While AI agents demonstrate remarkable capabilities in reasoning and tool use, they remain fundamentally reactive: they compute responses only after explicit user prompts. This paradigm ignores a critical opportunity: the idle time between interactions is largely wasted, leaving agents unable to prepare for future user needs. To bridge this gap, we introduce ProAct, a proactive agent architecture that leverages idle-time compute to anticipate and fulfill likely upcoming user needs. By analyzing evolving dialogue history together with persistent memory, ProAct predicts upcoming needs and iteratively acquires information, allowing the agent to resolve knowledge gaps and prepare evidence before the user initiates a query. To rigorously evaluate proactive capabilities, we also introduce ProActEval, a comprehensive benchmark comprising 200 scenarios across 40 domains, featuring predictable need chains and diverse user cognitive profiles. Empirical results demonstrate significant advantages over reactive baselines. ProAct accelerates task completion by reducing required turns by 14.8%, decreases user effort by 11.7%, and cuts hallucination rates by 28.1% on ProActEval. Furthermore, MemBench evaluations confirm that ProAct achieves state-of-the-art reflective accuracy, underscoring its sustained and robust performance.

翻译：尽管AI智能体在推理和工具使用方面展现出卓越能力，但其本质上仍是被动的：仅在用户明确提示后才进行计算。这种范式忽略了关键机遇：交互之间的空闲时间大部分被浪费，导致智能体无法为未来用户需求做好准备。为弥合这一差距，我们提出ProAct，一种利用空闲时计算来预测并满足潜在用户需求的主动式智能体架构。通过分析动态演变的对话历史与持久化记忆，ProAct预测即将到来的需求并迭代获取信息，使智能体能够在用户发起查询前消除知识空白并准备证据。为严格评估主动能力，我们同时引入ProActEval——一个包含40个领域200个场景的综合基准测试集，涵盖可预测的需求链与多样化的用户认知特征。实验结果表明，ProAct相比被动基线方法具有显著优势：在ProActEval上，它将任务完成所需交互轮次减少14.8%，用户努力降低11.7%，幻觉率降低28.1%。此外，MemBench评估证实ProAct达到最先进的反思准确率，彰显其持久而稳健的性能。