EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management
arXiv:2606.03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain fundamentally limited by their static action sets and lack of principled long-horizon context management, hindering their ability to accumulate reusable experience across tasks and operate reliably in multi-stage, iterative data science pipelines. To address these challenges, we introduce EvoDS, a self-evolving autonomous data science agent that learns to expand its skills and adaptively managing long-
Advances in large language models are enabling more sophisticated autonomous agents, pushing the boundaries of what these systems can achieve in complex domains like data science.
This development indicates a significant step towards more autonomous and effective AI agents, which could greatly accelerate scientific discovery and automate high-value white-collar tasks.
AI agents are moving beyond static action sets to self-evolving capabilities with improved context management, allowing them to accumulate experience and operate reliably in iterative workflows.
- · AI software developers
- · Data science industry
- · Research institutions
- · Early adopters of AI agents
- · Tasks requiring repetitive data science execution
- · Traditional data science consultancies
Increased efficiency and automation in data science workflows.
Democratization of advanced data science capabilities, reducing the barrier to entry for complex analysis.
New classes of AI-driven research leading to breakthroughs across various scientific and industrial sectors.
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Read at arXiv cs.AI