arXiv:2509. 22415v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved strong vision-language performance, yet their token-level visual evidence remains difficult to inspect.
By Jiawei Liang, Jianjie Huang, Ruoyu Chen, Xianghao Jiao, Siyuan Liang, Shiming Liu, Xiaochun Cao
The paper introduces the concept of Squint Text—text embedded in an image’s coarse structure that multimodal LLMs often miss but humans can read by squinting. It presents SquintBench, a dataset of 1,076 moderation images, and SquintCtrl, a synthetic suite for diagnosing the issue. The authors show that spatial selection is a key bottleneck and propose Squint OPSD, a training method that uses a downscaled teacher view and privileged transcription to supervise a student model, achieving a significant performance boost on SquintBench.
By Chen Peng, Shan Huang, Jianbo Huang, Linrui Qin, Qibo Sun, Mengwei Xu
The paper introduces IMAVB, a 500‑clip benchmark that tests whether omnimodal large language models can detect when a textual premise contradicts their visual or audio input. Experiments on eight open‑source models and Gemini 3.1 Pro reveal a Representation‑Action Gap: internal states encode mismatches, yet the models rarely reject false premises, exhibiting under‑rejection or over‑rejection. A probe‑guided logit adjustment improves rejection behavior, suggesting the main bottleneck is in translating perception to action rather than in perception itself.
By Trung Nguyen Quang, Yiming Gao, Fanyi Pu, Kaichen Zhang, Shuo Sun, Ziwei Liu
arXiv:2606. 17710v1 Announce Type: cross Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image.
By Mahshad Lotfinia, Sebastian Ziegelmayer, Lisa Adams, Daniel Truhn, Andreas Maier, Soroosh Tayebi Arasteh
arXiv:2606. 19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts.
By Yihao Wang, Zijian He, Jie Ren, Keze Wang
arXiv:2609.26986v1 Announce Type: new
Abstract: For multimodal large language models, when images or speech conflict with accompanying text, measured text reliance can entangle modality preference wi...
By Zhuoyun Li, Boxuan Wang, Xiaowei Huang, Yi Dong