The vast majority of materials science knowledge exists in unstructured natural language,
yet structured data is crucial for innovative and systematic materials design. Traditionally, the …

Large-scale endeavors like and widespread community efforts such as Open-X-Embodiment
have contributed to growing the scale of robot demonstration data. However, there is still an …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …

Audio-language models (ALMs) generate linguistic descriptions of sound-producing events
and scenes. Advances in dataset creation and computational power have led to significant …

Many clinical tasks require an understanding of specialized data, such as medical images
and genomics, which is not typically found in general-purpose large multimodal models …

Modern generative models demonstrate impressive capabilities, likely stemming from an
ability to identify and manipulate abstract concepts underlying their training data. However …

The rapid development of large language models (LLMs) has been witnessed in recent
years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from …

Traditional banks are increasingly challenged by FinTechs, particularly in leveraging
advanced technologies to enhance operational efficiency. Our study addresses this by …

Recent research suggests that the failures of Vision-Language Models (VLMs) at visual
reasoning often stem from erroneous agreements--when semantically distinct images are …

Large-scale multimodal representation learning successfully optimizes for zero-shot transfer
at test time. Yet the standard pretraining paradigm (contrastive learning on large amounts of …