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..
Query Complexity of Tournament Solutions
Authors: Palash Dey
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,291 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,426 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 prove tight bounds on the query complexity of commonly used tournament solutions. |
| Researcher Affiliation | Academia | Palash Dey Indian Institute of Science, Bangalore |
| Pseudocode | No | The paper describes algorithms in prose and mathematical notation (e.g., Theorem 6 and 7), but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes theoretical work and does not provide details on an experimental setup, hyperparameters, or training configurations. |