{"ID":6138134,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T07:41:35.149838596Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07108","arxiv_id":"2607.07108","title":"Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation","abstract":"Large language model (LLM)-based agentic recommender systems show promise in modeling user preferences through natural-language reasoning, yet they remain limited by text-centric inputs and coarse-grained memory updates, making agents prone to missing visual evidence, semantic noise, and preference drift. To address these limitations, we propose MMEACR, a Multimodal Memory-Enhanced Agent Collaboration framework for recommendation. MMEACR introduces a dual-track memory architecture that separates interpretable agent reasoning from fine-grained multimodal matching. In the reasoning track, collaborative User and Item Memory Agents maintain persistent multimodal memories and update them through an attribute-guided reinforcement-and-reflection mechanism. In the matching track, a decoupled multi-modal embedding memory is built from raw interaction narratives and item images to preserve detailed cross-modal signals beyond structured memory updates. The two tracks are integrated through weighted Reciprocal Rank Fusion to produce robust and interpretable rankings. Experiments on three real-world domains show that MMEACR achieves strong overall performance against competitive LLM-based and agent-based baselines, with notable gains in visually grounded recommendation scenarios.","short_abstract":"Large language model (LLM)-based agentic recommender systems show promise in modeling user preferences through natural-language reasoning, yet they remain limited by text-centric inputs and coarse-grained memory updates, making agents prone to missing visual evidence, semantic noise, and preference drift. To address th...","url_abs":"https://arxiv.org/abs/2607.07108","url_pdf":"https://arxiv.org/pdf/2607.07108v1","authors":"[\"Hao Cong\",\"Huizu Lin\",\"Zihan Wang\",\"Chengkai Huang\",\"Quan Z. Sheng\",\"Lina Yao\"]","published":"2026-07-08T07:50:03Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}