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arXiv AI By Xinyu Zhang, Weiwei Sun

Agent Memory as Recommendation: Recommender-System Design for Long-Term Memory of LLM Agents

Read the original on arXiv AI →

The paper proposes treating an LLM agent’s long‑term memory selection as a budgeted slate recommendation problem, framing each request as a user, stored memories as items, and the context window as a slate. By applying recommender‑system techniques—candidate generation, learning‑to‑rank, and slate construction—AgentMemRec selects relevant memories without any LLM calls. Experiments on LongMemEval and LoCoMo show that AgentMemRec outperforms existing memory and context‑selection methods by 10–31 accuracy points and achieves comparable or better performance than an LLM reranker while reducing serving cost and eliminating LLM calls at write time.

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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