EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence

The Evidence-Grounded Video Question Answering Benchmark (EG-VQA) has been introduced to address the gap between answer correctness and evidence grounding in VideoQA, consisting of 2,067 videos and 11,838 QA pairs with detailed temporal evidence annotations. The benchmark employs a new metric, Evidence-Grounded F1 (EG-F1), to evaluate both temporal alignment and semantic consistency of predictions against ground-truth evidence. Results indicate that existing models, including proprietary ones, struggle with evidence localization, highlighting the need for structured evidence supervision, which is addressed by the proposed EG-Reasoner model that achieves state-of-the-art performance among open-source models, particularly on reasoning-intensive tasks.