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.
Complexity of Reasoning with Cardinality Minimality Conditions
Authors: Nadia Creignou, Frédéric Olive, Johannes Schmidt
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,150 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,848 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 | Our main contribution is a complete complexity classification of the CARDMINSAT problem in Schaefer s framework, which opens the door for a better understanding of the complexity of many reasoning problems. |
| Researcher Affiliation | Academia | Nadia Creignou1, Fr ed eric Olive1, Johannes Schmidt2 1 Aix Marseille Univ, CNRS, LIS, Marseille, France 2 J onk oping University, Department of Computer Science and Informatics, School of Engineering, Sweden |
| Pseudocode | No | The paper presents theoretical proofs and mathematical derivations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or any other purpose. |
| Dataset Splits | No | The paper is theoretical and does not discuss datasets or their splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or mention specific hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup or list software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or provide details such as hyperparameters or training configurations. |