MemoType: type-based memory routing lifts agent recall by up to 16.18%
dair_ai · x · 2026-10-11
dair-ai highlights a new paper on agent memory, MemoType, whose core finding is that each memory type needs its own retrieval strategy rather than one embedding search over everything.
- Theoretical result: when the memory store holds several types, any single retrieval strategy has an upper bound on expected precision.
- Method: MemoType classifies each memory and each query by type, retrieves only type-matched memories, and applies the strategy for that type.
- A new dataset, TriMEM, provides the type labels needed to train the classifier.
- Results: Recall@1 improves by up to 16.18% across three datasets.
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