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标题: "V-FiLLM: Verified Financial LLM Reasoning Benchmark"
原文链接: "https://arxiv.org/abs/2608.11047"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.11047"
---

## Computer Science > Artificial Intelligence

## Title:V-FiLLM: Verified Financial LLM Reasoning Benchmark

[View PDF](https://arxiv.org/pdf/2608.11047) [HTML (experimental)](https://arxiv.org/html/2608.11047v1)

> Abstract:While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains comparatively less explored. We introduce V-FiLLM, a framework that generates financial reasoning benchmarks from executable computation trees grounded in real tables, yielding items whose answers are correct by construction. Trees are evaluated symbolically to obtain ground truth and rendered into natural-language questions, removing any model from the labeling loop, so items can be generated at arbitrary scale without annotation cost and without inheriting a generator's error rate. V-FiLLM exposes four independently controllable axes of difficulty including computation depth, expression breadth, financial concept complexity, and context size. By evaluating on open-source models, we find that accuracy falls up to 51% as reasoning depth increases, and up to 47% points under adversarial numerical perturbations, highlighting remaining challenges in robust financial reasoning over tables. We further show that lightweight LoRA fine-tuning on verified chain-of-thought traces improves accuracy from 81.1% to 85.6% on held-out problems and outperforms the base model by 5% points on FinQA (Chen et al., 2022a), s), suggesting that targeted, low-cost adaptation is a promising direction for compositional reasoning in financial QA.

| Comments: |  |
| --- | --- |
| Subjects: | Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG) |
| Cite as: | [arXiv:2608.11047](https://arxiv.org/abs/2608.11047) \[cs.AI\] |
|  | (or [arXiv:2608.11047v1](https://arxiv.org/abs/2608.11047v1) \[cs.AI\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11047](https://doi.org/10.48550/arXiv.2608.11047) |

## Submission history

From: Aulia Kharis Rakhmasari \[[view email](https://arxiv.org/show-email/37b8d6c1/2608.11047)\]  
**\[v1\]** Tue, 11 Aug 2026 15:18:47 UTC (64 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11047) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
