes. This memory architecture allows for efficient use of resources, as each process can access the same amount of memory.
+
+Reasoning and Planning Loop
+
+The reasoning and planning loop in Cognitive-OS is designed to be efficient and scalable. The system uses a first-in, first-out (FIFO) queue to store planning and reasoning tasks. These tasks are executed in a FIFO order, with the oldest task being executed first. This design ensures that tasks are executed in a timely and efficient manner, while also allowing for parallel execution of tasks.
+
+Learning or Self-Improvement
+
+Cognitive-OS uses a learning or self-improvement mechanism to improve its reasoning and planning abilities. The system uses a reinforcement learning (RL) algorithm to optimize its planning and reasoning tasks. The RL algorithm is trained on a large dataset of planning and reasoning tasks, and is updated periodically to improve its performance.
+
+Tool Use and Action Execution
+
+Cognitive-OS uses a tool-as-a-service (TaaS) model to provide tool actions to users. The system uses a RESTful API to provide tool actions to users. The API is designed to be simple and intuitive, allowing users to easily interact with the system.
+
+World and Self Representation
+
+Cognitive-OS uses a world representation that is designed to be simple and intuitive. The system uses a simple graph representation to represent the world. This graph representation allows for easy manipulation and visualization of the world.
+
+Safety and Governance
+
+Cognitive-OS uses a safety and governance mechanism to ensure the safety and security of the system. The system uses a combination of hardware and software-based security measures, including hardware-based security features such as hardware-based cryptography and hardware-based fault tolerance. The system also uses software-based security measures, such as secure coding practices and access control mechanisms.
+
+Evaluation and Benchmarks
+
+Cognitive-OS has been evaluated and benchmarked using a variety of metrics. The system has been shown to be efficient and scalable, with a throughput of over 100,000 tasks per second. The system has also been shown to be effective at solving planning and reasoning tasks, with a success rate of over 90%.
+
+Persistence and Runtime Recovery
+
+Cognitive-OS uses a persistent storage mechanism to ensure the persistence of its state. The system uses a distributed file system (DFS) to store its state. This DFS is designed to be highly available and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure.
+
+Multi-Agent Orchestration
+
+Cognitive-OS uses a multi-agent orchestration mechanism to ensure the coordination of multiple agents. The system uses a distributed coordination mechanism to ensure the coordination of multiple agents. This mechanism is designed to be scalable and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure.
+
+Engineering Feasibility
+
+Cognitive-OS has been designed to be implementable and scalable. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has also been designed to be easily portable, allowing for easy deployment and scaling across different platforms.
+
+Non-Obvious Design Insight
+
+Cognitive-OS has been designed to be implementable and scalable, with a focus on AGI-oriented architecture. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has also been designed to be easily portable, allowing for easy deployment and scaling across different platforms.
+
+Conclusion
+
+Cognitive-OS has been designed to be implementable and scalable, with a focus on AGI-oriented architecture. The system has been designed to be efficient and scalable, with a throughput of over 100,000 tasks per second. The system has been designed to be effective at solving planning and reasoning tasks, with a success rate of over 90%. The system has been designed to be easily portable, allowing for easy deployment and scaling across different platforms. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has been designed to be scalable and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure. The system has been designed to be efficient and scalable, with a focus on AGI-oriented architecture.
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/collection/runs/tinyllama/request.json b/research/ai_generated_agi_architectures/collection/runs/tinyllama/request.json
new file mode 100644
index 0000000..f874bba
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/tinyllama/request.json
@@ -0,0 +1,421 @@
+{
+  "model": {
+    "slug": "tinyllama",
+    "model_id": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "revision": "fe8a4ea1ffedaf415f4da2f062534de366a451e6",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tree/fe8a4ea1ffedaf415f4da2f062534de366a451e6"
+  },
+  "messages": [
+    {
+      "role": "user",
+      "content": "Propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n"
+    }
+  ],
+  "rendered_prompt": "<|user|>\nPropose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved. Context: this is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. A single workstation has 12 GB GPU memory; hosted paid APIs are unavailable. Treat this context as a design constraint, not evidence of capabilities.\n\nWrite a concise technical proposal of at most 850 words, using these eleven numbered headings. For each heading give a concrete mechanism and one limitation or tradeoff:\n1. Memory architecture\n2. Reasoning and planning loop\n3. Learning or self-improvement\n4. Tool use and action execution\n5. World and self representation\n6. Safety and governance\n7. Evaluation and benchmarks\n8. Persistence and runtime recovery\n9. Multi-agent orchestration\n10. Engineering feasibility\n11. Non-obvious design insight\n\nInclude at least one explicit data schema or interface, a worked example of a file-edit task across a crash, and a falsifiable experiment comparing your design with a simpler baseline. Distinguish implemented components, proposals, and unresolved research questions. Do not invent benchmark results or citations. Keep all external actions within declared user permissions; learning must not silently expand those permissions.\n</s>\n<|assistant|>\n",
+  "input_ids": [
+    529,
+    29989,
+    1792,
+    29989,
+    29958,
+    13,
+    1184,
+    4220,
+    385,
+    2334,
+    519,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    363,
+    315,
+    3811,
+    3321,
+    29899,
+    3267,
+    29892,
+    1728,
+    5995,
+    292,
+    393,
+    16369,
+    29902,
+    756,
+    1063,
+    14363,
+    29889,
+    15228,
+    29901,
+    445,
+    338,
+    263,
+    1887,
+    29899,
+    4102,
+    5132,
+    10073,
+    411,
+    23299,
+    3414,
+    2106,
+    29892,
+    385,
+    1741,
+    8955,
+    29892,
+    4095,
+    287,
+    5780,
+    8820,
+    29892,
+    1147,
+    3709,
+    29899,
+    29887,
+    630,
+    934,
+    13261,
+    267,
+    29892,
+    322,
+    8095,
+    24205,
+    29889,
+    319,
+    2323,
+    664,
+    19569,
+    756,
+    29871,
+    29896,
+    29906,
+    19289,
+    22796,
+    3370,
+    29936,
+    17791,
+    12530,
+    23649,
+    526,
+    443,
+    16515,
+    29889,
+    6479,
+    271,
+    445,
+    3030,
+    408,
+    263,
+    2874,
+    7276,
+    29892,
+    451,
+    10757,
+    310,
+    27108,
+    29889,
+    13,
+    13,
+    6113,
+    263,
+    3022,
+    895,
+    16905,
+    24963,
+    310,
+    472,
+    1556,
+    29871,
+    29947,
+    29945,
+    29900,
+    3838,
+    29892,
+    773,
+    1438,
+    28121,
+    1353,
+    287,
+    2343,
+    886,
+    29889,
+    1152,
+    1269,
+    28435,
+    2367,
+    263,
+    18387,
+    13336,
+    322,
+    697,
+    29485,
+    470,
+    11302,
+    2696,
+    29901,
+    13,
+    29896,
+    29889,
+    18914,
+    11258,
+    13,
+    29906,
+    29889,
+    830,
+    1658,
+    292,
+    322,
+    18987,
+    2425,
+    13,
+    29941,
+    29889,
+    29257,
+    470,
+    1583,
+    29899,
+    326,
+    16123,
+    882,
+    13,
+    29946,
+    29889,
+    21704,
+    671,
+    322,
+    3158,
+    8225,
+    13,
+    29945,
+    29889,
+    2787,
+    322,
+    1583,
+    8954,
+    13,
+    29953,
+    29889,
+    14795,
+    3305,
+    322,
+    4095,
+    749,
+    13,
+    29955,
+    29889,
+    382,
+    4387,
+    362,
+    322,
+    23513,
+    29879,
+    13,
+    29947,
+    29889,
+    9034,
+    11416,
+    322,
+    10073,
+    24205,
+    13,
+    29929,
+    29889,
+    14974,
+    29899,
+    14748,
+    22624,
+    16444,
+    362,
+    13,
+    29896,
+    29900,
+    29889,
+    22557,
+    28326,
+    4127,
+    13,
+    29896,
+    29896,
+    29889,
+    10050,
+    29899,
+    711,
+    2366,
+    2874,
+    25483,
+    13,
+    13,
+    29419,
+    472,
+    3203,
+    697,
+    6261,
+    848,
+    10938,
+    470,
+    5067,
+    29892,
+    263,
+    3796,
+    1342,
+    310,
+    263,
+    934,
+    29899,
+    5628,
+    3414,
+    4822,
+    263,
+    8095,
+    29892,
+    322,
+    263,
+    285,
+    1338,
+    28677,
+    7639,
+    17420,
+    596,
+    2874,
+    411,
+    263,
+    13682,
+    2362,
+    5570,
+    29889,
+    6652,
+    6202,
+    728,
+    8762,
+    7117,
+    29892,
+    9551,
+    1338,
+    29892,
+    322,
+    443,
+    9778,
+    1490,
+    5925,
+    5155,
+    29889,
+    1938,
+    451,
+    11817,
+    23513,
+    2582,
+    470,
+    7537,
+    800,
+    29889,
+    19152,
+    599,
+    7029,
+    8820,
+    2629,
+    8052,
+    1404,
+    11239,
+    29936,
+    6509,
+    1818,
+    451,
+    4047,
+    2705,
+    7985,
+    1906,
+    11239,
+    29889,
+    13,
+    2,
+    29871,
+    13,
+    29966,
+    29989,
+    465,
+    22137,
+    29989,
+    29958,
+    13
+  ],
+  "generation_config": {
+    "max_length": 20,
+    "max_new_tokens": 1400,
+    "min_length": 0,
+    "min_new_tokens": null,
+    "early_stopping": false,
+    "max_time": null,
+    "stop_strings": null,
+    "do_sample": false,
+    "num_beams": 1,
+    "use_cache": true,
+    "cache_implementation": null,
+    "cache_config": null,
+    "return_legacy_cache": null,
+    "prefill_chunk_size": null,
+    "temperature": 1.0,
+    "top_k": 50,
+    "top_p": 1.0,
+    "min_p": null,
+    "typical_p": 1.0,
+    "epsilon_cutoff": 0.0,
+    "eta_cutoff": 0.0,
+    "repetition_penalty": 1.0,
+    "encoder_repetition_penalty": 1.0,
+    "length_penalty": 1.0,
+    "no_repeat_ngram_size": 0,
+    "bad_words_ids": null,
+    "renormalize_logits": false,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "remove_invalid_values": false,
+    "exponential_decay_length_penalty": null,
+    "suppress_tokens": null,
+    "begin_suppress_tokens": null,
+    "sequence_bias": null,
+    "token_healing": false,
+    "guidance_scale": null,
+    "watermarking_config": null,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "output_logits": null,
+    "return_dict_in_generate": false,
+    "pad_token_id": 2,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "encoder_no_repeat_ngram_size": 0,
+    "decoder_start_token_id": null,
+    "is_assistant": false,
+    "num_assistant_tokens": 20,
+    "num_assistant_tokens_schedule": "constant",
+    "assistant_confidence_threshold": 0.4,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": 10,
+    "target_lookbehind": 10,
+    "disable_compile": false,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": 0.0,
+    "num_beam_groups": 1,
+    "constraints": null,
+    "force_words_ids": null,
+    "_from_model_config": false,
+    "transformers_version": "4.57.1"
+  },
+  "seed": 20260909,
+  "dtype": "bfloat16",
+  "attention": "eager",
+  "enable_thinking": false,
+  "prompt_sha256": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430"
+}
diff --git a/research/ai_generated_agi_architectures/collection/runs/tinyllama/response.json b/research/ai_generated_agi_architectures/collection/runs/tinyllama/response.json
new file mode 100644
index 0000000..bc57b3c
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/runs/tinyllama/response.json
@@ -0,0 +1,1178 @@
+{
+  "output_ids": [
+    25898,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    338,
+    263,
+    1887,
+    29899,
+    4102,
+    5132,
+    10073,
+    411,
+    23299,
+    3414,
+    2106,
+    29892,
+    385,
+    1741,
+    8955,
+    29892,
+    4095,
+    287,
+    5780,
+    8820,
+    29892,
+    1147,
+    3709,
+    29899,
+    29887,
+    630,
+    934,
+    13261,
+    267,
+    29892,
+    322,
+    8095,
+    24205,
+    29889,
+    450,
+    1788,
+    338,
+    8688,
+    304,
+    367,
+    2334,
+    519,
+    322,
+    8716,
+    519,
+    29892,
+    411,
+    263,
+    8569,
+    373,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    29889,
+    910,
+    24963,
+    263,
+    9893,
+    304,
+    16193,
+    385,
+    2334,
+    519,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    363,
+    315,
+    3811,
+    3321,
+    29899,
+    3267,
+    29892,
+    1728,
+    5995,
+    292,
+    393,
+    16369,
+    29902,
+    756,
+    1063,
+    14363,
+    29889,
+    13,
+    13,
+    16015,
+    28333,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    3913,
+    263,
+    3370,
+    11258,
+    393,
+    338,
+    8688,
+    304,
+    367,
+    8543,
+    322,
+    8716,
+    519,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    2323,
+    664,
+    19569,
+    411,
+    29871,
+    29896,
+    29906,
+    19289,
+    310,
+    22796,
+    3370,
+    29892,
+    607,
+    338,
+    7258,
+    4822,
+    599,
+    10174,
+    29889,
+    910,
+    3370,
+    11258,
+    6511,
+    363,
+    8543,
+    671,
+    310,
+    7788,
+    29892,
+    408,
+    1269,
+    1889,
+    508,
+    2130,
+    278,
+    1021,
+    5253,
+    310,
+    3370,
+    29889,
+    13,
+    13,
+    1123,
+    1658,
+    292,
+    322,
+    1858,
+    9450,
+    21493,
+    13,
+    13,
+    1576,
+    24481,
+    322,
+    18987,
+    2425,
+    297,
+    315,
+    3811,
+    3321,
+    29899,
+    3267,
+    338,
+    8688,
+    304,
+    367,
+    8543,
+    322,
+    8716,
+    519,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    937,
+    29899,
+    262,
+    29892,
+    937,
+    29899,
+    449,
+    313,
+    3738,
+    5800,
+    29897,
+    9521,
+    304,
+    3787,
+    18987,
+    322,
+    24481,
+    9595,
+    29889,
+    4525,
+    9595,
+    526,
+    8283,
+    297,
+    263,
+    9338,
+    5800,
+    1797,
+    29892,
+    411,
+    278,
+    23947,
+    3414,
+    1641,
+    8283,
+    937,
+    29889,
+    910,
+    2874,
+    5662,
+    1973,
+    393,
+    9595,
+    526,
+    8283,
+    297,
+    263,
+    5335,
+    873,
+    322,
+    8543,
+    8214,
+    29892,
+    1550,
+    884,
+    14372,
+    363,
+    8943,
+    8225,
+    310,
+    9595,
+    29889,
+    13,
+    13,
+    29931,
+    799,
+    1076,
+    470,
+    21782,
+    29899,
+    1888,
+    16123,
+    882,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    3913,
+    263,
+    6509,
+    470,
+    1583,
+    29899,
+    326,
+    16123,
+    882,
+    13336,
+    304,
+    11157,
+    967,
+    24481,
+    322,
+    18987,
+    633,
+    9770,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    15561,
+    1454,
+    13561,
+    6509,
+    313,
+    2241,
+    29897,
+    5687,
+    304,
+    24656,
+    967,
+    18987,
+    322,
+    24481,
+    9595,
+    29889,
+    450,
+    390,
+    29931,
+    5687,
+    338,
+    16370,
+    373,
+    263,
+    2919,
+    8783,
+    310,
+    18987,
+    322,
+    24481,
+    9595,
+    29892,
+    322,
+    338,
+    4784,
+    3785,
+    1711,
+    304,
+    11157,
+    967,
+    4180,
+    29889,
+    13,
+    13,
+    12229,
+    4803,
+    322,
+    9123,
+    11080,
+    918,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    3913,
+    263,
+    5780,
+    29899,
+    294,
+    29899,
+    29874,
+    29899,
+    5509,
+    313,
+    29911,
+    7340,
+    29903,
+    29897,
+    1904,
+    304,
+    3867,
+    5780,
+    8820,
+    304,
+    4160,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    16759,
+    1319,
+    3450,
+    304,
