{"ID":2823753,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.24903","arxiv_id":"2512.24903","title":"FinMMDocR: Benchmarking Financial Multimodal Reasoning with Scenario Awareness, Document Understanding, and Multi-Step Computation","abstract":"We introduce FinMMDocR, a novel bilingual multimodal benchmark for evaluating multimodal large language models (MLLMs) on real-world financial numerical reasoning. Compared to existing benchmarks, our work delivers three major advancements. (1) Scenario Awareness: 57.9% of 1,200 expert-annotated problems incorporate 12 types of implicit financial scenarios (e.g., Portfolio Management), challenging models to perform expert-level reasoning based on assumptions; (2) Document Understanding: 837 Chinese/English documents spanning 9 types (e.g., Company Research) average 50.8 pages with rich visual elements, significantly surpassing existing benchmarks in both breadth and depth of financial documents; (3) Multi-Step Computation: Problems demand 11-step reasoning on average (5.3 extraction + 5.7 calculation steps), with 65.0% requiring cross-page evidence (2.4 pages average). The best-performing MLLM achieves only 58.0% accuracy, and different retrieval-augmented generation (RAG) methods show significant performance variations on this task. We expect FinMMDocR to drive improvements in MLLMs and reasoning-enhanced methods on complex multimodal reasoning tasks in real-world scenarios.","short_abstract":"We introduce FinMMDocR, a novel bilingual multimodal benchmark for evaluating multimodal large language models (MLLMs) on real-world financial numerical reasoning. Compared to existing benchmarks, our work delivers three major advancements. (1) Scenario Awareness: 57.9% of 1,200 expert-annotated problems incorporate 12...","url_abs":"https://arxiv.org/abs/2512.24903","url_pdf":"https://arxiv.org/pdf/2512.24903v1","authors":"[\"Zichen Tang\",\"Haihong E\",\"Rongjin Li\",\"Jiacheng Liu\",\"Linwei Jia\",\"Zhuodi Hao\",\"Zhongjun Yang\",\"Yuanze Li\",\"Haolin Tian\",\"Xinyi Hu\",\"Peizhi Zhao\",\"Yuan Liu\",\"Zhengyu Wang\",\"Xianghe Wang\",\"Yiling Huang\",\"Xueyuan Lin\",\"Ruofei Bai\",\"Zijian Xie\",\"Qian Huang\",\"Ruining Cao\",\"Haocheng Gao\"]","published":"2025-12-31T15:00:03Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CE\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}