| # 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 |
|
|
| ```bash |
| 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. |
|
|
