Actualités
Meta AI Researchers Introduced a Scalable Byte-Level Autoregressive U-Net Model That Outperforms Token-Based Transformers Across Language Modeling Benchmarks
Language modeling plays a foundational role in natural language processing, enabling machines to predict and...
Meta AI Releases Sapiens2: A High-Resolution Human-Centric Vision Model for Pose, Segmentation, Normals, Pointmap, and Albedo
If you’ve ever watched a motion capture system struggle with a person’s fingers, or seen...
Meta AI Releases SAM Audio: A State-of-the-Art Unified Model that Uses Intuitive and Multimodal Prompts for Audio Separation
Meta has released SAM Audio, a prompt driven audio separation model that targets a common...
Meta AI Releases NeuralBench: A Unified Open-Source Framework to Benchmark NeuroAI Models Across 36 EEG Tasks and 94 Datasets
Evaluating AI models trained on brain signals has long been a messy, inconsistent topic. Different...
Meta AI Releases Muse Glimmer: A 30B Open-Weights Agentic Model That Runs on One Consumer GPU
Meta has released Muse Glimmer, a 30-billion-parameter multimodal model distilled from Muse Spark. It is...
Meta AI Releases Muse Code (Beta): A Terminal Coding Agent Powered by the New Muse Spark 1.2 Model
Meta AI has released Muse Code (in beta), a terminal coding agent in beta, powered...
Meta AI Releases EUPE: A Compact Vision Encoder Family Under 100M Parameters That Rivals Specialist Models Across Image Understanding, Dense Prediction, and VLM Tasks
Running powerful AI on your smartphone isn’t just a hardware problem — it’s a model...
Meta AI Released Muse Spark 1.3: An Agentic Coding Model That Uses ~20% Fewer Tool Calls and ~25% Fewer Tokens Than Muse Spark 1.2
This week, Meta Superintelligence Labs released Muse Spark 1.3. It is the fourth Muse Spark...
Meta AI Released MobileLLM-R1: A Edge Reasoning Model with less than 1B Parameters and Achieves 2x–5x Performance Boost Over Other Fully Open-Source AI Models
Table of contents What architecture powers MobileLLM-R1? How efficient is the training? How does it...
Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language Models
Multi-modal large language models (MLLMs) have shown great progress as versatile AI assistants capable of...
MemOS: A Memory-Centric Operating System for Evolving and Adaptive Large Language Models
LLMs are increasingly seen as key to achieving Artificial General Intelligence (AGI), but they face...
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs
arXiv:2601.02931v1 Announce Type: new Abstract: Autoregressive LLMs perform well on relational tasks that require linking...
