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.
Backdoors into Heterogeneous Classes of SAT and CSP
Authors: Serge Gaspers, Neeldhara Misra, Sebastian Ordyniak, Stefan Szeider, Stanislav Zivny
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 17,226 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,997 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 draw a detailed complexity landscape for the problem of detecting strong backdoor sets into heterogeneous base classes for SAT and CSP. We provide algorithms that establish fixedparameter tractability under natural parameterizations, and we contrast the tractability results with hardness results that pinpoint the theoretical limits. |
| Researcher Affiliation | Academia | Serge Gaspers UNSW and NICTA Sydney, Australia EMAIL Neeldhara Misra Indian Institute of Science Bangalore, India EMAIL Sebastian Ordyniak Masaryk Univ. Brno, Czech Republic EMAIL Stefan Szeider Vienna Univ. of Technology Vienna, Austria EMAIL Stanislav ˇZivn y Univ. of Oxford Oxford, UK EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependency versions for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |