In this edition:We introduce privacy enhancements for multiparty deep learning, a framework using smaller, open-source models to provide relevance judgments, and other notable new research.We congratulate Yasuyuki Matsushita, who was named an IEEE Computer Society Fellow.Weve included a recap of the extraordinary, far-reaching work done by researchers at Microsoft in 2024.NEW RESEARCHAI meets materials discoveryTwo of the transformative tools that play a central role in Microsofts work on AI for science are MatterGen and MatterSim. In the world of materials discovery, each plays a distinct yet complementary role in reshaping how researchers design and validate new materials.Read the storyNEW RESEARCHDistributed training enables multiple parties to jointly train a machine learning model on their respective datasets, which can help address the challenges posed by requirements in modern machine learning for large volumes of diverse data. However, this can raise security and privacy issues protecting each partys data during training and preventing leakage of private information from the model after training through various inference attacks.In a recent paper, Communication Efficient Secure and Private Multi-Party Deep Learning, researchers from Microsoft address these concerns simultaneously by designing efficient Differentially Private, secure Multiparty Computation (DP-MPC) protocols for jointly training a model on data distributed among multiple parties. This DP-MPC protocol in the two-party setting is 56-to-794 times more communication-efficient and 16-to-182 times faster than previous such protocols. This work simplifies and improves on previous attempts to combine techniques from secure multiparty computation and differential privacy, especially in the context of training machine learning models.Read the paperNEW RESEARCHTraining and evaluating retrieval systems requires significant relevance judgments, which are traditionally collected from human assessors. This process is both costly and time-consuming. Large language models (LLMs) have shown promise in generating relevance labels for search tasks, offering a potential alternative to manual assessments. Current approaches often rely on a single LLM. While effective, this approach can be expensive and prone to intra-model biases that can favor systems leveraging similar models.In a recent paper: JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment, researchers from Microsoft we introduce a framework that employs smaller, open-source models to provide relevance judgments by combining evaluations across multiple LLMs (LLMBlender) or multiple prompts (PromptBlender). By leveraging the LLMJudge benchmark, they compare JudgeBlender with state-of-the-art methods and the top performers in the LLMJudge challenge. This research shows that JudgeBlender achieves competitive performance, demonstrating that very large models are often unnecessary for reliable relevance assessments.Read the paperNEW RESEARCHCongestion games are used to describe the behavior of agents who share a set of resources. Each player chooses a combination of resources, which may become congested, decreasing utility for the players who choose them. Players can avoid congestion by choosing combinations that are less popular. This is useful for modeling a range of real-world scenarios, such as traffic flow, data routing, and wireless communication networks.In a recent paper: Convergence to Equilibrium of No-regret Dynamics in Congestion Games; researchers from Microsoft and external colleagues propose CongestEXP, a decentralized algorithm based on the classic exponential weights method. They evaluate CongestEXP in a traffic congestion game setting. As more drivers use a particular route, congestion increases, leading to higher travel times and lower utility. Players can choose a different route every day to optimize their utility, but the observed utility by each player may be subject to randomness due to uncertainty (e.g., bad weather). The researchers show that this approach provides both regret guarantees and convergence to Nash Equilibrium, where no player can unilaterally improve their outcome by changing their strategy.Read the paperNEW RESEARCHResearch and development (R&D) plays a pivotal role in boosting industrial productivity. However, the rapid advance of AI has exposed the limitations of traditional R&D automation. Current methods often lack the intelligence needed to support innovative research and complex development tasks, underperforming human experts with deep knowledge.LLMs trained on vast datasets spanning many subjects are equipped with extensive knowledge and reasoning capabilities that support complex decision-making in diverse workflows. By autonomously performing tasks and analyzing data, LLMs can significantly increase the efficiency and precision of R&D processes.In a recent article, researchers from Microsoft introduce RD-Agent, a tool that integrates data-driven R&D systems and harnesses advanced AI to automate innovation and development.At the heart of RD-Agent is an autonomous agent framework with two key components: a) Research and b) Development. Research focuses on actively exploring and generating new ideas, while Development implements these ideas. Both components improve through an iterative process, illustrated in Figure 1 of the article, ensures the system becomes increasingly effective over time.Read the articleSpotlight: Microsoft research newsletterMicrosoft Research NewsletterStay connected to the research community at Microsoft.Subscribe todayOpens in a new tab Microsoft Research | In case you missed itMicrosoft Research 2024: A year in reviewDecember 20, 2024Microsoft Research did extraordinary work this year, using AI and scientific research to make progress on real-world challenges like climate change, food security, global health, and human trafficking. Heres a look back at the broad range of accomplishments and advances in 2024. AIOpsLab: Building AI agents for autonomous cloudsDecember 20, 2024AIOpsLab is a holistic evaluation framework for researchers and developers, to enable the design, development, evaluation, and enhancement of AIOps agents, which also serves the purpose of reproducible, standardized, interoperable, and scalable benchmarks. Yasuyuki Matsushita, IEEE Computer Society 2025 FellowDecember 19, 2024Congratulations to Yasuyuki Matsushita, Senior Principal Research Manager at Microsoft Research, who was named a 2025 IEEE Computer Society Fellow. Matsushita was recognized for contributions to photometric 3D modeling and computational photography. View more news and awards Opens in a new tab