Proactive Computing

2026-08-12

AI Summary

This work proposes a novel paradigm of “proactive computing” to address the limitations of traditional systems that passively respond to explicit user commands and struggle to anticipate needs or mitigate risks in advance. It systematically delineates proactive computing from reactive, context-aware, adaptive, and predictive approaches, framing proactive behavior as a cross-layer integration problem encompassing perception, understanding, decision-making, action, and governance. The study emphasizes the critical roles of timing in action initiation and ethical governance. By integrating mobile and wearable sensing, edge computing, machine learning, foundation models, and physical actuation, the authors construct an end-to-end proactive reasoning framework and outline its design space, key technical challenges—such as uncertainty-triggered activation and the prediction-action gap—and sociotechnical considerations including trust, privacy, and fairness.

Abstract

Computing systems are moving from reactive tools toward systems that sense, interpret, predict, and act before explicit user requests. This transition is enabled by the global scale of mobile connectivity, the rapid expansion of wearable and ambient sensing, advances in machine learning and foundation models, distributed edge infrastructure, and physical actuation. We define \emph{proactive computing} as a paradigm in which systems infer user context, anticipate future needs or risks, and initiate information delivery or actions at an appropriate time. This survey distinguishes proactive computing from reactive, context-aware, adaptive, and predictive computing, and frames proactivity as a system-level integration problem across sensing, understanding, decision making, action, and governance. We review the technological enablers of proactive computing, organize its design space, analyze technical challenges such as uncertainty-aware triggering and the prediction-to-action gap, and discuss socio-technical issues involving user acceptance, trust, privacy, accountability, fairness, and sustainability. We argue that the key research challenge is not merely improving prediction accuracy, but determining when, how, and whether systems should act on behalf of users.

Problem

• proactive computing
• prediction-to-action gap
• uncertainty-aware triggering
• user trust
• system-level integration

Innovation

• proactive computing
• uncertainty-aware triggering
• prediction-to-action gap
• system-level integration
• foundation models