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.
Fast Algorithms for SAT with Bounded Occurrences of Variables
Authors: Junqiang Peng, Mingyu Xiao
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 22,792 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,172 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 | We present fast algorithms for the general CNF satisfiability problem (SAT) with running-time bound O (cdn)... In this paper, we study exact algorithms for SAT, where one is asked to decide the satisfiability of a given CNF with n variables. In exact algorithms, we are devoted to finding fast algorithms in which the correctness and the worst-case runningtime bound are theoretically guaranteed. |
| Researcher Affiliation | Academia | Junqiang Peng , Mingyu Xiao University of Electronic Science and Technology of China, Chengdu, China EMAIL, EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm complexity bounds, not empirical evaluation on specific datasets. Therefore, no dataset information is provided. |
| Dataset Splits | No | The paper is theoretical and focuses on algorithm complexity bounds, not empirical evaluation on specific datasets. Therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its algorithms or experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and complexity, not empirical experiments. Therefore, no experimental setup details like hyperparameters or training configurations are provided. |