ProactiveXBench: Benchmarking When and How to Intervene in the Physical World

Physical AI increasingly requires intelligent systems to operate autonomously in evolving real-world environments, where proactive assistance is a fundamental capability. However, existing benchmarks predominantly adopt reactive settings with explicit requests or goals, leaving proactive physical intelligence without systematic evaluation. A benchmark for this capability should assess whether a system can infer implicit intervention needs from ongoing observations, determine when intervention is appropriate, and sustain coherent communication and physical actions until the intended objective is achieved. To this end, we introduce ProactiveXBench, a benchmark for proactive physical intelligence comprising 1,000 real-world physical tasks with 166K fine-grained second-level annotations across diverse scenarios involving proactive communication and physical actions. ProactiveXBench evaluates the full intervention trajectory through three complementary dimensions: Timing, which measures alignment with the reference intervention time; Progress, which characterizes post-initiation execution duration; and Outcome, which measures event-level completion under entity-matching and ordering constraints. Evaluations of leading AI systems including GPT-6-Astra reveal substantial limitations in proactive physical intelligence: strong response or completion performance does not necessarily translate into accurate recognition of when intervention is needed, and timely initiation does not guarantee successful completion. These findings expose complementary challenges in intervention discovery and sustained execution, and establish ProactiveXBench as a unified framework for studying and advancing proactive physical intelligence.