Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict logical rigor and precise knowledge representation, such as instructional and educational media. To address this problem, we propose LAVES, a hierarchical LLM-based multi-agent system for generating high-quality instructional videos from educational problems. The LAVES formulates educational video generation as a multi-objective task that simultaneously demands correct step-by-step reasoning, pedagogically coherent narration, semantically faithful visual demonstrations, and precise audio--visual alignment. To address the limitations of prior approaches--including low procedural fidelity, high production cost, and limited controllability--LAVES decomposes the generation workflow into specialized agents coordinated by a central Orchestrating Agent with explicit quality gates and iterative critique mechanisms. Specifically, the Orchestrating Agent supervises a Solution Agent for rigorous problem solving, an Illustration Agent that produces executable visualization codes, and a Narration Agent for learner-oriented instructional scripts. In addition, all outputs from the working agents are subject to semantic critique, rule-based constraints, and tool-based compilation checks. Rather than directly synthesizing pixels, the system constructs a structured executable video script that is deterministically compiled into synchronized visuals and narration using template-driven assembly rules, enabling fully automated end-to-end production without manual editing. In large-scale deployments, LAVES achieves a throughput exceeding one million videos per day, delivering over a 95% reduction in cost compared to current industry-standard approaches while maintaining a high acceptance rate.

翻译：尽管近期端到端视频生成模型在视觉导向的内容创作中展现出令人瞩目的性能，但在需要严格逻辑严谨性和精确知识表示的场景（如教学与教育媒体）中，其能力仍然有限。为解决这一问题，我们提出了LAVES，一个基于大型语言模型（LLM）的分层多智能体系统，用于从教育问题生成高质量的教学视频。LAVES将教育视频生成定义为一个多目标任务，该任务同时要求正确的逐步推理、教学连贯的叙述、语义忠实的视觉演示以及精确的视听对齐。为克服先前方法（包括低程序保真度、高制作成本和有限可控性）的局限性，LAVES将生成工作流分解为由一个中央编排智能体协调的多个专业智能体，该编排智能体配备了明确的质量门控和迭代批判机制。具体而言，编排智能体监督一个用于严谨问题求解的解决方案智能体、一个生成可执行可视化代码的插图智能体，以及一个面向学习者的教学脚本叙述智能体。此外，所有工作智能体的输出均需经过语义批判、基于规则的约束和基于工具的编译检查。该系统并非直接合成像素，而是构建一个结构化的可执行视频脚本，该脚本通过模板驱动的组装规则确定性地编译成同步的视觉内容和叙述，从而实现无需人工编辑的全自动端到端生产。在大规模部署中，LAVES实现了每日超过一百万视频的吞吐量，与当前行业标准方法相比，成本降低了95%以上，同时保持了高接受率。