Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agents

Hugging Face Daily Papers Papers
proactive-agents idle-time-compute ai-agents benchmark task-completion user-effort hallucination-reduction

Summary

ProAct is a proactive agent architecture that leverages idle-time computation to anticipate user needs, improving task completion efficiency and accuracy. It introduces ProActEval, a benchmark spanning 200 scenarios across 40 domains, and achieves significant gains over reactive baselines: 14.8% reduction in required turns, 11.7% decrease in user effort, and 28.1% cut in hallucination rates.

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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Source: https://huggingface.co/papers/2605.25971

Abstract

ProAct is a proactive agent architecture that uses idle-time computation to anticipate user needs and improve task completion efficiency and accuracy.

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, aproactive agent architecturethat leveragesidle-time computeto anticipate and fulfill likely upcoming user needs. By analyzing evolvingdialogue historytogether withpersistent 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 acceleratestask completionby reducing required turns by 14.8%, decreasesuser effortby 11.7%, and cutshallucination ratesby 28.1% on ProActEval. Furthermore, MemBench evaluations confirm that ProAct achieves state-of-the-artreflective accuracy, underscoring its sustained and robust performance.

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