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..
A Fast Algorithm for Consistency Checking Partially Ordered Time
Authors: Leif Eriksson, Victor Lagerkvist
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We construct a much faster algorithm with a run-time bounded by O ((0.26n)n). This is achieved by a sophisticated enumeration of structures similar to total orders, which are then greedily expanded towards a solution. The algorithm, in particular, organizes variables into pairs where we only have to consider a relative ordering with n 2 other variables. This scheme leads to a runtime that is dominated by n!/2 n 2 . Demonstrating the correctness of this strategy is a nontrivial task, and the analysis itself is arguably as interesting as the precise bound we attain. |
| Researcher Affiliation | Academia | Leif Eriksson and Victor Lagerkvist Department of Computer and Information Science, Link oping University, Link oping, Sweden EMAIL |
| Pseudocode | No | No structured pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve empirical evaluation on datasets. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical evaluation on datasets, thus no dataset split information for validation is provided. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments requiring hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not report on experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not report on experiments requiring detailed experimental setup or hyperparameters. |