arXiv cs.AI· Julian Poeltl, Thomson Yen, Patrick Shea, Shubham Anil Varma, Dhruv Rastogi, Zihao Chen, Mengshi Zhang, Hongseok Namkoong·· 12 小时前AI 评分34
MBABench-2.0：评估 AI 智能体从零构建财务电子表格的能力
MBABench-2.0: Evaluating AI Agents on Constructing Financial Spreadsheets Beyond Simple Verifiability
AI 导读
MBABench-2.0 是一个包含 101 个原创电子表格构建任务的基准，最难任务估计需 3 年以上经验建模师耗时 33 小时。该基准用覆盖 12 类的 129 项标准评估可读性、可维护性等超出准确性的质量维度，对 8 个模型、4 种 harness 的 20 多个智能体测试中，最佳智能体通过率仅 16%。
正文 · 原文
Abstract:Spreadsheets are the lingua franca of modern finance, driving valuation, forecast, audit, and many other core workflows of the industry. As LLM agents advance to automate knowledge work, spreadsheet construction is a natural and pertinent testbed. However, most existing spreadsheet benchmarks evaluate agents on fill-in-the-blank style tasks, where an agent performs minor edits in otherwise complete spreadsheets. This protocol fails to capture the most central and time-consuming part of the spreadsheet workflow: the modeling decisions, format design, and sheet layout regular knowledge workers need to toil through to build a workbook from scratch. We introduce MBABench-2.0, a benchmark of 101 original spreadsheet construction tasks, the hardest of which is estimated to take 33 hours for a modeler with 3+ years of experience. Unlike coding or math, where a unit test or a final answer can verify an agent's output, the quality of a financial spreadsheet that concerns readability, maintainability, or auditability, is not so simply verifiable. To holistically evaluate spreadsheet construction, we introduce 129 criteria spanning 12 categories that operationalize quality beyond the accuracy of the spreadsheet. Evaluating over 20 agents across 8 models and 4 harness types, we find that frontier agents rarely construct spreadsheets that are both accurate and meet our quality criteria: the top agent achieves a pass rate of only 16% on the benchmark. The benchmark demonstrates a substantial gap in spreadsheet building capability of current agents.
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.31711 [cs.SE] |
| (or arXiv:2610.31711v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.31711 arXiv-issued DOI via DataCite |
Submission history
From: Thomson Yen [view email]
[v1]
Thu, 22 Oct 2026 02:30:05 UTC (2,385 KB)
来源：arXiv cs.AI · arxiv.org