让视觉语言模型像人一样专注看重点，节省计算还更准。
Foveated Reasoning: Stateful, Action-based Visual Focusing for Vision-Language Models
- 模型按需聚焦，只在关键区域获取高分辨率图像
- 在视觉标记受限时，准确率比传统方法提升12.3%
- 适合资源有限但要求精准推理的视觉任务
视觉语言模型受益于高分辨率图像，但视觉标记数量增加带来高昂计算开销。人类通过中央凹机制解决这一矛盾：先粗略观察确定关注区域，再选择性获取高精度证据以深化思考。我们提出Foveated Reasoner，一种自回归视觉语言框架，将中央凹与推理统一于同一解码过程。从低分辨率视图开始，仅在必要时触发中央凹，从选定区域检索高分辨率证据，并将其注入原解码轨迹。采用两阶段训练：冷启动监督引导中央凹行为，随后通过强化学习联合优化证据获取与任务准确率，抑制“全图扫描”等低效策略。实验表明，该方法学习到有效的中央凹策略，在多个视觉语言基准上，于严格的视觉标记预算下实现更强的准确性。
原文摘要 · Abstract (English)
Vision-language models benefit from high-resolution images, but the increase in visual-token count incurs high compute overhead. Humans resolve this tension via foveation: a coarse view guides "where to look", while selectively acquired high-acuity evidence refines "what to think". We introduce Foveated Reasoner, an autoregressive vision-language framework that unifies foveation and reasoning within a single decoding trajectory. Starting from a low-resolution view, the model triggers foveation only when needed, retrieves high-resolution evidence from selected regions, and injects it back into the same decoding trajectory. We train the method with a two-stage pipeline: coldstart supervision to bootstrap foveation behavior, followed by reinforcement learning to jointly improve evidence acquisition and task accuracy while discouraging trivial "see-everything" solutions. Experiments show that the method learns effective foveation policies and achieves stronger accuracy under tight visual-token budgets across multiple vision-language benchmarks.
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