# 2026-02-27 Session Notes

## Phase 1 Predictor Implementation Review

Nicholai completed and presented Phase 1 of the predictive memory scorer—a Rust crate scaffold implementing a complete training and inference pipeline for real-time memory preference scoring. He requested code review of the newly added packages/predictor/ crate.

## Implementation Summary

The Phase 1 deliverable includes:

- **Autograd engine** (autograd.rs): Operation-level automatic differentiation with Rng, Param, Tape abstractions and custom operations (Sigmoid, MeanPool, FeatureConcat, ListwiseLoss) plus comprehensive forward/backward implementations and unit tests.

- **Scorer core** (model.rs): Cross-attention-style ScorerConfig and CrossAttentionScorer implementing candidate scoring with softmax ranking for preference prediction.

- **Protocol layer** (protocol.rs): JSON-RPC protocol types defining the service contract.

- **Tokenization** (tokenizer.rs): HashTrick tokenizer for feature hashing.

- **Training infrastructure** (training.rs): Scaffolding including Adam optimizer.

- **Data pipeline** (data.rs): Placeholder reader for training data.

- **Persistence** (checkpoint.rs): Save/load/apply for model checkpoints.

- **Service entry point** (main.rs): JSON-line stdin/stdout service with status, score, and train handlers.

- **Build config** (Cargo.toml): Crate manifest and dependencies.

The session involved reading the implementation files to understand architecture and provide technical feedback. Build artifacts were properly ignored in .gitignore.