高分辨率图像感知是 MLLMs 落地的硬骨头，CVSearch 用零训练成本解决了覆盖率和效率的矛盾，做多模态感知或视觉问答的团队可以直接拿来用。
高分辨率图像感知是当前多模态大模型（MLLMs）的关键瓶颈。现有视觉搜索方法在覆盖率和效率之间难以平衡：专家辅助搜索高效但易遗漏，扫描式搜索全覆盖但计算冗余。CVSearch 提出了一种无需训练的“评估-搜索”自适应框架，先尝试专家辅助搜索，失败时再触发语义感知扫描，通过语义引导的自适应分块避免物体碎片化，并利用视觉复杂度驱动的动态自底向上搜索实现局部细节的高效迭代探索。实验表明，CVSearch 在高分辨率基准上达到最先进精度，同时显著提升搜索效率。代码已开源。
CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception
High-resolution (HR) image perception presents a key bottleneck for multimodal large language models (MLLMs). While visual search offers a promising solution, existing methods struggle with the trade-off between coverage and efficiency. Visual expert-assisted search is efficient but prone to blind spots when proposals fail, whereas scan-based search guarantees coverage at the cost of computational redundancy and semantic fragmentation. To address this dilemma, we introduce CVSearch, a training-free adaptive framework that dynamically schedules search strategies via an Assess-then-Search workflow. Specifically, CVSearch first invokes expert-assisted search when global information is insufficient, and only triggers a novel semantic-aware scanning mechanism upon failure. Distinct from rigid grid partitioning, this efficient scanning paradigm incorporates Semantic Guided Adaptive Patching to decompose images into semantically consistent regions, effectively mitigating object fragmentation. Furthermore, we devise a Dynamic Bottom-Up Search strategy driven by a Visual Complexity prior to enable efficient and precise iterative exploration of local details. Extensive experiments on HR benchmarks demonstrate that CVSearch achieves state-of-the-art accuracy while substantially improving search efficiency. Code is released at https://github.com/liliupeng28/ICML26-CVSearch.