Benchmark reveals LLM agents struggle with autonomous skill discovery and safe execution
Researchers have introduced Skill-Use, a benchmark that exposes a critical gap in how LLM agents handle structured task procedures. Rather than measuring whether agents can execute pre-loaded skills, this work tests whether they can independently recognize when a skill applies, retrieve its full specification from minimal cues, and follow it without deviation or unauthorized actions. The three-part evaluation framework (trigger recognition, procedural compliance, and boundary adherence) reveals that current agentic systems may struggle with autonomous skill discovery and safe execution, a foundational requirement for deploying LLMs in production environments where tool misuse carries real consequences.
Modelwire context
Skeptical readThe paper doesn't actually test whether LLMs can execute skills they already know. Instead it tests whether agents can discover, retrieve, and follow skills from minimal cues in an open-ended way. That's a different problem than 'can LLMs use skills' and the framing obscures whether the bottleneck is agent reasoning or skill specification design.
This connects directly to Control Under Compression (August 2nd), which showed that agent reliability degrades nonlinearly when system instructions are compressed. Skill-Use essentially asks the inverse question: if you strip away explicit skill definitions and force agents to infer them, what breaks? The two papers together suggest that agent control failures cluster around information density and retrieval, not core reasoning. However, Skill-Use doesn't address whether the failures are remediable through better prompting or training (as State2State from August 5th implies) or fundamental to current architectures.
If the same benchmark shows >80% performance when skills are explicitly loaded into context (versus discovered), that confirms the bottleneck is retrieval and inference, not execution. If performance stays flat regardless of skill presentation, the problem is deeper and Skill-Use's three-part decomposition becomes less actionable for practitioners.
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MentionsSkill-Use · LLM agents
Modelwire Editorial
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Skill-Use: Can LLMs Actually Use Skills in Agentic Harnesses?”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.