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..
Dividing a Graphical Cake
Authors: Xiaohui Bei, Warut Suksompong5159-5166
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider the classical cake-cutting problem... In this paper, we introduce a generalized setting... We determine the optimal approximation of proportionality that can be obtained... We also show that this bound is tight... We exhibit a moving-knife protocol that achieves the desired guarantee. |
| Researcher Affiliation | Academia | 1 School of Physical and Mathematical Sciences, Nanyang Technological University 2 School of Computing, National University of Singapore |
| Pseudocode | No | The paper describes algorithms (e.g., 'moving-knife protocol', 'our algorithm proceeds') but does not provide pseudocode or algorithm blocks. |
| Open Source Code | No | This is a theoretical paper that does not mention or provide any open-source code for its methodology. |
| Open Datasets | No | This is a theoretical paper and does not use any datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not use any datasets or describe data splitting for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any hardware specifications used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |