Explore Magma, a foundation model that can empower AI assistants to interpret environments, plan actions, and execute tasks across digital and physical spaces. Now available, learn how it advances the field of agentic AI. The post Magma: A foundation model for multimodal AI agents across digital and physical worlds appeared first on Microsoft Research.

Meet BioEmu-1 from Microsoft Research. This deep learning model can generate thousands of protein structures per hour, unlocking new possibilities for protein scientists and drug discovery and research. The post Exploring the structural changes driving protein function with BioEmu-1 appeared first on Microsoft Research.

Today Nature published Microsoft’s research detailing our WHAM, an AI model that generates video game visuals & controller actions. We are releasing the model weights, sample data, & WHAM Demonstrator on Azure AI Foundry, enabling researchers to build on the work. The post Introducing Muse: Our first generative AI model designed for gameplay ideation appeared first on Microsoft Research.

Limited resources, geography, and economic factors present barriers to quality education for many students in India. Learn how Microsoft Research is collaborating with Physics Wallah to make AI-based tutoring more accurate, reliable, and affordable. The post Microsoft Research and Physics Wallah team up to enhance AI-based tutoring appeared first on Microsoft Research.

ExACT combines Reflective-MCTS and Exploratory Learning to improve AI agents' decision-making, enabling test-time compute scaling. Learn how these methods help agents refine strategies for state-of-the-art performance and improved computational efficiency. The post ExACT: Improving AI agents’ decision-making via test-time compute scaling appeared first on Microsoft Research.

Advances in low-bit quantization techniques enable efficient operation of LLMs on resource-constrained edge devices. Discover how innovations like T-MAC, Ladder, and LUT Tensor Core improve computational efficiency and enhance hardware compatibility. The post Advances to low-bit quantization enable LLMs on edge devices appeared first on Microsoft Research.

In this issue: A new approach to multimodal pretraining for remote sensing; Managed-retention memory for the AI era; Improving detection of macular telangiectasia type 2; Generalizing symbolic automata. The post Research Focus: Week of January 27, 2025 appeared first on Microsoft Research.

In this edition: Privacy enhancements for multiparty deep learning; using smaller, open-source models to provide relevance judgments; new tool uses AI, data to automate innovation and development; Yasuyuki Matsushita named IEEE 2025 Computer Society Fellow. The post Research Focus: Week of January 13, 2025 appeared first on Microsoft Research.

Microsoft researchers introduce MatterGen, a model that can discover new materials tailored to specific needs—like efficient solar cells or CO2 recycling—advancing progress beyond trial-and-error experiments. The post MatterGen: A new paradigm of materials design with generative AI appeared first on Microsoft Research.

Announcing AutoGen 0.4, fully reimagined library for building advanced agentic AI systems, developed to improve code quality and robustness. Its asynchronous, event-driven architecture is designed to support dynamic, scalable workflows. The post AutoGen v0.4: Reimagining the foundation of agentic AI for scale, extensibility, and robustness appeared first on Microsoft Research.