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Large language models (LLMs) have shown strong performance on mathematical reasoning under well-defined conditions. However, real-world engineering problems involve uncertainty, context, and open-ended settings that extend beyond symbolic…

Artificial Intelligence · Computer Science 2026-05-05 Xiyuan Zhou , Xinlei Wang , Yirui He , Yang Wu , Ruixi Zou , Yuheng Cheng , Yulu Xie , Wenxuan Liu , Huan Zhao , Yan Xu , Jinjin Gu , Junhua Zhao

The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehensively evaluating these models in circuit design remains…

Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have…

Computation and Language · Computer Science 2024-07-18 Sahand Sabour , Siyang Liu , Zheyuan Zhang , June M. Liu , Jinfeng Zhou , Alvionna S. Sunaryo , Juanzi Li , Tatia M. C. Lee , Rada Mihalcea , Minlie Huang

Multi-modal Large Language Models (MLLMs) are gaining significant attention for their ability to process multi-modal data, providing enhanced contextual understanding of complex problems. MLLMs have demonstrated exceptional capabilities in…

Computer Vision and Pattern Recognition · Computer Science 2025-09-09 Pragati Shuddhodhan Meshram , Swetha Karthikeyan , Bhavya Bhavya , Suma Bhat

In response to the urgent demand for grid stability and the complex challenges posed by renewable energy integration and electricity market dynamics, the power sector increasingly seeks innovative technological solutions. In this context,…

Artificial Intelligence · Computer Science 2024-08-13 Xiyuan Zhou , Huan Zhao , Yuheng Cheng , Yuji Cao , Gaoqi Liang , Guolong Liu , Wenxuan Liu , Yan Xu , Junhua Zhao

Interleaved multimodal comprehension and generation, enabling models to produce and interpret both images and text in arbitrary sequences, have become a pivotal area in multimodal learning. Despite significant advancements, the evaluation…

Computer Vision and Pattern Recognition · Computer Science 2025-04-01 Peng Xia , Siwei Han , Shi Qiu , Yiyang Zhou , Zhaoyang Wang , Wenhao Zheng , Zhaorun Chen , Chenhang Cui , Mingyu Ding , Linjie Li , Lijuan Wang , Huaxiu Yao

Large Language Models (LLMs) are transformative not only for daily activities but also for engineering tasks. However, current evaluations of LLMs in engineering exhibit two critical shortcomings: (i) the reliance on simplified use cases,…

Artificial Intelligence · Computer Science 2025-05-21 Rene Heesch , Sebastian Eilermann , Alexander Windmann , Alexander Diedrich , Philipp Rosenthal , Oliver Niggemann

Large language models (LLMs) are gaining increasing popularity in software engineering (SE) due to their unprecedented performance across various applications. These models are increasingly being utilized for a range of SE tasks, including…

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Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial…

Large language models (LLMs), as a novel information technology, are seeing increasing adoption in the Architecture, Engineering, and Construction (AEC) field. They have shown their potential to streamline processes throughout the building…

Computation and Language · Computer Science 2026-02-17 Chen Liang , Zhaoqi Huang , Haofen Wang , Fu Chai , Chunying Yu , Huanhuan Wei , Zhengjie Liu , Yanpeng Li , Hongjun Wang , Ruifeng Luo , Xianzhong Zhao

The paradigm of Multimodal Large Language Models (MLLMs) offers a promising blueprint for advancing the electromagnetic (EM) domain. However, prevailing approaches often deviate from the native MLLM paradigm, instead using task-specific or…

Computer Vision and Pattern Recognition · Computer Science 2026-03-25 Junyu Shen , Zhendong She , Chenghanyu Zhang , Yuchuang Sun , Luqing Luo , Dingwei Tan , Zonghao Guo , Bo Guo , Zehua Han , Wupeng Xie , Yaxin Mu , Peng Zhang , Peipei Li , Fengxiang Wang , Yangang Sun , Maosong Sun

Large Language Models (LLMs) have achieved impressive results across a broad array of tasks, yet their capacity for complex, domain-specific mathematical reasoning-particularly in wireless communications-remains underexplored. In this work,…

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Large Language Models (LLMs) have demonstrated significant potential in various engineering tasks, including software development, digital logic generation, and companion document maintenance. However, their ability to perform board-level…

Hardware Architecture · Computer Science 2026-03-20 Weibo Qiu , Yinhao Xiao , Runyu Pan

Recent years have witnessed a significant interest in developing large multimodal models (LMMs) capable of performing various visual reasoning and understanding tasks. This has led to the introduction of multiple LMM benchmarks to evaluate…

Computer Vision and Pattern Recognition · Computer Science 2024-10-25 Sara Ghaboura , Ahmed Heakl , Omkar Thawakar , Ali Alharthi , Ines Riahi , Abduljalil Saif , Jorma Laaksonen , Fahad S. Khan , Salman Khan , Rao M. Anwer

Recently, multimodal large language models (MLLMs) have achieved significant advancements across various domains, and corresponding evaluation benchmarks have been continuously refined and improved. In this process, benchmarks in the…

Computation and Language · Computer Science 2025-08-20 Jiacheng Ruan , Dan Jiang , Xian Gao , Ting Liu , Yuzhuo Fu , Yangyang Kang

The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably more challenging benchmark dataset for evaluating the problem…

Computation and Language · Computer Science 2023-10-24 Daman Arora , Himanshu Gaurav Singh , Mausam

Recent evaluations of Large Multimodal Models (LMMs) have explored their capabilities in various domains, with only few benchmarks specifically focusing on urban environments. Moreover, existing urban benchmarks have been limited to…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Baichuan Zhou , Haote Yang , Dairong Chen , Junyan Ye , Tianyi Bai , Jinhua Yu , Songyang Zhang , Dahua Lin , Conghui He , Weijia Li

Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Chaoyou Fu , Peixian Chen , Yunhang Shen , Yulei Qin , Mengdan Zhang , Xu Lin , Jinrui Yang , Xiawu Zheng , Ke Li , Xing Sun , Yunsheng Wu , Rongrong Ji , Caifeng Shan , Ran He

With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this…

Computation and Language · Computer Science 2025-01-29 Dongyi Yi , Guibo Zhu , Chenglin Ding , Zongshu Li , Dong Yi , Jinqiao Wang

Large multimodal models (LMMs) have demonstrated outstanding capabilities in various visual perception tasks, which has in turn made the evaluation of LMMs significant. However, the capability of video aesthetic quality assessment, which is…

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