LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis
Existing benchmarks mostly evaluate isolated or short interactive tasks, failing to test agents' ability to track evolving analytical context over long horizons. Researchers introduce LongDS, a benchmark with 68 tasks from real Kaggle notebooks spanning 2,225 turns across six domains. Evaluation shows best model achieves only 48.45% average accuracy, performance drops 47 points from early to late turns, and long-horizon errors account for 52%-69% of failures. Additional steps don't necessarily improve performance; key bottleneck is maintaining correct analytical state.
[2605.30434] LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis
[Submitted on 28 May 2026]
Title:LongDS-Bench: On the Failure of Long-Horizon Agentic Data Analysis
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Abstract:Real-world data analysis is inherently iterative, yet existing benchmarks mostly evaluate isolated or short interactive tasks, leaving agents' ability to track evolving analytical context over long horizons untested. We introduce LongDS, a benchmark for long-horizon, multi-turn data analysis where agents must maintain, update, restore, and compose evolving analytical states. LongDS comprises 68 tasks constructed from real-world Kaggle notebooks, spanning 2,225 turns across six domains including Geoscience, Business, and Education. Tasks are designed around state-evolution patterns (e.g., counterfactual perturbation, rollback, multi-state composition), with an average dependency span of 11.3 turns. Evaluating five state-of-the-art models, we find that the best model reaches only 48.45% average accuracy, performance drops nearly 47 points from early to late turns, and long-horizon errors account for 52%--69% of failures. Further analysis shows that additional agent steps do not necessarily improve performance, suggesting that the key bottleneck is maintaining a correct analytical state rather than increasing interaction budget. We release LongDS to support research on reliable long-horizon agentic data analysis. Code and data will be released at this https URL.
Comments: Ongoing work
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
Cite as: arXiv:2605.30434 [cs.LG]
(or arXiv:2605.30434v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.30434
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Ningyu Zhang [view email] [v1] Thu, 28 May 2026 18:00:20 UTC (16,519 KB)
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