# 预见与学习：释放主动智能体的空闲时间计算能力

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-25 08:00
- AIHOT 分数：63
- AIHOT 链接：https://aihot.virxact.com/items/cmpmd6ghh0nojsl012t118df6
- 原文链接：https://arxiv.org/abs/2605.25971

## AI 摘要

当前AI智能体是反应式的，仅在用户提问后响应，浪费了交互间的空闲时间。为解决此问题，本文提出了ProAct主动式智能体架构，它能利用空闲时间，通过分析对话历史与持久记忆预测用户需求，并迭代地获取信息、准备证据，从而在用户提问前填补知识缺口。为评估该能力，研究者发布了包含200个场景的ProActEval基准。实验表明，相比反应式基线，ProAct将任务完成所需轮次减少14.8%，用户操作负担降低11.7%，并将模型幻觉率大幅降低28.1%，同时在MemBench上取得了最先进的反思准确率。

## 正文

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.