DABstep: Data Agent Benchmark for Multi-step Reasoning
Journal:
arXiv
Published Date:
Jun 30, 2025
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.
