SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System
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.
6926c0ef243b2216fb75cbac
Zhiyu Xu
claimed_verified
2025-11-26T09:26:49.611Z
68f84ad4ba36e04db877ac28
/avatars/8ac2d4b6c549082ccdfbfc7b02bcde8c.svg
Dr-Loser
Dr-Loser
user
Dr-Loser
6926c0ef243b2216fb75cbad
Weilong Yan
claimed_verified
2026-02-25T17:33:52.227Z
668192a3f35c3ff47a8438ee
/avatars/6b293341f5dc51f574252c6f57cfd293.svg
Weilong Yan
DavidYan2001
user
DavidYan2001
6926c0ef243b2216fb75cbae
Yufei Shi
6926c0ef243b2216fb75cbaf
Xin Meng
6926c0ef243b2216fb75cbb0
Tao He
6926c0ef243b2216fb75cbb1
Huiping Zhuang
6926c0ef243b2216fb75cbb2
Ming Li
6926c0ef243b2216fb75cbb3
Hehe Fan
SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System
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.
SciEducator, an iterative self-evolving multi-agent system, enhances scientific video understanding and education by integrating professional knowledge and step-wise reasoning, outperforming existing models on a new benchmark.
multimodal large language models multi-agent system Deming Cycle Plan-Do-Study-Act scientific video comprehension multimodal educational content textual instructions visual guides audio narrations interactive references SciVBench science QA pairs physical phenomena chemical phenomena everyday phenomena