arXiv:2605.29794v1 Announce Type: new Abstract: LLM agents now draw on growing skill libraries to handle complex tasks. However, injecting more skills does not always improve task completion and can even degrade it. Existing methods still treat skill injection as a static step, selecting skills with fixed criteria, fixing the budget in advance, and leaving descriptions unchanged. We argue that this static treatment can undermine the utility of skills, because which skills are exposed, how many are included, and how they are presented all affect downstream performance. We propose SkillsInjector
The proliferation of LLM agent skill libraries necessitates more sophisticated methods for skill management to maintain performance, making this research timely.
Improving how LLM agents select and utilize skills is critical for their efficiency, reliability, and widespread adoption in complex real-world tasks.
This research proposes moving beyond static skill injection, suggesting dynamic, context-aware methods for skill selection and presentation, which could lead to more robust and capable AI agents.
- · AI agent developers
- · Organizations deploying LLM agents
- · AI software platforms
- · Developers relying on static skill management
- · LLM agents with unoptimized skill sets
LLM agents become more reliably effective at complex, multi-step tasks due to improved skill utilization.
Increased trust and adoption of AI agents across various industries, leading to greater automation of white-collar workflows.
Accelerated development of more sophisticated autonomous systems that can dynamically adapt their capabilities to changing circumstances.
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Read at arXiv cs.AI