现有方法存在特征漂移或内容坍塌问题
Yale Song与Yiwen Song报告称，现有方法存在特征漂移（实体与环境逐渐发生非预期变化）或内容坍塌（叙事无法有意义地推进）的问题。
支持这项说法
现有方法存在特征漂移问题，即实体与环境逐渐发生非预期变化；或存在内容坍塌问题，即叙事无法有意义地推进。
原始摘录
existing methods suffer from feature drift , where entities and environments gradually change unintentionally, or content collapse , where narratives fail to progress meaningfully.
上下文
大多数现有智能体流水线通过链式模块实现该流程的自动化，但由于采用独立的手工提示词，会遭遇语义漂移（跨镜头的角色着装或场景出现细微变化）和级联故障（例如上游资产瑕疵破坏下游视频合成）。由于早期错误会传播并破坏长周期一致性，该流程往往需要详尽的人工干预。从结构角度看，这反映了经典的信用分配问题，因为终端故障难以追溯到具体的提示词。此外，
原始上下文
Most existing agentic pipelines automate this process via chained modules but suffer from semantic drift (subtle shifts in character attire or scenery across shots) and cascading failures (e.g., an upstream asset artifact corrupting downstream video synthesis) due to independent, handcrafted prompting. Because early errors propagate and break long-horizon consistency, the process often requires exhaustive manual intervention. From a structural perspective, this reflects the classical credit assignment problem, as terminal failures are difficult to trace back to specific prompts. Furthermore,