让AI在等待用户时主动预判需求，提前准备信息。
Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents
- 利用对话历史和记忆预测用户下一步需求，提前收集信息。
- 任务完成少14.8%轮次，用户付出减少11.7%，幻觉率降28.1%。
- 适合需要高效响应的智能助手场景，如客服、个人助理。
尽管AI代理在推理和工具使用方面表现卓越，但其本质仍为被动响应：仅在收到用户提示后才进行计算。这种模式忽略了关键机会——交互间隙的空闲时间被大量浪费，导致代理无法为未来用户需求做准备。为此，我们提出ProAct，一种主动式代理架构，通过利用空闲计算资源来预判并满足潜在用户需求。通过分析不断演化的对话历史与持久记忆，ProAct可预测未来需求，并迭代获取信息，从而在用户提问前填补知识空白并准备好证据。为严格评估主动能力，我们还引入ProActEval，一个涵盖40个领域共200个场景的综合基准，包含可预测的需求链和多样的用户认知特征。实验结果表明，与被动基线相比，ProAct在任务完成上减少14.8%的交互轮次，降低11.7%用户努力，将幻觉率降低28.1%。此外，MemBench评估证实ProAct达到当前最优的反思准确性，展现出持续且稳健的表现。
原文摘要 · Abstract (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.
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