DABstep: Data Agent Benchmark for Multi-step Reasoning

• URL: http://arxiv.org/abs/2506.23719v1
• Date: Mon, 30 Jun 2025 10:49:21 GMT
• Title: DABstep: Data Agent Benchmark for Multi-step Reasoning
• Authors: Alex Egg, Martin Iglesias Goyanes, Friso Kingma, Andreu Mora, Leandro von Werra, Thomas Wolf,
• Abstract summary: DABstep is a novel benchmark for evaluating AI agents on realistic multi-step data analysis tasks.<n>It comprises over 450 real-world challenges derived from a financial analytics platform.<n>Dabstep is released with a public leaderboard and toolkit to accelerate research in autonomous data analysis.
• Score: 2.6709582216950767
• License: http://creativecommons.org/licenses/by/4.0/
• 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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