Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation

Researchers introduce LASEV, an LLM-based multi-agent system that generates educational videos by decomposing production into specialized agents rather than relying on end-to-end video models. The system achieves 95% cost reduction and over one million videos daily while maintaining high quality through structured reasoning, semantic critique, and deterministic compilation.

LASEV represents a significant shift in how AI systems approach complex content generation tasks that demand both creative and logical rigor. Rather than attempting to synthesize videos directly through pixel-level generation, the system treats educational video production as a structured workflow problem, decomposing it into specialized agents handling reasoning, visualization, and narration. This architectural approach mirrors successful patterns in software engineering where specialized components collaborate through well-defined interfaces.

The innovation addresses fundamental limitations of current end-to-end video models, which excel at visually coherent content but struggle with logical consistency and pedagogical accuracy. Educational content requires multiple constraints to be satisfied simultaneously—correct mathematics, coherent explanations, synchronized audio-visual elements, and learner-appropriate pacing. LASEV's multi-agent framework with explicit quality gates and iterative critique mechanisms creates accountability at each stage, preventing errors from propagating.

The economic impact is substantial for educational technology providers. A 95% cost reduction compared to industry standards while scaling to one million videos daily opens possibilities for personalized, at-scale educational content generation. This particularly benefits online learning platforms, corporate training programs, and educational publishers seeking to automate content production without sacrificing quality.

The methodology demonstrates how decomposition and structured reasoning can outperform black-box end-to-end approaches for tasks requiring logical guarantees. The deterministic compilation step—converting abstract specifications into concrete multimedia—provides auditability and reproducibility absent in traditional generative AI. Future developments may extend this pattern to other domains requiring both creativity and logical precision, such as technical documentation, scientific visualization, and complex procedural training.

• →Multi-agent systems with specialized roles outperform end-to-end models for tasks requiring logical rigor and pedagogical coherence.
• →Structured, deterministic compilation reduces costs by 95% while enabling automated production at one million videos daily.
• →Quality gates and semantic critique mechanisms ensure educational accuracy where direct pixel synthesis cannot guarantee correctness.
• →The approach mirrors software engineering patterns, suggesting broader applicability beyond educational video generation.
• →Decomposing complex generation tasks into reasoning, visualization, and narration agents enables scaling without sacrificing content quality.