Adaptive Memory Index Evolution for Agents

Long-horizon agents continually accumulate experience, yet retaining a memory does not guarantee that it will be retrieved when needed. Existing retrieval-based memory systems primarily address this problem by modifying what is stored, while the retrieval index itself is typically treated as fixed. We introduce Memory Index Evolution, a complementary adaptation axis that learns how retained experience should be reached from deployment interactions, and instantiate it with Adaptive Memory Index Evolution (AMIE). AMIE maintains a bounded, persistent state for each memory and uses it to reshape future retrieval without modifying stored content or retraining the host memory system. Specifically, AMIE (i) derives a persistent interference state from deployment retrieval traffic, (ii) uses suppression dynamics to reduce repeated slot competition on subsequent queries, and (iii) performs index updates in the retrieval flow as a lightweight wrapper over existing memory systems. Across all evaluated memory hosts, AMIE consistently improves retrieval of fresh evidence, showing that index-side adaptation transfers across heterogeneous memory systems. Downstream evaluations further translate these retrieval gains into improved answer quality. Our results establish that how memories are reached is a powerful optimization axis distinct from what is stored.