ChainV: Atomic Visual Hints Make Multimodal Reasoning Shorter and Better

• URL: http://arxiv.org/abs/2511.17106v1
• Date: Fri, 21 Nov 2025 10:11:17 GMT
• Title: ChainV: Atomic Visual Hints Make Multimodal Reasoning Shorter and Better
• Authors: Yuan Zhang, Ming Lu, Junwen Pan, Tao Huang, Kuan Cheng, Qi She, Shanghang Zhang,
• Abstract summary: We propose ChainV, a framework that dynamically integrates visual hints into the reasoning process.<n>Our method significantly improves reasoning accuracy and efficiency, especially on math-intensive benchmarks.
• Score: 59.29940512530982
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in multimodal reasoning models have demonstrated impressive capabilities across text and vision. However, even leading models exhibit redundant self-reflection when generating lengthy reasoning chains. While training-free CoT compression methods have emerged in the LLMs domain, they rely on static visual references and thus provide limited gains for multimodal reasoning. Therefore, we propose ChainV, a framework that dynamically integrates visual hints into the reasoning process, thereby making multimodal reasoning shorter and better. Specifically, ChainV first performs a coarse visual patch selection based on the previous reasoning step, then refines it by identifying the most representative atomic visual hint according to the averaged attention intensity. Additionally, ChainV introduces a consistency-based evaluation mechanism to assess the reliability of the chosen hint, guiding the model to adaptively adjust its level of self-reflection. Eventually, the pixel coordinates of the selected visual hint and its reliability are incorporated into thinking with a Bernoulli stochastic process. Experiments indicate that our method significantly improves reasoning accuracy and efficiency, especially on math-intensive benchmarks where visual hints are crucial for multi-step symbolic reasoning. For example, ChainV achieves $2.3\%$ improvement on the MathVista within MIMO-VL-RL, while reducing inference latency by $51.4\%$ and shortening output token length by $24.5\%$.

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