Researchers introduced HarnessPro, a label-free method for recursively self-improving AI agent harnesses that uses Pareto optimization to trade off task performance against execution cost, addressing the higher execution costs that accompany performance gains in prior recursive self-improvement approaches. The work targets harness development for building capable AI agents, where earlier RSI methods improved performance but at increased cost.
Harness development plays a crucial role in building capable AI agents. Recent approaches improve harnesses through recursive self-improvement (RSI), but their performance gains often come with higher execution costs. We introduce HarnessPro, a label-free method for evolving harnesses to improve tas
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