While Transformers have been the main architecture behind deep learning's success in
language modeling, state-space models (SSMs) such as Mamba have recently been shown …

We introduce LlamaGen, a new family of image generation models that apply original``next-
token prediction''paradigm of large language models to visual generation domain. It is an …

PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m
vision encoder and the Gemma-2B language model. It is trained to be a versatile and …

We present VisionLLM v2, an end-to-end generalist multimodal large model (MLLM) that
unifies visual perception, understanding, and generation within a single framework. Unlike …

We present a unified transformer, ie, Show-o, that unifies multimodal understanding and
generation. Unlike fully autoregressive models, Show-o unifies autoregressive and …

While next-token prediction is considered a promising path towards artificial general
intelligence, it has struggled to excel in multimodal tasks, which are still dominated by …

Since the emergence of ChatGPT, research on large language models (LLMs) has actively
progressed across various fields. LLMs, pre-trained on vast text datasets, have exhibited …

Long-context capability is critical for multi-modal foundation models, especially for long
video understanding. We introduce LongVILA, a full-stack solution for long-context visual …

Large language models (LLMs) with vision capabilities (eg, GPT-4o, Gemini 1.5, and Claude
3) are powering countless image-text processing applications, enabling unprecedented …

Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in
processing vision-language tasks. One of the crux of MLLMs lies in vision tokenization …