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..
The Meta-Problem for Conservative Mal’tsev Constraints
Authors: Clement Carbonnel
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design an algorithm that decides in polynomial-time if a constraint language has a conservative Mal tsev polymorphism, and outputs one if one exists. |
| Researcher Affiliation | Academia | Clement Carbonnel LAAS-CNRS University of Toulouse, INP Toulouse, France EMAIL |
| Pseudocode | No | The paper describes algorithms in prose but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical experiments with datasets, and therefore does not mention publicly available training data. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical experiments with datasets, and therefore does not mention validation splits. |
| Hardware Specification | No | This is a theoretical paper that does not involve empirical experiments, and therefore does not specify hardware used. |
| Software Dependencies | No | This is a theoretical paper that does not detail specific software dependencies with version numbers for replication. |
| Experiment Setup | No | This is a theoretical paper that does not involve empirical experiments, and therefore does not detail an experimental setup or hyperparameters. |