Skill or Skip? Learning Selective Skill Invocation in Agentic Tasks via Dual-Granularity Preference Learning
arXiv:2606.00510v1 Announce Type: new Abstract: Agent skills are callable procedural modules that provide reusable knowledge and execution policies for complex agentic tasks. However, existing methods mainly focus on selecting relevant skills or improving the skills themselves, while overlooking whether a relevant skill should actually be invoked at the current decision point. Unhelpful invocations may introduce irrelevant context and disrupt an otherwise correct execution process. To address this issue, we propose SelSkill, a dual-granularity preference-learning framework for selective skill
The proliferation of AI agents highlights the emergent challenge of effectively managing their complex decision-making and skill invocation in real-world applications.
Improving the selectivity and efficiency of AI agent skill invocation directly impacts their reliability, performance, and ability to handle nuanced tasks with less human oversight.
This research introduces a novel framework for agents to intelligently decide when to use a skill versus when to bypass it, moving beyond simple relevance-based invocation.
- · AI agent developers
- · Businesses adopting AI agents
- · Enterprise software providers
- · Inefficient AI agent systems
- · Tasks requiring constant human intervention
More robust and autonomous AI agents capable of handling complex, unstructured tasks with fewer errors.
Reduced operational costs and increased efficiency across various industries as AI agents become more reliable.
Acceleration of 'lights-out' operations where AI agents autonomously manage entire workflows, further impacting white-collar employment.
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Read at arXiv cs.CL