Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Deterministic, Strategyproof, and Fair Cake Cutting
Authors: Vijay Menon, Kate Larson
IJCAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,656 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,868 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | All our negative (impossibility) results are in the direct-revelation model, while the algorithm we present is in the Robertson Webb model. |
| Researcher Affiliation | Academia | Vijay Menon and Kate Larson David R. Cheriton School of Computer Science University of Waterloo EMAIL |
| Pseudocode | Yes | Algorithm 1: ϵ-approximation of a piecewise constant function using 2k/ϵ cut queries. Algorithm 2: Modified Even-Paz algorithm. |
| Open Source Code | No | The paper does not contain any explicit statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and algorithm analysis; it does not report on empirical experiments that would involve training on a dataset. Therefore, no information about publicly available training datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; thus, no information about training, validation, or test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical proofs and algorithm analysis; it does not describe any empirical experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithm analysis; it does not describe specific software implementations with version numbers that would be required for replication. Therefore, no software dependencies are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Thus, no experimental setup details, hyperparameters, or training configurations are provided. |