arXiv:2606.15390v1 Announce Type: new Abstract: LLM agents can improve without weight updates by accumulating natural-language skills from experience, but current systems entrust every decision about which skills to keep and how to apply them to LLM judgment alone. We argue that this conflates two distinct roles: generating a skill from experience is a creative act that judgment handles well, while deciding whether that skill actually helps requires empirical evidence across many tasks. Measuring per-skill causal contributions via randomized masking, we find that skill libraries exhibit pervas
The rapid advancement of LLM agents demands empirical methods to enhance their efficiency and reliability, moving beyond sole reliance on LLM judgment.
This research provides a crucial methodology for optimizing AI agent performance by quantitatively evaluating the utility of skills, which directly impacts the scalability and trustworthiness of autonomous systems.
The development and deployment of AI agents will become more systematic and evidence-based, reducing reliance on qualitative assessments of skill effectiveness and leading to more robust systems.
- · AI Agent developers
- · Enterprises adopting AI agents
- · AI researchers
- · Automation software providers
- · Inefficient LLM agent architectures
- · Companies relying solely on heuristic AI agent development
AI agents will exhibit improved performance and reliability due to better skill management.
The cost and complexity of developing and maintaining sophisticated AI agents will decrease, accelerating their widespread adoption.
More reliable AI agents could enable fully autonomous workflows in sensitive sectors, leading to significant economic restructuring and new regulatory challenges.
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Read at arXiv cs.CL