Harness^2: Recursive Agent Harnessing for an Open World
LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a promising approach to automatically evolve these harnesses. However, deploying RSI in open-world settings still faces two critical challenges. First, open-world tasks are typically unseen during the harness design phase and novel a
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LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a promising approach to automatically evolve these harnesses. However, deploying RSI in open-world settings still faces two critical challenges. First, open-world tasks are typically unseen during the harness design phase and novel a
This work introduces a novel framework for recursive self-improvement of LLM agents, addressing key challenges in open-world deployment. ML engineers can leverage these techniques to build more robust and adaptable AI agents that can generalize to unseen tasks, reducing the need for constant manual intervention and task-specific engineering.