Omni-modality language models (OLMs) are a rapidly advancing area of AI that enables understanding and reasoning across multiple data types, including text, audio, video, and images. These models aim ...

One of the primary challenges in developing advanced text-to-speech (TTS) systems is the lack of expressivity when transcribing and generating speech. Traditionally, large language... Multimodal AI ...

The study investigates the emergence of intelligent behavior in artificial systems by examining how the complexity of rule-based systems influences the capabilities of models trained to predict those ...

Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, ...

Large language models (LLMs) have evolved to become powerful tools capable of understanding and responding to user instructions. Based on the transformer architecture, these models predict the next ...

Artificial intelligence (AI) research has increasingly focused on enhancing the efficiency & scalability of deep learning models. These models have revolutionized natural language processing, computer ...

Multimodal AI models are powerful tools capable of both understanding and generating visual content. However, existing approaches often use a single visual encoder for... The PyTorch community has ...

There is a growing demand for embedding models that balance accuracy, efficiency, and versatility. Existing models often struggle to achieve this balance, especially in scenarios ranging from ...

Mobile Vehicle-to-Microgrid (V2M) services enable electric vehicles to supply or store energy for localized power grids, enhancing grid stability and flexibility. AI is crucial in optimizing energy ...

Large Language Models (LLMs) need to be evaluated within the framework of embodied decision-making, i.e., the capacity to carry out activities in either digital or physical environments. Even with all ...

Current generative AI models face challenges related to robustness, accuracy, efficiency, cost, and handling nuanced human-like responses. There is a need for more scalable and efficient solutions ...

The challenge lies in generating effective agentic workflows for Large Language Models (LLMs). Despite their remarkable capabilities across diverse tasks, creating workflows that combine multiple LLMs ...