GroundedVDR: Grounding Video-to-Web Evidence Dependencies in Video Deep Research

Video large multimodal models have improved at identifying cross-frame visual evidence and conducting open-web retrieval, as reflected in recent video deep research (VDR) benchmarks. However, existing VDR benchmarks primarily evaluate tool-use trajectories and final responses without verifying whether models use temporal video information or retrieved web evidence. We introduce GroundedVDR, an evidence-grounded benchmark with three features. First, it combines human review and model testing to exclude questions that can be answered using parametric knowledge alone. Second, it provides structured video-to-web evidence annotation that links related video frames with their corresponding web evidence, while recording candidate-linked hard negatives for diagnostic analysis. Third, it measures video-state localization, evidence retrieval, video-to-web evidence alignment, and final answer synthesis separately. Together, these features allow GroundedVDR to evaluate both answer correctness and support from the required video and web evidence.