HomeACE: A Comprehensive Evaluation Benchmark for Home Anomaly Video Understanding

Understanding safety risks in household videos is essential for intelligent systems that support risk prevention and timely response. Achieving this requires a model that can as a forecaster of the household: perceiving subtle risk precursors, inferring causal chains of danger escalation, localizing the temporal window for intervention, and formulating actionable mitigation strategies. However, existing evaluations of multimodal large language models (MLLMs) focus predominantly on general video understanding, with no systematic assessment of the broad range of capabilities required in home settings. To address this gap, we introduce HomeACE, a large-scale Home Anomaly Comprehensive Evaluation dataset comprising more than 14K household videos, including nearly 3K real-world recordings and over 11K generated videos that span normal, risk-only (an emerging risk), and abnormal (a realized anomaly) situations across diverse home scenes. Based on this dataset, we design a systematic evaluation protocol that assesses models along four complementary dimensions: Perception, Cognition, Temporal, and Planning. Extensive experiments reveal substantial limitations in current MLLMs: limited anomaly perception, weak causal reasoning, imprecise temporal localization, and mitigation plans that are often insufficiently grounded in the observed hazards. HomeACE provides a standardized platform for diagnosing these deficiencies and advancing home anomaly understanding toward the vision of prevention-oriented intelligent home systems.