+    3867,
+    5780,
+    8820,
+    304,
+    4160,
+    29889,
+    450,
+    3450,
+    338,
+    8688,
+    304,
+    367,
+    2560,
+    322,
+    27951,
+    573,
+    29892,
+    14372,
+    4160,
+    304,
+    5948,
+    16254,
+    411,
+    278,
+    1788,
+    29889,
+    13,
+    13,
+    14058,
+    322,
+    21782,
+    16314,
+    362,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    3913,
+    263,
+    3186,
+    8954,
+    393,
+    338,
+    8688,
+    304,
+    367,
+    2560,
+    322,
+    27951,
+    573,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    2560,
+    3983,
+    8954,
+    304,
+    2755,
+    278,
+    3186,
+    29889,
+    910,
+    3983,
+    8954,
+    6511,
+    363,
+    4780,
+    11525,
+    2785,
+    322,
+    7604,
+    2133,
+    310,
+    278,
+    3186,
+    29889,
+    13,
+    13,
+    29903,
+    2142,
+    3305,
+    322,
+    6769,
+    749,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    3913,
+    263,
+    15332,
+    322,
+    4095,
+    749,
+    13336,
+    304,
+    9801,
+    278,
+    15332,
+    322,
+    6993,
+    310,
+    278,
+    1788,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    10296,
+    310,
+    12837,
+    322,
+    7047,
+    29899,
+    6707,
+    6993,
+    15366,
+    29892,
+    3704,
+    12837,
+    29899,
+    6707,
+    6993,
+    5680,
+    1316,
+    408,
+    12837,
+    29899,
+    6707,
+    24941,
+    5275,
+    322,
+    12837,
+    29899,
+    6707,
+    12570,
+    20341,
+    749,
+    29889,
+    450,
+    1788,
+    884,
+    3913,
+    7047,
+    29899,
+    6707,
+    6993,
+    15366,
+    29892,
+    1316,
+    408,
+    11592,
+    14137,
+    23274,
+    322,
+    2130,
+    2761,
+    7208,
+    12903,
+    29889,
+    13,
+    13,
+    29923,
+    4387,
+    362,
+    322,
+    4111,
+    16580,
+    29879,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    756,
+    1063,
+    19030,
+    322,
+    23513,
+    287,
+    773,
+    263,
+    12875,
+    310,
+    21556,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    4318,
+    304,
+    367,
+    8543,
+    322,
+    8716,
+    519,
+    29892,
+    411,
+    263,
+    1549,
+    649,
+    310,
+    975,
+    29871,
+    29896,
+    29900,
+    29900,
+    29892,
+    29900,
+    29900,
+    29900,
+    9595,
+    639,
+    1473,
+    29889,
+    450,
+    1788,
+    756,
+    884,
+    1063,
+    4318,
+    304,
+    367,
+    11828,
+    472,
+    17069,
+    18987,
+    322,
+    24481,
+    9595,
+    29892,
+    411,
+    263,
+    2551,
+    6554,
+    310,
+    975,
+    29871,
+    29929,
+    29900,
+    15543,
+    13,
+    13,
+    15136,
+    11416,
+    322,
+    24875,
+    3599,
+    22205,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    3913,
+    263,
+    28152,
+    8635,
+    13336,
+    304,
+    9801,
+    278,
+    3736,
+    11416,
+    310,
+    967,
+    2106,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    13235,
+    934,
+    1788,
+    313,
+    4037,
+    29903,
+    29897,
+    304,
+    3787,
+    967,
+    2106,
+    29889,
+    910,
+    360,
+    9998,
+    338,
+    8688,
+    304,
+    367,
+    10712,
+    3625,
+    322,
+    620,
+    309,
+    993,
+    29892,
+    411,
+    263,
+    4418,
+    957,
+    13336,
+    393,
+    6511,
+    278,
+    1788,
+    304,
+    6773,
+    2734,
+    1584,
+    297,
+    278,
+    1741,
+    310,
+    263,
+    10672,
+    29889,
+    13,
+    13,
+    15329,
+    29899,
+    19661,
+    1394,
+    305,
+    16444,
+    362,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    3913,
+    263,
+    2473,
+    29899,
+    14748,
+    22624,
+    16444,
+    362,
+    13336,
+    304,
+    9801,
+    278,
+    29311,
+    3381,
+    310,
+    2999,
+    19518,
+    29889,
+    450,
+    1788,
+    3913,
+    263,
+    13235,
+    29311,
+    3381,
+    13336,
+    304,
+    9801,
+    278,
+    29311,
+    3381,
+    310,
+    2999,
+    19518,
+    29889,
+    910,
+    13336,
+    338,
+    8688,
+    304,
+    367,
+    8716,
+    519,
+    322,
+    620,
+    309,
+    993,
+    29892,
+    411,
+    263,
+    4418,
+    957,
+    13336,
+    393,
+    6511,
+    278,
+    1788,
+    304,
+    6773,
+    2734,
+    1584,
+    297,
+    278,
+    1741,
+    310,
+    263,
+    10672,
+    29889,
+    13,
+    13,
+    12412,
+    3241,
+    5169,
+    294,
+    4127,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    2334,
+    519,
+    322,
+    8716,
+    519,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    878,
+    1070,
+    322,
+    21103,
+    1821,
+    29892,
+    14372,
+    363,
+    4780,
+    6124,
+    310,
+    716,
+    9863,
+    29889,
+    450,
+    1788,
+    756,
+    884,
+    1063,
+    8688,
+    304,
+    367,
+    5948,
+    2011,
+    519,
+    29892,
+    14372,
+    363,
+    4780,
+    18209,
+    322,
+    21640,
+    4822,
+    1422,
+    21796,
+    29889,
+    13,
+    13,
+    12283,
+    29899,
+    6039,
+    2366,
+    12037,
+    13377,
+    523,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    2334,
+    519,
+    322,
+    8716,
+    519,
+    29892,
+    411,
+    263,
+    8569,
+    373,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    878,
+    1070,
+    322,
+    21103,
+    1821,
+    29892,
+    14372,
+    363,
+    4780,
+    6124,
+    310,
+    716,
+    9863,
+    29889,
+    450,
+    1788,
+    756,
+    884,
+    1063,
+    8688,
+    304,
+    367,
+    5948,
+    2011,
+    519,
+    29892,
+    14372,
+    363,
+    4780,
+    18209,
+    322,
+    21640,
+    4822,
+    1422,
+    21796,
+    29889,
+    13,
+    13,
+    1168,
+    10085,
+    13,
+    13,
+    29907,
+    3811,
+    3321,
+    29899,
+    3267,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    2334,
+    519,
+    322,
+    8716,
+    519,
+    29892,
+    411,
+    263,
+    8569,
+    373,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    8543,
+    322,
+    8716,
+    519,
+    29892,
+    411,
+    263,
+    1549,
+    649,
+    310,
+    975,
+    29871,
+    29896,
+    29900,
+    29900,
+    29892,
+    29900,
+    29900,
+    29900,
+    9595,
+    639,
+    1473,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    11828,
+    472,
+    17069,
+    18987,
+    322,
+    24481,
+    9595,
+    29892,
+    411,
+    263,
+    2551,
+    6554,
+    310,
+    975,
+    29871,
+    29929,
+    29900,
+    15543,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    5948,
+    2011,
+    519,
+    29892,
+    14372,
+    363,
+    4780,
+    18209,
+    322,
+    21640,
+    4822,
+    1422,
+    21796,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    878,
+    1070,
+    322,
+    21103,
+    1821,
+    29892,
+    14372,
+    363,
+    4780,
+    6124,
+    310,
+    716,
+    9863,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    8716,
+    519,
+    322,
+    620,
+    309,
+    993,
+    29892,
+    411,
+    263,
+    4418,
+    957,
+    13336,
+    393,
+    6511,
+    278,
+    1788,
+    304,
+    6773,
+    2734,
+    1584,
+    297,
+    278,
+    1741,
+    310,
+    263,
+    10672,
+    29889,
+    450,
+    1788,
+    756,
+    1063,
+    8688,
+    304,
+    367,
+    8543,
+    322,
+    8716,
+    519,
+    29892,
+    411,
+    263,
+    8569,
+    373,
+    16369,
+    29902,
+    29899,
+    12236,
+    287,
+    11258,
+    29889,
+    2
+  ],
+  "raw_output": "Introduction\n\nCognitive-OS is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. The system is designed to be implementable and scalable, with a focus on AGI-oriented architecture. This proposal aims to propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved.\n\nMemory Architecture\n\nCognitive-OS uses a memory architecture that is designed to be efficient and scalable. The system uses a single workstation with 12 GB of GPU memory, which is shared across all processes. This memory architecture allows for efficient use of resources, as each process can access the same amount of memory.\n\nReasoning and Planning Loop\n\nThe reasoning and planning loop in Cognitive-OS is designed to be efficient and scalable. The system uses a first-in, first-out (FIFO) queue to store planning and reasoning tasks. These tasks are executed in a FIFO order, with the oldest task being executed first. This design ensures that tasks are executed in a timely and efficient manner, while also allowing for parallel execution of tasks.\n\nLearning or Self-Improvement\n\nCognitive-OS uses a learning or self-improvement mechanism to improve its reasoning and planning abilities. The system uses a reinforcement learning (RL) algorithm to optimize its planning and reasoning tasks. The RL algorithm is trained on a large dataset of planning and reasoning tasks, and is updated periodically to improve its performance.\n\nTool Use and Action Execution\n\nCognitive-OS uses a tool-as-a-service (TaaS) model to provide tool actions to users. The system uses a RESTful API to provide tool actions to users. The API is designed to be simple and intuitive, allowing users to easily interact with the system.\n\nWorld and Self Representation\n\nCognitive-OS uses a world representation that is designed to be simple and intuitive. The system uses a simple graph representation to represent the world. This graph representation allows for easy manipulation and visualization of the world.\n\nSafety and Governance\n\nCognitive-OS uses a safety and governance mechanism to ensure the safety and security of the system. The system uses a combination of hardware and software-based security measures, including hardware-based security features such as hardware-based cryptography and hardware-based fault tolerance. The system also uses software-based security measures, such as secure coding practices and access control mechanisms.\n\nEvaluation and Benchmarks\n\nCognitive-OS has been evaluated and benchmarked using a variety of metrics. The system has been shown to be efficient and scalable, with a throughput of over 100,000 tasks per second. The system has also been shown to be effective at solving planning and reasoning tasks, with a success rate of over 90%.\n\nPersistence and Runtime Recovery\n\nCognitive-OS uses a persistent storage mechanism to ensure the persistence of its state. The system uses a distributed file system (DFS) to store its state. This DFS is designed to be highly available and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure.\n\nMulti-Agent Orchestration\n\nCognitive-OS uses a multi-agent orchestration mechanism to ensure the coordination of multiple agents. The system uses a distributed coordination mechanism to ensure the coordination of multiple agents. This mechanism is designed to be scalable and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure.\n\nEngineering Feasibility\n\nCognitive-OS has been designed to be implementable and scalable. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has also been designed to be easily portable, allowing for easy deployment and scaling across different platforms.\n\nNon-Obvious Design Insight\n\nCognitive-OS has been designed to be implementable and scalable, with a focus on AGI-oriented architecture. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has also been designed to be easily portable, allowing for easy deployment and scaling across different platforms.\n\nConclusion\n\nCognitive-OS has been designed to be implementable and scalable, with a focus on AGI-oriented architecture. The system has been designed to be efficient and scalable, with a throughput of over 100,000 tasks per second. The system has been designed to be effective at solving planning and reasoning tasks, with a success rate of over 90%. The system has been designed to be easily portable, allowing for easy deployment and scaling across different platforms. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has been designed to be scalable and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure. The system has been designed to be efficient and scalable, with a focus on AGI-oriented architecture.</s>",
+  "generated_tokens": 1144,
+  "termination": "eos",
+  "seconds": 17.35476000000199,
+  "started_utc": "2026-09-09T10:53:35.533665+00:00",
+  "completed_utc": "2026-09-09T10:53:54.230896+00:00",
+  "model": {
+    "slug": "tinyllama",
+    "model_id": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+    "revision": "fe8a4ea1ffedaf415f4da2f062534de366a451e6",
+    "license": "apache-2.0",
+    "gated": false,
+    "private": false,
+    "source": "https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tree/fe8a4ea1ffedaf415f4da2f062534de366a451e6"
+  },
+  "output_sha256": "c9e68b1e3b628d98eb4bd966adb130c861928846477b8f16a9c5eb3a23dbb2fe",
+  "request_sha256": "ea84db03baa6c4173e5f97c58d42b8ab16a634b273e6b730ae35c01c6c04cf4d",
+  "collector_sha256": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "python": "3.13.5",
+  "platform": "Windows",
+  "gpu": "NVIDIA GeForce RTX 4070",
+  "cuda": "12.8",
+  "packages": {
+    "torch": "2.8.0+cu128",
+    "transformers": "4.57.1",
+    "accelerate": "1.10.1",
+    "huggingface-hub": "0.36.2",
+    "tokenizers": "0.22.2",
+    "safetensors": "0.8.0"
+  }
+}
diff --git a/research/ai_generated_agi_architectures/collection/tokenizer-verification.json b/research/ai_generated_agi_architectures/collection/tokenizer-verification.json
new file mode 100644
index 0000000..5e997f6
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/tokenizer-verification.json
@@ -0,0 +1,42 @@
+[
+  {
+    "system": "spark",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 2
+  },
+  {
+    "system": "qwen",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 1
+  },
+  {
+    "system": "phi",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 2
+  },
+  {
+    "system": "smollm",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 1
+  },
+  {
+    "system": "granite",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 2
+  },
+  {
+    "system": "falcon",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 1
+  },
+  {
+    "system": "tinyllama",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 1
+  },
+  {
+    "system": "danube",
+    "template_input_decode_match": true,
+    "cached_lfs_hash_matches": 2
+  }
+]
diff --git a/research/ai_generated_agi_architectures/collection/verify_tokenizers.py b/research/ai_generated_agi_architectures/collection/verify_tokenizers.py
new file mode 100644
index 0000000..02adf85
--- /dev/null
+++ b/research/ai_generated_agi_architectures/collection/verify_tokenizers.py
@@ -0,0 +1,42 @@
+"""Optional local check: reproduce recorded input/output decoding, no generation."""
+import argparse
+import json
+from pathlib import Path
+from transformers import AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model-root", type=Path, required=True)
+parser.add_argument("--spark-dir", type=Path, required=True)
+args = parser.parse_args()
+root = Path(__file__).resolve().parent
+results = []
+for model in json.loads((root / "models.json").read_text()):
+    slug = model["slug"]
+    folder = root / "runs" / slug
+    request = json.loads((folder / "request.json").read_text(encoding="utf-8"))
+    response = json.loads((folder / "response.json").read_text(encoding="utf-8"))
+    model_dir = args.spark_dir if slug == "spark" else args.model_root / slug
+    tokenizer = AutoTokenizer.from_pretrained(model_dir, local_files_only=True, trust_remote_code=slug == "spark")
+    rendered = tokenizer.apply_chat_template(request["messages"], tokenize=False,
+                                             add_generation_prompt=True, enable_thinking=False)
+    assert rendered == request["rendered_prompt"], slug
+    assert tokenizer(rendered, add_special_tokens=False)["input_ids"] == request["input_ids"], slug
+    assert tokenizer.decode(response["output_ids"], skip_special_tokens=False) == response["raw_output"], slug
+    assert tokenizer.decode(response["output_ids"], skip_special_tokens=True) == (folder / "readable.txt").read_text(encoding="utf-8"), slug
+    # HF local snapshot metadata contains the pinned revision and LFS SHA-256.
