ReKey: Benchmarking and Learning Agentic Keyframe Reflection for Video Generation

Recent video generation models have advanced substantially in visual fidelity and motion modeling, yet generated videos still suffer from semantic and temporal inconsistencies. This limitation has motivated Reflective Video Generation (RVG), a paradigm that introduces explicit reflection and correction into the generation process. We introduce ReKey-Bench to evaluate RVG. It comprises 400 video cases and 784 keyframe evaluation samples, covering keyframe verification, rectification, and downstream video improvement. Evaluation of existing vision-language models (VLMs) on ReKey-Bench reveals that they may overlook visual errors or produce rectifications that violate the keyframe plan. To address these limitations, we propose ReKey, an agent framework for evidence-grounded keyframe reflection. We train its reflection agent through on-policy self-distillation, providing a frozen teacher with question-answer-evidence (QAE) as privileged context. ReKey achieves gains of 13.3 points in keyframe verification F1 and 15.9 points in keyframe rectification score over Qwen3.5 on ReKey-Bench, while also improving downstream video generation and achieving consistent gains on T2V-CompBench and VBench-2.0. Our code and models will be publicly available.