M365 Research
Pioneering advancements in AI and Systems
Nouvelles et reportages
Research Focus: Week of January 13, 2025
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.
AIOpsLab: Building AI agents for autonomous clouds
| Minghua Ma, Gagan Somashekar, Rujia Wang, Chetan Bansal, et Saravan Rajmohan
AIOpsLab is an open-source framework designed to evaluate and improve AI agents for cloud operations, offering standardized, scalable benchmarks for real-world testing, enhancing cloud system reliability.
Research Focus: Week of November 11, 2024
Holistic motion-capture calibration technique without calibration, manual intervention or custom hardware; Research on AI agents for autonomous clouds; Automating proof-oriented program construction; One-to-many testing for natural language code generation.
Research Focus: Week of October 28, 2024
New Research | FLASH: Workflow automation agent for diagnosing recurring incidents; METAREFLECTION: Learning instructions for language agents using past reflections; Boosting LLM training efficiency through faster communication between GPUs; and more.
Research Focus: Week of September 23, 2024
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Time-series forecasting is a technique used to predict future values based on previously…
Research Focus: Week of April 29, 2024
In this edition: Can LLMs transform natural language into formal method postconditions; Semantically aligned question + code generation for automated insight generation; Explaining CLIP performance disparities on blind/low vision data; plus recent news.
Intelligent monitoring: Towards AI-assisted monitoring for cloud services
| Anjaly Parayil, Ayush Choure, Fiza Husain, Avi Nayak, Piyali Jana, Rujia Wang, Chetan Bansal, et Saravan Rajmohan
Integrating AI into cloud service monitoring improves incident detection accuracy, reduces unnecessary alerts, and enhances overall system reliability. This helps organizations better align with business goals and increase customer satisfaction.
TaskWeaver: A code-first agent framework for efficient data analytics and domain adaptation
| Shilin He, Liqun Li, Xu Zhang, Bo Qiao, Chaoyun Zhang, Yu Kang, Rujia Wang, Qingwei Lin 林庆维, Saravan Rajmohan, et Dongmei Zhang
AI-backed virtual assistants face challenges in handling complex data structures. TaskWeaver helps users build assistants that understand diverse domain questions, follow examples, and efficiently execute customizable algorithms on complex data structures.
LLMLingua: Innovating LLM efficiency with prompt compression
| Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, et Lili Qiu
Advanced prompting technologies for LLMs can lead to excessively long prompts, causing issues. Learn how LLMLingua compresses prompts up to 20x, maintaining quality, reducing latency, and supporting improved UX.