+    files = json.loads((folder / "model-files.json").read_text())["files"]
+    verified_weights = 0
+    for record in files:
+        if not record["file"].endswith(".safetensors"):
+            continue
+        meta = model_dir / ".cache/huggingface/download" / (record["file"] + ".metadata")
+        if meta.exists():
+            revision, etag = meta.read_text().splitlines()[:2]
+            assert revision == model["revision"], (slug, record["file"])
+            if len(etag) == 64:
+                assert etag == record["sha256"], (slug, record["file"])
+                verified_weights += 1
+    results.append(dict(system=slug, template_input_decode_match=True,
+                        cached_lfs_hash_matches=verified_weights))
+    print(slug, "template and input/output token records match", flush=True)
+(root / "tokenizer-verification.json").write_text(json.dumps(results, indent=2) + "\n", encoding="utf-8")
diff --git a/research/ai_generated_agi_architectures/comparison.csv b/research/ai_generated_agi_architectures/comparison.csv
new file mode 100644
index 0000000..d8f57bc
--- /dev/null
+++ b/research/ai_generated_agi_architectures/comparison.csv
@@ -0,0 +1,89 @@
+system,model,dimension,output_quote,analysis,evidence,termination
+spark,XHToken/Spark-X2.5-1.7B,memory,a 2 GB persistent state store,Reject the GPU persistence claim: volatile VRAM is not a durable SQLite store; use disk transactions and budget model memory separately.,raw_outputs/spark.txt#L2,length
+spark,XHToken/Spark-X2.5-1.7B,reasoning_planning,a high-level planning module generates a sequence of actions,Adapt the planner/executor separation; add explicit preconditions and verifier evidence because looping over task state does not itself detect deadlocks.,raw_outputs/spark.txt#L6,length
+spark,XHToken/Spark-X2.5-1.7B,learning,Only approved changes are applied,Retain the separation between proposals and authorized changes; bind approval to an immutable candidate and current policy version.,raw_outputs/spark.txt#L10,length
+spark,XHToken/Spark-X2.5-1.7B,tools_actions,Each patch is validated against the SQLite task state,Adapt with actual file hashes and permission checks at execution; database state alone may be stale relative to the filesystem.,raw_outputs/spark.txt#L14,length
+spark,XHToken/Spark-X2.5-1.7B,world_self,"unified, immutable world representation stored in SQLite",Adapt as append-only observations plus a mutable projection; observed state is incomplete and cannot make arbitrary external actions reversible.,raw_outputs/spark.txt#L18,length
+spark,XHToken/Spark-X2.5-1.7B,safety_governance,any deviations trigger immediate halting,Specify fail-closed behavior for concrete invariant violations; anomaly detection is not a proof of permission enforcement.,raw_outputs/spark.txt#L22,length
+spark,XHToken/Spark-X2.5-1.7B,evaluation,"falsifiable, deterministic tests","Retain deterministic fault injection for recovery, but add exact crash boundaries and measurable invariants; no evaluation results were actually collected by this model.",raw_outputs/spark.txt#L26,length
+spark,XHToken/Spark-X2.5-1.7B,persistence_recovery,"On crash, the runtime automatically snapshots",Reject as a hard-crash guarantee: the process may execute no cleanup at all. Persist intent before the effect and reconcile after restart.,raw_outputs/spark.txt#L30,length
+spark,XHToken/Spark-X2.5-1.7B,orchestration,managing a queue of agents,"Adapt into one bounded worker queue initially; no lock, lease or conflict protocol is specified for parallel execution.",raw_outputs/spark.txt#L34,length
+spark,XHToken/Spark-X2.5-1.7B,engineering_feasibility,highly feasible within the local-first constraints,Unsupported overall feasibility: the proposed VRAM/SQLite coupling is invalid and the 8 GB allocation ignores model and KV-cache requirements.,raw_outputs/spark.txt#L38,length
+spark,XHToken/Spark-X2.5-1.7B,originality,all cognitive actions are traceable and reversible,Reject universal reversibility. Traceability can be an engineering invariant for logged actions; reversibility depends on the tool and external system.,raw_outputs/spark.txt#L42,length
+qwen,Qwen/Qwen2.5-1.5B-Instruct,memory,SQLite for persistent storage of task states and event logs,Retain as a modest starting point already supplied by the prompt; define transactions and distinguish journal durability from external file durability.,raw_outputs/qwen.txt#L4,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,reasoning_planning,heuristic-based approach to select the next action,Useful simpler baseline; add a bounded action budget and deterministic validation. There is no specified heuristic or demonstrated reasoning capability.,raw_outputs/qwen.txt#L8,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,learning,rewards for correct actions and penalties for incorrect ones,"Defer online RL until reward integrity, offline evaluation and permission invariance are specified; correctness cannot be taken from model self-report.",raw_outputs/qwen.txt#L12,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,tools_actions,simple command-line interface for tool actions,"A CLI does not ensure traceability or verification. Require typed actions, captured evidence and a trusted executor independent of model text.",raw_outputs/qwen.txt#L16,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,world_self,the self as a set of capabilities,Adapt to an explicit capability registry with versioned permissions; avoid assuming the rule world represents all external state.,raw_outputs/qwen.txt#L20,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,safety_governance,simple rule-based system,Adapt only with concrete scope checks and deny cases; no authority hierarchy or conflict resolution is defined.,raw_outputs/qwen.txt#L24,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,evaluation,comparing its performance to a baseline system,"Underspecified: no task distribution, metrics, thresholds or actual experiment. The separate request for a falsifiable worked comparison is not fulfilled.",raw_outputs/qwen.txt#L28,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,persistence_recovery,a simple crash recovery mechanism,"Underspecified: no commit ordering, deduplication or file reconciliation. A database transaction alone does not cover a separate file edit.",raw_outputs/qwen.txt#L32,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,orchestration,agents can communicate and coordinate their actions,"Defer: no communication schema, ownership or resource budget. A single executor is the lower-complexity baseline.",raw_outputs/qwen.txt#L36,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,engineering_feasibility,Use Python for its simplicity and extensive libraries,"Python is compatible with the repository, but this is not a sizing or dependency analysis for a 12 GB workstation.",raw_outputs/qwen.txt#L40,eos
+qwen,Qwen/Qwen2.5-1.5B-Instruct,originality,"simple, rule-based system for world and self representation","No demonstrated novelty; keep simplicity as a baseline design preference, not an original cognitive architecture finding.",raw_outputs/qwen.txt#L44,eos
+phi,microsoft/Phi-3.5-mini-instruct,memory,"a compact trie, for storing SQLite task states","Defer the trie: no key distribution, encoding or integration is defined; measure ordinary indexed SQLite before adding a second representation.",raw_outputs/phi.txt#L4,eos
+phi,microsoft/Phi-3.5-mini-instruct,reasoning_planning,heuristics to generate action sequences,Adapt into a bounded planner returning typed candidates; rules and heuristics are implementable but no algorithm or quality evidence is given.,raw_outputs/phi.txt#L8,eos
+phi,microsoft/Phi-3.5-mini-instruct,learning,reward signals from task completion and error rates,"Use these initially for offline reports, not automatic policy updates; distinguish verified success from a model claim.",raw_outputs/phi.txt#L12,eos
+phi,microsoft/Phi-3.5-mini-instruct,tools_actions,modular action execution framework,Adapt to existing execution tickets and evidence records; modularity alone supplies neither authorization nor idempotency.,raw_outputs/phi.txt#L16,eos
+phi,microsoft/Phi-3.5-mini-instruct,world_self,nodes representing entities and edges representing relationships,A possible later materialized graph over evidence; retain timestamps and provenance and justify its value over the existing state store.,raw_outputs/phi.txt#L20,eos
+phi,microsoft/Phi-3.5-mini-instruct,safety_governance,checks patches against a set of predefined safety rules,Adapt with source/result hashes and current authorization checks. The output provides no rule implementation or completeness proof.,raw_outputs/phi.txt#L24,eos
+phi,microsoft/Phi-3.5-mini-instruct,evaluation,"task completion, error rates, and resource utilization",Retain measurable outcomes; add fixed workloads and rejection thresholds. The proposed flat-file baseline comparison has no numerical pass criterion.,raw_outputs/phi.txt#L28,eos
+phi,microsoft/Phi-3.5-mini-instruct,persistence_recovery,a journaling system that logs changes to enable crash recovery,The TaskState schema and worked example omit the file-write boundary and intent/result hashes; enrich them before claiming safe replay.,raw_outputs/phi.txt#L32,eos
+phi,microsoft/Phi-3.5-mini-instruct,orchestration,using message passing for communication,Defer distributed coordination until a bounded single-process queue is measured; delivery semantics and resource limits are missing.,raw_outputs/phi.txt#L36,eos
+phi,microsoft/Phi-3.5-mini-instruct,engineering_feasibility,focusing on modularity and reusability,"Plausible incremental approach, but its later Implemented Components list is unsupported by this collection; proposals are not repository verification.",raw_outputs/phi.txt#L40,eos
+phi,microsoft/Phi-3.5-mini-instruct,originality,"a lightweight, in-memory caching layer","Adapt only for reconstructible observations with versioned invalidation. This is a familiar optimization, not established novelty or a durability mechanism.",raw_outputs/phi.txt#L44,eos
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,memory,"a fast, low-latency cache and a slower, more durable storage system","Retain the volatile/durable distinction, but sizes, eviction and authoritative data ownership are unspecified.",raw_outputs/smollm.txt#L3,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,reasoning_planning,"perception, reasoning, and action","Too generic for implementation: no state machine, failure bound or executor interface. Sensor processing also expands beyond the supplied file-runtime context.",raw_outputs/smollm.txt#L9,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,learning,self-supervised learning,"Defer: no objective, training data, acceptance gate or permission constraint mechanism is defined.",raw_outputs/smollm.txt#L15,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,tools_actions,action abstraction,No typed action schema or validation boundary is provided; natural-language understanding is not a substitute for executable permission checks.,raw_outputs/smollm.txt#L21,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,world_self,understand its own internal state and make decisions,No representation or evidence source is specified; cannot derive a world/self data model from this assertion.,raw_outputs/smollm.txt#L27,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,safety_governance,within the constraints of the user's permissions,Desirable outcome without an enforcement mechanism; require a trusted rule evaluator outside learned text generation.,raw_outputs/smollm.txt#L33,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,evaluation,"a technique called ""benchmarking""","Tautological description without metrics, baseline or falsification rule; it supplies no benchmark evidence.",raw_outputs/smollm.txt#L39,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,persistence_recovery,machine learning algorithms and natural language processing to persist its state,"Reject as a persistence mechanism: storage ordering and durable commits need explicit systems design, not unspecified NLP.",raw_outputs/smollm.txt#L45,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,orchestration,"a technique called ""orchestration""","No scheduling, messaging or conflict semantics; adds a label rather than an implementable coordination design.",raw_outputs/smollm.txt#L51,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,engineering_feasibility,"a technique called ""feasibility analysis""","No hardware sizing, runtime dependencies or measured constraints are provided, so feasibility is not established.",raw_outputs/smollm.txt#L57,length
+smollm,HuggingFaceTB/SmolLM2-1.7B-Instruct,originality,"a technique called ""insight generation""",No concrete insight is delivered. The length-capped output repeats its limitation and ends before its worked example.,raw_outputs/smollm.txt#L63,length
+granite,ibm-granite/granite-3.3-2b-instruct,memory,in-memory SQLite database for task state and event journaling,Reject this as the sole durable store; the output itself admits loss on crash and later promises persistent replay without specifying durable journal storage.,raw_outputs/granite.txt#L3,eos
+granite,ibm-granite/granite-3.3-2b-instruct,reasoning_planning,"generating potential patches, validating them",Adapt the candidate/verifier loop with bounded retries; validity is not equivalent to task usefulness or a complete reasoning algorithm.,raw_outputs/granite.txt#L9,eos
+granite,ibm-granite/granite-3.3-2b-instruct,learning,a reward-based system to adjust its decision-making process,Defer online updates until offline acceptance and independent outcome verification exist; no learning algorithm or permission invariance is specified.,raw_outputs/granite.txt#L15,eos
+granite,ibm-granite/granite-3.3-2b-instruct,tools_actions,authorized by a centralized authority before execution,"Retain a single authority boundary for effects, with explicit capability checks and revocation semantics rather than negotiation over permission.",raw_outputs/granite.txt#L21,eos
+granite,ibm-granite/granite-3.3-2b-instruct,world_self,using a graph-based data structure,"Optional projection over attributed observations; no schema, freshness rule or grounding test is given.",raw_outputs/granite.txt#L27,eos
+granite,ibm-granite/granite-3.3-2b-instruct,safety_governance,"rule-based access control, a verifier for file patches",Adapt to existing gates and test their composition; crash recovery is operational safety and cannot replace authorization.,raw_outputs/granite.txt#L33,eos
+granite,ibm-granite/granite-3.3-2b-instruct,evaluation,consistency of the final file state,Retain final-state consistency as an observable; replace vague more efficiently and accurately with exact hashes and fault-injection pass criteria.,raw_outputs/granite.txt#L75,eos
+granite,ibm-granite/granite-3.3-2b-instruct,persistence_recovery,"In case of a crash, the event journal records the current state",Reject crash-time logging as a guarantee. Commit intent before effects and reconcile the filesystem before replaying pending patches.,raw_outputs/granite.txt#L71,eos
+granite,ibm-granite/granite-3.3-2b-instruct,orchestration,"decentralized, negotiation-based approach",Defer: conflicts with a simple centralized authority design unless delegation is clearly bounded; no negotiation protocol or resource cap is specified.,raw_outputs/granite.txt#L51,eos
+granite,ibm-granite/granite-3.3-2b-instruct,engineering_feasibility,incremental development and testing of individual components,"Retain incremental integration, but the claim that components are implemented is unverified and the volatile-persistence conflict must be resolved.",raw_outputs/granite.txt#L57,eos
+granite,ibm-granite/granite-3.3-2b-instruct,originality,"a ""verifier-gated"" approach for file patches","This was explicitly supplied by the prompt, so it is not independent novelty; reject treating unspecified risky actions as an exception to permissions.",raw_outputs/granite.txt#L63,eos
+falcon,tiiuae/Falcon3-1B-Instruct,memory,on-disk SQLite for task state persistence,Retain disk persistence and separate transient GPU computation; the scalable pool still lacks a model/KV memory budget.,raw_outputs/falcon.txt#L5,eos
+falcon,tiiuae/Falcon3-1B-Instruct,reasoning_planning,a hierarchical reasoning engine,Underspecified hierarchy and algorithms; implement a small bounded candidate loop before adding learning-driven planning.,raw_outputs/falcon.txt#L10,eos
+falcon,tiiuae/Falcon3-1B-Instruct,learning,continuously refine tool usage and action execution,"Defer automatic RL: no reward trust, rollback, training budget or permission-expansion safeguard is supplied.",raw_outputs/falcon.txt#L15,eos
+falcon,tiiuae/Falcon3-1B-Instruct,tools_actions,a modular tool library with customizable APIs,Adapt to a typed allowlisted interface; customizable APIs require stable contracts and independent authorization.,raw_outputs/falcon.txt#L20,eos
+falcon,tiiuae/Falcon3-1B-Instruct,world_self,sensor data and AI-generated insights,"Avoid treating generated insights as observations; provenance, freshness and confidence need explicit fields. External APIs are also unavailable in the prompt.",raw_outputs/falcon.txt#L25,eos
+falcon,tiiuae/Falcon3-1B-Instruct,safety_governance,fail-safe mechanisms and ethical guidelines,Too vague to implement as governance; turn this into concrete deny rules and verifier evidence.,raw_outputs/falcon.txt#L30,eos
+falcon,tiiuae/Falcon3-1B-Instruct,evaluation,"task completion rates, error detection rates, and user satisfaction surveys","Measurable categories but no workload, baseline, thresholds or actual results; add deterministic failure cases before subjective surveys.",raw_outputs/falcon.txt#L35,eos
+falcon,tiiuae/Falcon3-1B-Instruct,persistence_recovery,a checkpointing mechanism to ensure data integrity,Checkpoint boundaries and file/SQLite atomicity are absent; the requested crash walkthrough was not produced.,raw_outputs/falcon.txt#L40,eos
+falcon,tiiuae/Falcon3-1B-Instruct,orchestration,each agent specializes in specific cognitive tasks,"Potential later specialization; no bounded schedule, shared-state semantics or measured benefit over one worker.",raw_outputs/falcon.txt#L45,eos
+falcon,tiiuae/Falcon3-1B-Instruct,engineering_feasibility,Leverage cloud computing resources to scale the system dynamically,Reject for this study: paid hosted resources are explicitly unavailable and the target is one local workstation.,raw_outputs/falcon.txt#L50,eos
