Not All Skills Help: Measuring and Repairing Agent Knowledge
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 pervasive causal heterogeneity: individual skills routinely help on some task types while hurting on others, yet their opposing effects cancel in aggre...
| Authors | Yixuan Wang, Yiyang Zhou, Yiming Liang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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