Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories
Agents built around large language models continually accumulate interaction trajectories during deployment, yet their behavior typically remains fixed. Beyond updating model weights, these trajectories can improve the agent harness that constructs context, mediates tools, validates actions, and recovers execution. We introduce Harness-R1, the first method, to our knowledge, that makes failure-conditioned, lifecycle-wide editing of an existing executable runtime a learned capability. It post-tra
Record details
Published: 3 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Agents & autonomy
Retrieved: 4 August 2026
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ethics.ai (3 August 2026), “Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories,” evidence record 16110, https://ethics.ai/record/16110 (originally published by arXiv cs.AI).
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