mlsanityData to 14 Oct 2026

AtomRec: Evolving Atomic Memory for Agentic Recommendation

arXiv 2609.04882 ↗First posted 4 Sep 2026cs.IRcited by 0

On the map: LLM agents and tool use → Agentic recommender systems

Abstract (arXiv)

Agentic recommender systems use large language models to maintain semantic memory and support evidence-aware recommendation. However, existing memory mechanisms often compress user and item information into coarse summaries and connect them with scalar collaborative links, making it difficult to preserve fine-grained preference stages or retrieve interpretable evidence as user interests evolve. We propose textscAtomRec, an agentic recommender with evolving atomic collaborative memory. textscAtomRec represents user and item memories as structured atomic units, builds semantic links across related memories, and evolves related historical fields when new interactions arrive. During recommendation, it retrieves linked memories as multi-hop evidence paths rather than isolated neighbor summaries, allowing collaborative signals to support grounded ranking. Experiments on four public benchmarks show that textscAtomRec consistently outperforms state-of-the-art agentic and memory-augmented baselines, with around 8.5% average relative improvement across metrics.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 59 of its 84 references that are in the map. First posted on arXiv 4 Sep 2026.

Builds on

59 of 84 references.

Closest ancestors · 9

Same direction, newest first

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  2. 2026ChainRec: An Agentic Recommender Learning to Route Tool Chains for Diverse and Evolving Interests11 Feb
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  4. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval7 Mar
  5. 2025iAgent: LLM Agent as a Shield between User and Recommender Systems20 Feb
  6. 2025A Survey on LLM-powered Agents for Recommender Systems14 Feb
  7. 2024MACRec: a Multi-Agent Collaboration Framework for Recommendation23 Feb
  8. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations31 Aug
  9. 2023RecMind: Large Language Model Powered Agent For Recommendation28 Aug

Other references · 50

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  7. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  8. 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
  9. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  10. 2015Image-based Recommendations on Styles and Substitutescited by 706
  11. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
  12. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 463
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  13. 2026XSkill: Continual Learning from Experience and Skills in Multimodal Agentscited by 75
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  32. 2025Semantic-Preserving Prompt Hijacking: A Black-Box Adversarial Attack on Auto-Prompt Optimizationcited by 3
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  37. 2025LLM-Driven Completeness and Consistency Evaluation for Cultural Heritage Data Augmentation in Cross-Modal Retrievalcited by 1
  38. 2026OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Rankingcited by 1

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  5. 2026Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation
  6. 2026RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
  7. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
  8. 2023On Generative Agents in Recommendation