视觉搜索器：通过主动视觉推理实现视觉与自然语言多模态智能搜索
Visual-Seeker: Towards Visual-Native Multimodal Agentic Search via Active Visual Reasoning
摘要
多模态大型语言模型在许多视觉任务上展现出了令人印象深刻的性能，但在处理复杂、开放世界场景时，它们往往难以准确识别事实信息。虽然最近的一些多模态深度搜索算法试图通过利用外部工具来解决这一问题，但基于视觉的自然搜索方法仍然没有得到充分探索。现有的方法主要依赖于具有明确语义的简单图像以及仅包含文本信息的证据路径，这限制了智能体进行多跳、跨模态推理和搜索的能力。为了克服这些限制，我们提出了Visual-Seeker——一种基于主动视觉推理的多模态深度搜索智能体。与将视觉视为静态输入不同，我们的智能体能够主动关注细粒度的视觉细节，并在整个搜索过程中动态收集视觉证据。为了充分发挥其潜力，我们设计了一种主动视觉推理的数据处理流程，并为模型训练准备了5K数量级的高质量多模态数据。广泛的实验表明，该智能体在五个具有挑战性的多模态搜索基准测试中的表现处于领先地位，甚至超越了某些专有模型。这证明了其在真实网络环境中的强大视觉推理和搜索能力。代码和数据可以在以下链接获取：https://github.com/ZhengboZhang/Visual-Seeker
English Abstract
Multimodal large language models (MLLMs) have demonstrated impressive capabilities in many visual tasks, but they often struggle with factual grounding when confronted with complex, open-world scenarios. While recent multimodal deep search agents attempt to address this issue by utilizing external tools, the visual-native search paradigm remains underexplored. Existing methods primarily rely on simple images with explicit semantics and text-only evidence trajectories, limiting the agent's ability to perform multi-hop, cross-modal reasoning and search. To address these limitations, we propose Visual-Seeker, a visual-native multimodal deep search agent via active visual reasoning. Rather than treating vision as a static input, our agent actively attends to fine-grained visual details, dynamically harvests visual evidence throughout the search process. To unlock its visual-native potential, we design an active visual reasoning data pipeline and synthesize 5K high-quality multimodal trajectories for model training. Extensive experiments demonstrate the state-of-the-art performance across five challenging multimodal search benchmarks, even surpassing several proprietary models, validating robust visual-native reasoning and search in real-world web environments. The code and data can be accessed at: https://github.com/ZhengboZhang/Visual-Seeker.