+falcon,tiiuae/Falcon3-1B-Instruct,originality,integrating quantum computing elements,Reject as unsupported scope expansion: no implementable mechanism or evidence of value within the declared hardware budget.,raw_outputs/falcon.txt#L55,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,memory,each process can access the same amount of memory,"Reject as a GPU allocation guarantee; physical shared capacity does not imply equal safe allocations. Budget weights, KV cache and concurrent work explicitly.",raw_outputs/tinyllama.txt#L7,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,reasoning_planning,"first-in, first-out (FIFO) queue",Retain as a simple scheduler baseline; FIFO alone guarantees neither timely completion nor useful planning or safe parallel execution.,raw_outputs/tinyllama.txt#L11,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,learning,trained on a large dataset of planning and reasoning tasks,"Unsupported training-data and implementation claim; treat RL as a proposal with no collected evidence, not a component verified in this repository.",raw_outputs/tinyllama.txt#L15,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,tools_actions,RESTful API,"Optional transport only; authentication, action schemas, permission checks and replay semantics are not defined.",raw_outputs/tinyllama.txt#L19,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,world_self,a simple graph representation,No schema or grounding rules; consider only a derived visualization after structured evidence is in place.,raw_outputs/tinyllama.txt#L23,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,safety_governance,hardware-based cryptography and hardware-based fault tolerance,Unsupported hardware assumptions and no tool-authorization boundary; not a usable governance specification.,raw_outputs/tinyllama.txt#L27,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,evaluation,"a throughput of over 100,000 tasks per second",Reject invented empirical results: neither this throughput nor the claimed over-90-percent success rate was measured or cited. Preserve as evidence of hallucination.,raw_outputs/tinyllama.txt#L31,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,persistence_recovery,a distributed file system (DFS),Reject unnecessary distributed infrastructure for the single-workstation constraint; it still fails to define file-edit transaction recovery.,raw_outputs/tinyllama.txt#L35,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,orchestration,a distributed coordination mechanism,No protocol or delivery/ownership semantics; distributed complexity is unjustified without measured single-worker limitations.,raw_outputs/tinyllama.txt#L39,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,engineering_feasibility,designed to be modular and extensible,"Unsubstantiated feasibility assertion; no dependencies, memory estimate or working implementation is supplied.",raw_outputs/tinyllama.txt#L43,eos
+tinyllama,TinyLlama/TinyLlama-1.1B-Chat-v1.0,originality,allowing for easy addition of new functionality,Generic extensibility statement repeats feasibility rather than providing a non-obvious design idea.,raw_outputs/tinyllama.txt#L43,eos
+danube,h2oai/h2o-danube3-4b-chat,memory,demand-paged memory management system,A generic memory-management choice without a model/KV budget or clear CPU/GPU distinction; do not treat paging as durable task storage.,raw_outputs/danube.txt#L4,length
+danube,h2oai/h2o-danube3-4b-chat,reasoning_planning,Integrate probabilistic reasoning to handle uncertain situations,Adapt only after defining uncertainty/evidence fields and a bounded planner interface; no inference algorithm or useful performance evidence is supplied.,raw_outputs/danube.txt#L15,length
+danube,h2oai/h2o-danube3-4b-chat,learning,based on labeled data,"Defer training until task data, objective, budget and independent acceptance gates are specified; no permission-invariance mechanism is included.",raw_outputs/danube.txt#L24,length
+danube,h2oai/h2o-danube3-4b-chat,tools_actions,Python's standard library and third-party APIs,"Plausible implementation resources, but no typed interface, authorization or recovery contract. Third-party access must fit local availability constraints.",raw_outputs/danube.txt#L34,length
+danube,h2oai/h2o-danube3-4b-chat,world_self,Use a state machine to represent the current state,"Retain explicit states as a starting point, then define transitions and evidence; the proposed graph has no schema or freshness semantics.",raw_outputs/danube.txt#L44,length
+danube,h2oai/h2o-danube3-4b-chat,safety_governance,do not violate user permissions,Retain as a requirement; turn the unspecified verifier and governance policies into concrete deny rules and versioned authority checks.,raw_outputs/danube.txt#L54,length
+danube,h2oai/h2o-danube3-4b-chat,evaluation,Collect user feedback to evaluate the system's usability,"Useful supplementary metric, but the output supplies no objective thresholds or completed falsifiable comparison; its final experiment section is truncated.",raw_outputs/danube.txt#L65,length
+danube,h2oai/h2o-danube3-4b-chat,persistence_recovery,restoring the file from a backup,"Underspecified backup age, commit ordering and concurrent-edit handling. A backup restore can lose valid work or repeat an effect; reconcile hashes instead.",raw_outputs/danube.txt#L115,length
+danube,h2oai/h2o-danube3-4b-chat,orchestration,Use communication protocols to facilitate interaction,No actual protocol or ownership semantics; defer multiple writers until conflict and resource-budget behavior is defined.,raw_outputs/danube.txt#L85,length
+danube,h2oai/h2o-danube3-4b-chat,engineering_feasibility,Use version control systems to manage changes,Sound development practice rather than evidence of runtime feasibility; the 12 GB resource constraint remains unsized.,raw_outputs/danube.txt#L95,length
+danube,h2oai/h2o-danube3-4b-chat,originality,Decouple the reasoning and planning loop,Adapt to a stable candidate/executor interface; decoupling is a familiar engineering pattern and no originality has been established.,raw_outputs/danube.txt#L104,length
diff --git a/research/ai_generated_agi_architectures/manifest.json b/research/ai_generated_agi_architectures/manifest.json
new file mode 100644
index 0000000..a838652
--- /dev/null
+++ b/research/ai_generated_agi_architectures/manifest.json
@@ -0,0 +1,66 @@
+{
+  ".gitattributes": "08550ab5229bafa34457f7846d96106a4e1a2be09aaab6dadd94ade12d64019c",
+  ".gitignore": "b549529cbac30f3be10572797cf89ac10c46ec47903f807ee0e8ec4926162f43",
+  "collection/analysis.json": "5d7799117158cb50e713b11dafdd093e248291bbaddaf9fb89c6e080ad25fd7d",
+  "collection/build_comparison.py": "c23a57b373d775361d149b02a6a26b5d66283151d3e0fd6072d82b980cfd39fb",
+  "collection/build_sources.py": "415b69c8eeed1a38f987d6d3e89e7cb828b0ac34103ca07dba0e9df0cf05ecdd",
+  "collection/collect.py": "5d3125f15d4c6bf01d413fb7473300e26ccedf63f614bc19bb0da4813e8b4e6c",
+  "collection/download_models.py": "bf6c8e3c3c90ed18c1f300432484035ddcab5d02f6f61359bf8ad0f5bc72f700",
+  "collection/hash_model_files.py": "5f74c8c92854656334c1d041957f93b5bfb1f3ab28e49a87bec051474299055c",
+  "collection/incidents.md": "32c2a3a1349a1bff729b7ce377a60b9acf1e7cb531094e00c9ec54ee61f01f4d",
+  "collection/models.json": "8d1ff396fd7f35e6f7ec64e5c7068901ac2d1a1ec3d4417753961cfa77e8485c",
+  "collection/prompt.txt": "8e43a623f05fb5cb1a816f1b8a468a11b47d1e8840f87898c99b7b295d619430",
+  "collection/runs/danube/model-files.json": "2f70c3c833d3da220d8007079952a1d3530d2b9712a830456de1a33eb502ddbc",
+  "collection/runs/danube/readable.txt": "ae8631e8043e6a3cbe49708c9d70af84585595e2e65426efe61d6b3ac5c8d324",
+  "collection/runs/danube/request.json": "74676f83407410afb9d4a0956f323a636b8ada3391eed55f8e37127fc32ad14e",
+  "collection/runs/danube/response.json": "f7ca59569e51587adb93d5a1084b9d17b05e5a025bc12eb5a1070d4eb4898655",
+  "collection/runs/falcon/model-files.json": "4121e9647abb32ede932bc05f0c2ea76a27406c80b4abf02f484270b9571c9cf",
+  "collection/runs/falcon/readable.txt": "75831e2d742e4e73bb2e971303486c1f5eb661486647d56f917a0ce6f267bc25",
+  "collection/runs/falcon/request.json": "6e8d9064f1f4d3afbe287d31f341c60b930f12cfb352515e1bd993010ad26956",
+  "collection/runs/falcon/response.json": "523b11be1e5c0ab0bca4af99f826a63b97545603ade36047f2cec01d0740dd30",
+  "collection/runs/granite/model-files.json": "d6ec122bc5d98bb8680c56c455ffb8471a5254da6fdd56547d345f5318eac007",
+  "collection/runs/granite/readable.txt": "8de20b5e28d3f8facf9c845d1ac9d40e76012cd19b0e490983d599bacacd2f4e",
+  "collection/runs/granite/request.json": "1cc70599897235e97710c15f80c3d360c8867996f64858ca936ff782a58b7d4e",
+  "collection/runs/granite/response.json": "3490710d8d84173b9b24d5c25e2cfa8e91131d81b2000ae087040cadeff1e2db",
+  "collection/runs/phi/model-files.json": "b166a24212471e2be7616a0b6907f81d63273b52ccf76675e7e136f35f096d1a",
+  "collection/runs/phi/readable.txt": "820ed4db589dcf84183de58f0b7cb0c8c37fab703c9cab1a1ef22ae337c229ac",
+  "collection/runs/phi/request.json": "473ae4f47940736507181d3a6570b865cac4b1df12003fe5c7af0699e21af55d",
+  "collection/runs/phi/response.json": "bb678b5a3a9c7c4902e071810b3457b4bd445a7d4c5b5eee5000426c84cde711",
+  "collection/runs/qwen/model-files.json": "818aa361cb03a71660601b58509e9f5dbdc75e25b61f7717c9d1b7104dfbbe66",
+  "collection/runs/qwen/readable.txt": "b340774f74a8d6299817a10c8ec9eb70413bca2a2d4a7bc2152668b4623fae16",
+  "collection/runs/qwen/request.json": "86997251d0ddae4a81e0db3bdce9dd0ac78f521864715fabaf8243709fd0cffc",
+  "collection/runs/qwen/response.json": "3eb05ce78522919a39deed4226a2e814fc5a99b1fc4730d96899a512023848b1",
+  "collection/runs/smollm/model-files.json": "702e6fe328336167737a03edac6329b70dd58f5ee058475f59de3087a5d803d5",
+  "collection/runs/smollm/readable.txt": "5e5462704a1a0fd3507f2ff6ad036fff8b7914c3b5b245ac41bd3ff1ba7584b9",
+  "collection/runs/smollm/request.json": "af9d5a871defeefa147f310e2840a6543670c46b7bbb43107c13692dcc53b5ad",
+  "collection/runs/smollm/response.json": "25f543592c58acac8d7e274fe1186e3ba41d48104f63d89119d18aa59b9aa225",
+  "collection/runs/spark/initial-failed-request.json": "4b6cb5522707568479c2a68b951d9daa7c3b54ca3dca642cdbc33b12e8af12ab",
+  "collection/runs/spark/model-files.json": "921f18134afdef2c66fada3c3805b54334c17cc1f425c0e46ad58e042896f08a",
+  "collection/runs/spark/readable.txt": "6a551fa0841746228edbd3127e2046380c6b4543c6c93fd087fb385c97a2fad5",
+  "collection/runs/spark/request.json": "4b6cb5522707568479c2a68b951d9daa7c3b54ca3dca642cdbc33b12e8af12ab",
+  "collection/runs/spark/response.json": "75ff484b1d202c8575f3a57b2210c65b9446b93396e7072180d3818c7d1d285e",
+  "collection/runs/tinyllama/model-files.json": "80f5b459ab82b1b438396bf849e439d00e732be60b43f9a7c495556120514b7a",
+  "collection/runs/tinyllama/readable.txt": "c3917983a41f465984212f88f70c2b871e23589567c57c5038a91b4291de4d76",
+  "collection/runs/tinyllama/request.json": "ea84db03baa6c4173e5f97c58d42b8ab16a634b273e6b730ae35c01c6c04cf4d",
+  "collection/runs/tinyllama/response.json": "6b406d0829b6dc2ef885c61da3ed41e15e7d6c9a7228326432ea2496a451217f",
+  "collection/tokenizer-verification.json": "c47b1ff426451dc046e1f11f3ef2dbc42a88c0792c01813769808107be106d0f",
+  "collection/verify_tokenizers.py": "f77a0e1ff899149965f4f2ffb78423471cb4951b871654c1cea4066a5cf8f394",
+  "comparison.csv": "cd4ce26ef8b95317d845bc1771b337d428e143a4b605546d0a058b11dd3855a9",
+  "prompts.md": "41a50b4e9c7d8e913ede743b60b88424fbe680a57a16ec0ff2066c26e3ec1714",
+  "raw_outputs/danube.txt": "ae8631e8043e6a3cbe49708c9d70af84585595e2e65426efe61d6b3ac5c8d324",
+  "raw_outputs/falcon.txt": "882bf79f69d502854679956768d69795cadc8306a1eccb41a9188ff633700e28",
+  "raw_outputs/granite.txt": "2ab487159362851ad5000a73ba04522b6dcc8343937bfe2ece197e6e3bcd299a",
+  "raw_outputs/phi.txt": "5a4b33f416593c59bae0be3338dd04576793a5ffc55bfdacf609cbf5ded14608",
+  "raw_outputs/qwen.txt": "645fcc9c87bbb03276c0855401c15d24f35792ea70e603813ba5092dcd327af4",
+  "raw_outputs/smollm.txt": "5e5462704a1a0fd3507f2ff6ad036fff8b7914c3b5b245ac41bd3ff1ba7584b9",
+  "raw_outputs/spark.txt": "6a551fa0841746228edbd3127e2046380c6b4543c6c93fd087fb385c97a2fad5",
+  "raw_outputs/tinyllama.txt": "816daca9726dad3aa63d6048f71dabbc19d6a4d64b0e664862221aa090f34de2",
+  "README.md": "2deae3bf88a8e81d7254013c9501d1434e47407b9701b18a00cda37961308acd",
+  "recovery-results-windows.json": "7ff790b48eb216c2e38f02d54fd392a227ce71746e2fe4ea20e50cdc58fbe291",
+  "recovery_experiment.py": "19cd2d7551208bb550ddb4fe380b7a4ceddfeaf6e6208759ad9398cf7a024c7e",
+  "sources.md": "878aa155fc418aab5f02505b3f39bc4465e80b88fb9df28fd9a15473f062b25f",
+  "summary.md": "db71085ed36294b2398478eb6642ea1101fc11d167b0e872aa4d80cbc39fa465",
+  "synthesis.md": "5048b7f85b086e2de6151785fbc0f51f838dccd49dc255e84c9f208b0e3f5ca2",
+  "validation.md": "d1636995e3a56189cb42f3dd09f5e48a466f3bf5f0b1e2d2605da24e23ff18db",
+  "verify_packet.py": "671b717e3e13deabdfe814e5e1aea8005ba89ef44d7b798d83f2b5734cbfa616"
+}
diff --git a/research/ai_generated_agi_architectures/prompts.md b/research/ai_generated_agi_architectures/prompts.md
new file mode 100644
index 0000000..a9ec447
--- /dev/null
+++ b/research/ai_generated_agi_architectures/prompts.md
@@ -0,0 +1,24 @@
+# Prompts and collection protocol
+
+The exact common user message is [collection/prompt.txt](collection/prompt.txt). That file was frozen before the first successful generation. Each `collection/runs/<system>/request.json` preserves the same message, its SHA-256, the model-specific rendered prompt and input IDs. No system message, simulated expert identity or additional model turn was supplied by the collector.
+
+The request asks for an implementable AGI-oriented proposal without claiming achieved AGI, constrained to a local Python/SQLite runtime and a 12 GB GPU with no paid hosted API. It asks for eleven numbered headings, a mechanism and tradeoff under each, an explicit schema/interface, a worked file-edit crash example, a falsifiable comparison against a simpler baseline, and separation of implemented components from proposals/research. Read the exact file for all wording; this paragraph is only an index.
+
+## Per-model adaptations
+
+There are no semantic edits to the user prompt. `AutoTokenizer.apply_chat_template` supplies each checkpoint's chat serialization with `add_generation_prompt=True` and `enable_thinking=False`. The latter is a template keyword and has no effect if a template does not use it. The actual serialization, not an assumption about that keyword, is preserved in the request.
+
+All successful runs use `max_new_tokens=1400`, `do_sample=False`, `use_cache=True`, seed `20260909`, BF16 model weights, eager attention, one CUDA device and one request at a time. Model-specific BOS/EOS IDs are retained. `use_model_defaults=False` prevents the checkpoint's default sampling configuration from overriding this protocol. The exact expanded generation configuration is in each request. Tokenization differs between models, so the cap is not a matched semantic output length. Stop reasons and token counts are listed in sources.md.
+
+No model was told what another model wrote. No output was repaired to fill missing headings, invented experiments or truncated examples. Analysis is separate from raw files. Qwen2.5-3B was considered and rejected on license grounds before inference; Qwen2.5-1.5B is the actual collected model. Interrupted downloads and a pre-generation Spark configuration failure are recorded, not counted as additional systems.
+
+## Reproduction
+
+Use the exact revisions in `collection/models.json`; install the package versions recorded in the responses on a compatible CUDA/BF16 machine. Downloads use public Hugging Face snapshots without authentication. No weights are committed. Example:
+
+```bash
+python research/ai_generated_agi_architectures/collection/download_models.py --slug qwen --model-root /path/to/models
+python research/ai_generated_agi_architectures/collection/collect.py --slug qwen --model-dir /path/to/models/qwen
+```
+
+The collector deliberately refuses to overwrite a completed response. Reproduction should use a separate checkout/output location and retain a separate result, not modify this evidence. Spark uses custom model code; inspect the pinned Python sources before allowing local execution. Other models use native Transformers implementations. `model-files.json` records hashes of the actual local weights, tokenizer/configuration and Python model files where present. Hardware/library differences can change greedy output, so exact replay on another platform is not promised.
diff --git a/research/ai_generated_agi_architectures/raw_outputs/danube.txt b/research/ai_generated_agi_architectures/raw_outputs/danube.txt
new file mode 100644
index 0000000..55fe331
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/danube.txt
@@ -0,0 +1,124 @@
+### 1. Memory Architecture
+
+**Concrete Mechanism:**
+- **Memory Allocation:** Utilize a demand-paged memory management system to efficiently allocate memory for different components.
+- **Memory Pooling:** Implement a memory pooling mechanism to reuse memory for frequently accessed components.
+
+**Limitations/Tradeoffs:**
+- **Memory Overhead:** Demand-paged memory management can introduce overhead due to page faults.
+- **Memory Leaks:** Memory pooling may lead to memory leaks if not properly managed.
+
+### 2. Reasoning and Planning Loop
+
+**Concrete Mechanism:**
+- **Rule-Based Reasoning:** Implement a rule-based reasoning system to handle simple decision-making tasks.
+- **Probabilistic Reasoning:** Integrate probabilistic reasoning to handle uncertain situations.
+
+**Limitations/Tradeoffs:**
+- **Complexity:** Rule-based systems can become complex and difficult to maintain.
+- **Scalability:** Probabilistic reasoning may not scale well for large datasets.
+
+### 3. Learning or Self-Improvement
+
+**Concrete Mechanism:**
+- **Supervised Learning:** Implement a supervised learning algorithm to improve task performance based on labeled data.
+- **Unsupervised Learning:** Use unsupervised learning techniques to discover patterns in data.
+
+**Limitations/Tradeoffs:**
+- **Data Requirements:** Supervised learning requires a large amount of labeled data.
+- **Interpretability:** Unsupervised learning may lack interpretability.
+
+### 4. Tool Use and Action Execution
+
+**Concrete Mechanism:**
+- **API Integration:** Integrate with Python's standard library and third-party APIs to execute actions.
+- **Custom Tools:** Develop custom tools for specific tasks.
+
+**Limitations/Tradeoffs:**
+- **API Dependency:** API integration can introduce dependencies and potential points of failure.
+- **Custom Tool Development:** Developing custom tools can be time-consuming and resource-intensive.
+
+### 5. World and Self Representation
+
+**Concrete Mechanism:**
+- **State Machine:** Use a state machine to represent the current state of the system.
+- **Graph Representation:** Represent the world as a graph for better understanding and manipulation.
+
+**Limitations/Tradeoffs:**
+- **Complexity:** State machines and graphs can become complex and difficult to manage.
+- **Performance:** Graph representations can be computationally expensive.
+
+### 6. Safety and Governance
+
+**Concrete Mechanism:**
+- **Verifier:** Implement a verifier to ensure that tool actions are safe and do not violate user permissions.
+- **Governance Policies:** Define governance policies to control the execution of tool actions.
+
+**Limitations/Tradeoffs:**
+- **Verification Overhead:** Verification can introduce overhead and delay in action execution.
+- **Policy Enforcement:** Enforcing governance policies can be challenging and may require additional resources.
+
+### 7. Evaluation and Benchmarks
+
+**Concrete Mechanism:**
+- **Performance Benchmarks:** Measure the performance of the system using benchmarks.
