Research Tools: code, datasets, & models

Automatically synthesize proof annotations that help Verus prove the correctness of Rust code.

This dataset is the Version 2.0 of the FStar Data Set. This dataset’s primary objective is to train and evaluate Proof-oriented Programming with AI (PoPAI, in short). Given a specification of a program and proof…

This dataset contains programs and proofs in F* proof-oriented programming language. The data, proposed in Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming, is an archive of source code, build artifacts, and metadata assembled from eight…

This is a codebase to perform privacy-preserving in-context learning with differentially private few-shot generation.

This repository contains the code for the Eureka ML Insights, a framework for standardizing evaluations of large foundation models, beyond single-score reporting and rankings. The framework is designed to help researchers and practitioners run reproducible evaluations…

Transform data and create rich visualizations iteratively with AI.

A self-play mutual reasoning approach that significantly improves reasoning capabilities of small language models (SLMs) without fine-tuning or superior models. rStar decouples reasoning into a self-play mutual generation-discrimination process.

Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (

EASIER is a domain specific language embedded in PyTorch to automatically scale physical simulations up and out. It just-in-time (JIT) distributes tensor dataflows that describe physical simulations to any number of workers and compiles them…

RadFact is a framework for the evaluation of model-generated radiology reports given a ground-truth report, with or without grounding. Leveraging the logical inference capabilities of large language models, RadFact is not a single number but…