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…
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…
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…
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…
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…
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…
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…
This project tackles the challenge of cultural misalignment in AI agents, which often leads to unsafe or inappropriate behaviour in diverse, real-world interactions. CuRA introduces a dynamic routing framework that leverages specialised cultural adapters to…
This project focuses on creating robotic systems that can adapt and improve during deployment in dynamic, unstructured environments such as warehouses and industrial sites. It combines the reliability of classical robotics with the flexibility of…
This project introduces a novel architecture for agentic AI systems that ensures accuracy, efficiency, and safety during reasoning. It addresses two key challenges—lack of steerability and absence of verifiable guarantees—by developing verifiers that can interject…
