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.
We introduce Reprompting, an iterative sampling algorithm that automatically learns the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, Reprompting infers the CoT recipes that work consistently well for a…
Humans can learn to solve new tasks by inducing high-level strategies from example solutions to similar problems and then adapting these strategies to solve unseen problems. Can we use large language models to induce such…
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…
Large Language Models are typically trained with next-turn rewards, limiting their ability to optimize for long-term interaction. As a result, they often respond passively to ambiguous or open-ended user requests, failing to help users reach…
Recent advancements, such as DeepSeek-Prover-V2-671B and Kimina-Prover-Preview-72B, demonstrate a prevailing trend in leveraging reinforcement learning (RL)-based large-scale training for automated theorem proving. Surprisingly, we discover that even without any training, careful neuro-symbolic coordination of existing…
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.
