The paper introduces MPRBench, a benchmark designed to evaluate multimodal personalized reasoning in multimodal large language models (MLLMs). It comprises 12 sub-tasks and allows for interpretable analysis of error cases. Experiments show that current MLLMs struggle with personalized reasoning due to inaccurate concept recognition and sensitivity to irrelevant personal information.
By Dohyun Kim, Junyong Lhim, Kwanyong Park
arXiv:2505.03654v3 Announce Type: replace-cross
Abstract: Multimodal Large Language Models have shown strong performance across multimodal tasks, and recent personalized MLLMs can recognize user-spec...
By Yifan Xiang, Zhenxi Zhang, Bin Li, Yixuan Weng, Bo Gao, Shoujun Zhou, Yangfan He, Yilin Yuan, Keqin Li
arXiv:2511. 17731v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) prompting has proven remarkably effective for eliciting complex reasoning in large language models (LLMs).
By Lingxiao Li, Yifan Wang, Xinyan Gao, Chen Tang, Xiangyu Yue, Chenyu You
arXiv:2610.27341v1 Announce Type: new
Abstract: Vision-language models (VLMs) have advanced rapidly in multimodal reasoning, yet they remain limited on tasks requiring fine-grained understanding of s...
By Zerui Chen, Changrui Chen, Fei Ni, Jiankang Deng
arXiv:2606. 28357v1 Announce Type: cross Abstract: Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.
By Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Frank Shyu, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu
arXiv:2608.29374v1 Announce Type: new
Abstract: Tool-augmented multimodal reasoning integrates external tools (e.g., object detection, depth estimation) into multimodal large language models (MLLMs)...
By Yu Cheng, Arushi Goel, Hakan Bilen
arXiv:2507. 16518v3 Announce Type: replace-cross Abstract: Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities.
By Xiuwei Chen, Wentao Hu, Hanhui Li, Yongxin Wang Jun Zhou, Zisheng Chen, Meng Cao, Yihan Zeng, Kui Zhang, Yu-Jie Yuan, Jianhua Han, Hang Xu, Xiaodan Liang
CoMMET is a new multimodal benchmark designed to evaluate Theory of Mind (ToM) in Multimodal Large Language Models (MLLMs). It expands beyond existing text-only, belief-focused tests by covering a wider range of mental states, incorporating moral evaluation, and enabling multi-turn, open-ended interactions. The dataset is grounded in psychological theory and provides a comprehensive assessment across different model families and sizes, revealing strengths, limitations, and future improvement directions.
By Ruirui Chen, Weifeng Jiang, Chengwei Qin, Kaiwen Wei, Yanzhen Yue, Cheston Tan
arXiv:2508.13186v2 Announce Type: replace-cross
Abstract: AI agents with advanced reasoning and tool-use capabilities have demonstrated impressive performance in web browsing for deep search. However...
By Shilong Li, Xingyuan Bu, Wenjie Wang, Jiaheng Liu, Jun Dong, Haoyang He, Hao Lu, Haozhe Zhang, Chenchen Jing, Zhen Li, Chuanhao Li, Jiayi Tian, Chenchen Zhang, Tianhao Peng, Yancheng He, Jihao Gu, Hui Huang, Donghao Zhou, Yuanxing Zhang, Jian Yang, Ge Zhang, Wenhao Huang, Zhaoxiang Zhang, Qiangpeng Yang, Shilei Wen
MMEmb-R1 is a multimodal embedding framework that enhances reasoning by treating it as a latent variable and selecting beneficial reasoning paths through pair-aware selection and counterfactual intervention. It uses reinforcement learning to invoke reasoning only when necessary, reducing unnecessary computation and latency. On the MMEB-V2 benchmark, MMEmb-R1 achieves a state‑of‑the‑art score of 71.2 with just 4 B parameters.
By Yuchi Wang, Dingkang Yang, Haiyang Yu, Weikang Bian, Jiefeng Long, Xiao Liang, Chao Feng, Hongsheng Li
arXiv:2602.19001v2 Announce Type: replace
Abstract: As large language models increasingly power personal assistants, users expect them to reason over multimodal life histories, from recognizing peopl...
By Xia Hu, Honglei Zhuang, Brian Potetz, Alireza Fathi, Bo Hu, Babak Samari, Howard Zhou
arXiv:2604.13491v4 Announce Type: replace
Abstract: With the rapid progress of Multimodal Large Language Models (MLLMs), unified MLLMs that jointly perform image understanding and generation have adv...
By Yongjin Kim, Yoonjin Oh, Yerin Kim, Hyomin Kim, Jeeyoung Yun, Yujung Heo, Minjun Kim, Sungwoong Kim