AI写60集短剧，终于不靠套路硬撑了
短剧剧本越长，AI越容易陷入同一个套路：开头还新鲜，写到后面情节开始自我重复，甚至把前面埋的伏笔都忘了。DraMaze 把写剧本拆成多个 AI 角色协作，一边用「套路记忆」引导剧情往不同方向长，一边用一棵倒着长的「剧情依赖树」专门记住哪些历史信息是未来要用的。在最长 6 万字的 60 集剧本上，套路重复率降了 32.1%，逻辑连贯性评分比最强基线高 8.5 分。它不是你明天就能拿来写爆款短剧的工具，但说明一件事：AI 写长故事的天花板，正在从「能写多长」变成「能不能不重复自己」。
📄 原文摘要(英文)
The rapid growth of microdramas has increased demand for high-quality script production. Long-form microdrama script generation with LLMs requires both diverse plot progression and long-range narrative coherence. Our observations reveal that candidate continuations increasingly concentrate on fewer narrative mechanisms as generation progresses, while conventional retrieval may reinforce trope reuse and overlook historical information essential to future plots. To address these challenges, we propose DraMaze, a multi-agent pipeline that explores diverse causal paths through trope memory-guided evolutionary tree search and mutation. It further constructs a Backward Narrative Dependency Tree to organize, retain, and retrieve historical memories based on future narrative goals. We also introduce MiDramaBench, a bilingual benchmark for evaluating script logic and creative quality across different script lengths, together with causal-pattern metrics for narrative repetition and diversity. Experiments show consistent improvements across three backbone LLMs. For 60-episode scripts of up to 60k words, DraMaze reduces causal-pattern repetition by 32.1% and achieves a Logic score of 82.5, outperforming the best-performing baseline by 8.5 points.