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.
The Smoothed Complexity of Computing Kemeny and Slater Rankings
Authors: Lirong Xia, Weiqiang Zheng5742-5750
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,106 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,354 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 | In this paper, we develop the ο¬rst smoothed complexity results for winner determination in voting. We prove the smoothed hardness of Kemeny and Slater using the classical smoothed runtime analysis, and prove a parameterized typical-case smoothed easiness result for Kemeny. |
| Researcher Affiliation | Academia | Lirong Xia1 and Weiqiang Zheng2 1 RPI 2 CFCS, Computer Science Dept., Peking University EMAIL, EMAIL |
| Pseudocode | Yes | ALGORITHM 1: Algorithm for EFAS. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for its methodology is openly available. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical experiments with datasets that would be publicly available for access or training. |
| Dataset Splits | No | This is a theoretical paper and does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not describe specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup, hyperparameters, or training settings. |