Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MixSATGEN: Learning Graph Mixing for SAT Instance Generation
Authors: Xinyan Chen, Yang Li, Runzhong Wang, Junchi Yan
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show the superiority of our method with both resemblance in structure and hardness, and general applicability. |
| Researcher Affiliation | Academia | Xinyan Chen 12, Yang Li 1, Runzhong Wang1, Junchi Yan 12 1Department of Computer Science and Engineering & 2Zhiyuan College Shanghai Jiao Tong University EMAIL |
| Pseudocode | Yes | The algorithms for the training and generating process of Mix SATGEN in Sec. 3 are presented in Alg. 1 and Alg. 2. |
| Open Source Code | Yes | https://github.com/Thinklab-SJTU/Mix SATGEN |
| Open Datasets | Yes | The real-world SAT instances are collected from SATLIB benchmark library (Hoos & Stützle, 2000) and SAT Competition 2021 (Balyo et al., 2021). |
| Dataset Splits | No | The paper discusses the datasets used and refers to 'training dataset' and 'test instances' but does not provide specific training/validation/test dataset splits, percentages, or absolute counts for reproducibility. |
| Hardware Specification | Yes | All the experiments are performed on a single GPU of Ge Force RTX 3090. The affinity matrix K is calculated on an AMD Ryzen Threadripper 3970X 32-core CPU with 128GB memory. |
| Software Dependencies | No | The paper mentions software tools and solvers like Ca Di Ca L, Kissat, SBVA-Cadical, and Pygmtools, but it does not specify exact version numbers for these or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We first finetune the pretrained model with 0.0001 learning rate, 200 epoches and 4 iterations of message passing (Selsam et al., 2019). |