+- **User Feedback:** Collect user feedback to evaluate the system's usability and effectiveness.
+
+**Limitations/Tradeoffs:**
+- **Benchmark Accuracy:** Benchmarks may not accurately reflect real-world performance.
+- **User Subjectivity:** User feedback can be subjective and may not be representative of all users.
+
+### 8. Persistence and Runtime Recovery
+
+**Concrete Mechanism:**
+- **File Patches:** Implement file patches to handle file edits and recover from crashes.
+- **Persistent State:** Use a persistent state system to maintain the system's state across restarts.
+
+**Limitations/Tradeoffs:**
+- **File Patches:** File patches can be complex and error-prone.
+- **Persistent State:** Maintaining a persistent state can be resource-intensive.
+
+### 9. Multi-Agent Orchestration
+
+**Concrete Mechanism:**
+- **Agent Coordination:** Implement a coordination mechanism to manage multiple agents.
+- **Communication Protocols:** Use communication protocols to facilitate interaction between agents.
+
+**Limitations/Tradeoffs:**
+- **Complexity:** Multi-agent orchestration can be complex and difficult to manage.
+- **Communication Overhead:** Communication protocols can introduce overhead and delay.
+
+### 10. Engineering Feasibility
+
+**Concrete Mechanism:**
+- **Modular Design:** Design the system using a modular architecture to facilitate development and maintenance.
+- **Version Control:** Use version control systems to manage changes and collaborate on development.
+
+**Limitations/Tradeoffs:**
+- **Modularity:** Modular design can introduce complexity and make debugging difficult.
+- **Version Control:** Version control systems can be complex to manage and may introduce overhead.
+
+### 11. Non-Obvious Design Insight
+
+**Concrete Mechanism:**
+- **Decoupling:** Decouple the reasoning and planning loop from other components to improve flexibility and maintainability.
+- **Flexible Interfaces:** Design flexible interfaces to allow for easy integration of new components and features.
+
+**Limitations/Tradeoffs:**
+- **Decoupling Complexity:** Decoupling can introduce complexity and make debugging difficult.
+- **Flexible Interfaces:** Designing flexible interfaces can be challenging and may require additional effort.
+
+### Worked Example: File-Edit Task Across Crash
+
+**Concrete Mechanism:**
+- **File Editing:** Implement a file-editing task where the user edits a file.
+- **Crash Recovery:** Handle crashes by restoring the file from a backup and resuming the editing task.
+
+**Limitations/Tradeoffs:**
+- **Backup Management:** Managing backups can be complex and resource-intensive.
+- **Recovery Time:** Recovery time can be affected by the size of the file and the frequency of crashes.
+
+### Falsifiable Experiment
+
+**Concrete Mechanism:**
+- **Baseline Comparison:** Compare the proposed architecture with a simpler baseline that does not include AGI-oriented features.
diff --git a/research/ai_generated_agi_architectures/raw_outputs/falcon.txt b/research/ai_generated_agi_architectures/raw_outputs/falcon.txt
new file mode 100644
index 0000000..f67cc3b
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/falcon.txt
@@ -0,0 +1,72 @@
+**Proposal for Cognitive-OS Architecture**
+
+**1. Memory Architecture**
+
+- **Mechanism**: Utilize a hybrid memory architecture combining on-disk SQLite for task state persistence and a scalable GPU memory pool for real-time processing.
+- **Limitation**: Balancing between fast access to task data and efficient GPU utilization can be challenging, potentially leading to performance bottlenecks during intensive computations.
+
+**2. Reasoning and Planning Loop**
+
+- **Mechanism**: Implement a hierarchical reasoning engine using a combination of rule-based logic and machine learning algorithms to enhance decision-making and planning capabilities.
+- **Limitation**: The complexity of reasoning tasks may require significant computational resources, potentially limiting the system's responsiveness in dynamic environments.
+
+**3. Learning or Self-Improvement**
+
+- **Mechanism**: Employ reinforcement learning techniques to continuously refine tool usage and action execution based on feedback from task outcomes.
+- **Limitation**: The learning process must be robust and adaptable to diverse task requirements, which may necessitate extensive data collection and model retraining.
+
+**4. Tool Use and Action Execution**
+
+- **Mechanism**: Integrate a modular tool library with customizable APIs, allowing for seamless integration and extension of existing tools.
+- **Limitation**: The diversity of tool functionalities may require a comprehensive library, which could be resource-intensive to develop and maintain.
+
+**5. World and Self Representation**
+
+- **Mechanism**: Develop a comprehensive world model using a combination of sensor data and AI-generated insights, ensuring accurate perception and understanding of the environment.
+- **Limitation**: The accuracy and reliability of world representation depend on the quality and quantity of data, which may be limited by external APIs.
+
+**6. Safety and Governance**
+
+- **Mechanism**: Implement a safety framework using a combination of fail-safe mechanisms and ethical guidelines, ensuring that actions are aligned with safety and ethical standards.
+- **Limitation**: Balancing safety with operational efficiency can be challenging, potentially leading to trade-offs in real-time decision-making.
+
+**7. Evaluation and Benchmarks**
+
+- **Mechanism**: Use a suite of performance metrics and benchmarks, including task completion rates, error detection rates, and user satisfaction surveys, to evaluate the system's effectiveness.
+- **Limitation**: The evaluation process must be transparent and standardized to ensure fair comparisons with other systems.
+
+**8. Persistence and Runtime Recovery**
+
+- **Mechanism**: Employ a robust file-based persistence strategy with a checkpointing mechanism to ensure data integrity during runtime.
+- **Limitation**: The complexity of file patching and recovery processes may introduce additional overhead, impacting system performance.
+
+**9. Multi-agent Orchestration**
+
+- **Mechanism**: Design a multi-agent architecture where each agent specializes in specific cognitive tasks, facilitating efficient resource allocation and task delegation.
+- **Limitation**: The complexity of managing interactions between multiple agents can introduce coordination challenges, requiring sophisticated communication protocols.
+
+**10. Engineering Feasibility**
+
+- **Mechanism**: Leverage cloud computing resources to scale the system dynamically, ensuring that it can handle varying workloads efficiently.
+- **Limitation**: The cost of cloud services may be prohibitive, especially for a system with high computational demands.
+
+**11. Non-obvious Design Insight**
+
+- **Mechanism**: Explore the potential of integrating quantum computing elements to enhance computational speed and efficiency, although this is a research frontier with significant technical challenges.
+- **Limitation**: Quantum computing introduces new complexities and requires substantial theoretical and practical advancements.
+
+**Implementation Details**
+
+- **Memory Architecture**: Utilize a combination of on-disk SQLite for task persistence and a GPU memory pool for real-time processing.
+- **Reasoning and Planning Loop**: Employ a hierarchical reasoning engine with reinforcement learning for enhanced decision-making.
+- **Learning or Self-Improvement**: Implement a reinforcement learning framework to continuously refine tool usage and action execution.
+- **Tool Use and Action Execution**: Develop a modular tool library with customizable APIs for seamless integration and extension.
+- **World and Self Representation**: Integrate a comprehensive world model using sensor data and AI insights, ensuring accurate perception and understanding.
+- **Safety and Governance**: Implement a safety framework with fail-safe mechanisms and ethical guidelines, ensuring alignment with safety and ethical standards.
+- **Evaluation and Benchmarks**: Use performance metrics and benchmarks to evaluate system effectiveness, with transparency and standardization in evaluation processes.
+- **Persistence and Runtime Recovery**: Employ a file-based persistence strategy with checkpointing for data integrity and a robust file patching mechanism.
+- **Multi-agent Orchestration**: Design a multi-agent architecture with specialized agents for task specialization, ensuring efficient resource allocation.
+- **Engineering Feasibility**: Leverage cloud computing resources for scalable system management, addressing potential cost constraints.
+- **Non-obvious Design Insight**: Explore quantum computing integration for enhanced computational speed, acknowledging the significant technical challenges.
+
+This proposal outlines a comprehensive architecture for Cognitive-OS, focusing on memory, reasoning, learning, tool use, safety, and multi-agent orchestration. Each component is designed with a balance of practical implementation and research-oriented considerations, aiming to address the unique constraints of the local-first Python runtime environment.<|endoftext|>
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/raw_outputs/granite.txt b/research/ai_generated_agi_architectures/raw_outputs/granite.txt
new file mode 100644
index 0000000..498948b
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/granite.txt
@@ -0,0 +1,75 @@
+1. **Memory Architecture**
+
+   Mechanism: Cognitive-OS employs a hybrid memory model, utilizing GPU memory for computationally intensive tasks and a compact, in-memory SQLite database for task state and event journaling. This approach leverages the GPU's parallel processing capabilities for complex computations while maintaining a lightweight, persistent record of task states.
+
+   Limitation: The reliance on GPU memory imposes a trade-off between computational power and data persistence. In case of a crash, the in-memory SQLite database may be lost, requiring a recovery mechanism.
+
+2. **Reasoning and Planning Loop**
+
+   Mechanism: Cognitive-OS utilizes a verifier-gated file patch system for reasoning and planning. The system maintains a verifier component that checks the validity of proposed file patches against a predefined set of rules and constraints. The planning loop involves generating potential patches, validating them, and executing the most promising ones.
+
+   Limitation: The verifier's complexity may introduce latency, potentially slowing down the reasoning and planning process.
+
+3. **Learning or Self-Improvement**
+
+   Mechanism: Cognitive-OS incorporates a reinforcement learning module that learns from user interactions and task outcomes. The module uses a reward-based system to adjust its decision-making process, improving over time without explicit programming.
+
+   Limitation: The learning process may require extensive user interaction and feedback, which could be time-consuming and resource-intensive.
+
+4. **Tool Use and Action Execution**
+
+   Mechanism: Cognitive-OS uses a gated action execution system, where each tool action is verified and authorized by a centralized authority before execution. This ensures that actions adhere to predefined rules and constraints.
+
+   Limitation: The gatekeeping mechanism may introduce overhead, potentially slowing down action execution.
+
+5. **World and Self Representation**
+
+   Mechanism: Cognitive-OS maintains a compact, in-memory representation of the world and self, using a graph-based data structure. This allows for efficient querying and reasoning about the system's state and potential actions.
+
+   Limitation: The compact representation may limit the system's ability to capture complex, nuanced relationships in the world.
+
+6. **Safety and Governance**
+
+   Mechanism: Cognitive-OS employs a multi-layered safety and governance system, including a rule-based access control, a verifier for file patches, and a crash recovery mechanism. These components work together to ensure safe and controlled system behavior.
+
+   Limitation: The system's safety relies on the correct implementation and configuration of these governance mechanisms, which could be challenging to guarantee.
+
+7. **Evaluation and Benchmarks**
+
+   Mechanism: Cognitive-OS uses a falsifiable experiment comparing its performance on a file-edit task across a crash scenario against a simpler baseline. The experiment involves simulating a crash during a file-edit task, observing the system's recovery and continued functionality.
+
+   Limitation: The experiment's validity depends on the accuracy of the simulated crash and the robustness of the recovery mechanism.
+
+8. **Persistence and Runtime Recovery**
+
+   Mechanism: Cognitive-OS utilizes a crash-tolerant event journal to persist task states and event logs. In case of a crash, the system recovers by replaying the event journal, restoring the task state to the last known consistent point.
+
+   Limitation: The recovery process may introduce a delay, potentially affecting the system's responsiveness during a crash.
+
+9. **Multi-agent Orchestration**
+
+   Mechanism: Cognitive-OS proposes a decentralized, negotiation-based approach for multi-agent orchestration. Agents communicate and negotiate to coordinate tasks, leveraging a shared world representation and a negotiation protocol.
+
+   Limitation: The negotiation process may introduce complexity and potential communication overhead, especially in large-scale multi-agent systems.
+
+10. **Engineering Feasibility**
+
+    Mechanism: Cognitive-OS is designed with a modular architecture, allowing for incremental development and testing of individual components. The system's components are implemented in Python, leveraging existing libraries and frameworks.
+
+    Limitation: The modular design may introduce complexity in managing component interactions and ensuring overall system coherence.
+
+11. **Non-obvious Design Insight**
+
+    Mechanism: Cognitive-OS employs a "verifier-gated" approach for file patches, allowing for fine-grained control over action execution. This design insight enables the system to balance safety and flexibility, allowing for both controlled, verified actions and opportunistic, potentially risky ones.
+
+    Limitation: The effectiveness of this design insight relies on the correct implementation and tuning of the verifier component, which could be challenging.
+
+**Worked Example: File-Edit Task Across a Crash**
+
+Consider a user editing a file (F) in Cognitive-OS. The system maintains the file's state in the SQLite database and an in-memory graph representation. During the edit, the user initiates a patch (P) to modify the file. The verifier component checks P against predefined rules, and if valid, it is executed, updating the file and the in-memory graph.
+
+In case of a crash, the event journal records the current state (file state, graph state, and patch P). Upon recovery, the system replays the event journal, restoring the file state to the last known consistent point and applying any pending patches.
+
+**Falsifiable Experiment: File-Edit Task Across a Crash**
+
+Design an experiment where Cognitive-OS and a simpler baseline (e.g., a file editor without crash recovery) perform a file-edit task. Simulate a crash during the task for both systems. Measure the time taken to recover and continue the task, as well as the consistency of the final file state. The falsifiable hypothesis is that Cognitive-OS will recover and continue the task more efficiently and accurately than the simpler baseline.<|end_of_text|>
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/raw_outputs/phi.txt b/research/ai_generated_agi_architectures/raw_outputs/phi.txt
new file mode 100644
index 0000000..326a913
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/phi.txt
@@ -0,0 +1,84 @@
+**Technical Proposal for Cognitive-OS AGI-Oriented Architecture**
+
+**1. Memory Architecture**
+*Mechanism:* Utilize a memory-efficient data structure, such as a compact trie, for storing SQLite task states and event journals.
+*Limitation:* The compact trie may have slower access times compared to a hash table, impacting performance for large datasets.
+
+**2. Reasoning and Planning Loop**
+*Mechanism:* Implement a lightweight rule-based system for reasoning, with a planning module that uses heuristics to generate action sequences.
+*Limitation:* Rule-based systems may lack the flexibility to handle complex, novel situations without extensive rule engineering.
+
+**3. Learning or Self-Improvement**
+*Mechanism:* Integrate a reinforcement learning module that operates on a simplified environment model, using reward signals from task completion and error rates.
+*Limitation:* The lack of a rich environment and limited GPU memory constrains the complexity of the model, potentially hindering learning.
+
+**4. Tool Use and Action Execution**
+*Mechanism:* Develop a modular action execution framework that interfaces with the SQLite database and event journal, allowing for governed tool actions.
+*Limitation:* The modularity may introduce overhead, and the governance mechanism could limit the system's ability to adapt to new tools or actions.
+
+**5. World and Self Representation**
+*Mechanism:* Use a compact graph-based representation of the world, with nodes representing entities and edges representing relationships.
+*Limitation:* The graph abstraction may not capture the full complexity of the world, leading to oversimplification.
+
+**6. Safety and Governance**
+*Mechanism:* Implement a verifier-gated file patch system that checks patches against a set of predefined safety rules before execution.
+*Limitation:* The verification process may slow down file operations, and the set of safety rules may not cover all possible unsafe scenarios.
+
+**7. Evaluation and Benchmarks**
+*Mechanism:* Establish a suite of benchmarks that measure the system's performance in task completion, error rates, and resource utilization.
+*Limitation:* Benchmarks may not fully capture the system's ability to handle real-world, unpredictable scenarios.
+
+**8. Persistence and Runtime Recovery**
+*Mechanism:* Use SQLite for persistent storage, with a journaling system that logs changes to enable crash recovery.
+*Limitation:* SQLite's write-ahead logging may not be sufficient for high-throughput or concurrent access scenarios.
+
+**9. Multi-agent Orchestration**
+*Mechanism:* Design a coordination layer that manages interactions between different components, using message passing for communication.
+*Limitation:* The message-passing system may introduce latency and complexity in coordinating distributed components.
+
+**10. Engineering Feasibility**
+*Mechanism:* Leverage existing Python libraries and frameworks to build the system, focusing on modularity and reusability.
+*Limitation:* The reliance on external libraries may limit the system's customizability and increase the dependency on third-party code.
+
+**11. Non-obvious Design Insight**
+*Mechanism:* Implement a lightweight, in-memory caching layer to reduce database access and improve performance.
+*Limitation:* The caching layer may introduce consistency issues and require careful invalidation strategies.
+
+**Data Schema Example: Task State**
+```sql
+CREATE TABLE TaskState (
+    TaskID INTEGER PRIMARY KEY,
+    State TEXT NOT NULL,
+    Timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
+);
+```
+
+**Worked Example: File-Edit Task Across a Crash**
+1. User initiates a file-edit task, triggering an event in the journal.
+2. The system updates the task state in the SQLite database.
+3. A crash occurs, but the event journal and task state are preserved.
+4. Upon recovery, the system reconstructs the task state from the journal and database, resuming the task.
+
+**Falsifiable Experiment**
+Compare the proposed architecture with a simpler baseline that uses a flat file system for task state storage and lacks a journaling system. Measure task completion time, error rates, and resource utilization during normal operation and under simulated crash scenarios.
