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20242026
most citedCapabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra

20 citations · 40 across the 14 of their papers we have counts for

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7 papers · 1 filter

cs.AI2026

DuoCook: An Overcooked-Inspired Benchmark for Visual Planning and Coordination

Ziyi Jiao, Heng Zhang, Drishti Regmi +3

Coordinating two players in a cooking task requires a vision-language agent to track changing scene states, sequence recipe steps, and allocate shared resources under a time budget…

cs.AI2026

Code4Scene: Benchmarking Coding Agents for Constructing and Editing 3D Scenes

Xiaokang Ye, Siddhant Hitesh Mantri, Zimeng Chen +6

Frontier coding agents can now write and execute code that authors 3D environments, but whether they reliably understand 3D structure and precisely control scene state remains uncl…

cs.AI2026

SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning

Haoqiang Kang, Xiaokang Ye, Yuhan Liu +5

LLM/VLM-based digital agents have advanced rapidly thanks to scalable sandboxes for coding, web navigation, and computer use, which provide rich interactive training grounds. In co…

cs.AI2026

TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models

Fangxu Yu, Xingang Guo, Lingzhi Yuan +6

Time series are ubiquitous in real-world scenarios and crucial for applications ranging from energy management to traffic control. Consequently, the ability to reason over time ser…

cs.AI2026

PrivacyReasoner: Can LLM Emulate a Human-like Privacy Mind?

Yiwen Tu, Xuan Liu, Lianhui Qin +1

Prior work on LLM-based privacy focuses on norm judgment over synthetic vignettes, rather than how people think about a specific data practice and formulate their opinions. We addr…

cs.AI2024★ 2 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…

cs.AI2024

Flow of Reasoning: Training LLMs for Divergent Reasoning with Minimal Examples

Fangxu Yu, Lai Jiang, Haoqiang Kang +2

The ability to generate diverse solutions to a given problem is a hallmark of human creativity. This divergent reasoning is also crucial for machines, enhancing their robustness an…