arXiv:2606. 07861v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored.
By Lujun Li, Lama Sleem, Niccolo Gentile, Yangjie Xu, Yewei Song, Wenbo Wu, Radu State
arXiv:2605. 30170v2 Announce Type: replace-cross Abstract: While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visual counting.
By Xingzhou Pang, Yifan Hou, Junling Wang, Mrinmaya Sachan
PRISM‑VLM is a new benchmark for compact vision‑language models that evaluates each item across seven axes—task quality, behavioral robustness, and capability bottlenecks—rather than collapsing performance into a single accuracy score. It aggregates these axes into a single PScore while also providing per‑axis profiles, revealing differences that single‑axis benchmarks miss, such as sycophancy. The benchmark draws on items from fifteen public datasets and will be released with its full pipeline, prompts, and annotations.
By Sanghee Park, Kee-Eung Kim
Compact vision-language models (VLMs) now power a growing share of multimodal applications. The benchmarks used to compare them, however, inherit a frontier-centric design: each model is reduced to a...
arXiv:2609.37096v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) consistently struggle with fine-grained visual counting, yet the underlying causes remain poorly understood. I...
By Liwei Che, Yihao Quan, Sen Fang, Hongyi Wang, Ranjay Krishna, Ruixiang Tang, Vladimir Pavlovic
The paper investigates why multimodal large language models (MLLMs) struggle with vision‑centric tasks when visual evidence conflicts with pretrained language knowledge. Using image reconstruction and a new WhatIfVis benchmark, the authors show that MLLMs preserve coarse‑grained visual attributes but fail to consistently use them, and that supervised fine‑tuning and activation patching can improve controllability of visual context sensitivity. The study demonstrates that the main bottleneck lies in the models’ inability to reliably regulate their reliance on visual evidence rather than in visual perception itself.
By Jiaang Li, Chengzu Li, Zhaochong An, Yifei Yuan, Xi Liu, Serge Belongie, V\'esteinn Sn{\ae}bjarnarson