FinanceComplexQA: 面向工业级金融文档的智能体推理基准测试

摘要

智能体推理因其整合大规模信息并生成可靠准确内容的能力，已成为金融分析领域的变革性力量。然而在处理复杂现实问题时，不同智能体仍表现出显著性能差异。本研究设计了基于专家知识的金融文档复杂布局合成技能——Finance-LaTeX SKILL。通过基于该技能的智能体工作流，我们生成了2000份专业金融文档及6000个高质量的问答对。为评估智能体综合能力，我们提出了FinanceComplexQA——一个接近真实场景的综合性开放金融文档生成基准，包含针对1009份金融文档的2026项深度研究任务。该基准具备八大核心特征：双语支持；覆盖六类主流场景与七类任务；专家级文档推理问题；复杂布局深度研究；相对稳定且永久的参考答案；以及通过"智能体作为评判者"结合多评估指标的精准评价体系。我们利用FinanceComplexQA对金融文档问答领域的领先检索增强生成系统与智能体推理工具进行了全面评估。通过识别并分析失败案例，深入研究了其在数值计算、多跳推理、内容摘要及行业分析方面的能力。

English

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.