预见与学习：释放主动型代理中的空闲时间计算

摘要

尽管AI代理在推理和工具使用方面展现出卓越的能力，但它们本质上仍是被动的：仅在收到明确的用户提示后才计算响应。这一范式忽略了一个关键机遇：交互之间的空闲时间基本被浪费，导致代理无法为未来的用户需求做好准备。为弥补这一差距，我们提出ProAct——一种利用空闲期计算来预测并满足用户即将产生的需求的主动代理架构。通过分析不断演进的对话历史与持久化记忆，ProAct能够预测即将出现的需求，并迭代获取信息，从而在用户发起查询前解决知识盲区并准备证据。为严格评估主动能力，我们还引入ProActEval——一个涵盖40个领域200个场景的综合基准测试，包含可预测的需求链和多样化的用户认知特征。实证结果表明，与被动基线相比，ProAct具有显著优势：在ProActEval上，它将完成任务所需的交互轮次减少14.8%，用户工作量降低11.7%，幻觉率下降28.1%。此外，MemBench评估证实ProAct在反思准确性上达到最先进水平，凸显其持续且稳健的性能。

English

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.