在微软亚洲研究院（新加坡）用真实场景验证AI的 “破圈” 力量
编者按：当人工智能浪潮席卷全球，算法如何突破实验室围墙，在真实世界中创造价值？微软亚洲研究院（新加坡）首席研究员徐新兴博士用十年求索给出了自己的答案。从南洋理工大学的学术研究，到新加坡科技研究局的跨领域实践，再到成为微软亚洲研究院在新加坡的首位研究员，他始终在“算法研究”与“产业实践”的交汇中寻找创新的突破口。 尽管近年来人工智能持续快速发展，但始终面临一个难题：如何将算法模型从理论实验推向产业落...
Xinxing Xu bridges AI research and real-world impact at Microsoft Research Asia – Singapore
Xinxing Xu is helping shape the work of Microsoft Research Asia – Singapore by turning advanced AI research into real-world solutions. Learn how he collaborates across sectors and disciplines to drive responsible innovation throughout Southeast Asia.
Technical approach for classifying human-AI interactions at scale
| Amber Hoak, David Tittsworth, Kate Lytvynets, Scott Counts, Weiwei Yang, Ben Cutler, and Jonathan McLean
Semantic Telemetry helps LLMs run efficiently, reliably, and in near real-time. Learn about the engineering behind that system, including the trade-offs and lessons learned along the way—from batching strategies to token optimization and orchestration.
Given a language model, can we tell whether it is truly reasoning, or if its performance owes only to pattern recognition and memorization?
CoR：多范式思维加持，大模型也能学会“多思路解题”
编者按：大语言模型（LLMs）在语言生成与基础推理中已展现出强大的能力，但它们在数学解题上的能力仍存在明显短板，尤其是难以兼顾复杂计算与定理证明。这背后的关键原因在于，现有模型普遍依赖于单一的推理范式（如自然语言、代码或符号推理），缺乏人类思考问题时那种灵活的推理能力。 为此，微软亚洲研究院与清华大学联合提出了“推理链”（Chain-of-Reasoning, CoR）框架，引入了自然语言、代码与...
AI Testing and Evaluation: Reflections
| Kathleen Sullivan and Amanda Craig Deckard
In the series finale, Amanda Craig Deckard returns to examine what Microsoft has learned about testing as a governance tool. She also explores the roles of rigor, standardization, and interpretability in testing and what’s next for Microsoft’s AI governance work.
编者按：面对信息密集、时长数小时的长视频内容，即便是当下最强大的大语言模型（LLMs）与视觉语言模型（VLMs）也难以轻松应对。为此，微软亚洲研究院提出了 Deep Video Discovery（DVD）智能体，通过推理驱动与工具协同，探索更高效、更智能的视频理解。在多个挑战性基准测试中，DVD 展现出领先的性能，进一步推动长视频理解迈向“可用”、“可控”的智能时代。 近年来，大语言模型（LLM...
Lack of children in public medical imaging data points to growing age bias in biomedical AI
Alex Lu, Stan Hua (opens in new tab), Lauren Erdman (opens in new tab) Artificial intelligence (AI) is transforming healthcare with applications from interpreting electronic healthcare records (opens in new tab) to detecting cancer from medical images. We ask: are…