Agentic Recommender System with Hierarchical Belief-State Memory
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910223337259008 |
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| author | Shen, Xiang Zhou, Yuhang Wu, Yifan Zhao, Zhuokai Lin, Siyu Huang, Lei Zhong, Qianqian Zhang, Lizhu Zhang, Benyu Fan, Xiangjun Yan, Hong |
| author_facet | Shen, Xiang Zhou, Yuhang Wu, Yifan Zhao, Zhuokai Lin, Siyu Huang, Lei Zhong, Qianqian Zhang, Lizhu Zhang, Benyu Fan, Xiangjun Yan, Hong |
| contents | Memory-augmented LLM agents have advanced personalized recommendation, yet existing approaches universally adopt flat memory representations that conflate ephemeral signals with stable preferences, and none provides a complete lifecycle governing how memory should evolve. We propose MARS (Memory-Augmented Agentic Recommender System), a framework that treats recommendation as a partially observable problem and maintains a structured belief state that progressively abstracts noisy behavioral observations into a compact estimate of user preferences. MARS organizes this belief state into three tiers: event memory buffers raw signals, preference memory maintains fine-grained mutable chunks with explicit strength and evidence tracking, and profile memory distills all preferences into a coherent natural language narrative. A complete lifecycle of six operations -- extraction, reinforcement, weakening, consolidation, forgetting, and resynthesis -- is adaptively scheduled by an LLM-based planner rather than fixed-interval heuristics. Experiments on four InstructRec benchmark domains show that MARS achieves state-of-the-art performance with average improvements of 26.4% in HR@1 and 10.3% in NDCG@10 over the strongest baselines with further gains from agentic scheduling in evolving settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_14401 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Agentic Recommender System with Hierarchical Belief-State Memory Shen, Xiang Zhou, Yuhang Wu, Yifan Zhao, Zhuokai Lin, Siyu Huang, Lei Zhong, Qianqian Zhang, Lizhu Zhang, Benyu Fan, Xiangjun Yan, Hong Computation and Language Artificial Intelligence Memory-augmented LLM agents have advanced personalized recommendation, yet existing approaches universally adopt flat memory representations that conflate ephemeral signals with stable preferences, and none provides a complete lifecycle governing how memory should evolve. We propose MARS (Memory-Augmented Agentic Recommender System), a framework that treats recommendation as a partially observable problem and maintains a structured belief state that progressively abstracts noisy behavioral observations into a compact estimate of user preferences. MARS organizes this belief state into three tiers: event memory buffers raw signals, preference memory maintains fine-grained mutable chunks with explicit strength and evidence tracking, and profile memory distills all preferences into a coherent natural language narrative. A complete lifecycle of six operations -- extraction, reinforcement, weakening, consolidation, forgetting, and resynthesis -- is adaptively scheduled by an LLM-based planner rather than fixed-interval heuristics. Experiments on four InstructRec benchmark domains show that MARS achieves state-of-the-art performance with average improvements of 26.4% in HR@1 and 10.3% in NDCG@10 over the strongest baselines with further gains from agentic scheduling in evolving settings. |
| title | Agentic Recommender System with Hierarchical Belief-State Memory |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2605.14401 |