While Large Multimodal Models (LMMs) excel in general visual tasks, their deployment in specialized financial contexts remains insufficient. Existing benchmarks prioritize isolated charts, often overlooking the need to integrate data from text, tables, and images within comprehensive financial documents. To address this limitation, we introduce FINDOCMRE, a multi-image document-level benchmark designed for financial multimodal reasoning. We construct the dataset via a semi-automated pipeline that combines Visual-Centric Generation with Expert Verification, thereby minimizing text bias and ensuring high annotation quality. Spanning twelve domains, the benchmark comprises 12,207 samples derived from 2,878 financial reports, designed to evaluate multi-image processing and document-level understanding across five distinct task types. Extensive experiments with eleven representative LMMs reveal that no model surpasses an overall score of 65, highlighting challenges in integrating visual grounding with logical reasoning within complex document environments. Specifically, we observe a significant performance divergence across tasks, where models exhibit proficiency in semantic narrative construction but struggle with numerical estimation and cross-page visual grounding. FINDOCMRE serves as a rigorous benchmark to guide the evolution of financial LMMs towards expert-level document analysis and reasoning.

翻译：尽管大型多模态模型在通用视觉任务中表现优异，但其在金融领域的专业化部署仍显不足。现有基准多聚焦于孤立图表，往往忽视在综合金融文档中对文本、表格及图像等数据的整合需求。为解决此局限，我们提出FINDOCMRE——面向金融多模态推理的多图像文档级基准。通过结合视觉核心生成与专家验证的半自动化流水线构建数据集，从而最小化文本偏差并确保高质量标注。该基准覆盖十二个领域，包含源自2,878份金融报告的12,207个样本，旨在评估五项不同任务类型中的多图像处理与文档级理解能力。对十一种代表性大型多模态模型的实验表明，尚无模型总体得分超过65分，揭示了复杂文档环境中视觉定位与逻辑推理融合的挑战。具体而言，我们观察到模型在语义叙事构建任务中表现优异，但在数值估算与跨页视觉定位方面存在显著性能差异。FINDOCMRE作为严谨基准，将引导金融领域大型多模态模型向专家级文档分析与推理能力演进。