Under the model-harness co-evolution framework, harnesses are not merely inference-time scaffolds but data-generating components whose execution traces can shape future foundation models. This motivates harness-in-the-loop learning: optimizing harnesses for both immediate agent performance and the quality of traces used for future model training.
However, continually updating provider-built scaffolds is costly and labor-intensive. Therefore, we investigate whether optimizing user-constructed harnesses in a task-specific manner can improve execution-trace quality while remaining computationally lightweight and requiring only a few update iterations.
To this end, we introduce Recursive Harness Self-Improvement (RHI), which represents the harness as a prompt-level specification of the agent loop and iteratively refines it using pairwise feedback over its own revision history. Across 30 synthetic machine-learning research tasks spanning quantitative finance, robotics, and pharmacy, a few RHI iterations suffice to substantially raise the performance ceiling of low-reasoning-effort agents, exceeding the corresponding maximum-reasoning-effort setting while reducing inference cost by up to 60%.
We show that these gains arise primarily from improved task-specific context management through more effective inter-agent information flow rather than longer reasoning traces. Finally, we formalize this behavior as an information-theoretic hypothesis for RHI's implicit optimization objective, suggesting RHI as a practical algorithm for continual learning within the paradigm of model-harness co-evolution.
Blogger's Review: Recursive Harness Self-Improvement (RHI) presents a novel approach to enhancing agent performance through task-specific feedback mechanisms, significantly improving execution efficiency. This method not only reduces computational costs but also demonstrates adaptability and flexibility, indicating that future continual learning could become more efficient and intelligent. Its successful application opens new possibilities for agents performing complex tasks.