MBABench: Evaluating LLM Agents on End-to-End Spreadsheet Tasks in Finance
Thomson Yen, Julian Poeltl, Harshith Srinivas Gear, Yilin Meng, Joshua Fan, Adam Shen, Yili Liu, Ali Bauyrzhan, Patrick Shea, Siri Du, Haoyang Liu, Daniel Guetta, Hongseok Namkoong
cs.AI
Submitted: 2026-08-19
Updated: 2026-08-20
License: http://creativecommons.org/licenses/by/4.0/
The gist: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions.
Terminology
Abstract
LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions. To meet enterprise needs, frontier AI labs have developed agents that can construct entire spreadsheets from scratch. This is especially relevant in finance, where core workflows such as financial modeling, forecasting, and scenario analysis are commonly conducted through spreadsheets. Yet, existing spreadsheet benchmarks do not measure this new capability, focusing instead on question-answering or single-formula edits. To address this gap, we provide one of the first evaluations of agents on end-to-end spreadsheet tasks, focusing on economically critical financial workflows such as modeling and scenario analysis. Since deliverables therein are routinely reviewed and revised by multiple stakeholders, judging their quality necessarily involves high-level criteria such as readability or ease of modification. To reflect the multidimensional nature of solution quality, we develop an evaluation taxonomy comprising three dimensions: Accuracy, Formula, and Format, each comprising fine-grained criteria that reflect professional standards. Evaluating over 18 agents, the benchmark reveals that even the strongest agents fall short of basic professional finance standards, and their performance degrade sharply as the difficulty increases beyond a few chained calculations. This suggests that current agents are not yet able to reliably produce professional-quality spreadsheets at the level of complexity real-world workflows demand.
Sources
- SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models
- Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows
- GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks
- BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs
- SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
- WebArena: A Realistic Web Environment for Building Autonomous Agents
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