Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Improved Algorithms for Maximum Satisfiability and Its Special Cases
Authors: Kirill Brilliantov, Vasily Alferov, Ivan Bliznets
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,691 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,367 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For the (n, 3)-MAXSAT problem, we design a O (1.1749n) algorithm improving on the previous record running time of O (1.191n). For the (n, 4)-MAXSAT problem, we construct a O (1.3803n) algorithm improving on the previous best running time of O (1.4254n). Using the results, we develop a O (1.0911L) algorithm for the MAXSAT where L is a length of the input formula which improves previous algorithm with O (1.0927L) running time. |
| Researcher Affiliation | Academia | Kirill Brilliantov1, Vasily Alferov2, Ivan Bliznets3 1 Constructor University 2 Independent Researcher 3 Utrecht University EMAIL, EMAIL, EMAIL/EMAIL |
| Pseudocode | No | The paper describes reduction rules and branching rules, but it does not present them in structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for source code or state that it is open-source. |
| Open Datasets | No | This theoretical paper focuses on algorithm design and complexity analysis for MAXSAT problems, and as such, it does not use specific datasets or provide access information for them. |
| Dataset Splits | No | This theoretical paper does not discuss or provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running any experiments, consistent with its theoretical nature. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |