Zhuoshi Pan, ZhiYuan Feng, Yukuo Cen, Honglin Lin, Junru Lu, Zhongxia Chen, Shuo Yang, H. Vicky Zhao · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26770 · open-access PDF
Harness 개발은 능력있는 AI 에이전트를 구축하는 데 중요한 역할을합니다. 최근의 접근 방식은 재발전적 자기 향상 (RSI)을 통해 헬렌스를 개선하지만 성능 향상은 종종 더 높은 실행 비용과 관련이 있습니다. 우리는 HarnessPro를 도입합니다.이 환경에서 후보 헬렌스는 LLM 생성 테스트를 사용하여 진실 솔루션이나 공식 테스트에 액세스하지 않고 평가됩니다. 전문 에이전트는 실행 경로를 진단하고, 다른 성능 비용 우선 순위와 업데이트를 계획하며,
Machine translation (Meta M2M100); the English original follows.
Harness development plays a crucial role in building capable AI agents. Recent approaches improve harnesses through recursive self-improvement (RSI), but their performance gains often come with higher execution costs. We introduce HarnessPro, a label-free method for evolving harnesses to improve task performance while reducing execution cost. In this setting, candidate harnesses are evaluated using LLM-generated tests without access to ground-truth solutions or official tests. Specialized agents diagnose execution trajectories, plan updates with different performance-cost priorities, and imple
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.26770, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange