Self-Improving Recall: A Feedback Loop for AI Memory
A memory system that ranks facts the same way forever is dead weight. The signal that actually matters — did this fact help, or did it waste context — only exists during a real conversation. Memstore’s feedback loop captures that signal in-session and feeds it back into recall ranking, so the system gets better at surfacing useful knowledge the more it’s used.
The problem: static recall is stale recall
Memstore’s baseline ranking uses static signals — IDF, project boosts, semantic similarity, recency, surface-aware multipliers for project-level facts. All of them are derived from the fact itself, the query, or the static metadata around them. None answers the real question: when memstore injected this fact last time, did it help the agent or did it just crowd out something better?