Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.

A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.

Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.

By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.

“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.

A new algorithm solves complicated partial differential equations by breaking them down into simpler problems, potentially guiding computer graphics and geometry processing.

More efficient than other approaches, the “Thermometer” technique could help someone know when they should trust a large language model.

MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.

New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.

LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.