Research Tools: code, datasets, & models

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…

Raha (MetaOpt) uses our open-source heuristic analyzer to quantify the impact of failures on a traffic engineered WAN.

Implicit Language models are language models that calculate their outputs as a fixed-point iteration rather than a single forward pass. Implicit models offer increased representational power compared to transformers as we show in the accompanying…

This repository provides the code to construct the HORIZON benchmark — a large-scale, cross-domain benchmark built by refactoring the popular Amazon-Reviews 2023 dataset for evaluating sequential recommendation and user behavior modeling. We do not release…

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…

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…