DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Authors: name: Shaolei Zhang, name: Ju Fan, name: Meihao Fan, name: Guoliang Li, name: Xiaoyong Du
Organization: RUC-DataLab

Autonomous data science, from raw data sources to analyst-grade deep research
reports, has been a long-standing challenge, and is now becoming feasible with
the emergence of powerful large language models (LLMs). Recent workflow-based
data agents have shown promising results on specific data tasks but remain
fundamentally limited in achieving fully autonomous data science due to their
reliance on predefined workflows. In this paper, we introduce DeepAnalyze-8B,
the first agentic LLM designed for autonomous data science, capable of
automatically completing the end-toend pipeline from data sources to
analyst-grade deep research reports. To tackle high-complexity data science
tasks, we propose a curriculum-based agentic training paradigm that emulates
the learning trajectory of human data scientists, enabling LLMs to
progressively acquire and integrate multiple capabilities in real-world
environments. We also introduce a data-grounded trajectory synthesis framework
that constructs high-quality training data. Through agentic training,
DeepAnalyze learns to perform a broad spectrum of data tasks, ranging from data
question answering and specialized analytical tasks to open-ended data
research. Experiments demonstrate that, with only 8B parameters, DeepAnalyze
outperforms previous workflow-based agents built on most advanced proprietary
LLMs. The model, code, and training data of DeepAnalyze are open-sourced,
paving the way toward autonomous data science.