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..
How to Cut a Discrete Cake Fairly
Authors: Ayumi Igarashi
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that a connected division of indivisible items satisfying a discrete counterpart of envyfreeness, called envy-freeness up to one good (EF1), always exists for any number of agents n with monotone valuations. Our result settles an open question raised by Bil o et al. (2019), who proved that an EF1 connected division always exists for the number of agents n 4. Moreover, the proof can be extended to show the following (1) secretive and (2) extra versions: |
| Researcher Affiliation | Academia | Ayumi Igarashi The University of Tokyo, EMAIL |
| Pseudocode | Yes | Algorithm 1: Rounding into a division |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and does not use or reference any datasets for training, therefore no information about public availability is relevant or provided. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation with dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not describe or report on experiments requiring specific hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not involve software implementation or dependencies with specific version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |