[2603.00207] VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00207 (cs) [Submitted on 27 Feb 2026] Title:VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models Authors:Soumya Suvra Ghosal, Youngeun Kim, Zhuowei Li, Ritwick Chaudhry, Linghan Xu, Hongjing Zhang, Jakub Zablocki, Yifan Xing, Qin Zhang View a PDF of the paper titled VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models, by Soumya Suvra Ghosal and 8 other authors View PDF HTML (experimental) Abstract:Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended reasoning. However, recent studies observe that in vision-dependent tasks, extended textual reasoning at inference time can degrade performance as models progressively lose attention to visual tokens and increasingly rely on textual priors alone. To address this, prior works use reinforcement learning (RL)-based fine-tuning to route visual tokens or employ refocusing mechanisms during reasoning. While effective, these methods are computationally expensive, requiring large-scale data generation and policy optimization. To leverage the benefits of test-time compute without additional RL fine-tuning, we propose VisRef, a visually grounded test-time scaling framework. Our key idea is to actively guide the reasoning process by re-injecting a coreset of visual tokens that are ...