做视频的AI终于学会自己收拾烂摊子
现在的AI剪视频，大多是把活拆成几步、各干各的，一步出错后面全崩。这篇让一个9B的小模型当总导演：它手里攥着整个项目的状态，剪完一镜发现不对，能当场回头修，不用重来。训练数据来自3,750部成片——把成品倒推回剪辑决策，再回放验证，攒出13万条指令。结果：它跟Claude Opus 5打平，但每成功剪出一分钟视频，少用33倍的token。它不是你明天能用的工具，但「AI干活出错能自己补救」这件事，是视频自动化从玩具走向生产力的关键一步。
📄 原文摘要(英文)
Producing long-form videos from narration requires interdependent decisions that remain coherent across shots. Existing agentic editors often use multi-stage workflows driven by general models, leaving the quality-cost trade-off of compact, task-adapted policies unclear. We address this gap with WeVisCut, a long-form script-to-video system that connects tool execution with policy learning. Its agent harness maintains shared production state so that tool feedback informs subsequent decisions and repairs. Because finished videos do not expose these decisions, our four-stage data pipeline combines editing inversion with verified replay to turn recovered editing intent into executable supervision. Applied to 3,750 finished videos, this pipeline yields over 130K training examples. We use this supervision to train a single 9B policy that coordinates production and recovers from failures within the same harness. Moreover, we introduce a 100-task benchmark and five metrics assessing editing quality and delivery reliability. Under a shared tool environment, WeVisCut substantially outperforms its base model and achieves comparable performance to Claude Opus 5 while using nearly 33x fewer generated tokens per successful output minute. It also outperforms task-matched native editors on our proposed metrics.