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

Problem
This paper addresses the limitation of current AI agents, which predominantly operate in a reactive manner, responding only after user prompts. This reactive paradigm neglects the potential of utilizing idle computational time to anticipate user needs, thereby missing opportunities for proactive engagement. The authors propose a novel architecture, ProAct, to leverage this idle time for predictive capabilities. This work is presented as a preprint and has not yet undergone peer review.

Method
The core technical contribution is the ProAct architecture, which integrates a proactive approach to agent design. ProAct utilizes a combination of evolving dialogue history and persistent memory to predict user needs before they are explicitly stated. The architecture employs a multi-step process where the agent iteratively acquires relevant information, thereby resolving knowledge gaps and preparing evidence in advance of user queries. The authors also introduce ProActEval, a benchmark consisting of 200 scenarios across 40 domains, designed to rigorously evaluate the proactive capabilities of agents. The benchmark includes predictable need chains and diverse user cognitive profiles, facilitating a comprehensive assessment of the ProAct architecture.

Results
Empirical evaluations demonstrate that ProAct significantly outperforms reactive baselines on the ProActEval benchmark. Specifically, ProAct reduces the number of required interaction turns by 14.8%, decreases user effort by 11.7%, and lowers hallucination rates by 28.1%. Additionally, evaluations using MemBench indicate that ProAct achieves state-of-the-art reflective accuracy, highlighting its robust performance across various scenarios. These results suggest that proactive engagement can lead to more efficient and effective user-agent interactions.

Limitations
The authors acknowledge several limitations, including the potential for overfitting to the specific scenarios presented in ProActEval, which may not generalize to all real-world applications. They also note that the effectiveness of ProAct may vary depending on the complexity of user needs and the richness of the dialogue history. Furthermore, the reliance on persistent memory raises questions about scalability and computational overhead in more extensive applications. An additional limitation not explicitly mentioned is the potential for increased latency in response times due to the proactive information-gathering process.

Why it matters
The implications of this work are significant for the development of next-generation AI agents. By shifting from a reactive to a proactive paradigm, agents can enhance user experience through anticipatory actions, leading to more fluid and efficient interactions. This research opens avenues for further exploration into proactive AI systems, potentially influencing design principles in user-centered AI applications. The introduction of ProActEval as a benchmark also sets a foundation for future research in evaluating proactive capabilities, encouraging the development of more sophisticated and context-aware AI agents.

Authors: Haoyi Hu, Qirong Lyu, Xianghan Kong, Weiwen Liu, Jianghao Lin, Zixuan Guo, Yan Xu, Yasheng Wang et al.
Source: arXiv:2605.25971
URL: https://arxiv.org/abs/2605.25971v1