arXiv:2606.04970v1 Announce Type: cross Abstract: We envision a proactive multi-modal assistant system which gives users real-time step-by-step guidance on a procedural task, autonomously deciding \textit{when} to interrupt, and \textit{how} to coach. However, progress is limited by the absence of large-scale, cross-domain benchmarks that reflect realistic conditions, particularly the common case in which users deviate from the expected step sequence. We address this gap with four contributions: \textbf{(1)}~we release \textbf{EgoProactive}, a large-scale wearable-egocentric dataset for proact
The proliferation of advanced AI models and sensing capabilities makes proactive assistance systems more feasible, while the need for robust benchmarks addresses current limitations in development.
This research provides a critical benchmark and architectural insights for the development of AI agents capable of real-time, context-aware assistance, impacting various procedural tasks.
The explicit focus on handling user deviations in procedural tasks through a new large-scale dataset significantly unblocks progress towards more robust and adaptive AI assistance systems.
- · AI assistants developers
- · Robotics industry
- · Wearable technology companies
- · Manufacturing and logistics sectors
- · Manual instruction providers
- · Companies relying on static guidance
- · Inefficient training programs
The new benchmark accelerates the development of more capable and reliable AI agents for task assistance.
Ubiquitous proactive AI assistants could significantly improve efficiency and reduce errors across complex human operations.
As these systems become more sophisticated, they could reshape educational paradigms and skill transfer processes.
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Read at arXiv cs.AI