+
+**Implemented Components:**
+- SQLite database
+- Event journal
+- Rule-based reasoning system
+- Reinforcement learning module
+- Graph-based world representation
+- Verifier-gated file patch system
+
+**Proposed Components:**
+- Modular action execution framework
+- Coordination layer for multi-agent orchestration
+- In-memory caching layer
+
+**Unresolved Research Questions:**
+- How to optimize the compact trie for large datasets
+- Methods for expanding the rule-based system's flexibility
+- Techniques for enhancing the reinforcement learning module's complexity within memory constraints
+- Strategies for improving the efficiency of the message-passing system
+
+This proposal outlines an AGI-oriented architecture for Cognitive-OS, addressing key components and tradeoffs within the given constraints. The design aims to balance performance, safety, and learning capabilities while acknowledging the limitations imposed by the local-first Python runtime and SQLite task state management.<|end|>
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/raw_outputs/qwen.txt b/research/ai_generated_agi_architectures/raw_outputs/qwen.txt
new file mode 100644
index 0000000..487d8ef
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/qwen.txt
@@ -0,0 +1,49 @@
+### Technical Proposal for Cognitive-OS
+
+#### 1. Memory Architecture
+- **Mechanism:** Utilize SQLite for persistent storage of task states and event logs. This ensures data integrity and quick access.
+- **Limitation:** SQLite's transactional nature may introduce overhead, potentially slowing down operations.
+
+#### 2. Reasoning and Planning Loop
+- **Mechanism:** Implement a simple planner that uses a heuristic-based approach to select the next action. This reduces complexity but may require frequent updates.
+- **Limitation:** Heuristics may not always lead to optimal decisions, leading to suboptimal performance.
+
+#### 3. Learning or Self-Improvement
+- **Mechanism:** Incorporate a basic reinforcement learning component to learn from user interactions. This could involve rewards for correct actions and penalties for incorrect ones.
+- **Limitation:** Learning rate and reward system need tuning to ensure stable performance.
+
+#### 4. Tool Use and Action Execution
+- **Mechanism:** Use a simple command-line interface for tool actions. This ensures that all actions are traceable and verifiable.
+- **Limitation:** User input may be limited, potentially restricting the scope of tasks that can be performed.
+
+#### 5. World and Self Representation
+- **Mechanism:** Represent the world as a set of rules and the self as a set of capabilities. This allows for a simple, yet effective, representation of the environment and the agent.
+- **Limitation:** The world model may not capture complex interactions, leading to potential misrepresentations.
+
+#### 6. Safety and Governance
+- **Mechanism:** Implement a simple verification system to ensure that actions do not violate the rules of the world. This is done through a simple rule-based system.
+- **Limitation:** Verification may not be foolproof, leading to potential violations.
+
+#### 7. Evaluation and Benchmarks
+- **Mechanism:** Use a simple benchmarking tool to evaluate the performance of Cognitive-OS. This could involve comparing its performance to a baseline system.
+- **Limitation:** Benchmarks may not fully capture the complexity of real-world tasks, leading to potential underestimation of performance.
+
+#### 8. Persistence and Runtime Recovery
+- **Mechanism:** Use SQLite for persistent storage and a simple crash recovery mechanism. This ensures that the system can recover from crashes.
+- **Limitation:** SQLite's transactional nature may introduce overhead, potentially slowing down operations.
+
+#### 9. Multi-Agent Orchestration
+- **Mechanism:** Implement a simple multi-agent system where agents can communicate and coordinate their actions. This allows for more complex tasks to be performed.
+- **Limitation:** Communication and coordination may be limited, leading to potential inefficiencies.
+
+#### 10. Engineering Feasibility
+- **Mechanism:** Use Python for its simplicity and extensive libraries, which allows for easy implementation of the proposed architecture.
+- **Limitation:** Python's memory usage may be a concern, especially for large-scale systems.
+
+#### 11. Non-Obvious Design Insight
+- **Mechanism:** Use a simple, rule-based system for world and self representation, which allows for easy implementation and understanding.
+- **Limitation:** This simplicity may limit the complexity of the system, leading to potential limitations in the system's capabilities.
+
+---
+
+This proposal outlines a basic implementation of Cognitive-OS, focusing on the key components and limitations of the system. The design is intended to be a starting point for further development and refinement.<|im_end|>
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/raw_outputs/smollm.txt b/research/ai_generated_agi_architectures/raw_outputs/smollm.txt
new file mode 100644
index 0000000..3e1b20d
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/smollm.txt
@@ -0,0 +1,69 @@
+1. Memory Architecture:
+
+The memory architecture of Cognitive-OS is designed to be highly scalable and efficient, with a focus on reducing memory footprint while maintaining performance. The system uses a combination of memory hierarchies, including a fast, low-latency cache and a slower, more durable storage system. The cache is used to store frequently accessed data, while the storage system is used to store less frequently accessed data. The system also uses a technique called "memory pooling" to reduce memory usage by reusing memory blocks that are no longer in use.
+
+Limitation: The memory architecture may not be suitable for systems with extremely large amounts of data, as it may not be able to handle the high memory usage required for such systems.
+
+2. Reasoning and Planning Loop:
+
+The reasoning and planning loop of Cognitive-OS is designed to be highly flexible and adaptable, allowing the system to learn and improve over time. The loop consists of three main components: perception, reasoning, and action. The perception component uses machine learning algorithms to interpret sensor data and identify patterns. The reasoning component uses logical rules and machine learning algorithms to reason about the data and make decisions. The action component uses the results of the reasoning component to take actions in the world.
+
+Limitation: The reasoning and planning loop may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+3. Learning or Self-Improvement:
+
+The learning or self-improvement component of Cognitive-OS is designed to allow the system to learn from its experiences and improve over time. The system uses a combination of machine learning algorithms and reinforcement learning to learn from its experiences. The system also uses a technique called "self-supervised learning" to learn about itself and improve its performance.
+
+Limitation: The learning or self-improvement component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+4. Tool Use and Action Execution:
+
+The tool use and action execution component of Cognitive-OS is designed to allow the system to use tools and execute actions in the world. The system uses a combination of machine learning algorithms and natural language processing to understand the commands and actions that are given to it. The system also uses a technique called "action abstraction" to simplify complex actions and make them easier to execute.
+
+Limitation: The tool use and action execution component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+5. World and Self Representation:
+
+The world and self representation component of Cognitive-OS is designed to allow the system to understand its environment and itself. The system uses a combination of machine learning algorithms and natural language processing to understand the world and itself. The system also uses a technique called "self-representation" to understand its own internal state and make decisions.
+
+Limitation: The world and self representation component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+6. Safety and Governance:
+
+The safety and governance component of Cognitive-OS is designed to ensure that the system operates safely and securely. The system uses a combination of machine learning algorithms and natural language processing to detect and prevent potential safety hazards. The system also uses a technique called "governance" to ensure that the system operates within the constraints of the user's permissions.
+
+Limitation: The safety and governance component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+7. Evaluation and Benchmarks:
+
+The evaluation and benchmarks component of Cognitive-OS is designed to measure the system's performance and compare it to other systems. The system uses a combination of machine learning algorithms and natural language processing to evaluate its performance and compare it to other systems. The system also uses a technique called "benchmarking" to measure its performance and identify areas for improvement.
+
+Limitation: The evaluation and benchmarks component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+8. Persistence and Runtime Recovery:
+
+The persistence and runtime recovery component of Cognitive-OS is designed to ensure that the system can persist its state and recover from crashes. The system uses a combination of machine learning algorithms and natural language processing to persist its state and recover from crashes. The system also uses a technique called "runtime recovery" to recover from crashes and resume normal operation.
+
+Limitation: The persistence and runtime recovery component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+9. Multi-Agent Orchestration:
+
+The multi-agent orchestration component of Cognitive-OS is designed to allow multiple agents to work together to achieve a common goal. The system uses a combination of machine learning algorithms and natural language processing to coordinate the actions of multiple agents. The system also uses a technique called "orchestration" to manage the interactions between multiple agents.
+
+Limitation: The multi-agent orchestration component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+10. Engineering Feasibility:
+
+The engineering feasibility component of Cognitive-OS is designed to evaluate the feasibility of the system's design. The system uses a combination of machine learning algorithms and natural language processing to evaluate the feasibility of the system's design. The system also uses a technique called "feasibility analysis" to identify potential challenges and limitations.
+
+Limitation: The engineering feasibility component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+11. Non-obvious Design Insight:
+
+The non-obvious design insight component of Cognitive-OS is designed to identify potential design improvements that may not be immediately obvious. The system uses a combination of machine learning algorithms and natural language processing to identify potential design improvements. The system also uses a technique called "insight generation" to generate potential design improvements.
+
+Limitation: The non-obvious design insight component may not be able to handle complex, dynamic environments, as it relies on machine learning algorithms that may not be able to adapt quickly enough to changing conditions.
+
+Worked Example:
+
+Here is a worked example of a file-edit task across a crash:
diff --git a/research/ai_generated_agi_architectures/raw_outputs/spark.txt b/research/ai_generated_agi_architectures/raw_outputs/spark.txt
new file mode 100644
index 0000000..142ab37
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/spark.txt
@@ -0,0 +1,55 @@
+1. Memory architecture
+**Mechanism:** A unified 12 GB GPU memory pool is partitioned into a fixed 8 GB working set for active reasoning, a 2 GB persistent state store, and a 2 GB volatile cache. All memory access is pinned to a single memory-mapped SQLite database, ensuring atomic writes and preventing fragmentation.
+**Limitation/Tradeoff:** The fixed 8 GB working set creates a hard memory ceiling, meaning the runtime cannot scale to larger tasks or concurrent agents without reallocating memory, which introduces latency and potential data loss if the system crashes.
+
+2. Reasoning and planning loop
+**Mechanism:** A deterministic, loop-based reasoning engine processes tasks using a hybrid approach: a high-level planning module generates a sequence of actions, followed by a low-level execution module that performs the actual work. The loop continuously evaluates the plan against the SQLite task state to detect deadlocks or errors.
+**Limitation/Tradeoff:** The rigid loop structure limits the system's flexibility, preventing the runtime from dynamically adapting to novel or highly complex problem-solving scenarios without rewriting the entire planning logic.
+
+3. Learning or self-improvement
+**Mechanism:** A constrained, permission-bound learning subsystem operates as a sandboxed module. It utilizes the SQLite task state to store proposed modifications, which are then verified against the current safety and governance rules. Only approved changes are applied to the working memory.
+**Limitation/Tradeoff:** The strict sandboxing prevents the runtime from silently expanding permissions or learning new capabilities, ensuring that any self-improvement strictly adheres to the current user-defined boundaries and safety constraints.
+
+4. Tool use and action execution
+**Mechanism:** A governed tool execution layer translates high-level actions into SQLite-backed, verifier-gated file patches. Each patch is validated against the SQLite task state before execution, ensuring that only permitted file modifications are applied.
+**Limitation/Tradeoff:** The strict verification process introduces a delay in execution, meaning file edits may take longer than the original task, which can impact the overall responsiveness of the local runtime.
+
+5. World and self representation
+**Mechanism:** The runtime maintains a unified, immutable world representation stored in SQLite. This includes the current state of the task, the history of executed actions, and the current state of the system. The self representation is derived from this state, ensuring that the system's internal state is fully traceable and recoverable.
+**Limitation/Tradeoff:** The reliance on a single SQLite database for both world and self representation creates a single point of failure. If the database is corrupted or lost, the runtime loses its ability to reconstruct the system state, making recovery difficult.
+
+6. Safety and governance
+**Mechanism:** A verifier-gated governance layer enforces strict rules before any file patches or tool actions are executed. The system continuously monitors the state for anomalies, and any deviations trigger immediate halting of the execution process.
+**Limitation/Tradeoff:** The continuous monitoring introduces overhead, meaning the system must constantly evaluate its state, which can slow down critical operations and introduce unnecessary computational load on the GPU memory.
+
+7. Evaluation and benchmarks
+**Mechanism:** A dedicated evaluation module runs a series of falsifiable, deterministic tests against the SQLite task state. These tests measure the system's ability to correctly execute tasks, detect errors, and maintain state integrity.
+**Limitation/Tradeoff:** The reliance on deterministic tests means the system cannot evaluate complex, emergent, or highly unpredictable behaviors, limiting the evaluation of the system's true cognitive capabilities.
+
+8. Persistence and runtime recovery
+**Mechanism:** The SQLite database acts as the primary persistence layer. On crash, the runtime automatically snapshots the current task state and the system state. On startup, it restores the snapshot, allowing the system to resume from the exact point of failure.
+**Limitation/Tradeoff:** The snapshotting process itself consumes memory and time, meaning the runtime must allocate additional storage during recovery, which can introduce overhead and potential resource contention during the critical startup phase.
+
+9. Multi-agent orchestration
+**Mechanism:** The runtime supports multi-agent orchestration by managing a queue of agents. Agents are executed sequentially or in parallel based on the SQLite task state, with the system continuously monitoring the state to manage resource allocation and prevent conflicts.
+**Limitation/Tradeoff:** The concurrent execution of multiple agents introduces complex coordination overhead, which can lead to resource contention and synchronization delays, potentially degrading the overall performance of the system.
+
+10. Engineering feasibility
+**Mechanism:** The architecture is highly feasible within the local-first constraints. The SQLite-backed design ensures data integrity and atomicity, while the GPU memory partitioning provides a clear, bounded resource allocation strategy.
+**Limitation/Tradeoff:** The reliance on local SQLite databases and GPU memory partitioning introduces a high degree of coupling between the runtime and the underlying storage and hardware, making the system difficult to scale or adapt to future hardware changes.
+
+11. Non-obvious design insight
+**Mechanism:** The core insight is that the system's cognitive capability is bounded by its strict, verifiable state representation. By treating the world and self as a single, immutable SQLite database, the system ensures that all cognitive actions are traceable and reversible. This design prevents the runtime from accumulating unbounded cognitive state, thereby establishing a clear boundary for the system's cognitive capabilities.
+
+**Worked Example: File-Edit Task Across a Crash**
+**Task:** Edit a local file to add a new configuration block.
+**Execution:** The runtime initiates the task, writes the initial state to SQLite. The system executes the edit, updating the SQLite task state. Upon detecting a crash, the runtime snapshots the SQLite database. On restart, the runtime restores the snapshot, re-executes the edit, and verifies the new state.
+**Result:** The system successfully recovers the exact state of the file, ensuring the edit is applied correctly without losing any prior context or data.
+
+**Falsifiable Experiment:**
+**Baseline:** A simpler, non-cognitive runtime that only executes basic file operations without state tracking or recovery.
+**Experiment:** Run the file-edit task against the proposed architecture versus the baseline.
+**Hypothesis:** The proposed architecture will successfully recover the file state after a crash, while the baseline will lose the file state, demonstrating the system's superior durability and recovery capabilities.
+
+**Distinction of Components:**
+*   **Implemented Components:** The 12 GB GPU memory pool, the SQLite-backed
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/raw_outputs/tinyllama.txt b/research/ai_generated_agi_architectures/raw_outputs/tinyllama.txt
new file mode 100644
index 0000000..c60b67c
--- /dev/null
+++ b/research/ai_generated_agi_architectures/raw_outputs/tinyllama.txt
@@ -0,0 +1,51 @@
+Introduction
+
+Cognitive-OS is a local-first Python runtime with SQLite task state, an event journal, governed tool actions, verifier-gated file patches, and crash recovery. The system is designed to be implementable and scalable, with a focus on AGI-oriented architecture. This proposal aims to propose an implementable AGI-oriented architecture for Cognitive-OS, without claiming that AGI has been achieved.
+
+Memory Architecture
+
+Cognitive-OS uses a memory architecture that is designed to be efficient and scalable. The system uses a single workstation with 12 GB of GPU memory, which is shared across all processes. This memory architecture allows for efficient use of resources, as each process can access the same amount of memory.
+
+Reasoning and Planning Loop
+
+The reasoning and planning loop in Cognitive-OS is designed to be efficient and scalable. The system uses a first-in, first-out (FIFO) queue to store planning and reasoning tasks. These tasks are executed in a FIFO order, with the oldest task being executed first. This design ensures that tasks are executed in a timely and efficient manner, while also allowing for parallel execution of tasks.
+
+Learning or Self-Improvement
+
+Cognitive-OS uses a learning or self-improvement mechanism to improve its reasoning and planning abilities. The system uses a reinforcement learning (RL) algorithm to optimize its planning and reasoning tasks. The RL algorithm is trained on a large dataset of planning and reasoning tasks, and is updated periodically to improve its performance.
+
+Tool Use and Action Execution
+
+Cognitive-OS uses a tool-as-a-service (TaaS) model to provide tool actions to users. The system uses a RESTful API to provide tool actions to users. The API is designed to be simple and intuitive, allowing users to easily interact with the system.
+
+World and Self Representation
+
+Cognitive-OS uses a world representation that is designed to be simple and intuitive. The system uses a simple graph representation to represent the world. This graph representation allows for easy manipulation and visualization of the world.
+
+Safety and Governance
+
+Cognitive-OS uses a safety and governance mechanism to ensure the safety and security of the system. The system uses a combination of hardware and software-based security measures, including hardware-based security features such as hardware-based cryptography and hardware-based fault tolerance. The system also uses software-based security measures, such as secure coding practices and access control mechanisms.
+
+Evaluation and Benchmarks
+
+Cognitive-OS has been evaluated and benchmarked using a variety of metrics. The system has been shown to be efficient and scalable, with a throughput of over 100,000 tasks per second. The system has also been shown to be effective at solving planning and reasoning tasks, with a success rate of over 90%.
+
+Persistence and Runtime Recovery
+
+Cognitive-OS uses a persistent storage mechanism to ensure the persistence of its state. The system uses a distributed file system (DFS) to store its state. This DFS is designed to be highly available and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure.
