A Systematic Benchmark for Evaluating Multimodal Personalized Reasoning of Vision-Language Models

Authors: Dohyun Kim , Junyong Lhim , Kwanyong Park

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Abstract: People often make decisions by holistically reasoning over heterogeneous forms of personal information. In this paper, we study the relatively underexplored capability of multimodal personalized reasoning in multimodal large language models (MLLMs). To this end, we introduce \texttt{MPRBench}, a novel and comprehensive benchmark specifically designed to evaluate personalized reasoning across a wide range of tasks and real-world challenges. \texttt{MPRBench} consists of 12 sub-tasks and additionally supports interpretable analysis of representative error cases. Through extensive experiments, we find that current personalized MLLMs still struggle with personalized reasoning due to several key challenges, including inaccurate recognition of personalized concepts and sensitivity to irrelevant personal information. We hope that MPRBench will stimulate further research on personalized reasoning in MLLMs. Code and data are available at this https URL

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Accepted to WACV 2027

Subjects:

Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV)

Cite as:

arXiv:2611.06259 [cs.AI]

(or

arXiv:2611.06259v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.06259

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arXiv-issued DOI via DataCite (pending registration)

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From: Dohyun Kim [ view email ]

[v1]

Wed, 4 Nov 2026 10:08:26 UTC (9,286 KB)

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