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To circumvent these limitations through architectural design rather than modifying the LLM itself, we propose an agent-based recommendation framework, memory-based $\\textbf{P}$ersonalized $\\textbf{R}$ecommendation $\\textbf{T}$ool learning via autonomous language $\\textbf{A}$gents (PRTA), in which an LLM acts as a central planner interacting with multiple recommendation models as tools. The LLM-based agent is responsible for high-level reasoning and personalized tool selection, while traditional recommendation models perform full-ranking scoring, leveraging their scalability in modeling behavioral patterns. To support personaliz","title":"Personalized Recommendation Tool Learning via Autonomous Language Agents","url":"https://arxiv.org/abs/2607.19739","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.19739v1 Announce Type: cross \nAbstract: Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and extensive world knowledge, previous LLM-based agents suffer from hallucination and context-length limitations, and thus are not suitable for full-ranking recommendation tasks. To circumvent these limitations through architectural design rather than modifying the LLM itself, we propose an agent-based recommendation framework, memory-based $\\textbf{P}$ersonalized $\\textbf{R}$ecommendation $\\textbf{T}$ool learning via autonomous language $\\textbf{A}$gents (PRTA), in which an LLM acts as a central planner interacting with multiple recommendation models as tools. The LLM-based agent is responsible for high-level reasoning and personalized tool selection, while traditional recommendation models perform full-ranking scoring, leveraging their scalability in modeling behavioral patterns. 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