1.5 KiB
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:
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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.
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Scorer core (model.rs): Cross-attention-style ScorerConfig and CrossAttentionScorer implementing candidate scoring with softmax ranking for preference prediction.
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Protocol layer (protocol.rs): JSON-RPC protocol types defining the service contract.
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Tokenization (tokenizer.rs): HashTrick tokenizer for feature hashing.
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Training infrastructure (training.rs): Scaffolding including Adam optimizer.
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Data pipeline (data.rs): Placeholder reader for training data.
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Persistence (checkpoint.rs): Save/load/apply for model checkpoints.
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Service entry point (main.rs): JSON-line stdin/stdout service with status, score, and train handlers.
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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.