Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
The MCP Server for Azure AI Foundry Labs is designed to supercharge team velocity in adopting and evaluating breakthrough AI research. By equipping GitHub Copilot with custom tools for intelligent model discovery, tailored implementation guidance,…
With Project Amelie, we are unveiling our first Foundry autonomous agent that can perform machine learning engineering tasks. ML teams can use the agent to initiate complex machine learning tasks using prompts —such as, “Help…
Precio is a Rust library that implements the Precio protocol for computing private layered histograms and sums.
Lost in Conversation is a code repository to facilitate benchmarking LLMs on multi-turn task completion and the reproduction of experiments included in the accompanying paper: “LLMs Get Lost in Multi-Turn Conversation”.
Developed by Microsoft Research in collaboration with game studio Ninja Theory, Muse is a World and Human Action Model (WHAM) – a generative AI model of a video game that can generate game visuals, controller…
MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. To this end, it is most comparable to textract, but with a focus on…
Magma is a multimodal foundation model designed to both understand and act in digital and physical environments. Magma builds on the foundation models paradigm that pretraining on a larger amount of more diverse datasets allows…
OG-RAG enhances Large Language Models (LLMs) with domain-specific ontologies for improved factual accuracy and contextually relevant responses in fields with specialized workflows like agriculture, healthcare, knowledge work, and more. Paper: OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For…
Stochastic Optimal Control Fine-Tuning of Stable Diffusion. This repository provides an implementation of reward fine-tuning methods for Stable Diffusion 1.5 based on stochastic optimal control (SOC), focusing on Adjoint Matching. It adds specialized trainers, custom…
