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

CAVE-Mem is a training‑free framework that enhances memory search for long‑term memory agents by treating experience as a typed intervention operator with conditions on applicability, boundary, and utility. It first retrieves a base answer and then only applies an intervention if the operator matches the current memory substrate, answer contract, evidence boundary, and cross‑fitted utility; otherwise it abstains. Experiments on conversational memory, multi‑hop QA, and long‑document reasoning demonstrate consistent improvements over relevance‑only experience reuse.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents
• nlp