ReSyn — Partitioner
This repository contains the pre-trained Partitioner model presented in the paper ReSyn: A Generalized Recursive Regular Expression Synthesis Framework.
ReSyn is a synthesizer-agnostic divide-and-conquer framework that decomposes complex regular expression synthesis problems into manageable sub-problems by adaptively predicting whether to split examples sequentially (Concatenation) or group them by structural similarity (Union).
Partitioner performs the Union decomposition. Given a set of positive example strings, it predicts a group label for each string, clustering structurally similar strings together so that each cluster can be synthesized independently and combined with the union operator.
Links
- Paper: ReSyn: A Generalized Recursive Regular Expression Synthesis Framework
- GitHub Repository: mrseongminkim/ReSyn
- Dataset: mrseongminkim/ReSyn
Usage
These are custom PyTorch models that use PyTorchModelHubMixin. The model class is defined in the GitHub repository; clone it first so that the ReSyn package is importable, then:
from ReSyn.model import Partitioner
model = Partitioner.from_pretrained("mrseongminkim/ReSyn-Partitioner").eval()
See ReSyn/server.py for the full input encoding / output decoding used at inference time.
Citation
If you find this work useful, please cite:
@inproceedings{kim2026resyn,
title={ReSyn: A Generalized Recursive Regular Expression Synthesis Framework},
author={Kim, Seongmin and Cheon, Hyunjoon and Kim, Su-Hyeon and Han, Yo-Sub and Ko, Sang-Ki},
booktitle={Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-26)},
year={2026}
}