Microsoft Research Cambridge

Machine Intelligence

Advanced machine learning research, grounded in trust, efficiency, capability.

KBLaM blog | A flowchart illustrating the process of handling a prompt using a language model. The process begins with documents being used to construct and summarize a knowledge base (KB) offline. The summarized KB is then encoded and fed into the main process. A prompt goes through a tokenizer, followed by rectangular attention, and then into the large language model (LLM). The LLM retrieves information from the encoded KB to generate an answer.

Introducing KBLaM: Bringing plug-and-play external knowledge to LLMs

Layered image of Phi Silica, a state-of-the-art small language model integrated into Windows 11 Copilot+PCs

Phi Silica, small but mighty on-device SLM

A Ladder of Reasoning: Testing the power of imagination in LLMs

Cognition

The recent surge in Generative AI has revolutionized the creation of new systems and tools, transforming the way we work and live. Evaluating and improving the reasoning abilities of Generative AI is key to understanding their generalization abilities and safely deploy them in critical scenarios.

We are developing state-of-the art technologies to evaluate and improve the reasoning abilities of AI systems by focusing on principled machine learning approaches. This effort that requires a diversity of skills: Natural Language Processing, Formal Methods, Mathematical Logic, Machine Learning, Statistics, etc. We believe that advancing the reasoning capabilities of AI requires not only technical innovation but also interdisciplinary collaboration and rigorous evaluation.

By bringing together experts from diverse domains, we aim to build AI systems that can reason more reliably, generalize across tasks, and operate safely in high-stakes environments. Our work is grounded in both theoretical insights and empirical validation, ensuring that the systems we develop are robust, interpretable, and aligned with human values.

Learn more:

A Ladder of Reasoning: Testing the power of imagination in LLMs
MSR Blog | August 2025

Reasoning Elicitation in Language Models via Counterfactual Feedback
Publication | March 2025

Re-Imagine: Symbolic Benchmark Synthesis for Reasoning Evaluation
Publication | March 2025

Does Reasoning Emerge? Examining the Probabilities of Causation in Large Language Models
Publication | August 2024