Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Recognizing Top-Monotonic Preference Profiles in Polynomial Time
Authors: Krzysztof Magiera, Piotr Faliszewski
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 18,044 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,605 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 provide the ๏ฌrst polynomial-time algorithm for recognizing if a pro๏ฌle of (possibly weak) preference orders is top-monotonic. Our algorithm proceeds by reducing the recognition problem to the SAT-2CNF problem. |
| Researcher Affiliation | Academia | Krzysztof Magiera and Piotr Faliszewski AGH University, Krakow, Poland EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is openly available. |
| Open Datasets | No | This paper is theoretical and does not involve the use of a dataset for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not involve the use of dataset splits for validation or training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers). |
| Experiment Setup | No | This is a theoretical paper and does not include details on an experimental setup, hyperparameters, or training configurations. |