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 https://github.com/zjunlp/DataMind.

翻译：真实世界的数据分析本质上具有迭代性，然而现有基准测试主要评估孤立或短交互任务，未测试智能体在长周期中追踪不断演化的分析上下文的能力。我们提出LongDS——一个面向长周期多轮数据分析的基准测试，要求智能体必须维护、更新、恢复并组合不断演化的分析状态。LongDS包含基于真实世界Kaggle笔记本构建的68个任务，涵盖地球科学、商业和教育等六个领域的2,225轮交互。任务围绕状态演化模式（如反事实扰动、回滚、多状态组合）设计，平均依赖跨度为11.3轮。对五种最先进模型的评估显示，最优模型仅达到48.45%的平均准确率，从早期轮次到后期轮次性能下降近47个百分点，且长周期错误占总体失败的52%–69%。进一步分析表明，增加智能体交互步数并不必然提升性能，这表明关键瓶颈在于维持正确的分析状态而非增加交互预算。我们发布LongDS以支持可靠的长周期智能体数据分析研究。代码与数据将在https://github.com/zjunlp/DataMind 开源。