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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 proactive procedural assistance with explicit Out-of-Plan (OOP) annotations and recovery steps; \\textbf{(2)}~we augment five established benchmarks (Ego4D, EPIC-KITCHENS, EgoExo4D, HoloAssist, HowTo100M) into \\textbf{Pro\\textsuperscript{2}Bench} under a unified proactive-guidance schema; \\textbf{(3)}~we propose a \\textbf{decoupled planner--interaction architecture} specialized for procedural state, vis","title":"Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance","url":"https://arxiv.org/abs/2606.04970","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.04970v1 Announce Type: cross \nAbstract: 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. 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