MODEL_CARD.md

Model Card

Status: PR-M1 runtime implemented and locally accepted; the real-model smoke ran on 2026-11-05 (offline, CPU, pinned local snapshot). Planned versions: PR-M1/v0.1.0 (complete bounded runtime), PR-M2/v0.2.0 (deployment), PR-M3/v0.3.0 (experimental conversation and bounded web learning). See PROJECT-PLAN.zh-CN.md; future features are not evaluated capabilities.

Model identity

• Model name: convaiinnovations/laya (upstream frozen checkpoint; not trained, renamed or re-branded by this project)
• Source/revision: Hugging Face convaiinnovations/laya @ 55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851 (upstream lastModified 2026-09-24T05:39:22Z)
• SHA-256 of model.safetensors: 891102d372688fc2a094dac56a384bc537b87c63f21f9f3dac0be2b7cbc8d86c (842,609,210 bytes; local copy verified equal to the upstream LFS digest)
• Runtime package revision: cognitive-runtime 0.1.0
• License: upstream model card declares apache-2.0. This project's own runtime code is Apache-2.0 (LICENSE), decided by the owner on 2026-11-05; that licence covers the code only, not the checkpoint, and does not re-license it. The published package is thin — no weights are bundled.
• Encoder dependency: answerdotai/ModernBERT-large @ 45bb4654a4d5aaff24dd11d4781fa46d39bf8c13, apache-2.0. Its model.safetensors is 1,583,544,840 bytes, upstream LFS digest 44510fec5d3a81a1877f225637b869495f18e55f6f23a09abb9be0acc030295f; the owner downloaded it separately and the download was hash-verified. Measured 2026-11-05: at inference laya 0.3.20 does not read these weights — it builds the network untrained and loads every parameter, encoder included, from the Laya checkpoint; only the snapshot's encoder/config.json is read as configuration. The smoke run in acceptance/PR-M1.md item 2 executed with the hub in offline mode, which also proves no hidden fetch.

What the model actually does

laya.load(...).predict(state, questions) over one typed choice question with one option per candidate. It returns a candidate id, per-option probabilities and an answer confidence. It is a bounded selector over options the runtime hands it — not a free-text generator, and not able to cite its own sources.

rl_agent_config.json records max_len=512, head_max_len=192, max_prefixes=6, amp_dtype=bf16, and fine_tuned_from_checkpoint=true (7313 updates, 1 epoch). The option budget enforced by this package is 20.

Calibration

calibration_status is reported per run. Laya emits a temperature warning at load time, which this package maps to uncalibrated. Confidence values are therefore not probabilities of correctness and must not be read as such. The runtime never uses confidence to authorise an answer or a memory promotion.

Evaluated behavior

Verified in PR-M1, in a clean install outside this repository, using the mock provider:

• retrieval → verification → promotion → typed judgment → answer, persisted in SQLite and reused by a second, independent process;
• abstention with no evidence, with conflicting receipts, with a tampered record, with a duplicate candidate set, and with an unknown source ref;
• refusal of caller-supplied verified, gold or expected fields;
• a disagreeing model proposal does not change the authorized answer, and proposal_agrees_with_judgment compares candidate text on both sides;
• a provider that returns no usable proposal (schema_failure, timeout, runtime_error) exits 3 as provider_error instead of letting memory answer in the model's place — a model that declines (abstain) still flows through the judgment unchanged.

One end-to-end run then used the real frozen checkpoint (provider_class= "real", offline, CPU): answered/team-north with proposal_agrees_with_judgment=true, real token usage recorded (prompt_tokens=117), calibration_status=uncalibrated (the temperature warning actually fired), and the writeback promoted with verification_status=pass; a second independent process reused the memory with acquisition_calls=0. That is one smoke receipt on one synthetic example on one machine — it establishes that the pinned checkpoint loads and the chain runs end-to-end with the real model; it is not an accuracy evaluation, and nothing above the mock-provider list is claimed beyond it.

Not verified in PR-M1: any accuracy or quality evaluation of the real model, cross-platform installation, and every capability belonging to PR-M2/PR-M3.

The sealed experiments in this repository carry task- and protocol-bound evidence for bounded memory retrieval/replay, conflict and tamper rejection, persistence across controlled restarts, and writeback boundaries. That evidence was produced by the archived runners, not by this package, and is not re-attributed here.

Limitations

Long-horizon memory growth, open-domain autonomous search, universal transfer, stable positive long-term marginal gain, and model-parameter growth are not established. Verification in this package proves provenance consistency — that a record still matches the receipt or lineage it cites. It does not prove the cited claim is true in the world.

Reproduction

pip install ./dist/cognitive_runtime-0.1.0-py3-none-any.whl
python -m cognitive_runtime doctor
python -m cognitive_runtime infer \
  --input "$(python -c 'import cognitive_runtime.config as c;print(c.EXAMPLES_DIR/"request.json")')" \
  --store ./memory.sqlite

The real-model path additionally needs the model extra, network or a local copy of both upstream files, and provider: laya in the config. Until the real-model smoke is recorded, no claim in this card depends on it.