arXiv:2506.23149v2 Announce Type: replace Abstract: Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by the task and remain aligned with the target task. As a result, evolved skills could be incomplete or irrelevant. To address this limitation, we propose AlignEvoSkill, a skill-evolution framework that jointly models knowledge coverage and task alignment. Given failed task trajectories, AlignEvoSkill first identifies task-relevant knowledge tags, retrieves complementary
The rapid advancement of LLM-based agents currently highlights limitations in current skill evolution methods, necessitating more robust frameworks to prevent 'hallucinations' and improve reliability.
This research directly addresses a crucial bottleneck in the development of highly capable and autonomous AI agents, moving them closer to practical, reliable deployment in complex tasks.
The ability of AI agents to autonomously evolve and reuse skills becomes significantly more aligned with knowledge and task requirements, leading to more robust and less error-prone agentic systems.
- · AI Agent developers
- · Enterprise automation platforms
- · General AI research
- · Tasks requiring manual oversight of AI agents
- · Less robust AI agent frameworks
More capable and trustworthy autonomous AI agents can be deployed across various industries.
Reduced human intervention in complex computational and decision-making processes, accelerating automation adoption.
Increased economic productivity and the restructuring of white-collar work roles as agents handle more sophisticated tasks.
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Read at arXiv cs.CL