arXiv AI July 28, 2026 By Thomas Monks, Alison Harper, Amy Heather, Navonil Mustafee

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 3

Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies

arXiv:2607. 29389v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated a strong ability to generate syntactically correct code from natural-language specifications.

By Jan Marius St\"urmer, Jascha Knack, Tobias Koch, Andreas Weinmann

llms benchmarks

arXiv Machine Learning

Jul 28

Benchmarking LLMs for Verilog Design Flows

arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.

By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola

llms benchmarks

arXiv AI

Aug 7

MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation

arXiv:2511. 14967v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown great promise in generating structured diagrams from natural language descriptions, particularly Mermaid sequence diagrams for software engineering.

By Basel Shbita, Farhan Ahmed, Chad DeLuca

llms benchmarks

arXiv Machine Learning

Aug 11

Math-Vision Diagrams: A Comprehensive Benchmark for Evaluating LLM Mathematical Diagram Generation Capabilities

arXiv:2608. 08964v1 Announce Type: new Abstract: The generation of mathematically precise diagrams from tex- tual prompts has emerged as a critical yet underexplored capability of Large Language Models (LLMs).

By Harish Kashyap, Kiran Byadarhaly, Sriram Chakaravarthy, Sanyukta Tuti, Aryan Mistry

llms diffusion benchmarks

arXiv AI

Jul 28

Knowledge-Centric Agents for Workflow Generation in ComfyUI

arXiv:2607. 15845v2 Announce Type: replace Abstract: Workflow generation in visual creation systems such as ComfyUI demands not only syntactic accuracy but also expert-level reasoning over modular compositions.

By Zhendong Li, Lei Sun, Ruibo Ming, He Zhang, Danda Pani Paudel, Luc Van Gool, Jinjin Gu

llms agents fine-tuning

arXiv AI

Jul 17

StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows

arXiv:2607. 14896v1 Announce Type: cross Abstract: Addressing a structural-engineering request requires more than a single answer; it requires a chain of interdependent artifacts: interpreted requirements, a computable model, validation records, solver outputs, code-check records, and a final report.

By Sizhong Qin, Yi Gu, Yao Jiang, Ao Cai, Changjian Zhou, Shaoxuan Shuai, Jiachang Wang, Tianhao Shen, Yueqiang Li, Xinhao Li, Li Zeng, Yueshi Chen, Dachen Gao, Genrong Xu, Wenjie Liao, Xinzheng Lu

llms agents nlp multimodal benchmarks

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Simulation Code Generation for Fluid Systems using Large Language Models: Benchmarking Models and Prompting Strategies

Benchmarking LLMs for Verilog Design Flows

MermaidSeqBench: An Evaluation Benchmark for NL-to-Mermaid Sequence Diagram Generation

Math-Vision Diagrams: A Comprehensive Benchmark for Evaluating LLM Mathematical Diagram Generation Capabilities

Knowledge-Centric Agents for Workflow Generation in ComfyUI

StructureClaw: Traceable LLM Agents and an Executable Benchmark for Structural Engineering Workflows