DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
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.
68f6ed0424c4489363111848
Shaolei Zhang
68f6ed0424c4489363111849
Ju Fan
68f6ed0424c448936311184a
Meihao Fan
68f6ed0424c448936311184b
Guoliang Li
68f6ed0424c448936311184c
Xiaoyong Du
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
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.
DeepAnalyze-8B, an agentic LLM, autonomously completes the data science pipeline from raw data to research reports using curriculum-based training and data-grounded trajectory synthesis.
large language models LLMs workflow-based data agents agentic LLM curriculum-based agentic training data-grounded trajectory synthesis data question answering specialized analytical tasks open-ended data research