A team led by Yanfei Chen released Harness², a plug‑and‑play framework that automatically refines the code that runs large language model agents. The system uses two layers of recursion: a task‑level step that proposes and tests edits without needing ground‑truth answers, and a harness‑level step that learns from those edits for future tasks. In tests on three professional benchmarks, a single recursion step lifted GLM‑5 and Gemini 3.5 Flash scores by up to 11.5 points on WorkBuddy‑Bench, 4.5 points on JobBench, and 11.8 percentage points on LAB.
Why it matters
The method could let AI assistants adapt to new real‑world tasks without hand‑tuned code, benefiting developers who deploy LLM agents in changing environments.
