用强化学习让大模型在终端环境中学会通用行为，打开日常自动化新可能。
arXiv:2608.22631v1 Announce Type: new Abstract: Terminal agents are a compelling application of large language models (LLMs), with the potential to in…
为解决终端智能体训练数据稀缺而生，用可验证任务合成引擎大幅提升Agent能力测试的可靠性与覆盖面。
arXiv:2606.22883v1 Announce Type: new Abstract: While recent LLM-based terminal agents have demonstrated promising capabilities, the scarcity of high-…
挑战强代码代理是更好教师的假设，揭秘交互轨迹训练终端代理的关键——Terminal-Lego流水线可扩展解耦多因素。
arXiv:2606.03461v1 Announce Type: new Abstract: Stronger code agents are commonly assumed to be superior teachers for post-training, yet this assumpti…
论文提出通过Agent Skills扩展终端智能体环境的规模化方法，为AI研究开辟新思路。
arXiv:2605.20876v1 Announce Type: new Abstract: Terminal agents extend Large Language Models with the ability to execute tasks directly in command-lin…