Multimodal Large Language Models (MLLMs) remain unreliable on fine-grained visual perception, even when high-resolution inputs preserve the necessary local details. We identify this limitation as visual context rot: decisive evidence may exist in the full image, yet fail to be reliably selected and used amid redundant visual context. We propose LOCUS (LOcal visual CUe Search), a training framework that teaches MLLMs to internalize local evidence search through a verifiable proxy task. During training, LOCUS provides a local crop as a visual cue and optimizes the model to recover its spatial support in the full image using an IoU-based reward. The visual cue is used only during training, leaving the standard image-question inference interface unchanged. Experiments across fine-grained perception, hallucination, general understanding, and reasoning benchmarks show that LOCUS improves localization-sensitive visual understanding while preserving broad capabilities. Attention analyses further indicate stronger focus on task-relevant evidence regions, suggesting that training-time visual cue search provides an effective route to internalized fine-grained evidence selection.

翻译：多模态大语言模型在细粒度视觉感知任务中仍存在不可靠性，即便高分辨率输入保留了必要的局部细节。我们将此限制归因于"视觉上下文衰减"现象：决定性证据可能存在于完整图像中，却无法在冗余的视觉上下文中被可靠地选择与运用。为此，我们提出LOCUS（局部视觉线索搜索）训练框架，通过可验证的代理任务教会多模态大语言模型内化局部证据搜索能力。训练阶段，LOCUS以局部裁剪区域作为视觉线索，并基于IoU奖励机制优化模型在完整图像中恢复该线索空间位置的能力。视觉线索仅在训练环节使用，不改变标准的图像-问题推理接口。在细粒度感知、幻觉抑制、通用理解及推理基准上的实验表明，LOCUS在保持广泛能力的同时提升了定位敏感的视觉理解。注意力分析进一步揭示模型对任务相关证据区域关注度的增强，由此证明训练阶段的视觉线索搜索为内化细粒度证据选择提供了有效途径。