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ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

Research LLM Agents

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Merged summary

TL;DR - ScienceBuddy is an interactive workspace for scientific agents that continually improves by learning from researchers’ requests, feedback, and execution evidence. Its “recursive-in-recursive” approach jointly evolves the agent harness and trains the underlying model.

  • Inner recursion refines the harness while keeping the model fixed.
  • Outer recursion applies reinforcement learning to the model under the improved harness.
  • User interactions and execution traces become training tasks and evaluation rubrics.
  • Case studies span four scientific task families, though the abstract reports no quantitative results.

Sources (1)

ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

arXiv cs.AI Shuhan Xue, Jianyuan Zhong, Ziyuan Nan, Wenbin Li, Zhaochen Yu, Jinchao Ding, Qiang Gao, Pengyu Zhan, Yuntong Zhang, Tian Cheng, Zhenfei Yin, Yingcheng Wu, Ling Yang 2026-09-15 arXiv:2609.17523
Public signals Hugging Face upvotes 30
Providers: Hugging Face · Upvotes 30 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:19:26.888948 UTC

TL;DR - ScienceBuddy is an interactive workspace for scientific agents that continually improves by learning from researchers’ requests, feedback, and execution evidence. Its “recursive-in-recursive” approach jointly evolves the agent harness and trains the underlying model.

  • Inner recursion refines the harness while keeping the model fixed.
  • Outer recursion applies reinforcement learning to the model under the improved harness.
  • User interactions and execution traces become training tasks and evaluation rubrics.
  • Case studies span four scientific task families, though the abstract reports no quantitative results.
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