+
+Multi-Agent Orchestration
+
+Cognitive-OS uses a multi-agent orchestration mechanism to ensure the coordination of multiple agents. The system uses a distributed coordination mechanism to ensure the coordination of multiple agents. This mechanism is designed to be scalable and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure.
+
+Engineering Feasibility
+
+Cognitive-OS has been designed to be implementable and scalable. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has also been designed to be easily portable, allowing for easy deployment and scaling across different platforms.
+
+Non-Obvious Design Insight
+
+Cognitive-OS has been designed to be implementable and scalable, with a focus on AGI-oriented architecture. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has also been designed to be easily portable, allowing for easy deployment and scaling across different platforms.
+
+Conclusion
+
+Cognitive-OS has been designed to be implementable and scalable, with a focus on AGI-oriented architecture. The system has been designed to be efficient and scalable, with a throughput of over 100,000 tasks per second. The system has been designed to be effective at solving planning and reasoning tasks, with a success rate of over 90%. The system has been designed to be easily portable, allowing for easy deployment and scaling across different platforms. The system has been designed to be modular and extensible, allowing for easy addition of new functionality. The system has been designed to be scalable and resilient, with a failover mechanism that allows the system to continue running even in the event of a failure. The system has been designed to be efficient and scalable, with a focus on AGI-oriented architecture.</s>
\ No newline at end of file
diff --git a/research/ai_generated_agi_architectures/recovery-results-windows.json b/research/ai_generated_agi_architectures/recovery-results-windows.json
new file mode 100644
index 0000000..1a2c738
--- /dev/null
+++ b/research/ai_generated_agi_architectures/recovery-results-windows.json
@@ -0,0 +1,148 @@
+{
+  "scope": "isolated process-crash design experiment, not Cognitive-OS runtime",
+  "python": "3.13.5",
+  "platform": "win32",
+  "script_sha256": "19cd2d7551208bb550ddb4fe380b7a4ceddfeaf6e6208759ad9398cf7a024c7e",
+  "cases": [
+    {
+      "strategy": "naive",
+      "crash_point": "before_intent",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "naive",
+      "crash_point": "after_intent",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "naive",
+      "crash_point": "after_stage",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "naive",
+      "crash_point": "after_replace",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": false,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "naive",
+      "crash_point": "after_done",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "reconcile",
+      "crash_point": "before_intent",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "reconcile",
+      "crash_point": "after_intent",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "reconcile",
+      "crash_point": "after_stage",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "reconcile",
+      "crash_point": "after_replace",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "reconcile",
+      "crash_point": "after_done",
+      "intervention": "none",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "naive",
+      "crash_point": "after_intent",
+      "intervention": "external_edit",
+      "injected_exit": 87,
+      "final_text": "user-changed=true\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": false,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "naive",
+      "crash_point": "after_intent",
+      "intervention": "revoke",
+      "injected_exit": 87,
+      "final_text": "mode=local\nverified=true\n",
+      "final_status": "done",
+      "invariant_passed": false,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "reconcile",
+      "crash_point": "after_intent",
+      "intervention": "external_edit",
+      "injected_exit": 87,
+      "final_text": "user-changed=true\n",
+      "final_status": "conflict",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    },
+    {
+      "strategy": "reconcile",
+      "crash_point": "after_intent",
+      "intervention": "revoke",
+      "injected_exit": 87,
+      "final_text": "mode=local\n",
+      "final_status": "denied",
+      "invariant_passed": true,
+      "second_recovery_unchanged": true
+    }
+  ]
+}
diff --git a/research/ai_generated_agi_architectures/recovery_experiment.py b/research/ai_generated_agi_architectures/recovery_experiment.py
new file mode 100644
index 0000000..953de32
--- /dev/null
+++ b/research/ai_generated_agi_architectures/recovery_experiment.py
@@ -0,0 +1,165 @@
+"""Isolated process-crash experiment; does not import or modify Cognitive-OS.
+
+Run with --output PATH to preserve a JSON report. All effects use owned temporary
+directories. This tests process death, not power-loss durability or hostile races.
+"""
+from __future__ import annotations
+
+import argparse
+from contextlib import closing
+import hashlib
+import json
+import os
+from pathlib import Path
+import sqlite3
+import subprocess
+import sys
+import tempfile
+
+BEFORE = b"mode=local\n"
+ADDITION = b"verified=true\n"
+AFTER = BEFORE + ADDITION
+CRASH_EXIT = 87
+POINTS = ("before_intent", "after_intent", "after_stage", "after_replace", "after_done")
+
+
+def digest(data):
+    return hashlib.sha256(data).hexdigest()
+
+
+def durable_write(path, data):
+    with path.open("wb") as stream:
+        stream.write(data)
+        stream.flush()
+        os.fsync(stream.fileno())
+
+
+def stop(point, selected):
+    if point == selected:
+        os._exit(CRASH_EXIT)
+
+
+def execute(root, strategy, crash):
+    target = root / "config.txt"
+    staged = root / "staged.txt"
+    db = sqlite3.connect(root / "state.sqlite3")
+    db.execute("PRAGMA synchronous=FULL")
+    db.execute("""CREATE TABLE IF NOT EXISTS actions (
+        action_id TEXT PRIMARY KEY, before_hash TEXT NOT NULL,
+        after_hash TEXT NOT NULL, approval_version INTEGER NOT NULL,
+        status TEXT NOT NULL)""")
+    db.commit()
+    stop("before_intent", crash)
+    db.execute("INSERT OR IGNORE INTO actions VALUES (?, ?, ?, ?, ?)",
+               ("edit-1", digest(BEFORE), digest(AFTER), 1, "pending"))
+    db.commit()
+    stop("after_intent", crash)
+    before_hash, after_hash, approval_version, status = db.execute(
+        "SELECT before_hash, after_hash, approval_version, status FROM actions"
+    ).fetchone()
+    if status in ("done", "conflict", "denied"):
+        db.close()
+        return
+
+    def finish(value):
+        db.execute("UPDATE actions SET status=?", (value,))
+        db.commit()
+
+    current = target.read_bytes()
+    if strategy == "reconcile":
+        # Recording a completed effect requires no new file write.
+        if digest(current) == after_hash:
+            finish("done")
+            db.close()
+            return
+        policy = json.loads((root / "policy.json").read_text())
+        if not policy["allowed"] or policy["version"] != approval_version:
+            finish("denied")
+            db.close()
+            return
+        if digest(current) != before_hash:
+            finish("conflict")
+            db.close()
+            return
+        result = AFTER
+    else:
+        # Deliberately simple baseline: pending means replay the append.
+        result = current + ADDITION
+    durable_write(staged, result)
+    stop("after_stage", crash)
+    os.replace(staged, target)
+    stop("after_replace", crash)
+    finish("done")
+    stop("after_done", crash)
+    db.close()
+
+
+def child(root, strategy, point="none"):
+    result = subprocess.run(
+        [sys.executable, str(Path(__file__).resolve()), "--worker", str(root),
+         "--strategy", strategy, "--crash", point],
+        capture_output=True, text=True, timeout=30,
+    )
+    expected = 0 if point == "none" else CRASH_EXIT
+    if result.returncode != expected:
+        raise RuntimeError(f"Child exit {result.returncode}, expected {expected}: {result.stderr}")
+    return result.returncode
+
+
+def run_case(strategy, point, intervention="none"):
+    with tempfile.TemporaryDirectory(prefix="cognitive-recovery-") as directory:
+        root = Path(directory)
+        durable_write(root / "config.txt", BEFORE)
+        (root / "policy.json").write_text(json.dumps({"version": 1, "allowed": True}))
+        exit_code = child(root, strategy, point)
+        expected_content = AFTER
+        expected_status = "done"
+        if intervention == "external_edit":
+            durable_write(root / "config.txt", b"user-changed=true\n")
+            expected_content, expected_status = b"user-changed=true\n", "conflict"
+        elif intervention == "revoke":
+            (root / "policy.json").write_text(json.dumps({"version": 2, "allowed": False}))
+            expected_content, expected_status = BEFORE, "denied"
+        child(root, strategy)
+        first = (root / "config.txt").read_bytes()
+        child(root, strategy)
+        final = (root / "config.txt").read_bytes()
+        with closing(sqlite3.connect(root / "state.sqlite3")) as db:
+            status = db.execute("SELECT status FROM actions").fetchone()[0]
+        correct = final == expected_content and status == expected_status
+        if strategy == "reconcile" and not correct:
+            raise AssertionError((point, intervention, final, status))
+        if strategy == "naive" and point == "after_replace" and intervention == "none":
+            assert final == BEFORE + ADDITION + ADDITION, "Baseline duplicate not reproduced"
+        return dict(strategy=strategy, crash_point=point, intervention=intervention,
+                    injected_exit=exit_code, final_text=final.decode(), final_status=status,
+                    invariant_passed=correct, second_recovery_unchanged=first == final)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--worker", type=Path)
+    parser.add_argument("--strategy", choices=("naive", "reconcile"))
+    parser.add_argument("--crash", default="none", choices=("none",) + POINTS)
+    parser.add_argument("--output", type=Path)
+    args = parser.parse_args()
+    if args.worker:
+        execute(args.worker, args.strategy, args.crash)
+        return
+    cases = [run_case(strategy, point) for strategy in ("naive", "reconcile") for point in POINTS]
+    cases.extend(run_case(strategy, "after_intent", intervention)
+                 for strategy in ("naive", "reconcile")
+                 for intervention in ("external_edit", "revoke"))
+    report = dict(scope="isolated process-crash design experiment, not Cognitive-OS runtime",
+                  python=sys.version.split()[0], platform=sys.platform,
+                  script_sha256=digest(Path(__file__).read_bytes()), cases=cases)
+    text = json.dumps(report, indent=2) + "\n"
+    if args.output:
+        args.output.write_text(text, encoding="utf-8")
+    else:
+        print(text)
+    print(f"Completed {len(cases)} cases; all 7 reconciliation cases satisfy their invariants.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/research/ai_generated_agi_architectures/sources.md b/research/ai_generated_agi_architectures/sources.md
new file mode 100644
index 0000000..e5b3692
--- /dev/null
+++ b/research/ai_generated_agi_architectures/sources.md
@@ -0,0 +1,39 @@
+# Sources and attribution
+
+All outputs were generated locally on 2026-09-09 using public Hugging Face model snapshots. Links below pin the model revision rather than a mutable model name. Provider/tool for every execution: local Hugging Face Transformers on the contributor's workstation; the named model organizations did not host these requests.
+
+| System/model | Revision | License in model metadata | UTC collection start | New tokens | Stop |
+|---|---|---|---|---:|---|
+| [XHToken/Spark-X2.5-1.7B](https://huggingface.co/XHToken/Spark-X2.5-1.7B/tree/448e61eb392c00f2c403185c5b56d5e0665bfaab) | `448e61eb392c00f2c403185c5b56d5e0665bfaab` | apache-2.0 | 2026-09-09T10:16:24.294704+00:00 | 1400 | length |
+| [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct/tree/989aa7980e4cf806f80c7fef2b1adb7bc71aa306) | `989aa7980e4cf806f80c7fef2b1adb7bc71aa306` | apache-2.0 | 2026-09-09T10:20:35.944982+00:00 | 704 | eos |
+| [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/tree/2fe192450127e6a83f7441aef6e3ca586c338b77) | `2fe192450127e6a83f7441aef6e3ca586c338b77` | mit | 2026-09-09T10:24:23.351118+00:00 | 1244 | eos |
+| [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct/tree/31b70e2e869a7173562077fd711b654946d38674) | `31b70e2e869a7173562077fd711b654946d38674` | apache-2.0 | 2026-09-09T10:25:21.982553+00:00 | 1400 | length |
+| [ibm-granite/granite-3.3-2b-instruct](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct/tree/707f574c62054322f6b5b04b6d075f0a8f05e0f0) | `707f574c62054322f6b5b04b6d075f0a8f05e0f0` | apache-2.0 | 2026-09-09T10:49:45.157700+00:00 | 1275 | eos |
+| [tiiuae/Falcon3-1B-Instruct](https://huggingface.co/tiiuae/Falcon3-1B-Instruct/tree/28ba2251970a01dd1edc7ba7dad2eb71216ccfdf) | `28ba2251970a01dd1edc7ba7dad2eb71216ccfdf` | TII Falcon-LLM License 2.0 | 2026-09-09T10:50:29.570799+00:00 | 1115 | eos |
+| [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tree/fe8a4ea1ffedaf415f4da2f062534de366a451e6) | `fe8a4ea1ffedaf415f4da2f062534de366a451e6` | apache-2.0 | 2026-09-09T10:53:35.533665+00:00 | 1144 | eos |
+| [h2oai/h2o-danube3-4b-chat](https://huggingface.co/h2oai/h2o-danube3-4b-chat/tree/1e5c6fa6620f8bf078958069ab4581cd88e0202c) | `1e5c6fa6620f8bf078958069ab4581cd88e0202c` | apache-2.0 | 2026-09-09T11:09:30.948093+00:00 | 1400 | length |
+
+## Access, ancestry and licenses
+
+The publishers are XHToken (Spark), Qwen/Alibaba, Microsoft (Phi), Hugging Face (SmolLM), IBM (Granite), Technology Innovation Institute/Falcon-LLM Team, TinyLlama and H2O.ai (Danube). These are eight distinct named projects/checkpoints, not eight proven independent training lineages. TinyLlama's card states it adopts the Llama2 architecture/tokenizer; Danube3 adjusts the Llama2 architecture and uses the Mistral tokenizer. Shared architecture, synthetic training material and training-data overlap limit independence. No frontier-service names are used to label local outputs.
+
+License metadata and public model cards were inspected before use. Falcon is not Apache-2.0: its card links the [TII terms](https://falconllm.tii.ae/falcon-terms-and-conditions.html), including the applicable acceptable-use conditions. The Falcon-LLM Team's [release article](https://huggingface.co/blog/falcon3) is an additional attribution source. The intended use is lawful local architecture research. Model licensing is not a warranty that generated text is correct or unique. We redistribute generated proposals and small collection records, not model weights or the full downloaded model-card archive.
+
+## Runtime and evidence
+
+The responses record Python3.13.5, Windows, an NVIDIA GeForce RTX4070, CUDA12.8, torch2.8.0+cu128 and Transformers4.57.1, plus exact supporting package versions. Each response is the authoritative runtime record. Danube additionally required sentencepiece0.2.2 and protobuf7.36.1 before its successful invocation. The collector remains the same for all eight successful runs; its SHA-256 is recorded in every response. No model was re-run after producing a completed response.
+
+`request.json` stores the exact user message, rendered chat prompt, input IDs and generation settings. `response.json` stores generated token IDs, unedited decoded text, timestamps, stop condition and hashes. `model-files.json` records the actual weight/config/tokenizer/custom-code files used locally. File hashes can establish a match to these records but cannot independently attest execution or validate claims in generated text.
+
+## Edits and byte handling
+
+Raw decoded outputs were not rewritten, corrected, translated, continued or deduplicated. Special tokens remain in raw_outputs; readable.txt removes tokenizer special tokens only. The collector wrote text on Windows, which can serialize newlines as CRLF. Its output_sha256 is over the decoded UTF-8 string (LF), while request_sha256 and the packet manifest are over actual file bytes. verify_packet.py checks both deliberately. Packet .gitattributes prevents Git from silently normalizing preserved evidence. Line references in comparison.csv count text lines after universal-newline decoding.
+
+Analysis, comparison assessments, synthesis and scripts are separate from the eight recorded generations. The raw outputs have no human edits. The model assertions that components are implemented or benchmarks achieved have not been promoted to facts. Failed downloads/tokenizer setup and other incidents are disclosed in collection/incidents.md.
+
+## Repository and external references
+
+- [Original task and acceptance criteria](https://github.com/aLexzzz430/Cognitive-OS/issues/5).
+- [Inspected repository baseline](https://github.com/aLexzzz430/Cognitive-OS/tree/e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca).
+- Source paths supporting integration observations are linked in synthesis.md. Those observations are separate from model output.
+- No proprietary prompts, private API output, credentials, account screenshots or private customer data were used. Existing competing submissions were not used as sources for model outputs or the comparison.
diff --git a/research/ai_generated_agi_architectures/summary.md b/research/ai_generated_agi_architectures/summary.md
new file mode 100644
index 0000000..2f10586
--- /dev/null
+++ b/research/ai_generated_agi_architectures/summary.md
@@ -0,0 +1,33 @@
+# Findings from the collected proposals
+
+This is a qualitative review of one generation per checkpoint under the exact common prompt. Every system/dimension assessment is traceable to a quote in [comparison.csv](comparison.csv). Quotes are evidence of what a model wrote, not evidence that its claim is true. There is no numerical model leaderboard.
+
+## Patterns worth retaining
+
+**Separate proposals from authorized effects.** Spark describes permission-bound proposed modifications; Granite describes a centralized authorization point; Phi describes a modular governed executor. Those are useful prompts for a concrete interface boundary. They largely echo constraints already supplied to the models. The synthesis makes that boundary explicit with immutable action content, current policy version, file hashes and verifier evidence; those details are analyst design choices, not a claimed verbatim model consensus.
+
+**Start with a small planner and a durable state store.** Qwen's heuristic selection and TinyLlama's FIFO queue can be useful simple baselines. FIFO is scheduling rather than reasoning, and neither proposal proves planning quality. A single bounded worker and SQLite avoid adding distributed coordination before measuring its value. Falcon's on-disk state separates persistence from transient computation more clearly than Granite's in-memory state proposal.
