APeB: Benchmarking Personalization Ability of Large Language Model Agents

[2607.03162] APeB: Benchmarking Personalization Ability of Large Language Model Agents Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2607.03162 (cs) [Submitted on 3 Jul 2026 ( v1 ), last revised 16 Oct 2026 (this version, v3)] Title: APeB: Benchmarking Personalization Ability of Large Language Model Agents Authors: Garry Yang , Zizhe Chen , Xinru Chen , Yongqiang Chen , Jianxiang Wang , Deyu Zou , Linyi Ding , Jialiang Wu , Yunzhong He , Yu Gong , James Cheng , Huaixiao Tou View PDF HTML (experimental) Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract preferences from noisy interaction histories, and select among competing alternatives. Existing benchmarks rarely test this capability, as they often rely on user-refined queries or simplified histories. We introduce personalized product search (PPS), a testbed for agentic personalization under raw queries and diverse histories. We construct Agent Personalized Benchmark (APeB) from action logs, pairing underspecified intents with rich histories and user-viewed candidate items. Evaluating state-of-the-art LLMs with multi-step agent workflows, we find that models handle explicit queries well but struggle with early-stage queries requiring intent and preference discovery. Rubric analysis attributes this gap mainly to ineffective history use. A simple history-aware query-refinement pipeline, VQRA, yields consistent gains, highlighting the need for dedicated history-utilization modules in personalized agents. Comments: Accepted to Findings of EMNLP 2026. Code and data: this https URL Subjects: Artificial Intelligence (cs.AI) ; Human-Computer Interaction (cs.HC) Cite as: arXiv:2607.03162 [cs.AI] (or arXiv:2607.03162v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.03162 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haochen Yang Mr [ view email ] [v1] Fri, 3 Jul 2026 10:03:35 UTC (33,705 KB) [v2] Thu, 27 Aug 2026 14:22:53 UTC (33,706 KB) [v3] Fri, 16 Oct 2026 07:23:05 UTC (33,706 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-07 Change to browse by: cs cs.HC References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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