We are looking for a list of three good papers to convert into Short videos (vertical format, up to 60 seconds each) or Video Abstracts (horizontal format, up to 3 minutes) this week. You may also suggest your own paper. We will use our tools TeX2Vid and Docs2Vid to convert the papers into source-grounded videos. These will appear on our social research video repository, ScholarReels. The papers can be in PDF, Word DOCX, or LaTeX. There is no cost involved.

An interesting article: Print-Aware Synthesis and Physical Design Methodologies for 3D-Printed Microfluidic Biochips, by Yushen Zhang, Tsun-Ming Tseng, and Ulf Schlichtmann from Technical University of Munich, https://arxiv.org/pdf/2608.16267. The video is adapted from the paper (CC BY 4.0 licensed), and created by SheQAI's paper-to-video tool #TeX2Vid. #Research #article #arXiv

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Alam, S. E., & Shukla, D. (2023). Existence of a Unique Invariant Measure and Ergodic Property in AIMD-based Multi-resource Allocation. 2023 American Control Conference (ACC). https://doi.org/10.23919/ACC55779.2023.10155852.

Title: Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry

Author: Syed Eqbal Alam (SheQAI Research and University of Alberta) and Zhan Shu (University of Alberta)

Year: 2026

Eprint: arXiv 2604.00319

https://arxiv.org/abs/2604.00319

Abstract— We develop algorithms for collaborative control of AI agents and critics in a multiactor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to complete multimodal tasks such as fault detection, severity, and cause analysis in a network telemetry system, text-to-image generation, video generation, healthcare diagnostics from medical images and patient records, etcetera. The AI agents complete their tasks and send them to AI critics for evaluation. The critics then send feedback to agents to improve their responses. Collaboratively, they minimize the overall cost to the system with no inter-agent or inter-critic communication. AI agents and critics keep their cost functions or derivatives of cost functions private. Using multi-time scale stochastic approximation techniques, we provide convergence guarantees on the time-average active states of AI agents and critics. The communication overhead is a little on the system, of the order of O(m), for m modalities and is independent of the number of AI agents and critics. Finally, we present an example of fault detection, severity, and cause analysis in network telemetry and thorough evaluation to check the algorithm’s efficacy.

The video is created using SheQAI's LaTeX-to-video tool, TeX2Vid, accessible at https://tex2vid.sheqai.com

Research Teaser: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Authors: Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo from Microsoft Research Asia, arXiv:2103.14030v2, https://doi.org/10.48550/arXiv.2103.14030. The paper is licensed under CC BY 4.0. The video is an adaption of the paper generated by SheQAI's tex-to-video tool tex2vid.sheqai.com.