most citedBenchmarking the Capabilities of Large Language Models in Transportation System Engineering: Accuracy, Consistency, and Reasoning Behaviors

2 citations · 4 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Beyond Seeing: Evaluating Multimodal LLMs on Tool-Enabled Image Perception, Transformation, and Reasoning

Xingang Guo, Utkarsh Tyagi, Advait Gosai +8

Multimodal Large Language Models (MLLMs) are increasingly applied in real-world scenarios where user-provided images are often imperfect, requiring active image manipulations such…

cs.CE2025

Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs

Xingang Guo, Yaxin Li, Xiangyi Kong +62

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. Ho…

cs.CV20242 cited

DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models

Chengke Zou, Xingang Guo, Rui Yang +3

The rapid advancements in Vision-Language Models (VLMs) have shown great potential in tackling mathematical reasoning tasks that involve visual context. Unlike humans who can relia…

eess.SY2024

ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise

Xingang Guo, Darioush Keivan, Usman Syed +5

Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors including aerospace, automotive systems, power grids, and robo…

cs.AI20242 cited

Benchmarking the Capabilities of Large Language Models in Transportation System Engineering: Accuracy, Consistency, and Reasoning Behaviors

Usman Syed, Ethan Light, Xingang Guo +4

In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, GPT-4o, Claude 3.5 Sonnet, Claude 3 Opus, Gemini 1.5 Pro, Llama 3, and Ll…