MPRBench：系统性评测视觉语言模型多模态个性化推理能力的基准
A Systematic Benchmark for Evaluating Multimodal Personalized Reasoning of Vision-Language Models
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研究者发布 MPRBench，用于评测多模态大语言模型（MLLM）的个性化推理能力。该基准包含 12 个子任务，并支持对代表性错误案例进行可解释分析。实验发现，现有个性化 MLLM 在个性化概念识别不准确、对无关个人信息敏感等关键挑战下仍难以胜任个性化推理，代码与数据已公开。
正文 · 原文
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
| Comments: | 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 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Dohyun Kim [view email]
[v1]
Wed, 4 Nov 2026 10:08:26 UTC (9,286 KB)
来源：arXiv cs.AI · arxiv.org