6 papers
Unveiling the Visual Counting Bottleneck in Vision-Language Models
Xingzhou Pang, Yifan Hou, Junling Wang +1
While Large Vision-Language Models (VLMs) excel at interpolation, they suffer catastrophic failures in systematic generalization, most notably in visual counting. In this work, we…
Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models
Yijie Tong, Yifan Hou, Shaobo Cui +2
Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their application and benefits for vision-lan…
Compose and Fuse: Revisiting the Foundational Bottlenecks in Multimodal Reasoning
Yucheng Wang, Yifan Hou, Aydin Javadov +2
Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio. Yet cross-modal reasoning remains underexplored,…
Chimera: Diagnosing Shortcut Learning in Visual-Language Understanding
Ziheng Chi, Yifan Hou, Chenxi Pang +3
Diagrams convey symbolic information in a visual format rather than a linear stream of words, making them especially challenging for AI models to process. While recent evaluations…
Can Vision-Language Models Solve Visual Math Equations?
Monjoy Narayan Choudhury, Junling Wang, Yifan Hou +1
Despite strong performance in visual understanding and language-based reasoning, Vision-Language Models (VLMs) struggle with tasks requiring integrated perception and symbolic comp…
Do Vision-Language Models Really Understand Visual Language?
Yifan Hou, Buse Giledereli, Yilei Tu +1
Visual language is a system of communication that conveys information through symbols, shapes, and spatial arrangements. Diagrams are a typical example of a visual language depicti…