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Source note2 min read

Learning Globally Reusable Skills for Coding Agents

Source

arXiv

arxiv.org/abs/2608.06153v1

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Source

Plain-Language Brief

Researchers propose a new method called GSE (Globalized Skill Evolution) to help Large Language Model agents continuously improve their skills without needing expensive retraining. The proposed method optimizes skill compatibility and generalization, preserves consistency across the skill bank by maintaining a Skill Relation Graph, and improves generalization through cluster-based skill consolidation and replay-driven verification. GSE was tested on two software engineering tasks: bug-revealing test generation and false-positive bug report filtering. It outperformed other evolution techniques across various metrics and showed promising results in an industrial agent deployment.

What the Document Says

  • The research focuses on Automated skill evolution for Large Language Model agents.
  • GSE is a proposed globalized skill evolution framework that optimizes skill compatibility and generalization.
  • SRG (Skill Relation Graph) is maintained to model and co-evolve inter-skill relationships.
  • Cluster-based skill consolidation abstracts reusable capabilities from local updates.
  • Replay-driven verification is employed to prevent overfitting and behavioral regressions.
  • The research was evaluated on two software engineering tasks: bug-revealing test generation and false-positive bug report filtering.
  • GSE achieved the best precision, recall, and F1-score across two state-of-the-art coding agents, OpenHands and mini-SWE-agent.

Key Concepts Mentioned

  • Automated skill evolution
  • Large Language Model (LLM) agents
  • GSE framework
  • Skill Relation Graph (SRG)
  • Cluster-based skill consolidation
  • Replay-driven verification

Claims Made by the Document

  • Existing approaches treat skill evolution as a sequence of local updates.
  • Overlooking relationships among skills leads to overfitted skill updates that fail to generalize across tasks.

Evidence Used

  • The proposed GSE framework aims to address these issues by optimizing skill compatibility and generalization.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Automated skill evolution enables Large Language Model (LLM) agents to continuously improve without expensive retraining. However, existing approaches typically treat skill evolution as a sequence of local updates, overlooking relationships among skills and often producing overfitted skill updates that fail to generalize across tasks. We propose GSE, a globalized skill evolution framework that jointly optimizes skill compatibility and skill generalization. To preserve consistency across the skill bank, GSE maintains a Skill Relation Graph (SRG) that explicitly models and co-evolves inter-skill relationships. To improve generalization, GSE performs cluster-based skill consolidation to abstract reusable capabilities from local updates and employs replay-driven verification to prevent overfitting and behavioral regressions. We evaluate GSE on two representative software engineering tasks: bug-revealing test generation and false-positive bug report filtering. Across two state-of-the-art coding agents, OpenHands and mini-SWE-agent, GSE consistently achieves the best precision, recall, and F1-score. Compared with existing evolution techniques, GSE improves precision and recall by 6.1%~34.1% and 31.8%~180.0% for test generation, and by 15.4%~96.4% and 13.1%~19.8% for false-positive filtering. Deployment on an internal industrial agent further yields a 61.4% improvement in F1-score, demonstrating the effectiveness and generalizability of GSE for evolving effective skills.