提出可自动验证长视频生成质量的新方法，解决人工审核难、评估慢的问题。

While text-to-video diffusion models have advanced significantly, creating coherent long-form content remains unreliable due to stochastic sampling artifacts. This necessitates generating multiple candidates, yet verifying them creates a severe bottleneck; manual review is unscalable, and existing automated metrics lack the adaptability and speed required for runtime monitoring. Another critical issue is the trade-off between evaluation quality and run-time performance: metrics that best capture human-like judgment are often too slow to support iterative generation. These challenges, originating from the lack of an effective evaluation, motivate our work toward a novel solution. To address this, we propose a scalable automated verification framework for long-form video. First, we introduce the MSG(Multi-Scene Generation) score, a hierarchical attention-based metric that adaptively evaluates narrative and visual consistency. This serves as the core verifier within our CGS (Candidate Generation and Selection) framework, which automatically identifies and filters high-quality outputs. Furthermore, we introduce Implicit Insight Distillation (IID) to resolve the trade-off between evaluation reliability and inference speed, distilling complex metric insights into a lightweight student model. Our approach offers the first comprehensive solution for reliable and scalable long-form video production.