TRACE: Temporal Audit and Condition-aware Evaluation of Streaming Video Understanding
This study addresses the limitation of existing streaming video evaluation methods, which overlook evidence timeliness and triggering mechanisms, allowing similar scores to obscure fundamental differences in system behavior. To this end, we propose TRACE, a framework that explicitly quantifies information availability and event responsiveness through temporal auditing and condition-aware evaluation. Furthermore, it introduces a unified causal Core-Adapter protocol to enable multi-dimensional reporting of conditioned execution behaviors. Evaluations based on temporal annotations, causal controls, and a multi-dimensional metric suite reveal significant disparities in system workload and reliability despite identical accuracy. Consequently, this work establishes a new paradigm for streaming video evaluation.