Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024
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Updated
Oct 2, 2025 - Python
Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024
A Dataset of Python Challenges for AI Research
APPS: Automated Programming Progress Standard (NeurIPS 2021)
multilspy is a lsp client library in Python intended to be used to build applications around language servers.
[ICLR'25] BigCodeBench: Benchmarking Code Generation Towards AGI
A relation-aware semantic parsing model from English to SQL
Code for Parsel 🐍 - generate complex programs with language models
Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing.
🌌️Fuzz4All: Universal Fuzzing with Large Language Models
Code and Data artifact for NeurIPS 2023 paper - "Monitor-Guided Decoding of Code LMs with Static Analysis of Repository Context". `multispy` is a lsp client library in Python intended to be used to build applications around language servers.
An inductive logic programming system
A Python framework for program synthesis with a focus on Automated Machine Learning.
[ICLR 2025 Oral] This is the official repo for the paper "LLM-SR" on Scientific Equation Discovery and Symbolic Regression with Large Language Models
Repilot, a patch generation tool introduced in the ESEC/FSE'23 paper "Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair"
A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement, ASE 2024 (Distinguished Paper Award)
Re-implement DeepCoder (https://openreview.net/pdf?id=ByldLrqlx)
An official TensorFlow implementation of "Neural Program Synthesis from Diverse Demonstration Videos" (ICML 2018) by Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, and Joseph J. Lim
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms
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