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.
An Improved Upper Bound for SAT
Authors: Huairui Chu, Mingyu Xiao, Zhe Zhang3707-3714
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,986 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,590 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 | In this paper, we study exact algorithms for SAT with guaranteed theoretical running time bounds. |
| Researcher Affiliation | Academia | Huairui Chu, Mingyu Xiao , Zhe Zhang School of Computer Science and Engineering, University of Electronic Science and Technology of China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 SAT(F) |
| Open Source Code | No | The paper references an arXiv preprint URL (https://arxiv.org/abs/2007.03829) but does not provide a link to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper on algorithm complexity, not one involving training models on datasets. Therefore, no dataset information is applicable. |
| Dataset Splits | No | This is a theoretical paper on algorithm complexity, not one involving training models on datasets with splits. Therefore, no dataset split information is applicable. |
| Hardware Specification | No | This is a theoretical paper on algorithm complexity. It does not describe any specific hardware used for running empirical experiments. |
| Software Dependencies | No | This is a theoretical paper focused on algorithm design and analysis. It does not mention any specific software dependencies or versions required for empirical experiments. |
| Experiment Setup | No | This is a theoretical paper. It does not describe an experimental setup with hyperparameters or training configurations for empirical validation. |