Interesting direction. Treating decay/confidence as engine-native primitives is closer to what multi-agent systems need than raw similarity search. One practical thing to watch in production: expose provenance + freshness semantics at query time so downstream agents can decide whether to trust, refresh, or ignore a recalled memory.
This is the project I just posted. Happy to dive into any details... the exact ACT-R decay formula, how the Hebbian graph updates in log space, the 6-phase Activate pipeline, or why I went with embedded Pebble. Fire away!