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arXiv:2603.03321v1 Announce Type: new Abstract: Evaluating instruction following in Large Language Models requires decomposing instructions into verifiable requirements and assessing...
How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3%...
Scott Shambaugh didn’t think twice when he denied an AI agent’s request to contribute to matplotlib, a software library that...
The race to prevent the worst wildfires has been an increasingly high-tech one. Companies are proposing AI fire detection systems...
arXiv:2506.03922v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of...
arXiv:2603.02830v1 Announce Type: new Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it...
arXiv:2603.03198v1 Announce Type: cross Abstract: Universal embodied intelligence demands robust generalization across heterogeneous embodiments, such as autonomous driving, robotics, and...
Meta AI Releases SAM Audio: A State-of-the-Art Unified Model that Uses Intuitive and Multimodal Prompts for Audio Separation
Meta AI Releases NeuralBench: A Unified Open-Source Framework to Benchmark NeuroAI Models Across 36 EEG Tasks and 94 Datasets
Meta AI Releases Muse Glimmer: A 30B Open-Weights Agentic Model That Runs on One Consumer GPU
Meta AI Releases Muse Code (Beta): A Terminal Coding Agent Powered by the New Muse Spark 1.2 Model
Meta AI Releases EUPE: A Compact Vision Encoder Family Under 100M Parameters That Rivals Specialist Models Across Image Understanding, Dense Prediction, and VLM Tasks
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
Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language Models
MemOS: A Memory-Centric Operating System for Evolving and Adaptive Large Language Models
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs
MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents
