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..
Fine-grained Complexity of Partial Minimum Satisfiability
Authors: Ivan Bliznets, Danil Sagunov, Kirill Simonov
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our goal is to fix the issue and show a O ((2 ϵ)m) lower bound under the SETH assumption (here m is the total number of clauses), as well as several other lower bounds and parameterized exact algorithms with better-than-trivial running time. |
| Researcher Affiliation | Academia | 1HSE University, St.Petersburg, Russia 2St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, St.Petersburg, Russia 3Algorithms and Complexity Group, TU Wien, Austria |
| Pseudocode | No | The paper describes algorithms and rules in prose (e.g., "Reduction rule 1", "Branching rule 1") but does not provide any formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | This is a theoretical paper focused on complexity analysis and algorithm design. It does not conduct empirical studies using datasets, therefore, there is no mention of publicly available or open datasets for training. |
| Dataset Splits | No | This is a theoretical paper focused on complexity analysis and algorithm design. It does not conduct empirical studies using datasets, therefore, there is no mention of training/validation/test splits. |
| Hardware Specification | No | This is a theoretical paper. It does not discuss any experimental setup, and therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper. It does not discuss any experimental setup, and therefore no specific software dependencies or version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper focused on complexity analysis and algorithm design. It does not conduct empirical studies or experiments, and therefore no details about experimental setup, such as hyperparameters or system-level training settings, are provided. |