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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
When Does Schwartz Conjecture Hold?
Authors: Matthias Mnich, Yash Raj Shrestha, Yongjie Yang
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we prove sufficient conditions for infinite classes of tournaments that satisfy Schwartz s Conjecture and Brandt s Conjecture. Moreover, we prove that τ can be calculated in polynomial time in several infinite classes of tournaments. Furthermore, our results reveal some structures that are forbidden in every counterexample to Schwartz s Conjecture. |
| Researcher Affiliation | Academia | Matthias Mnich Universit at Bonn Bonn, Germany Yash Raj Shrestha ETH Z urich Z urich, Switzerland Yongjie Yang Universit at des Saarlandes Saarbr ucken, Germany |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not describe the use of datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not describe dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any hardware specifications for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not describe any specific ancillary software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or system-level training settings. |