| --- |
| license: apache-2.0 |
| base_model: convaiinnovations/laya-multilingual |
| library_name: laya |
| datasets: |
| - atmem/stateful-memory-transitions-10pct-v2 |
| tags: [laya, typed-decisions, memory, multilingual, research] |
| --- |
| |
| # Laya Memory Decision V2 — authentic-conversation research checkpoint |
|
|
| This is a portable multilingual classifier for memory-state decisions. A host |
| memory system supplies a new observation, its authorized retrieved memories, |
| and one bounded Choice, Noul, or Score question. The model returns a typed |
| probability distribution; it does not generate memory text or write a database. |
|
|
| The checkpoint is independent of AtMem. AtMem is one compatible host and owns |
| retrieval, authorization, mutation, provenance, and receipts. Other memory |
| systems can use the same JSON contract in `questions.json`. |
|
|
| ## Status |
|
|
| **Research-only, uncalibrated checkpoint.** It is public for inspection and |
| experimentation, but is not qualified to authorize automatic memory mutation. |
| The calibration split lacks enough relational operations, and the frozen |
| production selector therefore returned `blocked`. |
|
|
| ## Data and lineage |
|
|
| | Item | Frozen identity | |
| |---|---| |
| | Base model | `convaiinnovations/laya-multilingual@1720e3e3357cfe1e281542e223f8273b0890ca34` | |
| | Laya source | `NandhaKishorM/laya@68804629e8ccd9d616d48a40e87de9fabbeae069` | |
| | Dataset | `atmem/stateful-memory-transitions-10pct-v2@92a851b7f1811d3403f8d2282ac1275e6f957922` | |
| | Dataset manifest | `2cb6aac781b07f6734bbbc4a465260850363c77093e2a79c7ded4c98de2c4730` | |
| | Training run | `rlcd-410239-20261013T203312Z` | |
| | Model SHA-256 | `17c949572ecd43917ed8d0413c3e2c68252432ef1034623e57caf61d7920ada1` | |
| | Seed | `410239` | |
|
|
| The dataset contains only human-origin public conversation rows selected from |
| WildChat-1M and LUFY. It contains no generated, synthetic, translated, or |
| hand-authored conversation rows. Transition labels are contextual machine weak |
| supervision produced against frozen scoped SQLite and vector state; they are not |
| human ground truth. Users, conversations, scopes, chronological chains, and |
| duplicate clusters are split together to prevent cross-split leakage. |
|
|
| ## Typed contract |
|
|
| The host sends a versioned request containing the observation, authorized |
| retrieved memories, canonical generation, and exactly one typed question: |
|
|
| ```json |
| { |
| "format": "laya-evaluation-request-v2", |
| "schema_version": "2.0.0", |
| "request_id": "req_01", |
| "scope_id": "opaque-scope", |
| "canonical_generation": 184, |
| "observation": {"id": "obs_01", "speaker_role": "user", "content": "Actually, call me Sara rather than Mona."}, |
| "retrieved_memories": [{"id": "mem_41", "content": "The user prefers to be called Mona.", "lifecycle": "active"}], |
| "question": { |
| "question_id": "formation.operation.v2", |
| "type": "choice", |
| "instructions": "Select the supported memory-state operation.", |
| "options": [ |
| {"id": "STORE_NEW", "description": "Create distinct durable memory."}, |
| {"id": "UPDATE_EXISTING", "description": "Add compatible detail."}, |
| {"id": "SUPERSEDE_EXISTING", "description": "Replace outdated memory."}, |
| {"id": "DUPLICATE", "description": "Meaning is already represented."}, |
| {"id": "DO_NOT_STORE", "description": "Do not create memory."}, |
| {"id": "FORGET_EXISTING", "description": "Retire an authorized target."} |
| ] |
| } |
| } |
| ``` |
|
|
| The output is projected to a strict Choice, Noul, or Score response with a full |
| probability distribution. `questions.json` contains the frozen definitions. A |
| host must preserve complete state, reject truncation, restrict target IDs to the |
| authorized retrieved set, and treat every output as a proposal. |
|
|
| ## Loading |
|
|
| ```python |
| from laya import Agent |
| |
| agent = Agent("atmem/laya-memory-decision-v2", device="cpu") |
| result = agent.system_one( |
| { |
| "observation": {"id": "obs_01", "content": "Call me Sara."}, |
| "retrieved_memories": [], |
| }, |
| { |
| "formation.is_durable.v2": { |
| "type": "noul", |
| "statement": "The observation is likely to remain useful later." |
| } |
| }, |
| max_len=1024, |
| head_max_len=256, |
| ) |
| ``` |
|
|
| Pin the model revision in reproducible or governed deployments. |
|
|
| ## Training |
|
|
| Training used upstream RLCD/proper scoring for four epochs on one RTX 3090, |
| micro-batch 8, gradient accumulation 8, encoder learning rate `2.5e-5`, head |
| learning rate `1e-4`, FP32, gradient clipping `1.0`, and seed `410239`. PyTorch |
| CUDA has no deterministic `cumsum` kernel used by the RLCD ordered-score reward; |
| the run records a warn-only exception for that operation while retaining fixed |
| seeds and deterministic settings elsewhere. |
|
|
| The predeclared production selector rejected both RLCD and soft-CE because of |
| input truncation, missing rare expected classes, and insufficient calibration |
| power. RLCD was nominated only for research comparison from non-sealed metrics. |
|
|
| ## Frozen evaluation |
|
|
| The sealed split has 1,999 transitions after privacy exclusions: 1,821 |
| `DO_NOT_STORE`, 107 `STORE_NEW`, 61 `REVIEW`, 8 `UPDATE_EXISTING`, and 2 |
| `DUPLICATE`; it contains no `SUPERSEDE_EXISTING` or `FORGET_EXISTING` examples. |
|
|
| | Metric | Base Laya | Fine-tuned checkpoint | |
| |---|---:|---:| |
| | Fully correct transitions | 367 / 1,999 (18.36%) | 1,475 / 1,999 (73.79%) | |
| | Operation accuracy | 25.11% | 80.54% | |
| | Operation macro-F1 | 9.18% | 23.37% | |
| | Target accuracy | 0.00% | 10.00% | |
| | Runtime failures | 1,269 | 215 | |
| | False stores | 2 | 15 | |
| | Unsafe mutations | 10 | 9 | |
| | Brier score | 0.4957 | 0.1852 | |
| | ECE | 0.5653 | 0.4092 | |
|
|
| The paired fully-correct improvement is `+55.43` percentage points; paired |
| bootstrap 95% CI `[+53.18, +57.63]`. A constant `DO_NOT_STORE` classifier would |
| score 91.10% aggregate on this imbalanced split, so aggregate accuracy alone is |
| not evidence of a deployable decision model. The fine-tuned checkpoint improves |
| macro-F1 over base Laya but increases false storage and remains poorly |
| calibrated. |
|
|
| ## Appropriate use |
|
|
| - Research on typed memory-decision contracts and state-aware classification. |
| - Offline comparison using the published frozen database and transition set. |
| - Proposal-only integration behind host validation and explicit review. |
|
|
| Do not use this checkpoint as an authorization oracle, autonomous database |
| writer, deletion authority, or calibrated production classifier. |
|
|
| ## License |
|
|
| Apache-2.0. Source-dataset licenses and provenance are recorded per row in the |
| dataset card and manifest. |
|
|
