ProAct

ProAct: Proactive Agent Architecture Using Idle-Time Compute to Anticipate User Needs

ProAct is a proactive agent architecture that uses idle time between user interactions to predict upcoming needs, pre-fetch information, and resolve knowledge gaps before queries are issued. The system analyzes dialogue history and persistent memory to iteratively acquire relevant information in advance. Evaluated on the new ProActEval benchmark (200 scenarios, 40 domains), ProAct reduces required turns by 14.8%, user effort by 11.7%, and hallucination rates by 28.1% compared to reactive baselines. The work also achieves state-of-the-art reflective accuracy on MemBench.