Recursive Harness Self-Improvement

# Recursive Harness Self-Improvement
Source: [https://arxiv.org/abs/2607.15524](https://arxiv.org/abs/2607.15524)
[View PDF](https://arxiv.org/pdf/2607.15524)

> Abstract:Under model\-\-harness co\-evolution, 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\. We therefore 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\.

## Submission history

From: Hyunin Lee \[[view email](https://arxiv.org/show-email/97dc9d73/2607.15524)\] **\[v1\]**Fri, 17 Jul 2026 00:21:19 UTC \(2,357 KB\)