Microsoft Research Lab – Asia
News & features
As large language models (LLMs) continue to improve at writing code, a key challenge has emerged: enabling them to generate complex, high-quality training data that actually reflects real-world programming. Currently, most data synthesis methods rely on simple code snippets as…
One of the driving forces behind AI’s rapid progress is access to large-scale, high-quality data, essential to enable training models to continuously improve and perform reliably. But that well is running dry. As the supply of usable internet data shrinks,…
New methods boost reasoning in small and large language models
| Li Lyna Zhang, Xian Zhang, Xueting Han, and Dongdong Zhang
New techniques are reimagining how LLMs reason. By combining symbolic logic, mathematical rigor, and adaptive planning, these methods enable models to tackle complex, real-world problems across a variety of fields.
World models are a key concept in AI, used to simulate how agents behave in virtual environments and enable immersive, interactive experiences. They’re not only transforming game and media generation, they’re also opening new frontiers for using AI in complex,…
Time-series data—measurements collected over time like stock prices or heart rates—plays a vital role in AI forecasting systems across industries. As these systems advance, the need for time-series data is increasing, especially synthetic data, which offers numerous advantages over real-world…
Math is more than a school subject—it’s the engine behind scientific discovery, driving advances in everything from climate modeling to AI. At Microsoft Research Asia, senior researcher Xian Zhang is leading efforts to help AI move beyond surface-level pattern recognition toward…
Predicting and explaining AI model performance: A new approach to evaluation
| Lexin Zhou and Xing Xie
ADeLe, a new evaluation method, explains what AI systems are good at—and where they’re likely to fail. By breaking tasks into ability-based requirements, it has the potential to provide a clearer way to evaluate and predict AI model performance.
Abstracts: Societal AI with Xing Xie
| Gretchen Huizinga and Xing Xie
New AI models aren’t just changing the world of research; they’re also poised to impact society. Xing Xie talks about Societal AI, a white paper that explores the changing landscape with an eye to future research and improved communication across…
Societal AI: Building human-centered AI systems
| Beibei Shi, Haotian Li, and Xing Xie
Learn about a new white paper on Societal AI, an interdisciplinary framework for guiding AI development that reflects shared human values. It presents key research challenges and emphasizes collaboration across disciplines.