Minimal Coding Agent Matches Elaborate ML Engineering Harnesses on Autonomous Tasks

Under equal time budgets and the same frontier LLM backbone, a single session of a minimal-harness coding agent with read, write, and bash primitives matched open-source state-of-the-art autonomous machine learning engineering harnesses.

Apple researchers found the added orchestration and retrieval machinery redundant in large-scale ablation studies, pointing to the backbone model as the main driver of performance.

They conclude that hand-crafted harnesses around strong models yield poor returns on current MLE benchmarks.