Episode Details

This paper introduces Meta-Harness, an innovative system designed to automate harness engineering for large language models. Unlike traditional methods that rely on manual coding or compressed feedback, this system uses an agentic proposer to search through and optimize the code that governs how models store, retrieve, and process information. By utilizing a filesystem to access full execution traces and prior performance logs, the proposer can perform targeted edits and sophisticated program rewrites. Experimental results demonstrate that Meta-Harness outperforms human-engineered baselines and existing text optimizers across diverse tasks, including text classification, mathematical reasoning, and agentic coding. Ultimately, the research shows that providing automated agents with unfiltered access to historical experience enables the discovery of highly efficient, high-performance system architectures.