Paper artifact note — PR-M1 / v0.1.0

Date: 2026-11-05. Written because ARTIFACT-SPEC.zh-CN.md requires this note from M1 onward, and because a reader must not be able to mistake this package for the experiments behind the sealed research report.

Identity and availability

Field	Value
Artifact	`cognitive-runtime` 0.1.0 (PR-M1)
Wheel	`dist/cognitive_runtime-0.1.0-py3-none-any.whl`, 46,448 B, SHA-256 `64e02c1ab36023f4ae66c6da0d0ba6fba959bd88664d6b37590b3905c71f28e1`
Source package	`dist/cognitive_runtime-0.1.0.tar.gz`, 51,237 B, SHA-256 `d69ad3ce98d3a63fdf2519724675f599920b4173cf3a4a381bee00b6fc9b0213`
Public URL	`https://huggingface.co/WayneRR/cognitive-learning-lab` (model repo, published 2026-11-05; initial publication commit `4225068d`; live commit hashes are tracked in `RELEASE-MANIFEST.json` → `publication`). Artifacts are also attached to GitHub Release v0.1.0 of the source repository
Source-of-truth repository	the private working repository, branch `public-runtime-m1`; this bundle is the public-safe subset. The public source copy lives at `https://github.com/kialajin-l/cognitive-learning-lab`
Code license	`Apache-2.0` (`LICENSE`), owner decision 2026-11-05; covers the package's code only, not the upstream checkpoint. Package is thin — no weights bundled

How to obtain and run it

python -m pip install ./dist/cognitive_runtime-0.1.0-py3-none-any.whl   # or: pip install .
python -m cognitive_runtime doctor --store ./memory.sqlite
python -m cognitive_runtime infer --input "$(python -c \
  'import cognitive_runtime.config as c;print(c.EXAMPLES_DIR/"request.json")')" \
  --store ./memory.sqlite --namespace demo
python -m pytest tests -q --basetemp=<tmp> -p no:cacheprovider   # 28 tests, from the source tree

The --basetemp and -p no:cacheprovider flags were needed on this audit machine (PermissionError [WinError 5] on a pytest-of-<user> temp directory it cannot write). They are harness workarounds, not part of the package; whether a plain python -m pytest tests -q works elsewhere is not verified here.

The default provider is mock. The real provider needs the model extra and one upstream download (convaiinnovations/laya@55cf4c4e…) — or a local directory, which is what the smoke in acceptance/PR-M1.md used. Measured correction to earlier text: the answerdotai/ModernBERT-large weights are not read at inference on laya 0.3.20; see MODEL_CARD.md.

What this package is a contribution of

An engineering artifact: the runtime boundary the research line kept needing — input sanitising before the gate, integrity-checked memory, retrieval, a fail-closed typed rule judgment, a three-layer boundary that separates a model proposal from an authorised answer, receipt/lineage verification, controlled writeback, and cross-process replay — packaged so it installs and runs without the private repository, and without any answer keys or task sets.

Correspondence with the sealed experiments

The frozen research record for this mechanism is docs/LSG-SG4-R3-D708-FINAL-VERDICT-v1.zh-CN.md (2026-10-29) inside docs/LSG-SG4-R3-PROJECT-BOUNDARY-CLOSEOUT-SCOPE-v1.zh-CN.md, status PROJECT_SCOPE_CLOSED. Mapping of ideas, not of numbers:

Sealed experiment line	What it studied	What this package does with that
R23–R25, R27, R35–R37 memory participating in decisions	retrieval + evidence into the decision	reimplemented as `retrieval.py` + `judgment.py`, on a new synthetic task domain
R16–R19, R29–R30 correct writeback and reuse	verified writeback surviving replay	reimplemented as `pipeline._writeback` + `lineage-consistency-v1`
R26/R28, R40 rejecting contaminated memory and conservative abstention	pollution rejection, refuse when undecidable	reimplemented as fail-closed `select_rule` reasons and `sanitize.py` refusal of caller trust fields
R39/R44/R46/R47 conflict handling	multi-source conflict, partially unresolved	only the narrow single-assignment conflict is implemented: two promoted rules that disagree → `conflicting_verified_rules`, no answer, no writeback
R31–R34, R37, R48 open-web acquisition	autonomous search, `3/8` admission on open web	not implemented — that is PR-M3 scope, and the sealed evidence does not support it being reliable
long-horizon positive gain	explicitly unverified	explicitly not claimed; nothing here measures learning over time

Differences a reader must not overlook

Different code path. The sealed numbers were produced by archived runners/ scripts against sealed task sets and local-state model assets. This package re-implements the boundary in cognitive_runtime and has never been run on those task sets. Equal semantics are not assumed, and no R-number result is re-stated as a property of this package.
Different evidence class. The acceptance evidence in acceptance/PR-M1.md comes from the mock provider plus synthetic Directory record: X is assigned to Y fixtures. The single real-model receipt (2026-11-05, offline CPU, provider_class="real") is a load-and-run smoke on one of those same synthetic examples — it proves the pinned checkpoint drives the chain end-to-end; it is not an accuracy result and adds no evidence toward any paper claim. This package has never been run on the sealed task sets.
No answer keys, no trust root. The paper experiments used held-out answer keys in a separate evaluation path. This package refuses fields that look like answer keys or trust assertions (gold, expected, verified, trusted, ground truth) at the input boundary, and ships no signing key — verification means hash + cited receipt + lineage, which is provenance consistency, not truth.
Scope of "memory growth". The closed project's question was whether memory can participate in decisions and show observable gain, plateau or degradation under a controlled loop. This package answers the narrower engineering question of whether that boundary can be installed and reproduced at all. Gain is not measured here.

Reading order for a reviewer

README.md → acceptance/PR-M1.md (what actually passed, what did not) → SOURCE-INVENTORY.md (licenses, revisions, hashes, audit gaps) → MODEL_CARD.md (model identity and limitations) → the sealed verdict document above for the research claims, which this note neither repeats nor extends.

M2 and M3 will update this note with their own version, run method and correspondence changes. This note does not modify any sealed experiment, figure, or research conclusion, and adds no new D/R numbering.