2026-02-25T16-05-30_auto_memory/memories.db-wal, memory/2026-02-25-predicti

memory/2026-02-25-predictive-memory-scorer-spec-review-feedback-inte.md

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+# 2026-02-25 Session Notes
+
+## Predictive Memory Scorer Spec Review & Feedback Integration
+
+Nicholai received detailed external code review feedback on the predictive memory scorer specification (`docs/wip/predictive-memory-scorer.md`). The reviewer provided high-level validation of the overall architecture—calling it "exceptionally well-thought-out" and praising the North Star vision ("difference between a tool that remembers and a mind that persists")—while identifying five concrete technical refinements.
+
+### Feedback Highlights
+
+The reviewer validated three core design strengths: (1) Dynamic baseline weighting via RRF with EMA success rate ensures graceful degradation; (2) Zero-dependency Rust sidecar keeps binary size negligible and inference sub-millisecond; (3) Outcome-driven labels via continuity scorer avoids hand-labeling overhead.
+
+### Five Technical Refinements Identified
+
+1. **HashTrick bucket count**: Bump from 4,096 to 16,384 to reduce collisions in code-heavy memories (still ~1.1M parameters, fits L2 cache)
+2. **Listwise loss temperature**: Use T < 1.0 to sharpen soft label distributions; flat P_true from continuity scorer soft labels requires aggressive sharpening
+3. **Negative sample filtering**: Apply cosine similarity filter before assigning strict 0.0 labels; prevents training model to replicate baseline mistakes
+4. **Drift reset strategy**: Use replay buffer (80% recent + 20% historical by continuity score) instead of 2x learning rate to avoid catastrophic forgetting
+5. **RRF constant k**: Drop from 60 to 10–15 for ~50 candidate memories; k=60 compresses rank variance when candidate list is small
+
+All five recommendations are sound and likely to be incorporated into the specification.