MSR Synth is a research project exploring how to design AI agents with social intelligence—agents that can communicate, collaborate, and participate in human interaction in ways that are context‑aware, trustworthy, and aligned with human values. Drawing on human–computer interaction, AI‑mediated communication, and social computing, the…
This project studies the data protection challenges when models and agents interact with sensitive data across multiple users. Large language models (LLMs) are increasingly deployed in enterprise settings where thLarge language models (LLMs) are increasingly deployed in enterprise environments, where they serve multiple users and…
Communication-centered computing Communication shapes how societies create, share, and preserve knowledge. Yet today’s digital tools remain organized around static formats that enforce rigid separations between creation and consumption, author and reader, expression and interpretation. These structural constraints fragment collaboration and limit how knowledge evolves over…
< AI for Good Lab Language is more than communication—it sustains culture, identity, and opportunity. Yet most of the world’s languages remain underrepresented in today’s AI systems because they have limited digital content, few high-quality datasets, and little benchmark data to measure progress. Our work…
Automated evaluation and testing of AI agents AI agents are rapidly transforming software, with projections of over a billion agents in operation by 2028. These agents, embedded in products like VS Code and M365 Copilot, perform increasingly complex tasks—writing code, conducting research, and automating workflows.…
Small foundation models for the electric grid GridFM is a Microsoft Research initiative to build a foundation model (FM) for electric power grids, applying modern AI methods—similar to large language/weather models—to complex grid physics. Traditional power‑flow solvers (like AC‑OPF) are accurate but extremely slow, taking…
This project aims to push the boundaries of agentic AI by addressing three critical challenges: long-horizon memory, safe and aligned tool usage, and adaptive reasoning. Current language models excel at text generation but lack agency—the ability to plan, act, and adapt in dynamic environments. The…
This project explores how Generative AI can transform supply chain management from rule-based automation to true autonomy. Building on the MIT autonomous supply chain testbed, it integrates multiple AI agents that learn, adapt, and coordinate decisions across forecasting, inventory, and replenishment—cutting costs by up to…
This project develops a unified framework for physically grounded world modelling that combines video-based temporal prediction with Gaussian Splatting for photorealistic 3D representation. A Physics Vision-Language Model translates natural-language instructions into transformations that respect physical constraints, enabling interpretable and goal-directed control in dynamic scenes. By…
This project introduces AgentGuard, a monitoring and routing system designed to improve reliability and cost-efficiency in Azure-based agent workflows. By analysing early trajectory signals—such as reasoning patterns and tool usage—within the first 10–30% of an agent’s execution, AgentGuard predicts task outcomes and dynamically halts or…
This project addresses the challenge of enabling AI agents to operate effectively in complex, realistic environments such as web navigation, computer use, and mobile interfaces. While current models excel in structured domains like mathematics and coding, they struggle with tasks requiring long-horizon planning, exploration, and…
Human intelligence is defined by the interplay of semantic and episodic memory. AI research almost exclusively develops semantic memory systems, but episodic memory has many open challenges, especially for vision. Episodic memory is the ability to recall specific past experiences and use the information together…
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