SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System
- URL: http://arxiv.org/abs/2511.17943v1
- Date: Sat, 22 Nov 2025 06:54:16 GMT
- Title: SciEducator: Scientific Video Understanding and Educating via Deming-Cycle Multi-Agent System
- Authors: Zhiyu Xu, Weilong Yan, Yufei Shi, Xin Meng, Tao He, Huiping Zhuang, Ming Li, Hehe Fan,
- Abstract summary: SciEducator is a self-evolving multi-agent system for scientific video comprehension and education.<n>Our design reformulates its Plan-Do-Study-Act philosophy into a self-evolving reasoning and feedback mechanism.<n>It can produce multimodal educational content tailored to specific scientific processes.
- Score: 42.24867393406526
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- ExpVid: A Benchmark for Experiment Video Understanding & Reasoning [65.17173232816818]
We introduce ExpVid, the first benchmark designed to systematically evaluate MLLMs on scientific experiment videos.<n>We evaluate 19 leading MLLMs on ExpVid and find that while they excel at coarse-grained recognition, they struggle with disambiguating fine details, tracking state changes over time, and linking experimental procedures to scientific outcomes.<n>Our results reveal a notable performance gap between proprietary and open-source models, particularly in high-order reasoning.
arXiv Detail & Related papers (2025-10-13T16:45:28Z) - Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics [82.55776608452017]
Large language models (LLMs) provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics.<n>This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle.<n>We identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents.
arXiv Detail & Related papers (2025-10-10T22:26:26Z) - SciVideoBench: Benchmarking Scientific Video Reasoning in Large Multimodal Models [89.10286051587151]
We introduce SciVideoBench, a rigorous benchmark designed to assess advanced video reasoning in scientific contexts.<n>SciVideoBench consists of 1,000 carefully crafted multiple-choice questions derived from cutting-edge scientific experimental videos.<n>Our evaluation highlights significant performance deficits in state-of-the-art proprietary and open-source LMMs.
arXiv Detail & Related papers (2025-10-09T17:59:23Z) - Learning Progression-Guided AI Evaluation of Scientific Models To Support Diverse Multi-Modal Understanding in NGSS Classroom [2.6572245224872835]
We build on a validated NGSS-aligned multi-modal LP reflecting diverse ways of modeling and explaining electrostatic phenomena.<n>We show how LP guides the design of personalized ML-driven feedback grounded in the diversity of student thinking on both assessment modes.
arXiv Detail & Related papers (2025-09-16T22:12:15Z) - VideoAgent: Personalized Synthesis of Scientific Videos [24.440349159498286]
VideoAgent is a novel multi-agent framework that synthesizes personalized scientific videos through a conversational interface.<n>VideoAgent parses a source paper into a fine-grained asset library and orchestrates a narrative flow that synthesizes both static slides and dynamic animations to explain complex concepts.<n>SciVidEval is the first comprehensive suite for this task, which combines automated metrics for multimodal content quality and synchronization with a Video-Quiz-based human evaluation to measure knowledge transfer.
arXiv Detail & Related papers (2025-09-14T12:54:21Z) - Many Heads Are Better Than One: Improved Scientific Idea Generation by A LLM-Based Multi-Agent System [62.832818186789545]
Virtual Scientists (VirSci) is a multi-agent system designed to mimic the teamwork inherent in scientific research.<n>VirSci organizes a team of agents to collaboratively generate, evaluate, and refine research ideas.<n>We show that this multi-agent approach outperforms the state-of-the-art method in producing novel scientific ideas.
arXiv Detail & Related papers (2024-10-12T07:16:22Z) - LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations.
We introduce Scientific Generative Agent (SGA), a bilevel optimization framework.
We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
arXiv Detail & Related papers (2024-05-16T03:04:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.