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arXiv · 2506.23719

DABstep: Data Agent Benchmark for Multi-step Reasoning

Alex Egg·Martin Iglesias Goyanes·Friso Kingma·Andreu Mora·Leandro von Werra·Thomas Wolf
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Abstract

We introduce DABstep, a novel benchmark for evaluating AI agents on realistic multi-step data analysis tasks. DABstep comprises over 450 real-world challenges derived from a financial analytics platform, requiring models to combine code-based data processing with contextual reasoning over heterogeneous documentation. Each task demands an iterative, multi-step problem-solving approach, testing capabilities in data manipulation, cross-referencing multiple sources, and precise result reporting. The benchmark provides a factoid-style answer format with automatic correctness checks for objective scoring at scale. We evaluate leading LLM-based agents, revealing a substantial performance gap: even the best agent achieves only 14.55% accuracy on the hardest tasks. We detail our benchmark's design, dataset composition, task formulation, evaluation protocol, report baseline results and analyze failure modes. DABstep is released with a public leaderboard and toolkit to accelerate research in autonomous data analysis.

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Alex Egg, Martin Iglesias Goyanes, Friso Kingma, Andreu Mora, Leandro von Werra, Thomas Wolf. 2025-06-30. DABstep: Data Agent Benchmark for Multi-step Reasoning. https://arxiv.org/abs/2506.23719

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