From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning

• URL: http://arxiv.org/abs/2603.03825v1
• Date: Wed, 04 Mar 2026 08:22:27 GMT
• Title: From Narrow to Panoramic Vision: Attention-Guided Cold-Start Reshapes Multimodal Reasoning
• Authors: Ruilin Luo, Chufan Shi, Yizhen Zhang, Cheng Yang, Songtao Jiang, Tongkun Guan, Ruizhe Chen, Ruihang Chu, Peng Wang, Mingkun Yang, Yujiu Yang, Junyang Lin, Zhibo Yang,
• Abstract summary: We find that reasoning performance is strongly correlated with Visual Attention Score (VAS)<n>To validate its causal role, we design training-free interventions that directly modulate attention allocation during inference.<n>We propose Attention-Guided Visual Anchoring and Reflection, a comprehensive cold-start framework that integrates visual-anchored data synthesis, attention-guided objectives, and visual-anchored reward shaping.
• Score: 72.4876727619987
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The cold-start initialization stage plays a pivotal role in training Multimodal Large Reasoning Models (MLRMs), yet its mechanisms remain insufficiently understood. To analyze this stage, we introduce the Visual Attention Score (VAS), an attention-based metric that quantifies how much a model attends to visual tokens. We find that reasoning performance is strongly correlated with VAS (r=0.9616): models with higher VAS achieve substantially stronger multimodal reasoning. Surprisingly, multimodal cold-start fails to elevate VAS, resulting in attention distributions close to the base model, whereas text-only cold-start leads to a clear increase. We term this counter-intuitive phenomenon Lazy Attention Localization. To validate its causal role, we design training-free interventions that directly modulate attention allocation during inference, performance gains of 1$-$2% without any retraining. Building on these insights, we further propose Attention-Guided Visual Anchoring and Reflection (AVAR), a comprehensive cold-start framework that integrates visual-anchored data synthesis, attention-guided objectives, and visual-anchored reward shaping. Applied to Qwen2.5-VL-7B, AVAR achieves an average gain of 7.0% across 7 multimodal reasoning benchmarks. Ablation studies further confirm that each component of AVAR contributes step-wise to the overall gains. The code, data, and models are available at https://github.com/lrlbbzl/Qwen-AVAR.

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