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Introducing AutoMind: Adaptive Knowledgeable Agent for Automated Data Science Paper: https://t.co/Ts3cE0SDXd Code (will be released soon): https://t.co/NgMD9b7gIG Our latest work AutoMind​ is a new LLM agent framework that automates end-to-end machine learning pipelines by dynamically integrating domain expertise, strategic solution exploration, and adaptive code generation. Unlike rigid workflow-based agents, AutoMind mimics human practitioners' empirical insights to solve complex, real-world data science challenges. AutoMind​ is evaluated on two automated data science benchmarks using different foundation model families. Our results demonstrate superior performance over baselines on both benchmarks: 🏆 On the OpenAI's MLE-bench, AutoMind surpassed ​56.8%​​ of human participants - representing improvement​ over prior state-of-the-art (AIDE). 🔬 Comprehensive efficiency analysis revealed ​300% increased efficiency and ​63% lower token costs​ compared to previous SOTA approaches. Specifically, AutoMind improves automated data science with these breakthrough features: 🔍 Expert Knowledge Base: Curated from domain expertise to ground the agent in empirical best practices, overcoming LLMs' inherent lack of human practitioner experience. This enables handling of complex, innovative tasks beyond classical problems. 🌳 Agentic Knowledge Tree Search: Strategically explores solution spaces through a structured search approach, dynamically navigating possible paths to optimize problem-solving efficiency and effectiveness. ⌨️ Self-Adaptive Coding Strategy: Dynamically adjusts code generation complexity based on task requirements, moving beyond rigid workflows to deliver context-aware implementations—from simple scripts to cutting-edge solutions.