Large language model (LLM)-powered agents are transforming digital devices from passive tools into proactive intelligent collaborators. However, most existing frameworks remain confined to a single OS or device, making cross-device workflows brittle and largely manual. We present UFO$^3$, a system that unifies heterogeneous endpoints, desktops, servers, mobile devices, and edge, into a single orchestration fabric. UFO$^3$ models each user request as a mutable TaskConstellation: a distributed DAG of atomic subtasks (TaskStars) with explicit control and data dependencies (TaskStarLines). The TaskConstellation continuously evolves as results stream in from distributed devices, enabling asynchronous execution, adaptive recovery, and dynamic optimization. A Constellation Orchestrator} executes tasks safely and asynchronously while applying dynamic DAG updates, and the Agent Interaction Protocol (AIP) provides persistent, low-latency channels for reliable task dispatch and result streaming. These designs dissolve the traditional boundaries between devices and platforms, allowing agents to collaborate seamlessly and amplify their collective intelligence. We evaluate UFO$^3$ on NebulaBench, a benchmark of 55 cross-device tasks across 5 machines and 10 categories. UFO$^3$ achieves 83.3% subtask completion, 70.9% task success, exposes parallelism with an average width of 1.72, and reduces end-to-end latency by 31% relative to a sequential baseline. Fault-injection experiments demonstrate graceful degradation and recovery under transient and permanent agent failures. These results show that UFO$^3$ achieves accurate, efficient, and resilient task orchestration across heterogeneous devices, uniting isolated agents into a coherent, adaptive computing fabric that extends across the landscape of ubiquitous computing.

翻译：基于大语言模型（LLM）的智能体正在将数字设备从被动工具转变为主动的智能协作伙伴。然而，现有的大多数框架仍局限于单一操作系统或设备，导致跨设备工作流脆弱且高度依赖人工操作。本文提出 UFO$^3$，该系统将异构的终端设备、桌面系统、服务器、移动设备及边缘节点统一整合为单一编排架构。UFO$^3$ 将每个用户请求建模为一个可变的“任务星座”：这是一个由原子子任务（TaskStars）组成的分布式有向无环图，其中包含显式的控制流与数据流依赖关系（TaskStarLines）。随着分布式设备的结果持续流入，任务星座不断演化，从而实现异步执行、自适应恢复与动态优化。星座编排器（Constellation Orchestrator）在应用动态 DAG 更新的同时，安全且异步地执行任务；而智能体交互协议（Agent Interaction Protocol, AIP）则提供持久、低延迟的通道，以实现可靠的任务分发与结果流式传输。这些设计打破了设备与平台之间的传统界限，使得智能体能够无缝协作，并增强其集体智能。我们在 NebulaBench 基准上对 UFO$^3$ 进行评估，该基准包含跨越 5 台机器、10 个类别的 55 项跨设备任务。实验结果表明，UFO$^3$ 实现了 83.3% 的子任务完成率与 70.9% 的任务成功率，平均并行宽度为 1.72，并且相较于串行基线将端到端延迟降低了 31%。故障注入实验表明，系统在瞬时性与永久性智能体故障下均能实现优雅的性能降级与恢复。这些结果证明，UFO$^3$ 能够在异构设备间实现准确、高效且鲁棒的任务编排，将孤立的智能体整合为一个连贯、自适应的计算架构，从而延伸至普适计算的广阔图景中。