这个新基准EG-VQA把视频问答的答案和证据绑定在一起，测出来一大票模型只会蒙答案不会找证据。开源模型EG-Reasoner靠证据监督训练，反事实推理直接碾压好几家专有模型。
EG-VQA是一个开放式的视频问答基准，包含2,067个视频和11,838个QA对，每个问题都标注了精确的时间证据区间。提出EG-F1指标，统一衡量预测证据的时间对齐和语义一致性。实验发现即使最强模型（如专有模型）在证据定位上表现不佳，存在答案正确但定位偏差的问题。为此提出EG-Reasoner模型，通过显式证据监督训练，在开源模型中达到最优，并在反事实等推理密集型任务上表现突出。该研究证明仅靠扩展规模不足以实现鲁棒的视频理解，结构化证据监督是关键。
EG-VQA: Benchmarking Verifiable Video Question Answering with Grounded Temporal Evidence
Recent advances in Video Large Language Models (Video-LLMs) have yielded promising performance on video question answering (VideoQA). Nevertheless, existing benchmarks are predominantly evaluated through answer correctness, while the grounding of predictions in relevant video evidence remains largely unexamined. This disconnect between answer generation and evidence understanding motivates the construction of the Evidence-Grounded Video Question Answering Benchmark (EG-VQA), an open-ended evaluation protocol in which each QA pair is explicitly annotated with supporting temporal evidence, thereby requiring joint reasoning and precise evidence localization. EG-VQA is comprised of 2,067 videos and 11,838 QA pairs with fine-grained evidence annotations. To evaluate predicted evidence, Evidence-Grounded F1 (EG-F1) is introduced as a unified metric in which temporal alignment and semantic consistency against ground-truth evidence are jointly measured. Experimental evaluation reveals that even strong proprietary models struggle to accurately ground their predictions, exposing a fundamental discrepancy between answer correctness and faithful evidence localization. To bridge this gap, EG-Reasoner, an evidence-grounded reasoning model trained with explicit supervision, is proposed. State-of-the-art performance is achieved among open-source models, with results competitive against proprietary systems, particularly pronounced gains are observed on reasoning-intensive tasks such as counterfactual questions. These findings demonstrate that scaling alone is insufficient for robust video understanding and that structured evidence supervision is essential for the development of more reliable and interpretable VideoQA systems.