LazyMem: Retrieve Broadly, Construct Selectively for Efficient Long-Term Agent Memory
Long-term memory enables LLM agents to leverage past interactions, but dialogue histories quickly exceed the context window, forcing agents to retrieve relevant subsets at query time. Because useful evidence is sparse and scattered across verbose conversations, retrieval faces a fundamental tension: broadening recall improves coverage but floods downstream reasoning with noise, while compressing memories at write time eases retrieval but irreversibly discards details that future queries may need. We introduce LazyMem, which resolves this tension by deferring all memory construction to query time. Given a retrieved candidate pool, a lightweight model processes it in overlapping parallel wi...
| Authors | Jing Yu, Yibo Zhao, Jiaming Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 17 July 2026 |
| Last updated at source | 28 July 2026 |
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