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The advent of Multimodal Large Language Models, leveraging the power of Large Language Models, has recently demonstrated superior multimodal understanding and reasoning abilities, heralding a new era for artificial general intelligence.…

Artificial Intelligence · Computer Science 2025-04-14 Lu Qiu , Yi Chen , Yuying Ge , Yixiao Ge , Ying Shan , Xihui Liu

The goal of achieving Artificial General Intelligence (AGI) is to imitate humans and surpass them. Models such as OpenAI's o1, o3, and DeepSeek's R1 have demonstrated that large language models (LLMs) with human-like reasoning capabilities…

Computer Vision and Pattern Recognition · Computer Science 2025-05-27 Yansheng Qiu , Li Xiao , Zhaopan Xu , Pengfei Zhou , Zheng Wang , Kaipeng Zhang

The Multimodal Large Language Model (MLLM) is currently experiencing rapid growth, driven by the advanced capabilities of LLMs. Unlike earlier specialists, existing MLLMs are evolving towards a Multimodal Generalist paradigm. Initially…

Strong Artificial Intelligence (Strong AI) or Artificial General Intelligence (AGI) with abstract reasoning ability is the goal of next-generation AI. Recent advancements in Large Language Models (LLMs), along with the emerging field of…

Computation and Language · Computer Science 2024-01-19 Yiqi Wang , Wentao Chen , Xiaotian Han , Xudong Lin , Haiteng Zhao , Yongfei Liu , Bohan Zhai , Jianbo Yuan , Quanzeng You , Hongxia Yang

Mathematical reasoning, a core aspect of human cognition, is vital across many domains, from educational problem-solving to scientific advancements. As artificial general intelligence (AGI) progresses, integrating large language models…

Computation and Language · Computer Science 2025-05-21 Yibo Yan , Jiamin Su , Jianxiang He , Fangteng Fu , Xu Zheng , Yuanhuiyi Lyu , Kun Wang , Shen Wang , Qingsong Wen , Xuming Hu

With the integration of multimodal large language models (MLLMs) into robotic systems and AI applications, embedding emotional intelligence (EI) capabilities is essential for enabling these models to perceive, interpret, and respond to…

Computation and Language · Computer Science 2026-04-28 He Hu , Lianzhong You , Hongbo Xu , Qianning Wang , Fei Richard Yu , Fei Ma , Zebang Cheng , Zheng Lian , Yucheng Zhou , Laizhong Cui

Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite advancements in Multimodal Large Language Models (MLLMs), current systems often fail to…

Computer Vision and Pattern Recognition · Computer Science 2026-04-24 Chentao Li , Zirui Gao , Mingze Gao , Yinglian Ren , Jianjiang Feng , Jie Zhou

Multimodal Large Language Models (MLLMs) mimic human perception and reasoning system by integrating powerful Large Language Models (LLMs) with various modality encoders (e.g., vision, audio), positioning LLMs as the "brain" and various…

Computer Vision and Pattern Recognition · Computer Science 2024-08-29 Jiaxing Huang , Jingyi Zhang

The advent of Large Language Models (LLMs) holds promise for revolutionizing various fields traditionally dominated by human expertise. Urban planning, a professional discipline that fundamentally shapes our daily surroundings, is one such…

Computation and Language · Computer Science 2025-05-01 Yu Zheng , Longyi Liu , Yuming Lin , Jie Feng , Guozhen Zhang , Depeng Jin , Yong Li

The planning ability of Large Language Models (LLMs) has garnered increasing attention in recent years due to their remarkable capacity for multi-step reasoning and their ability to generalize across a wide range of domains. While some…

Artificial Intelligence · Computer Science 2025-02-19 Mohamed Aghzal , Erion Plaku , Gregory J. Stein , Ziyu Yao

Large language models (LLMs) perform well on step-by-step reasoning benchmarks such as mathematics and code generation, yet their ability to carry out robust long-horizon planning under realistic constraints remains insufficiently…

Artificial Intelligence · Computer Science 2026-04-21 Petr Anokhin , Roman Khalikov , Stefan Rebrikov , Viktor Volkov , Artyom Sorokin , Vincent Bissonnette

Multimodal Large Language Models (MLLMs) are widely regarded as crucial in the exploration of Artificial General Intelligence (AGI). The core of MLLMs lies in their capability to achieve cross-modal alignment. To attain this goal, current…

Computation and Language · Computer Science 2024-11-26 Fei Zhao , Taotian Pang , Chunhui Li , Zhen Wu , Junjie Guo , Shangyu Xing , Xinyu Dai

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Chaoyou Fu , Yi-Fan Zhang , Shukang Yin , Bo Li , Xinyu Fang , Sirui Zhao , Haodong Duan , Xing Sun , Ziwei Liu , Liang Wang , Caifeng Shan , Ran He

In recent years, large language models (LLMs) have driven major advances in language understanding, marking a significant step toward artificial general intelligence (AGI). With increasing demands for higher-level semantics and cross-modal…

Computation and Language · Computer Science 2025-09-30 Yuntao Shou , Tao Meng , Wei Ai , Keqin Li

Large language models (LLMs) like ChatGPT have revealed amazing intelligence. How to evaluate the question-solving abilities of LLMs and their degrees of intelligence is a hot-spot but challenging issue. First, the question-solving…

Computation and Language · Computer Science 2023-09-14 Fei Tang , Wanling Gao , Luzhou Peng , Jianfeng Zhan

Humans constantly reason about 3D proximity, the relations between their body and surrounding objects, to guide perception and action in daily life. Whether multimodal large language models (MLLMs) can perform such embodied 3D reasoning…

Computer Vision and Pattern Recognition · Computer Science 2026-05-27 Jinzhao Li , Yinuo Chen , Dongxu Piao , Panwang Pan , Yifan Yu , Dong Wang , Honglei Yan , Liang Yue , Shaofei Wang , Yixin Chen , Siyuan Huang , Miao Liu

Multimodal large language models (MLLMs), which integrate language and visual cues for problem-solving, are crucial for advancing artificial general intelligence (AGI). However, current benchmarks for measuring the intelligence of MLLMs…

The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have…

Artificial Intelligence · Computer Science 2024-09-30 Lin Li , Guikun Chen , Hanrong Shi , Jun Xiao , Long Chen

Large language models (LLMs) have demonstrated significant potential to accelerate scientific discovery as valuable tools for analyzing data, generating hypotheses, and supporting innovative approaches in various scientific fields. In this…

Computation and Language · Computer Science 2025-10-30 Jin Huang , Silviu Cucerzan , Sujay Kumar Jauhar , Ryen W. White

Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks, but their ability to interpret subjective, emotionally nuanced multimodal content remains largely unexplored. Thus, it…

Computer Vision and Pattern Recognition · Computer Science 2024-07-02 Qu Yang , Mang Ye , Bo Du
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