AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
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.
684b8e193b733ba333687028
Yixin Ou
claimed_verified
2025-06-13T07:39:36.148Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1669210243382-6241749cf80bd930bd99f3dd.jpeg
Ou Yixin
user
OE-Heart
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Yujie Luo
684b8e193b733ba33368702a
Jingsheng Zheng
684b8e193b733ba33368702b
Lanning Wei
684b8e193b733ba33368702c
Shuofei Qiao
684b8e193b733ba33368702d
Jintian Zhang
684b8e193b733ba33368702e
Da Zheng
684b8e193b733ba33368702f
Huajun Chen
684b8e193b733ba333687030
Ningyu Zhang
claimed_verified
2025-06-13T07:39:37.984Z
620b3bbb0668e435407c8d0a
/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
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.
AutoMind, a flexible and knowledgeable LLM-agent framework, improves automated data science through expert knowledge integration, strategic solution exploration, and adaptive coding, outperforming existing systems.
LLM data science agents machine learning pipeline expert knowledge base agentic knowledgeable tree search self-adaptive coding strategy