Harness^2: Recursive Agent Harnessing for an Open World

Ruiyao Xu, Yanfei Chen, Zhongying CuiZhu and others propose Harness^2, a plug-and-play recursive agent framework aimed at addressing two major difficulties faced by recursive self-improvement (RSI) in open-world scenarios: test-time tasks are novel and lack a ground-truth verifier to guide online harness editing, and the RSI process is highly task-specific and difficult to generalize. The framework establishes two levels of recursion: task-level recursion uses a contrastive "propose-probe-compose" procedure to reliably guide editing without a ground-truth verifier, and can be scaled through parallel or serial recursion; framework-level recursion extracts experience from these edits and updates the continuously improving harness, enabling the agent to better adapt to future tasks. The paper states that, on three specialized benchmarks and four model-harness configurations, a single step of recursion continuously improves the performance of GLM-5 and Gemini 3.5 Flash relative to their base harnesses, with WorkBuddy-Bench improving by up to 11.5 points, JobBench by 4.5 points, and LAB by 11.8 percentage points, and the gains further increase as the task-level recursion budget increases.