3 papers
cs.AI2026
Do MLLMs Really See It: Reinforcing Visual Attention in Multimodal LLMs
Siqu Ou, Tianrui Wan, Zhiyuan Zhao +2
While chain-of-thought (CoT) reasoning has substantially improved multimodal large language models (MLLMs) on complex reasoning tasks, existing approaches largely rely on long text…
cs.CL2025
When Seeing Is not Enough: Revealing the Limits of Active Reasoning in MLLMs
Hongcheng Liu, Pingjie Wang, Yuhao Wang +3
Multimodal large language models (MLLMs) have shown strong capabilities across a broad range of benchmarks. However, most existing evaluations focus on passive inference, where mod…
cs.AI2025
Bridging the Dynamic Perception Gap: Training-Free Draft Chain-of-Thought for Dynamic Multimodal Spatial Reasoning
Siqu Ou, Hongcheng Liu, Pingjie Wang +4
While chains-of-thought (CoT) have advanced complex reasoning in multimodal large language models (MLLMs), existing methods remain confined to text or static visual domains, often…