CAVE-Mem: Boundary-Aware Experience Validation for Memory Search

Abstract:Long-term memory agents increasingly rely on it- erative search and reusable experience to answer questions over large personal, factual, or narrative histories. However, current experience-memory systems largely optimize relevance: they re- trieve past search lessons that appear similar to the current state and inject them into the prompt. A relevant experience can still be harmful when the memory substrate, question intent, answer granularity, or evidence boundary changes. We propose CAVE- Mem, a training-free framework that represents experience as a typed intervention operator with applicability, boundary, and utility conditions. CAVE-Mem first obtains a base memory-search answer, then allows an operator to change it only if the oper- ator matches the current substrate, answer contract, evidence boundary, and cross-fitted utility; otherwise the system abstains. Experiments across long-term conversational memory, multi-hop question answering, and long-document narrative reasoning show consistent gains over relevance-only experience reuse.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.00238 [cs.CL]

(or arXiv:2610.00238v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.00238 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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