118 citations · 160 across the 7 of their papers we have counts for
8 papers
Margin-Regularized Structured Semantic Alignment for Brain-Language Correspondence
Jiaqi Wang, Huawen Hu, Shu Zhang
With the rapid advancement of large language models, brain-language decoding has achieved remarkable progress. However, it remains unclear whether decoded content genuinely reflect…
World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
Arif Hassan Zidan, Yi Pan, Hanqi Jiang +23
World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, ena…
Structured Diversity Control: A Dual-Level Framework for Group-Aware Multi-Agent Coordination
Shuocun Yang, Huawen Hu, Xuan Liu +3
Controlling the behavioral diversity is a pivotal challenge in multi-agent reinforcement learning (MARL), particularly in complex collaborative scenarios. While existing methods at…
FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling
Enze Shi, Kui Zhao, Qilong Yuan +4
Electroencephalography (EEG) is a vital tool to measure and record brain activity in neuroscience and clinical applications, yet its potential is constrained by signal heterogeneit…
HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement Learning
Huawen Hu, Enze Shi, Chenxi Yue +7
Human-in-the-loop reinforcement learning integrates human expertise to accelerate agent learning and provide critical guidance and feedback in complex fields. However, many existin…
Evaluation of OpenAI o1: Opportunities and Challenges of AGI
Tianyang Zhong, Zhengliang Liu, Yi Pan +74
This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, includi…
Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
Jiaqi Wang, Zihao Wu, Yiwei Li +17
Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. Notably, in the realm of robot task planning, LLMs h…
Prompt Engineering for Healthcare: Methodologies and Applications
Jiaqi Wang, Enze Shi, Sigang Yu +21
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aim…