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.
A collection of packages for building consent management systems with audit trails, granular permissions, and flexible storage backends. Designed for transparency, compliance with privacy regulations, and easy integration into existing applications.
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…
These are the prompts and qrels used for the experiments in Thomas et al., “System Comparison using Automated Generation of Relevance Judgements in Multiple Languages”, SIGIR 2025.
BenchmarkQED is a suite of tools designed for automated benchmarking of retrieval-augmented generation (RAG) systems. It provides components for query generation, evaluation, and dataset preparation to facilitate reproducible testing at scale.
Magentic-UI is a research prototype of an agentic web interface for solving complex web tasks. An Orchestrator coordinates four AutoGen agents—WebSurfer, Coder, FileSurfer, and UserProxy—to handle browsing, coding, file management, and user feedback, etc. It…
Chemically transferable wave function model for molecules. This package provides an implementation of the Orbformer wave function foundation model.
FlexCAD aims at achieving controllable CAD generation across all CAD construction hierarchies. It takes an original CAD model along with the part the user intends to modify (highlighted in blude) as input, and generates multiple…
Repo for “Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective”
We employ GloVe and random projection theory to infer immunologically meaningful T-cell receptor embeddings from adaptive immune repertoires. This repository contains the Pytorch code to replicate experiments in our paper “Scalable Universal T-Cell Receptor Embeddings…
