Author

Qingyao Li

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Preprint Aug 2026

Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories

This work introduces Harness-R1, the first method, to the authors' knowledge, that makes failure-conditioned, lifecycle-wide editing of an existing executable runtime a learned capability, and post-trains a dedicated harness engineer with online reinforcement learning so that its edits are optimized for the realized task success they produce.

Shuai Shao, Kangning Zhang, Qingyao Li et al. · 6 citations · ⚡2

#artificial intelligence Preprint Aug 2026

EMAN: Optimization-Driven Capacity Growth through Path Emergence in Multi-Task Learning

The Emergent Modular Atomic Network (EMAN), an optimization-driven framework for exposing an antisymmetric growth direction through latent relative phases without instantiating a second path, and for monitoring multiple decision signals during training to transform local optimization evidence into a structural decision is proposed.

Chen Fang, Jingchen Li, Hongzong Li et al. · 0 citations

#artificial intelligence Preprint Aug 2026

SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents

SkillGate lifts a 9B policy from 40.8% to 53.2% trial success, well ahead of the identical budget spent on outcome reward alone, while cutting exposure to misleading candidates by two thirds and reading fewer skills.

Qingyao Li, Wenxiang Jiao, Shuai Shao et al. · 0 citations