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.
On the Kernelization of Global Constraints
Authors: Clément Carbonnel, Emmanuel Hebrard
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,788 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,906 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 introduce novel loss-less kernelization variants that are tailored for constraint propagation. We showcase the theoretical interest of our ideas on two constraints, VERTEXCOVER and EDGEDOMINATINGSET. |
| Researcher Affiliation | Academia | Cl ement Carbonnel University of Oxford EMAIL Emmanuel Hebrard CNRS, LAAS-CNRS, Universit e de Toulouse EMAIL |
| Pseudocode | Yes | Algorithm 1: z Crown(G = (V, E), k, z) |
| Open Source Code | No | No statement explicitly providing a link to source code or stating its public availability for the described methodology was found. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and proofs for constraint satisfaction problems (VERTEX COVER, EDGE DOMINATING SET). It does not involve empirical studies with specific datasets, and therefore no mention of publicly available training datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications were mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments involving specific experimental setups or hyperparameters. |