AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
Authors: name: Yixin Ou, name: Yujie Luo, name: Jingsheng Zheng, name: Lanning Wei, name: Shuofei Qiao, name: Jintian Zhang, name: Da Zheng, name: Huajun Chen, name: Ningyu Zhang

Large Language Model (LLM) agents have shown great potential in addressing
real-world data science problems. LLM-driven data science agents promise to
automate the entire machine learning pipeline, yet their real-world
effectiveness remains limited. Existing frameworks depend on rigid, pre-defined
workflows and inflexible coding strategies; consequently, they excel only on
relatively simple, classical problems and fail to capture the empirical
expertise that human practitioners bring to complex, innovative tasks. In this
work, we introduce AutoMind, an adaptive, knowledgeable LLM-agent framework
that overcomes these deficiencies through three key advances: (1) a curated
expert knowledge base that grounds the agent in domain expert knowledge, (2) an
agentic knowledgeable tree search algorithm that strategically explores
possible solutions, and (3) a self-adaptive coding strategy that dynamically
tailors code generation to task complexity. Evaluations on two automated data
science benchmarks demonstrate that AutoMind delivers superior performance
versus state-of-the-art baselines. Additional analyses confirm favorable
effectiveness, efficiency, and qualitative solution quality, highlighting
AutoMind as an efficient and robust step toward fully automated data science.