统一智能体：管理跨设备交互

中文摘要 AI 辅助

随着能力的快速提升，AI智能体可从在单个应用内运行，逐步发展为跨用户多设备执行任务。然而现有智能体系统在该场景下仍存在不足，原因在于观测数据分散在不同设备和不同时刻，但主流系统并非围绕这一事实设计：单一智能体将设备视为工具，缺乏对跨时间、跨设备的有效状态管理；多智能体系统虽能实现智能体间的协调，却无法维持跨设备、跨时间请求所需的紧凑携带状态。本文提出，智能体应维护精心设计的状态，以紧凑、可执行的形式组织交互证据、陈述事实及待处理请求，从而结合当前观测结果决定行动。为对比不同状态设计的效果，本文构建了用户-智能体跨设备、跨时间交互的基准数据集。我们将该原则实例化为统一智能体（Unified Agent），这是一种带状态的智能体，可在不同设备和时刻间携带交互证据，并结合当前观测结果执行行动。在默认设置下，其性能显著优于我们对4种已发表设计的适配版本；在多模态大语言模型（MLLM）系列、能力及推理复杂度发生变化时，它仍优于所有对比系统，证明该状态设计优势在不同MLLM设置下具有鲁棒性。我们的代码和数据将在GitHub上公开。

英文摘要

As capabilities rapidly increase, AI agents can move from running inside one app to acting across a user's devices over time. Yet existing agent systems still fall short in this scenario. This is because observations are scattered across devices and moments, but mainstream systems are not designed around this fact: a single agent that treats devices as tools lacks effective state management for all devices across time, and multi-agent systems coordinate across agents but do not maintain the compact carried state a cross-device, cross-time request needs. We argue that the agent should maintain an effectively designed state that organizes engagement evidence, stated facts, and the standing request in a compact, action-ready form for deciding its action given the current observation. To compare state designs, we construct a benchmark of user-agent interaction across devices and time. We instantiate this principle in Unified Agent, a stateful agent that carries interaction evidence across devices and moments and uses it with the current observation to act. In the default setting, it significantly outperforms our adaptations of four published designs. Across changes in multimodal large language model (MLLM) family, capability, and reasoning effort, it remains ahead of all compared systems, demonstrating that the state-design advantage is robust across MLLM settings. Our code and data will be publicly available on GitHub.

发表机构

• University of California, San Diego（加利福尼亚大学圣迭戈分校）

机构由 AI 辅助整理，请以论文原文为准。