The Retriever Should Remember: Experience-Amortized Reranking for Long-Term Agent Memory
Long-term language-model agents accumulate memories across interactions, but their retrievers typically do not accumulate retrieval experience. Semantic retrieval is efficient, but embedding similarity does not always reflect whether a memory contains evidence relevant to the current query. Large language model (LLM) rerankers provide stronger query-conditioned relevance scores, yet stateless reranking repeatedly scores a large candidate pool and discards these scores after each query. We introduce EARM, an experience-amortized reranking framework that treats previously acquired LLM relevance scores as reusable retrieval experience. EARM stores sparse query--memory relevance scores in an ...
| Authors | Qi Feng, Chris Ding, Jicong Fan |
|---|---|
| Primary category | cs.AI |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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