The effective modelling of long-term dependencies enables conditioning new model outputs on previous inputs and is critical when dealing with longer text, audio or video contexts. However, when ...

The recent Nobel Prize for groundbreaking advancements in protein discovery underscores the transformative potential of foundation models (FMs) in exploring vast combinatorial spaces. These models are ...

The transformative impact of Transformers on natural language processing (NLP) and computer vision (CV) is undeniable. Their scalability and effectiveness have propelled advancements across these ...

Large Language Models (LLMs) have become indispensable tools for diverse natural language processing (NLP) tasks. Traditional LLMs operate at the token level, generating output one word or subword at ...

Recent advancements in training large multimodal models have been driven by efforts to eliminate modeling constraints and unify architectures across domains. Despite these strides, many existing ...

A research team introduces Automated Search for Artificial Life (ASAL). This novel framework leverages vision-language FMs to automate and enhance the discovery process in ALife research.

In a new paper Time-Reversal Provides Unsupervised Feedback to LLMs, a research team from Google DeepMind and Indian Institute of Science proposes Time Reversed Language Models (TRLMs), a framework ...

In a new paper Navigation World Models, a research team from Meta, New York University and Berkeley AI Research proposes a Navigation World Model (NWM), a controllable video generation model that ...

An NVIDIA research team proposes Hymba, a family of small language models that blend transformer attention with state space models, which outperforms the Llama-3.2-3B model with a 1.32% higher average ...

Recent advancements in large language models (LLMs) have primarily focused on enhancing their capacity to predict text in a forward, time-linear manner. However, emerging research suggests that ...

An Apple research team introduces AIMV2, a family of vision encoders that is designed to predict both image patches and text tokens within a unified sequence. This combined objective enables the model ...