# MBABench: Evaluating LLM Agents on End-to-End Spreadsheet Tasks in Finance

arXiv: https://arxiv.org/abs/2605.22664 · HTML: https://mlsanity.com/paper/2605.22664/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 21 May 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 13 of 13)

- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [The BrowserGym Ecosystem for Web Agent Research](https://mlsanity.com/paper/2412.05467/) (2024)
- [GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks](https://mlsanity.com/paper/2510.04374/) (2025)
- [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024)
- [StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models](https://mlsanity.com/paper/2403.07714/) (2024)
- [SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation](https://mlsanity.com/paper/2406.14991/) (2024)
- [BizBench: A Quantitative Reasoning Benchmark for Business and Finance](https://mlsanity.com/paper/2311.06602/) (2023)
- [BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs](https://mlsanity.com/paper/2505.19457/) (2025)
- [SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models](https://mlsanity.com/paper/2403.03636/) (2024)
- [XFinBench: Benchmarking LLMs in Complex Financial Problem Solving and Reasoning](https://mlsanity.com/paper/2508.15861/) (2025)
- [NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries](https://mlsanity.com/paper/2402.14853/) (2024)

## Built on by (most cited first, 4 of 4)

- [FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents](https://mlsanity.com/paper/2608.11683/) (2026)
- [IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier](https://mlsanity.com/paper/2609.10494/) (2026)
- [FinancialAuditBench: Benchmark Construction under Differential Privacy Using Real-World Priors](https://mlsanity.com/paper/2609.32835/) (2026)
- [Better Deck or Different Judge? Evaluating Agentic Harness Gains in Corporate and Investment Banking](https://mlsanity.com/paper/2609.39958/) (2026)

## Abstract (arXiv)

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.