Are the Financial Reasoning from LLMs Credible? A Real World Test over Long-Horizon Statements
Do Large Language Models (LLMs) possess genuine structural reasoning, or merely rely on surface-level pattern matching? The financial domain, demanding numerical precision and multi-step logic over long contexts, is an ideal testbed. Existing benchmarks fail to capture real-world industrial complexity, predominantly relying on multiple-choice questions or single-hop QA over cropped tables while ignoring intricate cross-statement dynamics and temporal de-cumulation. To bridge this gap, we introduce FinIndices, a large-scale benchmark evaluating data-processing fidelity over uncropped financial statements (up to 32K tokens). Utilizing an automated synthesis pipeline with adversarial traps, ...
| Authors | Xinke Tong, Xuanming Zhang, Tianyi Tang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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