arXiv AI By Rizwaan Malik, Ashna Khetan, Isabel Sieh, Samin Khan

Exploring Agentic Workflows for Generating High Quality Math Visual Aids

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arXiv:2607. 09839v1 Announce Type: new Abstract: Mathematical diagrams play a crucial role in K 12 education, both as problem components and as scaffolding for student comprehension.

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arXiv AI
Jun 2

Beyond End-to-End Video Models: An LLM-Based Multi-Agent System for Educational Video Generation

arXiv:2602. 11790v2 Announce Type: replace Abstract: Although recent end-to-end video generation models demonstrate impressive performance in visually oriented content creation, they remain limited in scenarios that require strict logical rigor and precise knowledge representation, such as instructional and educational media.

By Lingyong Yan, Jiulong Wu, Dong Xie, Weixian Shi, Deguo Xia, Jizhou Huang
arXiv AI
Jun 11

MentisOculi: Revealing the Limits of Reasoning with Mental Imagery

arXiv:2602. 02465v2 Announce Type: replace Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation.

By Jana Zeller, Thadd\"aus Wiedemer, Fanfei Li, Thomas Klein, Prasanna Mayilvahanan, Matthias Bethge, Felix Wichmann, Ryan Cotterell, Wieland Brendel
arXiv AI
Aug 5

Automated Visualization Code Synthesis via Multi-Path Reasoning and Feedback-Driven Optimization

arXiv:2502. 11140v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have become a cornerstone for automated visualization code generation, enabling users to create charts through natural language instructions.

By Wonduk Seo, Daye Kang, Hyunjin An, Taehan Kim, Soohyuk Cho, Seungyong Lee, Minhyeong Yu, Jian Park, Yi Bu, Seunghyun Lee
arXiv AI
Jun 10

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.

By Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Sumitra Ganesh, Manuela Veloso
arXiv AI
Jul 28

Generative Artificial Intelligence (GenAI) to convert images of queuing networks into verifiable simulation models: an open-weight LLM workflow approach

arXiv:2607. 24259v1 Announce Type: new Abstract: Recent work has explored the use of Large Language Models (LLMs) to automate simulation model building, typically by generating executable code directly from natural language descriptions.

By Thomas Monks, Alison Harper, Amy Heather, Navonil Mustafee