Research
In this issue, we examine a new conversation segmentation method that delivers more coherent and personalized agent conversation, and we review efforts to improve MLLMs’ understanding of geologic maps. Check out the latest research and other updates.
Claimify, created by Microsoft Research, is a novel LLM-based claim-extraction method that outperforms prior solutions to produce more accurate, comprehensive, and substantiated claims from LLM outputs.
Metasurfaces explore engineered 2D materials that manipulate electromagnetic and mechanical waves, offering advances in wireless tech. They can power indoor GPS, extend 5G/6G coverage, and enable wireless sensing and imaging.
Introducing KBLaM, an approach that encodes and stores structured knowledge within an LLM itself. By integrating knowledge without retraining, it offers a scalable alternative to traditional methods.
AI interactions can be iterative and complex. Learn how the Semantic Telemetry project at Microsoft Research is developing a new data science approach to understand human-AI interactions and their value.
Our recent study in Nature Scientific Reports identified key challenges in the biomedical data lifecycle and offered 7 actionable recommendations.
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.
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.
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.
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.
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.
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.