Struct-Searcher：代理式结构化思维推进多模态深度信息检索
Struct-Searcher: Agentic Structural Thinking Advances Multimodal Deep Information Seeking
AI总结：
提出基于信念修正理论的结构化代理工作流Struct-Searcher，通过维护多模态结构图实现冲突感知的深度信息检索，在多个基准上平均相对准确率提升17.2%。
摘要：
深度研究代理因其收集大规模在线信息以获取目标知识的能力而受到越来越多的关注，最近的研究工作从纯文本信息检索转向多模态设置。然而，现有的代理工作流大多与证据积累模型一致，该模型线性地聚合证据，缺乏处理跨异构模态矛盾信息的原则性机制。为此，我们提出了Struct-Searcher，一种基于信念修正理论的结构化代理工作流，它在整个推理过程中显式地维护一个不断演变的多模态结构图，从而实现有效的冲突感知多模态深度信息检索。在多个基准数据集和骨干模型上的大量实验表明，Struct-Searcher是（1）即插即用且模型无关的，在BrowseComp-VL上使用五种不同骨干模型平均相对准确率提升17.2%。（2）性能最优，持续优于最先进的视觉语言模型（VLM）和深度研究代理，在MM-BrowseComp上相对准确率提升3.7%，在HLE-VL上提升1.5%，在BrowseComp-VL上提升0.7%，均超过第二名的竞争方法。
英文摘要：
Deep research agents have attracted increasing attention for their ability to collect large-scale online information to acquire target knowledge, with recent efforts shifting from purely text-based information seeking to multimodal settings. However, existing agentic workflows are largely aligned with evidence accumulation models, which linearly aggregate evidence and lack principled mechanisms for handling contradictory information across heterogeneous modalities. Towards this end, we propose Struct-Searcher, a structural agentic workflow grounded in belief revision theory that explicitly maintains an evolving multimodal structural graph throughout the reasoning process, enabling effective conflict-aware multimodal deep information seeking. Extensive experiments across multiple benchmark datasets and backbone models demonstrate that Struct-Searcher is (1) plug-and-play and model-agnostic, yielding an average relative accuracy improvement of 17.2% on BrowseComp-VL across five different backbones. (2) top-performing, consistently outperforming state-of-the-art vision-language models (VLMs) and deep research agents, with relative accuracy improvements of 3.7% on MM-BrowseComp, 1.5% on HLE-VL, and 0.7% on BrowseComp-VL over the second-best competing approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
