Memory scoring now uses the graph as source of truth:
- last_scored timestamp on each node (new capnp field @22)
- Nodes scored when older than scoring_interval_secs (default 1hr)
- Oldest-scored-first ordering
- Window: scoring_response_window assistant responses (default 100)
- First-quarter memories scored even without full window
- Per-response normalization (raw divergence / response count)
- Asymmetric weight update: alpha=0.5 up, alpha=0.1 down
(responds fast to importance, decays slowly — memories stay
surfaced even if only useful 1/4 of the time)
Graph writes disabled pending normalization calibration.
Also: configurable scoring_interval_secs and scoring_response_window.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
Separate the scoring into two distinct functions:
- memory_score(key): scores one memory's importance by measuring
divergence in the 50 messages after it was surfaced. Two API calls
(baseline vs without that memory).
- finetune_score(count): scores recent messages with all memories
stripped to identify fine-tuning candidates. Responses with high
divergence depend on memories the model hasn't internalized yet.
The existing score_memories() with the full NxM matrix is preserved
for the debug screen.
Co-Authored-By: Proof of Concept <poc@bcachefs.org>
