The Regression Tax: Decomposing Why Skills Help and Hurt LLM Agents

Adding "skills" (procedural guidance) to LLM agents is usually judged by average performance gains, but this masks cases where skills actively cause failures on previously solvable tasks. Analyzing nearly 6,000 runs across office automation benchmarks, the authors identify three regression mechanisms: skill presence alone altering behavior, skills overriding correct input interpretation, and skills suppressing self-verification. They find top-performing skills win mainly by regressing less, not gaining more. This has direct applications for designing and evaluating agent tooling, suggesting skill libraries should emphasize grounding and verification support rather than pure procedural instructions to improve real-world agent reliability. Authors: Darshan Tank, Baran Nama Paper: https://arxiv.org/abs/2607.22520v1