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.
Text Adventure Learning Environment Suite (TALES) – Benchmark to evaluate language models on interactive text environments. This repository contains the files needed to benchmark language agents on a curated list of text-based games from the…
Implementation of Discrete Attn Transformer. Includes from scratch training of transformer for Templatic Generation Tasks (dataset available at: https://huggingface.co/datasets/rfernand/templatic_generation_tasks), along with code to evaluate LLMs on same dataset. See our paper: “Mechanisms of Symbol Processing…
Scalable long-context LLM decoding that leverages sparsity—by treating the KV cache as a vector storage system. RetroInfer is a novel system that rethinks the KV cache as vector storage within a GPU–CPU co-execution setup to…
Structured Entity Extraction and the Approximate Entity Set OverlaP (AESOP) metric are designed to appropriately assess model performance.
AttentionEngine accelerates transformer attention variants by generating efficient custom kernels, enabling model designers to easily create new variants with our flexible API.
Understanding land use over time is critical to tracking events related to climate change, like deforestation. However, satellite-based remote sensing tools which are used for monitoring struggle to differentiate vegetation types in farms and orchards…
SeerAttention is a learning-based method to enable block-level sparse attention for long-context LLM without using prefined static pattern or heuristic methods. It can be applied in Post-training or Fine-tuning stages. The Attention Gate units learn…
OmniParser is an advanced vision-based screen parsing module that converts user interface (UI) screenshots into structured elements, allowing agents to execute actions across various applications using visual data . By harnessing large vision-language model capabilities,…
This is the repository for paper “Causal integration of chemical structures in self-supervised learning improves representations of microscopy images for morphological profiling”. Learning effective representations of cells in microscopy images can fuel many applications. Here,…
