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arXiv AI By Hanchong Chen, Xing Tang, Lingjie Li, Xiongfeng Shan, Xiuqiang He

When Memory Takes Gradients: Collaborative Vector Memory for Agentic Recommender Systems

Read the original on arXiv AI →

The paper introduces CoVeMem, a Collaborative Vector Memory system that replaces text-based memory in agentic recommender systems with vectorized user and item states derived from a frozen LightGCN model. By retrieving relevant historical states at each decision and integrating them as soft tokens in the LLM’s context, CoVeMem enables contrastive alignment and listwise co‑training to learn how to read and rank these states, achieving performance on par with or better than existing text‑memory agents across multiple benchmarks without extra LLM calls for memory updates.

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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