In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …

The human ability to learn, generalize, and control complex manipulation tasks through multi-
modality feedback suggests a unique capability, which we refer to as dexterity intelligence …

Large multimodal models (LMM) have recently shown encouraging progress with visual
instruction tuning. In this paper we present the first systematic study to investigate the design …

Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …

We introduce MMMU: a new benchmark designed to evaluate multimodal models on
massive multi-discipline tasks demanding college-level subject knowledge and deliberate …

We introduce CogVLM, a powerful open-source visual language foundation model. Different
from the popular\emph {shallow alignment} method which maps image features into the …

Visual language models (VLMs) rapidly progressed with the recent success of large
language models. There have been growing efforts on visual instruction tuning to extend the …

The growing interest in vision-language models (VLMs) has been driven by improvements in
large language models and vision transformers. Despite the abundance of literature on this …

People are spending an enormous amount of time on digital devices through graphical user
interfaces (GUIs) eg computer or smartphone screens. Large language models (LLMs) such …

A big convergence of language, multimodal perception, action, and world modeling is a key
step toward artificial general intelligence. In this work, we introduce KOSMOS-1, a …