Microsoft Research Blog

Research Blog

  1. Stylized digital illustration of a multi-layered circuit board. A glowing blue microchip sits at the top center, with intricate circuitry radiating outward. Beneath it, four stacked layers transition in color from blue to orange, each featuring circuit-like patterns. Smaller rectangular and circular components are connected around the layers, all set against a dark background with scattered geometric shapes.

    Project Ire autonomously identifies malware at scale 

    August 5, 2025

    Designed to classify software without context, Project Ire replicates the gold standard in malware analysis through reverse engineering. It streamlines a complex, expert-driven process, making large-scale malware detection faster & more consistent.

  2. The image features four white icons on a gradient background that transitions from blue on the left to green on the right. The first icon is a network or molecule structure with interconnected nodes. The second icon shows a stylized person in front of a computer screen. The third icon shows an organization tree with one main node and three nodes branching out side by side below it.

    Technical approach for classifying human-AI interactions at scale 

    July 23, 2025

    Semantic Telemetry helps LLMs run efficiently, reliably, and in near real-time. Learn about the engineering behind that system, including the trade-offs and lessons learned along the way—from batching strategies to token optimization and orchestration.

  3. CollabLLM blog hero | flowchart diagram starting in the upper left corner with an icon of two overlapping chat bubbles; arrow pointing right to an LLM network node icon; branching down to show three simulated users; right arrow to a "Reward" box

    CollabLLM: Teaching LLMs to collaborate with users 

    July 15, 2025

    Recipient of an ICML 2025 Outstanding Paper Award, CollabLLM improves how LLMs collaborate with users, including knowing when to ask questions and how to adapt tone and communication style to different situations. This approach helps move AI toward more user-centric and trustworthy systems.