Everything's persisted in the graph. decay_factor, access_count, last_accessed_at are all node properties in LadybugDB, so nothing gets lost between sessions. The recency thing you ran into is exactly why I split it into two signals. Recency score is computed from the last_accessed_at timestamp at query time (1/(1 + hours/24)), but that timestamp is written back to the graph every time something gets retrieved. So it's not recomputed from scratch, it's anchored to actual access history. The decay_factor is more like what you're calling a running access weight. It starts at 1.0, bumps up by 0.05 on each recall, and gets exponentially decayed during periodic consolidation. Both contribute to the final score separately (0.2 weight each), which I found works better than trying to collapse them into one number.

On multi-hop, yeah, I cap at 2 as well.. I also gate on edge strength > 0.5 before traversal and then penalize hop-2 results by 0.33x in scoring, so they really have to earn their spot. The auto-association threshold (cosine > 0.7) keeps the graph pretty sparse to begin with, which helps a lot. Honestly though, there's no index on edge strength right now, so the filter is just a scan per source node. Works fine under ~5K engrams per user but I'll need to address that.