Accelerating inference in large language models (LLMs) is challenging due to their high computational and memory requirements, leading to significant financial and energy costs. Current solutions, such as sparsity, quantization, or pruning, often require specialized hardware or result in decreased model accuracy, making efficient deployment difficult. Researchers from FAIR at Meta, GenAI at Meta, Reality… →

Proteins, vital macromolecules, are characterized by their amino acid sequences, which dictate their three-dimensional structures and functions in living organisms. Effective generative protein modeling requires a multimodal approach to simultaneously understand and generate sequences and structures. Current methods often rely on separate models for each modality, limiting their effectiveness. While advancements like diffusion models and… →

Large language models (LLMs) can understand and generate human-like text across various applications. However, despite their success, LLMs often need help in mathematical reasoning, especially when solving complex problems requiring logical, step-by-step thinking. This research field is evolving rapidly as AI researchers explore new methods to enhance LLMs’ capabilities in handling advanced reasoning tasks, particularly… →

Large Language Models (LLMs) have gained significant attention in AI research due to their impressive capabilities. However,  their limitation lies with long-term planning and complex problem-solving. While explicit search methods like Monte Carlo Tree Search (MCTS) have been employed to enhance decision-making in various AI systems, including chess engines and game-playing algorithms, they present challenges… →