In the news | NBER
Economics of Transformative AI Workshop, Fall 2025
Watch workshop on the Economics of Transformative AI, held on September 18-19 at Stanford University. Supported by the Alfred P. Sloan Foundation grant #G-2023-19618 and Microsoft.
In the news | The Korea Times
IITP, Microsoft collaborate to build global network for fostering AI workers
In the news | Mongabay
How AI helps conservationists better understand and protect giraffes
Scientists have deployed artificial intelligence models to identify and re-identify endangered giraffes in Tanzania. The Wild Nature Institute partnered with Microsoft’s AI For Good Lab to launch Project GIRAFFE which uses open-source AI tools to identify and re-identify individual giraffes…
Tool-space interference in the MCP era: Designing for agent compatibility at scale
| Adam Fourney, Tyler Payne, Maya Murad, and Saleema Amershi
As agentic AI ushers in a new era marked by tool expansion, systems are converging, and complexity is rising. Microsoft Research explores the Model Context Protocol (MCP) as a new standard for agent collaboration across fragmented tool ecosystems.
In the news | VentureBeat
Microsoft’s new AI framework trains powerful reasoning models with a fraction of the cost
RenderFormer: How neural networks are reshaping 3D rendering
| Yue Dong
RenderFormer, from Microsoft Research, is the first model to show that a neural network can learn a complete graphics rendering pipeline. It’s designed to support full-featured 3D rendering using only machine learning—no traditional graphics computation required.
Breaking the networking wall in AI infrastructure
| Paolo Costa
Datacenter memory and network limits are restraining AI system performance. MOSAIC uses microLEDs and a wide-and-slow optical architecture to deliver faster, longer, more reliable, and energy efficient connections that could transform AI cluster designs.
BRIDGE：突破SFT-RL两阶段训练瓶颈，开辟推理模型协同优化新路径
编者按：在数学、编程等高度挑战的推理任务中，大语言模型正在展现前所未有的突破。然而，支撑其背后的传统训练范式仍存在效率低下与“灾难性遗忘”的困境。为此，微软亚洲研究院与香港中文大学联合提出了 BRIDGE 框架，通过双层优化机制让监督微调（SFT）与强化学习（RL）“携手并进”，突破了长期以来的两阶段训练瓶颈。BRIDGE 不仅在多个权威测试中实现了显著的性能提升，还为未来大模型后训练开辟了新方向...
Awards | MIT Technology Review
Tian Xie named to MIT Technology Review’s 2025 Innovators Under 35
Tian led the development of MatterGen, our generative AI model for materials discovery.