arXiv:2607. 13027v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and iteratively deciding the next action.
By Hongru Cai, Yongqi Li, Ran Wei, Wenjie Li
The paper introduces a framework that selectively reuses parts of previously executed mobile GUI control traces to reduce LLM inference overhead. It identifies six categories of variation that can affect trace reuse and shows that individual transitions can still be valid when checked against the current task context and UI state. Using graph-text templates, the system achieves roughly 80% fewer LLM tokens and up to 2‑fold speedups on Android and HarmonyOS devices.
By Bingyu Bai, Cheng Zhang, Erhu Feng
AI agents are driving a new software paradigm, with the ability to autonomously call tools, extract information, manage memory, and complete tasks that span applications and data sources. Most existing end-user operating systems, however, are designed for application-centric workflows and offer little native support for AI agents.
arXiv:2609.25373v1 Announce Type: new
Abstract: On-device assistants require function-calling models that map natural language to local system actions, but existing resources emphasize web APIs or na...
By Ali Rezagholizadeh, Soheila Samiee
arXiv:2610.34735v1 Announce Type: new
Abstract: An agent that truly knows and acts on behalf of its user has been a long-standing promise of AI assistants. Computer-use agents bring this promise with...
By Yuting Ning, Ruijia Cheng, Sushobhan Ghosh, Peitong Duan, HungShi Lin, Abhishek Sundararajan, Shreyash Pandey, Huan Sun, Jeffrey Nichols, Eldon Schoop
arXiv:2607. 15367v1 Announce Type: new Abstract: Desktop voice assistants are still dominated by cloud pipelines that ship raw audio off the machine and expose a fixed set of skills.
By Raunak B Sinha