← 研踪 PaperTrailCVF · CVPR 2026:cvf:dbc3f9e2ab9f77cb5a1e61578cfb39c2 · 记录 #1
VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 21:42
摘要
Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended inference-time thinking. However, recent studies observe that in vision-dependent tasks, extended textual reasoning at inference time can often degrade performance as models progressively lose attention to visual tokens, increasingly relying on textual priors alone. To address this, prior works used 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 inference-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 through re-injecting a coreset of visual tokens that are semantically relevant to the reasoning context yet diverse and globally representative of the image for more grounded multi-modal reasoning. Experiments on three visual reasoning benchmarks with state-of-the-art multi-modal large reasoning models demonstrate that under fixed inference-time compute budgets, VisRef consistently outperforms existing test-time scaling approaches by up to 6.4%.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models
Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended inference-time thinking. However, recent studies observe that in vision-dependent tasks, extended textual reasoning at inference time can often degrade performance as models progressively lose attention to visual tokens, increasingly relying on textual priors alone. To address this, prior works used 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 inference-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 through re-injecting a coreset of visual tokens that are semantically relevant to the reasoning context yet diverse and globally representative of the image for more grounded multi-modal reasoning. Experiments on three visual reasoning benchmarks with state-of-the-art multi-modal large reasoning models demonstrate that under fixed inference-time compute budgets, VisRef consistently outperforms existing test-time scaling approaches by up to 6.4%.
查看此版本解读 →查看此版本原文 ↗