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When we look around and perform complex tasks, how we see and selectivelyprocess what we see is crucial. However, the lack of this visual searchmechanism in current multimodal LLMs (MLLMs) hinders their ability to focus onimportant visual details, especially when handling high-resolution and visuallycrowded images. To address this, we introduce V*, an LLM-guided visual searchmechanism that employs the world knowledge in LLMs for efficient visualquerying. When combined with an MLLM, this mechanism enhances collaborativereasoning, contextual understanding, and precise targeting of specific visualelements. This integration results in a new MLLM meta-architecture, named Show,sEArch, and TelL (SEAL). We further create V*Bench, a benchmark specificallydesigned to evaluate MLLMs in their ability to process high-resolution imagesand focus on visual details. Our study highlights the necessity ofincorporating visual search capabilities into multimodal systems. The code isavailable https://github.com/penghao-wu/vstar.

V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs