mlsanityData to 7 Oct 2026

AgentWebRec: Compact Evidence Fusion over the Agent Web for Personalized Recommendation

arXiv 2610.01705 ↗First posted 1 Oct 2026cs.IRcited by 0

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

Abstract (arXiv)

LLM-based personal agents are emerging as persistent carriers of user semantics and intermediaries between users and recommendation platforms, maintaining richer user knowledge locally. As agents interact with one another, the conventional textitUser--Platform relation evolves into a textitUser--Agent Web--Platform information pathway, enabling distributed user-side information to complement item-side information. This new pathway, however, defies conventional recommendation: evidence is scattered across mutually opaque agents and reachable only through bounded queries, only a small portion of it is relevant to the current recommendation decision, and the responses returned by different agents are semantically heterogeneous. We therefore recast recommendation over the agent web as a task-time evidence acquisition and fusion problem under a finite evidence budget by deciding what to ask and what to keep, rather than learning from aggregated data. We propose AgentWebRec, a user-agent-oriented framework that progressively acquires and fuses distributed evidence for each user-item decision while keeping underlying agent memories local. It grounds each decision in platform-provided item semantics and task-relevant evidence from the target user agent's private memory, and conditionally queries neighboring user agents for complementary preference patterns when local evidence is insufficient. Experiments on four InstructRec datasets show that AgentWebRec consistently outperforms baseline recommenders, and ablations verify that the evidence layers contribute complementary gains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 31 of its 46 references that are in the map. First posted on arXiv 1 Oct 2026.

Builds on

31 of 46 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026MemRec: Collaborative Memory-Augmented Agentic Recommender System13 Jan
  2. 2025PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequence4 Mar
  3. 2025AgentCF++: Memory-enhanced LLM-based Agents for Popularity-aware Cross-domain Recommendations19 Feb
  4. 2024MACRec: a Multi-Agent Collaboration Framework for Recommendation23 Feb
  5. 2023AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems13 Oct
  6. 2023Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations31 Aug
  7. 2023RecMind: Large Language Model Powered Agent For Recommendation28 Aug

Other references · 24

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  4. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  5. 2023LongBench: A Bilingual, Multitask Benchmark for Long Context Understandingcited by 1,378
  6. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  7. 2018Self-Attentive Sequential Recommendationcited by 1,169
  8. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  9. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  10. 2023A Survey on Large Language Models for Recommendationcited by 378
  11. 2023Making Retrieval-Augmented Language Models Robust to Irrelevant Contextcited by 364
  12. 2023Recommender Systems with Generative Retrievalcited by 191
12 more
  1. 2023LLMRec: Large Language Models with Graph Augmentation for Recommendationcited by 171
  2. 2023Adapting Large Language Models by Integrating Collaborative Semantics for Recommendationcited by 171
  3. 2023Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approachcited by 168
  4. 2023Text Is All You Need: Learning Language Representations for Sequential Recommendationcited by 149
  5. 2024Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligencecited by 80
  6. 2023ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendationcited by 76
  7. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 72
  8. 2025Agentic Web: Weaving the Next Web with AI Agentscited by 43
  9. 2025Collaborative Retrieval for Large Language Model-based Conversational Recommender Systemscited by 16
  10. 2025AI-native Memory 2.0: Second Mecited by 14
  11. 2025Graph Retrieval-Augmented LLM for Conversational Recommendation Systemscited by 10
  12. 2025Homophily-induced Emergence of Biased Structures in LLM-based Multi-Agent AI Systemscited by 8

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  2. 2026RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
  3. 2025AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
  4. 2026Agentic Recommender System with Hierarchical Belief-State Memory
  5. 2025AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systems
  6. 2026Position: Recommender Systems Should Move Beyond Platform-Centric Ranking toward Personal Agent-Mediated Recommendation
  7. 2025Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations
  8. 2026RecThinker: An Agentic Framework for Tool-Augmented Reasoning in Recommendation