arXiv — NLP / Computation & Language · · 4 min read

Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation

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Computer Science > Computation and Language

arXiv:2607.27816 (cs)
[Submitted on 30 Jul 2026]

Title:Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation

Authors:Yuhang Zhu (1), Mingxuan Du (1), Benfeng Xu (1 and 2), Jie Gao (2), Lingyun Yu (1), Hongtao Xie (1) ((1) University of Science and Technology of China, (2) MetaStone Technology, Beijing, China)
View a PDF of the paper titled Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation, by Yuhang Zhu (1) and 8 other authors
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Abstract:Role-playing agents (RPAs) have become one of the most important consumer applications of large language models. Users engage in multi-turn conversations with RPAs for experiences such as emotional comfort, making reliable evaluation essential for measuring capability, comparing systems, and guiding further improvement. Existing benchmarks, however, typically require an RPA to continue a fixed dialogue history and then evaluate the continuation using a fixed rubric detached from the user. We identify and empirically demonstrate two limitations of this design. First, an RPA's output is shaped by the preceding dialogue history, preventing a scientifically grounded assessment of its role-playing ability in real multi-turn settings. Second, user experience varies substantially across individuals, and conventional fixed rubrics need not align with user satisfaction. We therefore introduce PALATE (Person-Aligned LLM-Simulated-User Assessment with Tailored Evaluation), a scalable RPA benchmark built on user simulators. PALATE is accompanied by a pool of 300 character profiles. Its main evaluation trains five per-user simulators and lets them engage candidate RPAs in free-form, multi-turn conversations over a pre-frozen panel of character profiles. Alongside a general quality rubric, we construct personalized rubrics to measure user satisfaction; on held-out annotated data, the personalized rubrics show higher agreement with human judgments than the general rubric. In the main evaluation of 16 candidates, PALATE separately characterizes generic turn quality, long-horizon session capability, and per-user experience on multi-turn trajectories co-constructed by each candidate. It thereby produces interpretable evaluations of specific user-RPA pairs rather than compressing systems into a single user-independent ranking.
Comments: 29 pages, 3 figures, including supplementary material. Resources: this https URL
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.27816 [cs.CL]
  (or arXiv:2607.27816v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2607.27816
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yuhang Zhu [view email]
[v1] Thu, 30 Jul 2026 07:55:35 UTC (2,016 KB)
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