預測與學習：於主動式代理中釋放閒置時段的算力

摘要

儘管AI代理在推理與工具使用方面展現出卓越能力，其本質上仍屬被動反應型：僅在收到明確的使用者提示後才計算回應。這種運作模式忽略了一個關鍵契機：互動間的空閒時間大多被浪費，使代理無法為未來的使用者需求預作準備。為填補此缺口，我們提出ProAct，一種主動式代理架構，藉由運用空閒時間的計算能力來預測並滿足使用者即將產生的需求。透過分析持續演進的對話歷史及持久記憶，ProAct能預測即將到來的需求，並以疊代方式獲取資訊，使代理能在使用者發起查詢前填補知識缺口與準備佐證。為嚴謹評估主動能力，我們亦引入ProActEval，這是一個涵蓋40個領域、共200個場景的全面基準測試，其中包含可預測的需求鏈與多樣化的使用者認知特徵。實驗結果顯示，相較於被動反應型基準方法，ProAct展現顯著優勢。在ProActEval上，ProAct將任務完成所需回合數減少14.8%，降低使用者努力11.7%，並將幻覺率降低28.1%。此外，MemBench評估證實ProAct在反思準確度上達到當前最佳水準，突顯其持續穩健的表現。

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.