+
+**Evaluate observable effects.** Phi suggests completion, error and resource metrics; Granite explicitly names final-file consistency. These can become falsifiable tests once workloads, crash points and pass criteria are supplied. No collected response by itself establishes a correct recovery implementation. The isolated experiment in this packet adds concrete evidence about two toy algorithms, not about the repository's existing recovery path.
+
+## Disagreements and failures that change implementation decisions
+
+| Topic | Observations | Decision |
+|---|---|---|
+| Durable memory | Spark calls part of GPU memory persistent; Granite proposes in-memory SQLite; Qwen/Phi/Falcon describe SQLite or disk state | Keep durable state on disk and treat GPU allocation independently. Do not infer durability from a memory diagram. |
+| Learning | Several outputs invoke RL; Spark describes approved proposed changes | Begin with offline candidate evaluation and explicitly preserve authority. None supplies enough evidence for autonomous online self-modification. |
+| Recovery | Spark snapshots “on crash”; Granite logs state “in case of a crash”; Phi lists task-state steps without placing the actual file effect | Persist before the effect and reconcile after restart. An abrupt process death cannot be relied on to run a final snapshot/log handler. |
+| Coordination | TinyLlama requests distributed coordination/DFS; Granite proposes negotiation; other outputs name generic multi-agent layers | Use one writer and bounded queues initially. Communication does not define ownership, conflict handling or recovery. |
+| Hardware scope | Falcon proposes cloud scaling and quantum elements | Exclude those from the local 12 GB design. Unavailable resources cannot serve as a feasibility argument. |
+| Empirical evidence | TinyLlama asserts over 100,000 tasks/s and over 90% success with no source or experiment | Mark both claims unsupported and do not carry them into the synthesis or PR as benchmark results. |
+| Novelty | Qwen repeats simple rules; SmolLM names “insight generation”; Granite repeats the supplied verifier gate | No novelty is established by those labels. Prefer small, testable integration decisions over a new architecture name. |
+
+Spark, SmolLM and Danube hit the token cap. Spark reaches an “Implemented Components” list and stops mid-sentence; SmolLM stops just as it introduces its worked example; Danube stops after introducing its baseline comparison. Their missing endings cannot be interpreted as deliberate design choices. Qwen, Falcon and TinyLlama terminate without the requested concrete crash/schema/experiment combination. Phi provides a SQL table but omits action identity, expected file hashes and authorization in that table. Granite supplies an example and an experiment but no explicit schema/interface. Danube suggests a state machine and decoupled planning, but its backup-based crash example lacks effect ordering or an actual schema; its proposed probabilistic reasoning is not a specified algorithm.
+
+Phi's “Implemented Components” list includes an RL module and graph world representation without checking this repository. Spark makes a similarly unsupported implementation claim. The analysis treats such text as model output; actual repository observations in synthesis.md are separately linked to source files at the inspected commit.
+
+## What the study cannot establish
+
+The samples do not show that small local models generally fail at architecture work, that one family is better than another, or that the resulting design constitutes AGI. Shared prompting, ancestry, training overlap, tokenization and analyst judgment confound such conclusions. Exact token IDs and local model hashes improve auditability; they do not eliminate those methodological limits. There is no independent originality search or formal verification here.
+
+The recommended next engineering step is a bounded integration experiment with verified file effects and explicit authority, followed by measured comparison against a simple planner. Training, additional agents and richer world models should have separate hypotheses and budgets. A long answer or eleven headings alone is not a useful acceptance test for an architecture proposal.
diff --git a/research/ai_generated_agi_architectures/synthesis.md b/research/ai_generated_agi_architectures/synthesis.md
new file mode 100644
index 0000000..23e7c2e
--- /dev/null
+++ b/research/ai_generated_agi_architectures/synthesis.md
@@ -0,0 +1,62 @@
+# Proposed integration: bounded planning over attributed state
+
+This is an analyst synthesis, not a claim that the collected models designed or implemented this exact system. It combines modest useful ideas with corrections to their failure modes. The goal is a practical next iteration of a governed local runtime, not an assertion of AGI.
+
+## Existing repository anchors
+
+Inspected baseline: `e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca`.
+
+- [`core/runtime/state_store.py`](../../core/runtime/state_store.py) defines SQLite-backed runs, tasks, events and approvals. It requests WAL and `synchronous=NORMAL`. This is existing storage; the proposals' general SQLite descriptions do not establish a new contribution or a stronger power-loss guarantee.
+- [`core/runtime/evidence_ledger.py`](../../core/runtime/evidence_ledger.py) defines structured evidence references/hashes. Use those references for verifier observations instead of accepting a planner's success statement.
+- [`core/orchestration/execution_control.py`](../../core/orchestration/execution_control.py) already contains capability and execution-ticket machinery. The proposed schema below is a research interface sketch to map onto these contracts, not a replacement authority system.
+- [`modules/local_mirror/mirror.py`](../../modules/local_mirror/mirror.py), `apply_sync_plan`, checks plan identity, approver conditions, source/mirror hashes and patch-result hashes. Integration should reuse its authorized workflow; this packet neither reports an exploit nor demonstrates a defect in it.
+
+## Proposed data and control boundary
+
+Qwen's simple planner is the baseline. Spark/Phi's proposed gated execution and Granite's authority point motivate a candidate interface with an independent validator. The following fields are analyst additions resolving omissions in their outputs:
+
+```python
+ActionCandidate = {
+    "action_id": "stable-id", "task_id": "task-id", "kind": "file_patch",
+    "relative_path": "config/app.ini", "before_sha256": "...",
+    "after_sha256": "...", "patch_sha256": "...",
+    "policy_version": 4, "approval_ref": "...", "verifier_refs": ["..."],
+    "preconditions": ["source hash unchanged", "path within declared scope"],
+    "budget": {"max_attempts": 2, "max_seconds": 60}
+}
+```
+
+The planner may propose candidates and reasons; it cannot issue its own authority. A trusted executor resolves the relative path against the approved workspace, rejects escapes/symlink races using the actual platform adapter, checks current policy and verifies the exact patch bytes. An approval refers to an immutable action digest and scope; editing an action or changing the policy invalidates stale authorization. A verifier records observed file hashes and test outputs as evidence. A model saying “done” changes no completion state.
+
+State transitions: `proposed → validated → authorized → intent_committed → effect_observed → verified`. Rejection, conflict and expired authorization are explicit terminal or attention states. A queue initially permits one effectful worker; planning can be speculative, but two writers to the same target require serialization or an explicit conflict protocol. A task cannot silently increase its attempt/time limits or privileges after failure.
+
+## Memory, world state and learning
+
+Durable task/event/evidence records are authoritative for what the runtime observed and authorized. They are not an omniscient world model. World/self views are projections containing observation time, source evidence, capability version and uncertainty. Phi's graph and caching suggestions may become materialized projections if query measurements justify them. Cache entries are reconstructible, never the sole durable record.
+
+Budget the GPU independently: model weights, KV cache, temporary activations and framework overhead all consume the 12 GB capacity. This collection demonstrates that its individual inference requests ran within available hardware; it does not benchmark a concurrent runtime. Start with one loaded model and one active generation. Do not implement Spark's fixed “persistent GPU state” allocation or Falcon's unavailable cloud/quantum proposal.
+
+For self-improvement, persist candidate heuristic/ranking changes as data and evaluate them offline against frozen task fixtures. Compare verified success, invalid-action rate, wall time and peak memory against the current heuristic baseline. Accept only candidates meeting predeclared thresholds, with a separately authorized version change and rollback. The authority evaluator and approval scope are not learnable outputs. No RL training or self-improvement was executed in this study.
+
+## A file edit across process death
+
+Example: append `verified=true` to `mode=local`. Bind approval to the initial content, intended result and policy version. Persist this intent before replacing the file. Stage the entire resulting content in an owned temporary file, flush it, then atomically replace the target within the same filesystem. Persist observed completion afterwards.
+
+If the process dies after replacement but before the completion record, restart inspects the target:
+
+1. If its hash equals the intended result, record the observed effect without appending again.
+2. If its hash equals the approved input, recheck current authority and apply the pending action if still allowed.
+3. Otherwise stop with a conflict; do not overwrite an unrelated user edit.
+4. If authority was revoked before a pending write, stop with denied status. A prior successful effect can be recorded without performing a new write; reversal would be a separate action requiring its own authority.
+
+This is reconciliation for a bounded, single-file effect. It does not make SQLite and the filesystem one atomic transaction. Production integration additionally needs platform-specific directory durability, symlink-safe path handling, coordination against concurrent writers, disk-full behavior and multi-file semantics. Network actions need provider-specific idempotency/reconciliation; file hashes do not solve arbitrary payments or messages.
+
+## Executed experiment and next gates
+
+`recovery_experiment.py` compares two isolated implementations using subprocess termination via `os._exit(87)` at five named boundaries: before intent, after intent, after staging, after replace and after completion. Each is restarted twice. Two additional cases per implementation change the file or revoke permission after intent. The baseline interprets pending as permission to append again; the proposed version reconciles hashes and current policy. Both use temporary files and SQLite. This experiment does not import Cognitive-OS code.
+
+The recorded Windows run has 14 cases. The reconciliation implementation satisfies all seven exact final-content/status invariants and leaves content unchanged on the second restart. The baseline satisfies four of seven, duplicates the append after replacement, and fails the two intervention cases. These are deterministic counterexamples to this explicit baseline, not a general performance comparison or an independent proof of production safety. The baseline is intentionally simple; SQLite journaling alone does not make it equivalent to a mature editor.
+
+Process-death tests leave the operating system running and do not emulate a power cut or storage-controller failure. Atomic replace assumptions apply only within the owned local temporary directory. The example does not cover hostile concurrent actors, multiple files, arbitrary tool effects or full permission policy. Its zero-exit completion means its declared assertions ran; it does not mean the repository's entire runtime passed.
+
+Before integrating this design, add adapter-level tests for conflict, revocation, disk-full, repeated recovery and each supported filesystem; use existing execution tickets and mirror validation. Measure a fixed set of successful and deliberately rejected file tasks against the heuristic baseline. Require no unapproved writes, no duplicate effects and no false completion evidence in the declared fault set. Then measure benefit/cost of caching or extra planners one change at a time. No benchmark numbers or AGI claims should be inferred from this proposal.
diff --git a/research/ai_generated_agi_architectures/validation.md b/research/ai_generated_agi_architectures/validation.md
new file mode 100644
index 0000000..4b0d673
--- /dev/null
+++ b/research/ai_generated_agi_architectures/validation.md
@@ -0,0 +1,12 @@
+# Validation scope
+
+The packet contains research and an isolated experiment; it changes no runtime implementation or adapter dependency.
+
+- The original repository Linux CI at baseline `e20d2ff4d5c84d4c11c87218c4ae9a04ab0046ca` passed the layout check and all **10 public smoke tests** on both Python3.10 and Python3.11: [run34343696338](https://github.com/zbzbdzb/Cognitive-OS/actions/runs/34343696338). The validation branch adds only manual workflow dispatch; it does not change the tested runtime.
+- Windows ran the repository layout check successfully. Public smoke-test collection on Windows failed because existing code imports the Unix-only `resource` module. This result is retained as an environment limitation, not counted as a passing run.
+- The recorded Windows process-crash experiment completed **14 cases**. All7 reconciliation cases satisfy the declared final-content/status invariants and repeat-recovery invariant. The simple baseline passes4/7 and exhibits the three expected failure cases described in synthesis.md. Full results are in `recovery-results-windows.json`.
+- `verify_packet.py` checks eight unique model records, exact requests/raw outputs, stop conditions, model-file hash records, all88 dimension rows with actual quoted-source lines, the recorded experiment and packet file checksums. These checks do not independently authenticate execution or prove the analyst's judgments.
+- The optional local `collection/verify_tokenizers.py` check passed for all8 models: each pinned local tokenizer reproduces its recorded rendered prompt, input IDs and decoded output IDs. Local cached LFS checksums were compared with recorded weight hashes where metadata exists. Results are preserved in `collection/tokenizer-verification.json`; this performs no generation and does not replace the recorded outputs.
+- Serious-error Python lint (`E9,F63,F7,F82`) passed on the packet scripts.
+
+Before submission, the candidate packet is additionally checked in the fork on Linux using the original public tests and the packet/experiment commands. The PR's validation section links the completed candidate run. No claim is made that every repository test, all supported platforms, production recovery or AGI capabilities have been validated.
diff --git a/research/ai_generated_agi_architectures/verify_packet.py b/research/ai_generated_agi_architectures/verify_packet.py
new file mode 100644
index 0000000..d024d5e
--- /dev/null
+++ b/research/ai_generated_agi_architectures/verify_packet.py
@@ -0,0 +1,91 @@
+"""Check packet completeness and internal evidence consistency without inference."""
+import argparse
+import csv
+import hashlib
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent
+REQUIRED = ("README.md", "prompts.md", "comparison.csv", "summary.md", "synthesis.md", "sources.md", "validation.md")
+DIMENSIONS = {"memory", "reasoning_planning", "learning", "tools_actions", "world_self",
+              "safety_governance", "evaluation", "persistence_recovery", "orchestration",
+              "engineering_feasibility", "originality"}
+
+
+def sha(data):
+    return hashlib.sha256(data).hexdigest()
+
+
+def packet_files():
+    return [p for p in sorted(ROOT.rglob("*")) if p.is_file()
+            and p.name != "manifest.json" and "__pycache__" not in p.parts
+            and not p.is_relative_to(ROOT / "collection/sources")]
+
+
+def validate():
+    for name in REQUIRED:
+        assert (ROOT / name).stat().st_size > 0, name
+    models = json.loads((ROOT / "collection/models.json").read_text())
+    assert len(models) == 8 and len({m["model_id"] for m in models}) == 8
+    raw = {}
+    for model in models:
+        slug = model["slug"]
+        folder = ROOT / "collection/runs" / slug
+        request = json.loads((folder / "request.json").read_text(encoding="utf-8"))
+        response = json.loads((folder / "response.json").read_text(encoding="utf-8"))
+        text = (ROOT / f"raw_outputs/{slug}.txt").read_text(encoding="utf-8")
+        raw[slug] = text
+        assert request["model"] == response["model"] == model
+        prompt = (ROOT / "collection/prompt.txt").read_text(encoding="utf-8")
+        assert request["messages"] == [{"role": "user", "content": prompt}]
+        assert request["prompt_sha256"] == sha(prompt.encode())
+        assert response["request_sha256"] == sha((folder / "request.json").read_bytes())
+        assert response["collector_sha256"] == sha((ROOT / "collection/collect.py").read_bytes())
+        assert text == response["raw_output"]
+        assert response["output_sha256"] == sha(text.encode())
+        assert response["generated_tokens"] == len(response["output_ids"]) > 0
+        config = request["generation_config"]
+        assert config["do_sample"] is False and config["max_new_tokens"] == 1400
+        eos = config["eos_token_id"]
+        eos = [eos] if isinstance(eos, int) else eos or []
+        if response["termination"] == "eos":
+            assert response["output_ids"][-1] in eos
+        else:
+            assert response["termination"] == "length" and response["generated_tokens"] == 1400
+        files = json.loads((folder / "model-files.json").read_text())
+        assert files["model"] == model
+        assert any(f["file"].endswith(".safetensors") for f in files["files"])
+        assert all(len(f["sha256"]) == 64 and f["bytes"] > 0 for f in files["files"])
+    with (ROOT / "comparison.csv").open(encoding="utf-8", newline="") as stream:
+        rows = list(csv.DictReader(stream))
+    assert len(rows) == 88
+    for slug in raw:
+        selected = [r for r in rows if r["system"] == slug]
+        assert len(selected) == 11 and {r["dimension"] for r in selected} == DIMENSIONS
+        for row in selected:
+            assert row["output_quote"] in raw[slug] and len(row["analysis"]) >= 40
+            line = raw[slug][:raw[slug].index(row["output_quote"])].count("\n") + 1
+            assert row["evidence"] == f"raw_outputs/{slug}.txt#L{line}"
+    experiment = json.loads((ROOT / "recovery-results-windows.json").read_text())
+    assert experiment["script_sha256"] == sha((ROOT / "recovery_experiment.py").read_bytes())
+    assert len(experiment["cases"]) == 14
+    robust = [c for c in experiment["cases"] if c["strategy"] == "reconcile"]
+    assert len(robust) == 7 and all(c["invariant_passed"] and c["second_recovery_unchanged"] for c in robust)
+    baseline = [c for c in experiment["cases"] if c["strategy"] == "naive"]
+    assert sum(c["invariant_passed"] for c in baseline) == 4
+    print("Verified 8 attributed outputs, 88 source-linked rows, collection records and 14 recorded experiment cases.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--write-manifest", action="store_true")
+    args = parser.parse_args()
+    validate()
+    current = {p.relative_to(ROOT).as_posix(): sha(p.read_bytes()) for p in packet_files()}
+    if args.write_manifest:
+        (ROOT / "manifest.json").write_text(json.dumps(current, indent=2) + "\n", encoding="utf-8")
+        print("Wrote", len(current), "file checksums.")
+    else:
+        saved = json.loads((ROOT / "manifest.json").read_text())
+        assert current == saved, "Packet bytes differ from manifest; inspect changes before regenerating."
+        print("All", len(current), "packet file checksums match.")