EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management

📰 ArXiv cs.AI

Learn how EvoDS, a self-evolving autonomous data science agent, improves automated data science with skill learning and context management, enabling reusable experience across tasks

advanced Published 3 Jun 2026
Action Steps
  1. Build a self-evolving agent using Large Language Model (LLM) architecture
  2. Implement skill learning to enable the agent to accumulate reusable experience
  3. Configure context management to facilitate long-horizon planning
  4. Test the agent in multi-stage data science pipelines
  5. Apply EvoDS to real-world data science tasks to evaluate its performance
Who Needs to Know This

Data scientists and AI engineers on a team can benefit from EvoDS as it enhances automated data science pipelines, while product managers can leverage it to improve overall project efficiency

Key Insight

💡 EvoDS overcomes limitations of static action sets and lack of context management in existing automated data science approaches

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🤖 EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management 💡
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