SOURCE-LINKED INTELLIGENCE
Harness-Zero: Harness Distillation via Agent-as-Harness
Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain tied to the harness at deployment. Because the best harness varies across domains, instances, and models, a general-purpose agent must either settle for a suboptimal shared harness or route among an ever-growing set of specialized ones. We therefore study agent harness distillation: using a domain- or instance-optimized harness as training-time guidance and transferring the behaviors it induces into model weights, so that its gains survive under
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-21T17:55:20.000Z
First collected: 2026-09-23T06:11:12.848Z. This is not the publication date.