papersSEP 10 04:00 UTC
Paper proposes measuring and optimizing LLM agent harnesses without retraining models
A new arXiv paper studies how LLM tool agents can be improved by modifying the runtime harness around a fixed model, including prompts, tool interfaces, middleware, state handling, and recovery logic. The authors frame this as a resource-bounded harness selection problem, arguing that agent performance can be improved without retraining. The work offers ways to measure and optimize these harness components systematically.