On-policy distillation (OPD) transfers the capabilities of a large language model to a smaller student by providing teacher supervision on the student's own rollouts. In long-horizon agentic tasks, however, uniform token-level matching can allocate supervision poorly: a large local discrepancy need not improve future b...
Yu-Hao Sun, Bin-Rui Wu, Zhuo-Er Xu et al.· 0 citations
LLM-based agents increasingly rely on persistent skills, i.e., reusable procedural prompts, to adapt without weight updates. Existing skill self-evolution methods directly revise skill text based on execution feedback, but each oracle evaluation requires a full agent rollout, creating a supervision bottleneck that conf...
Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than generated content alone. Existing guard models target static prompts and responses and are poorly suited to agent execution; existing executa...
Yun-Hao Feng, Rui-Xiao Lin, Ming Wen et al.· 0 citations
LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions. Developers often trust evidence such as test results and execution logs. We identify a response path integrity gap in Bring Your Own Key configurations used by roughly 88 percent of mainstrea...
Mingyu Luo, Zihan Zhang, Zesen Liu et al.· 0 citations
UI-Venus-2 is presented, a general-purpose foundation GUI agent designed to operate across mobile, web, and desktop environments through a unified closed-loop reasoning-action framework that integrates safety-aware mechanisms to ensure controlled execution of consequential actions.
Venus Team, Zhuo-Hang Cai, Hao-Xin Chen et al.· 1 citation
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