Recent advancements in multimodal large language models (MLLMs) have achieved
significant multimodal generation capabilities, akin to GPT-4. These models predominantly …

We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-
centric approach. While stronger language models can enhance multimodal capabilities, the …

Predicting future trajectories of traffic agents in highly interactive environments is an
essential and challenging problem for the safe operation of autonomous driving systems. On …

The rapid advancements in pre-trained Large Language Models (LLMs) and Large
Multimodal Models (LMMs) have ushered in a new era of intelligent applications …

We propose SPHINX-X, an extensive Multi-modality Large Language Model (MLLM) series
developed upon SPHINX. To improve the architecture and training efficiency, we modify the …

The applications of Vision-Language Models (VLMs) in the field of Autonomous Driving (AD)
have attracted widespread attention due to their outstanding performance and the ability to …

The research community has increasing interest in autonomous driving research, despite
the resource intensity of obtaining representative real world data. Existing self-driving …

Multimodal Large Language Models (MLLMs) have demonstrated profound capabilities in
understanding multimodal information, covering from Image LLMs to the more complex …

In the pursuit of Artificial General Intelligence (AGI), the integration of vision in language
models has marked a significant milestone. The advent of vision-language models (MLLMs) …

While large language models (LLMs) are still being adopted to new domains and utilized in
novel applications, we are experiencing an influx of the new generation of foundation …