Results 61 to 70 of about 16,504,576 (273)

Large Multimodal Models: Notes on CVPR 2023 Tutorial

, 2023
This tutorial note summarizes the presentation on ``Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4'', a part of CVPR 2023 tutorial on ``Recent Advances in Vision Foundation Models''. The tutorial consists of three parts.
Li, Chunyuan
core  

Gradient‐Conductivity Architecture Enables Synchronous Enhancement of Sensitivity and Detection Range in Multifunctional All‐Paper Sensors

Advanced Functional Materials, EarlyView.
A gradient‐engineered all‐paper sensor is fabricated by integrating MXene and in situ grown AgNPs within hierarchical cellulose networks. The device breaks the sensitivity–detection range trade‐off through cascaded conductive pathways, enabling ultrahigh pressure sensitivity, humidity–pressure decoupled dual‐mode sensing, and outstanding EMI shielding,
Ao Li, Jun Xu, Dezhong Xu, Zhaohui Zhang, Jiyi Li, Daxian Cao, Kefu Chen, Mizi Fan   +7 more
wiley   +1 more source

Evaluating large language models on multimodal chemistry olympiad exams

Communications Chemistry
Multimodal scientific reasoning remains a significant challenge for large language models (LLMs), particularly in chemistry, where problem-solving relies on symbolic diagrams, molecular structures, and structured visual data.
Yiming Cui, Xin Yao, Yuxuan Qin, Xin Li, Shijin Wang, Guoping Hu   +5 more
doaj   +1 more source

MMaDA: Multimodal Large Diffusion Language Models

Advances in Neural Information Processing Systems 38
NeurIPS 2025.
Ling Yang 0006, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, Mengdi Wang 0001   +6 more
openaire   +3 more sources

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Embracing Large Language and Multimodal Models for Prosthetic Technologies [PDF]

This article presents a vision for the future of prosthetic devices, leveraging the advancements in large language models (LLMs) and Large Multimodal Models (LMMs) to revolutionize the interaction between humans and assistive technologies.
Schilling, Arndt F., Dey, Sharmita
core   +1 more source

Enhancing Multimodal Recommender System Performance Using Large Language Models

IEEE Access
With the rapid advancement of e-commerce platforms and the overwhelming amount of information available online, recommender systems have emerged as essential tools for helping users efficiently discover items aligned with their preferences.
Soohyun Woo, Seonu Park, Xinzhe Li, Seongkook Lee, Jaekyeong Kim   +4 more
doaj   +1 more source

Hallucination of Multimodal Large Language Models: A Survey

CoRR
This survey presents a comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), which have demonstrated significant advancements and remarkable abilities in multimodal tasks.
Zechen Bai, Pichao Wang, Tianjun Xiao, Tong He 0002, Zongbo Han, Zheng Zhang 0001, Mike Zheng Shou   +6 more
openaire   +2 more sources

Fiber‐Based Wearable Electronics as Adaptive Biointerfaces for Healthcare Applications

Advanced Functional Materials, EarlyView.
Fiber‐based wearable electronics have been extensively studied as adaptive healthcare biointerfaces. In this review, we classify and introduce fiber‐based systems according to biointerface design, conductive materials, transduction mechanisms, and healthcare applications, and provide a perspective on their translation toward reliable, continuous ...
Sangwoo Hong, Jinsol Kim, Yoonjae Jeong, Sejong Kang, Daeho Lee, Seung Hwan Ko   +5 more
wiley   +1 more source

Ce‐LLMs: Status and trends of education‐specific large language models developed in China

Future in Educational Research
The prevalence of AI hallucination in general‐purpose large language models (LLMs) poses significant pedagogical challenges, particularly in terms of content credibility and reliability.
Tao Xie, Yingli Zhou, Jiazhen Yu
doaj   +1 more source