arXiv:2608. 06301v1 Announce Type: new Abstract: As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding them.
By Varun Ursekar, Apaar Shanker, Yash Maurya, Shehab Yasser, Vijay S. Kalmath, Veronica Chatrath, Yuan Xue
arXiv:2609.05992v1 Announce Type: new
Abstract: As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task complet...
By Hengle Jiang, Qijun Cai, Ziying Luo, Ke Tang
arXiv:2610.26770v2 Announce Type: replace
Abstract: Harness development plays a crucial role in building capable AI agents. Recent approaches improve harnesses through recursive self-improvement (RSI...
By Zhuoshi Pan, ZhiYuan Feng, Yukuo Cen, Honglin Lin, Junru Lu, Zhongxia Chen, Shuo Yang, H. Vicky Zhao, Jie Tang
arXiv:2609.05736v2 Announce Type: new
Abstract: LLM tool agents can be improved without retraining by modifying the runtime harness around a fixed model: prompts, tool interfaces, middleware, state h...
By Cen Mia Zhao, Haibo Ruan, Wenjie Chen, Pei-fen Tu, Usman Abbasi, Joel Hesch
arXiv:2605.19330v2 Announce Type: replace-cross
Abstract: LLM agents organize behavior through skills -- structured natural-language specifications that govern how an agent reasons, retrieves, and re...
By Md Mehrab Tanjim, Jayakumar Subramanian, Branislav Kveton, Xiang Chen, Subhojyoti Mukherjee, Anlan Zhang, Sungchul Kim, Sunav Choudhury, Somdeb Sarkhel
arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.
By Yicheng Feng, Yan Zhang, Yan Cheng, Wei Qi