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..
On Recognising Nearly Single-Crossing Preferences
Authors: Florian Jaeckle, Dominik Peters, Edith Elkind
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that it can be efficiently decided if voters can be split into two single-crossing groups. Also, for every fixed k ≥ 1 we can decide in polynomial time if a profile can be made single-crossing by performing at most k candidate swaps per vote. In contrast, for each k ≥ 3 it is NP-complete to decide whether candidates can be partitioned into k sets so that the restriction of the input profile to each set is single-crossing. Our results are summarised in Table 1. |
| Researcher Affiliation | Academia | Florian Jaeckle, Dominik Peters, Edith Elkind Department of Computer Science University of Oxford, UK |
| Pseudocode | No | The paper describes algorithms in prose, for example, 'Our algorithm proceeds by creating and solving several instances of 2SAT', but it does not include formally structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical research and does not use or provide access to any dataset for training. |
| Dataset Splits | No | This paper is theoretical research and does not specify any dataset splits for validation. |
| Hardware Specification | No | This paper is theoretical research and does not describe any specific hardware used for running experiments. |
| Software Dependencies | No | This paper is theoretical research and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical research and does not describe any experimental setup details such as hyperparameters or training configurations. |