AutoMind: Adaptive Knowledgeable Agent for Automated Data Science (2506.10974v1)

Abstract: 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.

• The paper introduces AutoMind, a framework that combines a curated expert knowledge base, a knowledgeable tree search algorithm, and a self-adaptive coding strategy to tackle complex data science tasks.
• The methodology strategically explores the solution space by evaluating complete candidate solutions, optimizing for task-specific metrics and reducing token costs by 63%.
• Benchmark evaluations showed AutoMind outperformed 56.8% of human participants and achieved a 13.5% improvement over prior state-of-the-art, demonstrating significant practical impact.

AutoMind: Adaptive Knowledgeable Agent for Automated Data Science

The paper "AutoMind: Adaptive Knowledgeable Agent for Automated Data Science" introduces an innovative framework designed to enhance the automation capabilities of LLM agents in the data science domain. Recognizing the limitations of existing data science agents that primarily rely on pre-defined workflows and simplistic coding strategies, the authors present AutoMind, which distinguishes itself through three core advancements: the integration of a curated expert knowledge base, an agentic knowledgeable tree search algorithm, and a self-adaptive coding strategy.

AutoMind aims to address the shortcomings of current frameworks that excel in solving straightforward, conventional problems but falter when faced with more complex, innovative tasks requiring empirical expertise and flexibility. The expert knowledge base is constructed from domain-specific resources, including high-quality papers from esteemed journals and expert-derived solutions from top-ranked competitions. This repository allows AutoMind to ground its operations in robust, empirical knowledge.

The agentic knowledgeable tree search algorithm employed by AutoMind allows for strategic exploration of the solution space, dynamically adapting to the problem's complexity. Instead of sequential decision-making, AutoMind compares complete potential solutions, optimizing directly for task-specific metrics. This is achieved by encapsulating solutions in a tree structure, each node representing a candidate solution comprised of a textual plan, executable code, and a validation metric.

One of AutoMind's significant contributions is its self-adaptive coding strategy, which reconciles plan complexity with LLM coding abilities. For simpler plans, it utilizes a one-pass coding approach for efficiency. Conversely, for more complex solutions, it applies a stepwise decomposition strategy paired with execution feedback to ensure robust implementation.

Two benchmark evaluations—MLE-Bench and recent AI competitions—demonstrate AutoMind's superior metrics compared to state-of-the-art baselines. Notably, AutoMind outperformed 56.8% of human participants in MLE-Bench, representing a 13.5% improvement over AIDE, the prior SOTA. This signifies a substantial leap in efficiency, with AutoMind achieving similar performance to AIDE in just 6 hours—a three-fold boost in time efficiency and a 63% reduction in token costs.

The framework suggests several theoretical and practical implications. On the theoretical front, AutoMind advances automatic data pipelines by incorporating empirical knowledge, potentially paving the way for more sophisticated AI-driven scientific discovery. Practically, it offers a scalable solution that leverages human-like expertise in data science tasks, suggesting that such frameworks could reduce human involvement in routine data science processes, thereby enhancing productivity.

Future research could explore further integration of empirical knowledge across broader AI domains and refine self-adaptive coding mechanics to handle emerging challenges in automated data science workflows. Additionally, evaluating AutoMind's performance with other widely recognized foundation models might provide insights into its adaptability and efficiency across diverse computational environments.

In summary, AutoMind represents a significant step towards fully automated data science by leveraging robust empirical resources and dynamic, context-aware strategies, offering promising directions for future advancements in AI-driven scientific methodologies.