Paper Detail
Xianfu Cheng, Shiwei Zhang, Jiyu Zhao, Jian Yang, Xinyuan Wang, Ming Zhou, Weixiao Zhou, Xiangyuan Guan, Xiang Li, Zhenhe Wu, Ziyi Ni, Zhoujun Li, Bingjing Xu
Agentic Reasoning has become a transformative force in financial analysis due to its ability to integrate large-scale information and generate reliable and accurate content. However, when handling complex real-world problems, different agents still show significant performance variation. In this work, we design Finance-LaTeX SKILL, a skill for synthesizing financial documents with complex layouts based on expert knowledge. Using an agent workflow built on this skill, we generate 2,000 professional financial documents along with 6,000 high-quality question-answer pairs. To evaluate the overall capability of agents, we introduce FinanceComplexQA, a comprehensive open-ended generation benchmark for financial documents that closely resembles real-world scenarios. It contains 2,026 deep research tasks targeting 1009 financial documents. FinanceComplexQA has 8 key features: bilingual support; coverage of six mainstream scenarios and seven tasks; expert-level document reasoning questions; deep research of complex layouts; relatively stable and permanent reference answers; and precise evaluation through an Agent-as-a-Judge with multiple evaluation metrics. Using FinanceComplexQA, we conduct a comprehensive evaluation of leading RAG systems and agentic reasoning tools for financial document QA. Through identifying and analyzing failure cases, we provide an in-depth study of their capabilities in numerical computation, multi-hop reasoning, content summarization, and industry analysis.
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@misc{cheng2026financecomplexqa,
title = {FinanceComplexQA: Benchmarking Agentic Reasoning on Industrial-grade Financial Documents},
author = {Xianfu Cheng and Shiwei Zhang and Jiyu Zhao and Jian Yang and Xinyuan Wang and Ming Zhou and Weixiao Zhou and Xiangyuan Guan and Xiang Li and Zhenhe Wu and Ziyi Ni and Zhoujun Li and Bingjing Xu},
year = {2026},
abstract = {Agentic Reasoning has become a transformative force in financial analysis due to its ability to integrate large-scale information and generate reliable and accurate content. However, when handling complex real-world problems, different agents still show significant performance variation. In this work, we design Finance-LaTeX SKILL, a skill for synthesizing financial documents with complex layouts based on expert knowledge. Using an agent workflow built on this skill, we generate 2,000 profession},
url = {https://huggingface.co/papers/2607.19238},
keywords = {code available, huggingface daily},
eprint = {2607.19238},
archiveprefix = {arXiv},
}
{}