| # TMCRA Training Notes | |
| This document summarizes the training direction behind the graph scorer package included in this release. | |
| ## Training Goal | |
| TMCRA trains graph-scoring components for long-memory retrieval. The goal is to help the runtime decide which memory nodes and graph paths should be surfaced to an answer model for a given user query. | |
| The trained model is not intended to replace the answer LLM. It is responsible for memory selection: | |
| - identify relevant memory nodes | |
| - score graph paths between related memory events | |
| - preserve useful cross-turn and cross-session links | |
| - reduce noisy or stale evidence before answer generation | |
| ## Model Components | |
| The released model directory contains two main runtime scorers: | |
| - `node_scorer.pt`: scores candidate memory nodes. | |
| - `path_scorer.pt`: scores graph paths and tunnel links between memory nodes. | |
| The training output also includes: | |
| - best checkpoints | |
| - last checkpoints | |
| - epoch and step checkpoints | |
| - training summary and logs | |
| - export manifest | |
| ## Training Data Direction | |
| Training data is built around dialogue-memory behavior rather than isolated QA pairs. Samples are designed to teach the model how memory should connect across turns and sessions. | |
| The major training directions include: | |
| - direct user facts | |
| - assistant-provided details | |
| - preference/profile signals | |
| - temporal state changes | |
| - old-value vs current-value selection | |
| - cross-session event links | |
| - multi-evidence aggregation | |
| - evidence-positive vs noise/negative memory separation | |
| - unit-to-unit coverage for count/sum/compare tasks | |
| ## Graph Memory Supervision | |
| Each training example is converted into graph-oriented supervision. Instead of only asking whether a text chunk is relevant, TMCRA trains over: | |
| - memory node relevance | |
| - event-unit relevance | |
| - path usefulness | |
| - tunnel/link usefulness | |
| - evidence role | |
| - currentness and temporal state | |
| - whether a candidate should be injected into answer context | |
| This allows the runtime to learn memory structure, not only lexical similarity. | |
| ## Writer and Scorer Separation | |
| TMCRA separates memory writing from graph scoring. | |
| The writer extracts candidate memory records from dialogue. The graph model then learns how those records should be selected and connected during retrieval. | |
| This separation is important because a long-memory system needs two different abilities: | |
| - write useful memory units from conversation | |
| - retrieve and connect the right units later under noise | |
| ## Training Output Included | |
| The packaged model output is located at: | |
| ```text | |
| models/action_frame_tunnel_graph548_tunnel_fusion_train_20260524_042557/ | |
| ``` | |
| Runtime files: | |
| ```text | |
| node_scorer.pt | |
| path_scorer.pt | |
| export_manifest.json | |
| ``` | |
| Full training trace: | |
| ```text | |
| checkpoints/ | |
| node_scorer_best.pt | |
| path_scorer_best.pt | |
| node_scorer_last.pt | |
| path_scorer_last.pt | |
| train_summary.json | |
| train.log | |
| training_issues.jsonl | |
| ``` | |
| ## Current Training Lessons | |
| The current baseline shows that TMCRA has strong single-session fact recall and assistant-detail recall. It also has working temporal and preference layers. | |
| The main remaining training targets are: | |
| - stronger multi-session aggregation | |
| - better unit coverage for count/sum/compare questions | |
| - deeper temporal graph planning | |
| - query-graph to memory-graph matching | |
| - more stable preference abstraction under indirect user requests | |
| These directions are the next step for improving TMCRA from a working long-memory runtime into a stronger general agent-memory layer. | |