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About this paper
Xianfu Cheng, Shiwei Zhang, Jiyu Zhao, Jian Yang, Xinyuan Wang, Ming Zhou, Weixiao Zhou, Xiangyuan Guan and 5 more
Researchers introduced FinanceComplexQA, a large-scale, open-ended generation benchmark for evaluating agentic reasoning on industrial-grade financial documents, alongside Finance-LaTeX SKILL for scalable data synthesis. Their evaluation revealed a substantial performance gap in current AI systems for multi-step numerical reasoning and cross-layout evidence fusion in complex financial analysis.
Key insights
- A significant performance gap exists between current state-of-the-art RAG and agentic systems and the demands of industrial-grade financial reasoning, especially in long-chain numerical and cross-layout evidence integration.
- Layout-aware retrieval mechanisms (e.g., PageIndex) consistently improve overall scene scores by preserving structural context in complex financial documents, highlighting their importance for evidence integration.
- Agentic systems generally achieve higher scores but incur substantial computational costs, and their performance varies by underlying foundation model and orchestration strategy, suggesting fragmented capabilities across tasks.
Summary by alphaXiv.
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