Video AI turns out to be surprisingly bad at pointing to "when and where," even when it can answer the question itself. Title: OmniCompass: A Holistic Benchmark for Omni-Modal Video Grounding and Understanding URL: 💡 Overview Researchers from Hong Kong Polytechnic University and OPPO Research Institute introduce OmniCompass, a new holistic benchmark for video understanding, evaluating 50 models on temporal/spatial grounding and question answering on the same footing. 🎯 The problem Existing benchmarks cover either vision-only or audio-visual evidence, test either QA or grounding but rarely both, and had no way to check whether a model's predictions actually agree when the target is specified by text alone versus text plus a reference image. 🧪 The method Across 820 videos (80.6 hours, 7 domains, 52 subcategories), each clip gets a matched Text setting and Text+Image setting sharing the same target and ground truth. Two tracks, OmniCompass-VL for vision-language models and OmniCompass-AV for omni-modal models, each evaluate three unified tasks: temporal grounding (TG), spatial grounding (SG), and QA. A new Cross-Specification Consistency (CS) score measures how well predictions agree between the two settings. 📊 Results QA reaches up to 69.1% (VL) and 78.2% (AV), while temporal grounding tops out at just 27.1% (VL) and 40.6% (AV), exposing a huge gap between answering and pointing. The best open-source model also trails the best proprietary model, Gemini-3.8-Flash, by 32.8 points on CS average in the AV track (24.6% vs. 57.4%). #VideoAI# #Benchmarks#