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..
Achieving Proportionality up to the Maximin Item with Indivisible Goods
Authors: Artem Baklanov, Pranav Garimidi, Vasilis Gkatzelis, Daniel Schoepflin5143-5150
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a constructive argument proving the existence of a PROPm allocation for any instance with up to five agents. |
| Researcher Affiliation | Academia | 1 HSE University, Russian Federation 2 Conestoga High School 3 Drexel University |
| Pseudocode | No | The paper describes constructive proofs but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve data splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or system-level training settings. |