Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents. DataMind tackles three key challenges in building open-source data-analytic agents, including insufficient data resources, improper training strategy, and unstable code-based multi-turn rollout. Concretely, DataMind applies 1) a fine-grained task taxonomy and a recursive easy-to-hard task composition mechanism to increase the diversity and difficulty of synthesized queries; 2) a knowledge-augmented trajectory sampling strategy followed by model-based and rule-based filtering; 3) a dynamically adjustable training objective combining both SFT and RL losses; 4) a memory-frugal and stable code-based multi-turn rollout framework. Built on DataMind, we curate DataMind-12K, a high-quality trajectory set spanning diverse domains, task categories, and data file formats for data-analytic tasks. Trained on DataMind-12K, our DataMind-14B achieves state-of-the-art with an average score of 71.16% on multiple data analysis benchmarks, outperforming the strongest proprietary baselines DeepSeek-V3.1 and GPT-5. Our DataMind-7B also performs best among all open-source models with a score of 68.10%. We also incorporate some empirical insights gained from our exploratory trials into the analysis experiments, aiming to provide actionable insights about agentic training for the community. We will release DataMind-12K and DataMind-7B,14B for the community's future research.

翻译：数据分析智能体正成为自动化科学发现与"创新AI"愿景的关键催化剂。然而，当前方法严重依赖基于专有模型的提示工程，而开源模型难以应对现实世界分析任务所要求的多样化格式、大规模数据文件以及长跨度、多步骤推理。本文提出DataMind，一种可扩展的数据合成与智能体训练方案，旨在构建通用数据分析智能体。DataMind解决了构建开源数据分析智能体的三个关键挑战：数据资源不足、训练策略不当以及基于代码的多轮次执行不稳定。具体而言，DataMind采用：1）细粒度任务分类法与递归式由易到难任务组合机制，以提升合成查询的多样性与难度；2）知识增强的轨迹采样策略，辅以基于模型和基于规则的过滤；3）结合监督微调与强化学习损失的动态可调训练目标；4）内存高效且稳定的基于代码多轮次执行框架。基于DataMind，我们构建了DataMind-12K——一个涵盖多领域、多任务类别及多数据文件格式的高质量数据分析任务轨迹数据集。在DataMind-12K上训练的DataMind-14B模型，在多个数据分析基准测试中平均得分达71.16%，超越最强的专有基线模型DeepSeek-V3.1与GPT-5，达到最先进水平。我们的DataMind-7B模型同样以68.10%的得分在所有开源模型中表现最佳。我们还将探索性试验中获得的实证见解融入分析实验，旨在为学界提供关于智能体训练的可操作性洞见。我们将向社区开源DataMind-12K数据集及DataMind-7B、14B模型，以促进未来研究。