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..
Augment with Care: Contrastive Learning for Combinatorial Problems
Authors: Haonan Duan, Pashootan Vaezipoor, Max B Paulus, Yangjun Ruan, Chris Maddison
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a scientific study of the effect of augmentation design on contrastive pretraining for the Boolean satisfiability problem. |
| Researcher Affiliation | Academia | 1University of Toronto 2Vector Institute 3ETH Zürich. |
| Pseudocode | No | The paper describes the framework's components and processes, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/ h4duan/contrastive-sat. |
| Open Datasets | Yes | Datasets. We experimented using four generators: SR (Selsam et al., 2018), Power Random 3SAT (PR) (Ansótegui et al., 2009), Double Power (DP) and Popularity Similarity (PS) (Giráldez-Cru & Levy, 2017). |
| Dataset Splits | Yes | We generated 100 separate labelled instances to train our linear evaluators, and another 500 as the validation set to pick the hyperparameters (ranging from 10 3 to 103) of L2 regularization. We used 200 instances as validation sets for early stopping of all methods. |
| Hardware Specification | No | The paper does not specify the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions software like 'Adam optimizer', 'sklearn', 'Neuro SAT', and 'Crypto Mini Sat solvers', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Architecture. We primarily used the encoder of Neuro SAT (Selsam et al., 2018) as the GNN architecture. [...] The dimension of literal representations was chosen to be 128. [...] Experimental Setting. We used the contrastive loss in Equation 1 with the temperature 0.5. For the projection head, we used a 2-layer MLP, with the dimension of hidden and output layer being 64. We used Adam optimizer with learning rate 2 10 4 and weight decay 10 5. The batch size was 128 and the maximum training epoch was 5000. |