Popular GitHub Repositories for Formal Specification

OpenAPI specification for the OpenAI API

CodeMachine is a CLI-native orchestration platform that uses coordinated multi-agent AI workflows to adaptively transform specification files into production-ready code. ⚡️

LLMs as Copilots for Theorem Proving in Lean

A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.

alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, 2023, and 2024)

An efficient API documentation management tool that fully adheres to the OpenAPI specification and incorporates advanced LLM technology. This tool can automate the generation of API documentation, data models, and test cases, greatly enhancing development efficiency and documentation quality.

Crystal - C++ implementation of a unified framework for multilingual TTS synthesis engine with SSML specification as interface.

Transform AI-generated text into formal, human-like, and academic writing with ease, avoids AI detector!

Neural Network Verification Software Tool

Tableau-based Theorem Prover for Natural Logic and Language

用于强化神经网络逻辑规范的工具包

This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation

Formal Verification of Neural Feedback Loops (NFLs)

Lyapunov-stable Neural Control for State and Output Feedback

Formalizing "Proofs from THE BOOK"

Automated .NET SDKs for your APIs

This repository contains my full work and notes on Deeplearning.ai GAN Specialization (Generative Adversarial Networks)

C# SDK based on official HuggingFace OpenAPI specification

LeanAgent is a novel lifelong learning framework for formal theorem proving that continuously generalizes to and improves on ever-expanding mathematical knowledge without forgetting previously learned knowledge.