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🧠 AI🟢 BullishImportance 7/10
Scaling Generalist Data-Analytic Agents
arXiv – CS AI|Shuofei Qiao, Yanqiu Zhao, Zhisong Qiu, Xiaobin Wang, Jintian Zhang, Zhao Bin, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen||5 views
🤖AI Summary
Researchers introduce DataMind, a new training framework for building open-source data-analytic AI agents that can handle complex, multi-step data analysis tasks. The DataMind-14B model achieves state-of-the-art performance with 71.16% average score, outperforming proprietary models like DeepSeek-V3.1 and GPT-5 on data analysis benchmarks.
Key Takeaways
- →DataMind framework addresses key challenges in training open-source data-analytic agents including insufficient data resources and unstable multi-turn reasoning.
- →The approach uses fine-grained task taxonomy and recursive composition to create diverse training scenarios across different data formats and domains.
- →DataMind-14B achieves 71.16% average score, surpassing leading proprietary models DeepSeek-V3.1 and GPT-5 on multiple benchmarks.
- →DataMind-7B leads all open-source models with 68.10% performance score, demonstrating scalability across model sizes.
- →The team will release DataMind-12K dataset and both 7B and 14B models to support community research and development.
#datamind#data-analysis#ai-agents#open-source#machine-learning#automated-discovery#benchmark#model-training#scientific-computing
Read Original →via arXiv – CS AI
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