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arXiv AI By Jie Huang, Cuiling Lan, Qize Yu, Xiulian Peng, Yan Lu

MemSteer: Turning User Histories into Supervision for Agent Memory Optimization

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MemSteer transforms unlabeled user histories into question‑answer supervision with supporting evidence, enabling memory‑design optimization for agents. It builds evidence‑grounded answer plans that guide question generation and uses source‑grounded auditing to validate annotations. Experiments on ATM‑Bench and RHELM show that MemSteer‑Opt, which leverages this supervision, outperforms baseline BM25 and improves accuracy without updating model parameters.

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.

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