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.
Switch-List Representations in a Knowledge Compilation Map
Authors: Ondřej Čepek, Miloš Chromý
IJCAI 2020 | Venue PDF | LLM Run Details | Input Tokens: 15,953 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,554 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 | This is a theory paper which establishes the properties of the SL language and places it in KCM. |
| Researcher Affiliation | Academia | Ondˇrej ˇCepek and Miloˇs Chrom y Charles University, Faculty of Mathematics and Physics, Department of Theoretical Computer Science and Mathematical Logic, Malostransk e n am. 25, 118 00 Praha 1, Czech Republic EMAIL |
| Pseudocode | No | The paper describes algorithms and procedures in prose, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code related to the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training, validation, or testing. Therefore, it does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments with datasets, so it does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |