用管理循环设计多智能体系统，让AI能像老师一样讲懂科学视频。
SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System
- 基于戴明循环构建自进化推理机制，分步解析科学实验视频。
- 在500个专家验证的科学问题上超越Gemini、GPT-4o等主流模型。
- 可生成图文音互动教学内容，适合科研教育场景使用。
多模态大语言模型和视频智能体系统虽提升了通用视频理解能力，但在需要外部专业知识和严谨步骤推理的科学视频领域仍显不足。为此，我们提出SciEducator，首个用于科学视频理解与教育的迭代自进化多智能体系统。该系统借鉴管理学中的戴明循环（Plan-Do-Study-Act），将其转化为自我演化的推理与反馈机制，以精准解析复杂科学活动。此外，SciEducator可生成针对特定科学过程的多模态教学内容，包括文本说明、视觉指引、音频讲解和交互参考。为支持评估，我们构建了SciVBench基准，包含500个由专家验证且基于文献的科学问答对，覆盖物理、化学及日常现象五大类别。大量实验表明，SciEducator在该基准上显著优于主流闭源MLLMs（如Gemini、GPT-4o）及先进视频代理系统，确立了新的研究范式。
原文摘要 · Abstract (English)
Recent advancements in multimodal large language models (MLLMs) and video agent systems have significantly improved general video understanding. However, when applied to scientific video understanding and educating, a domain that demands external professional knowledge integration and rigorous step-wise reasoning, existing approaches often struggle. To bridge this gap, we propose SciEducator, the first iterative self-evolving multi-agent system for scientific video comprehension and education. Rooted in the classical Deming Cycle from management science, our design reformulates its Plan-Do-Study-Act philosophy into a self-evolving reasoning and feedback mechanism, which facilitates the interpretation of intricate scientific activities in videos. Moreover, SciEducator can produce multimodal educational content tailored to specific scientific processes, including textual instructions, visual guides, audio narrations, and interactive references. To support evaluation, we construct SciVBench, a benchmark consisting of 500 expert-verified and literature-grounded science QA pairs across five categories, covering physical, chemical, and everyday phenomena. Extensive experiments demonstrate that SciEducator substantially outperforms leading closed-source MLLMs (e.g., Gemini, GPT-4o) and state-of-the-art video agents on the benchmark, establishing a new paradigm for the community.
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