One of the roadblocks for training generalist robotic models today is heterogeneity. Previous
robot learning methods often collect data to train with one specific embodiment for one task …

Multi-modal large language models (MLLMs) have demonstrated impressive performance in
vision-language tasks across a wide range of domains. However, the large model scale and …

Existing open-source multimodal large language models (MLLMs) generally follow a
training process involving pre-training and supervised fine-tuning. However, these models …

Information comes in diverse modalities. Multimodal native AI models are essential to
integrate real-world information and deliver comprehensive understanding. While …

While large language models (LLMs) excel on generation tasks, their decoder-only
architecture often limits their potential as embedding models if no further representation …

The development of large language models (LLMs) has expanded to multi-modal systems
capable of processing text, images, and speech within a unified framework. Training these …

Mixture of Experts (MoEs) plays an important role in the development of more efficient and
effective large language models (LLMs). Due to the enormous resource requirements …

Healthcare systems worldwide face persistent challenges in efficiency, accessibility, and
personalization. Powered by modern AI technologies such as multimodal large language …

We present LlamaFusion, a framework for empowering pretrained text-only large language
models (LLMs) with multimodal generative capabilities, enabling them to understand and …

Existing encoder-free vision-language models (VLMs) are rapidly narrowing the
performance gap with their encoder-based counterparts, highlighting the promising potential …