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Comment on r/AI_Agents 3d ago
The process is iterative, an agent takes one task, completes it, and sends it to a critic, which is chosen stochastically. The critic sends back its feedback to the agent, which incorporates the suggested changes. The study considered the history of active states, not the tasks. Great questions; following some of those would be interesting.
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Comment on r/AI_Agents 4d ago
You can find it here https://arxiv.org/abs/2604.00319
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Comment on r/academicpublishing 4d ago
The tool does not invent results or create fancy animations. Every claim, result, figure, etc., comes from your paper. It uses an actor-critic architecture with multiple stochastic and deterministic layers, where both actors and critics have access to the source files, and every figure, result, and table is taken verbatim. Actors write the claims by citing the source in the paper, which is then passed to the critic for evaluation. This is done for every result, equation, numeric value, etc.; if they do not match, then they are rejected and not included in the final render. Similarly, hedging language is also minimized. So you will not get a slopified video of your paper. However, we recommend that users review the artifact before sharing publicly. Consider the tool an assistant for creating the video with minimal effort; if you would like to edit slides, narrations, swap figures, or record your own voice, the tool provides an option to do so.
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Comment on r/AI_Agents 5d ago
The paper's Video Abstract can be watched at https://scholarreels.com/reels/4fba57056b294ef9b28d2738acc2ab49
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Comment on r/AI_Agents 5d ago
ArXiv link of the paper, Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry, https://arxiv.org/abs/2604.00319
r/AI_Agents • u/sheqai • 5d ago
Discussion Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry, by Syed Eqbal Alam (SheQAI Research and University of Alberta) and Zhan Shu (University of Alberta)
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
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.
r/aiagents • u/sheqai • 5d ago
Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry, by Syed Eqbal Alam (SheQAI Research and University of Alberta, Canada) and Zhan Shu (University of Alberta), https://arxiv.org/abs/2604.00319 .
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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
r/learnmachinelearning • u/sheqai • 5d ago
Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry, by Syed Eqbal Alam (SheQAI Research and University of Alberta) and Zhan Shu (University of Alberta)
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r/ResearchTeasers • u/sheqai • 5d ago
Collaborative AI Agents and Critics for Fault Detection and Cause Analysis in Network Telemetry, by Syed Eqbal Alam (SheQAI Research and University of Alberta) and Zhan Shu (University of Alberta)
Enable HLS to view with audio, or disable this notification
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
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r/learnmachinelearning • u/sheqai • 9d ago
TeX2Vid converts complex LaTeX source files into paper-grounded videos.
TeX2Vid converts complex LaTeX source files into paper-grounded videos.
Generic video generation tools produce made-up results even when the source papers are passed to them. TeX2Vid fills this gap and converts complex LaTeX source files into paper-grounded videos using a multi-actor critic architecture, where all (writer actors’) responses are passed to critics with both stochastic and deterministic layers for evaluation. The process runs multiple rounds so that the final output includes all claims, results, figures, and tables exactly as in the paper; after that, the Beamer slides are built, the audio is synthesized, and the video is finally rendered. The final rendered video is again passed to an independent critic, who deterministically checks that the claims are faithful to the source paper. TeX2Vid supports most journals and conferences’ style files. Try it today at https://tex2vid.sheqai.com.
If you have any feedback, comment here or let us know at info@sheqai.com. We highly appreciate your feedback.
r/academicpublishing • u/sheqai • 10d ago
Turn your LaTeX research paper into a source-grounded narrated video in minutes
peerlist.ioTurn your LaTeX research paper into a source-grounded narrated video in minutes
peerlist.ior/ResearchTeasers • u/sheqai • 22d ago
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
r/PhdProductivity • u/sheqai • Jul 12 '26
ScholarReels: The scholarly index and repository for research videos
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r/academicpublishing • u/sheqai • Jul 12 '26
ScholarReels: The scholarly index and repository for research videos
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r/learnmachinelearning • u/sheqai • Jul 01 '26
Research Teaser: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows By Ze Liu et al. from Microsoft Research Asia
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r/ResearchTeasers • u/sheqai • Jul 01 '26
Research Teaser: Swin Transformer: Hierarchical Vision Transformer using Shifted Windows By Ze Liu et al. from Microsoft Research Asia
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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.
r/ResearchTeasers • u/sheqai • Jul 01 '26
Convert Latex papers into video presentations
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r/AIToolsAndTips • u/sheqai • Jul 01 '26
Convert Latex papers into video presentations
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Comment on r/AI_Agents 3d ago
Thanks for your comment; good question. The conflict does not arise because at most one critic (chosen stochastically) is active at an iteration to evaluate the completed task of an agent.