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.
Envy-Free Mechanisms with Minimum Number of Cuts
Authors: Reza Alijani, Majid Farhadi, Mohammad Ghodsi, Masoud Seddighin, Ahmad Tajik
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,839 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,274 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 | We provide methods, namely expansion process and expansion process with unlocking, for dividing the cake under different assumptions on the valuation functions of the players. |
| Researcher Affiliation | Academia | Sharif University of Technology , Duke University , University of Michigan Ann Arbor , Institute for Research in Fundamental Sciences (IPM) School of Computer Science EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 EFISM algorithm |
| Open Source Code | No | The paper does not provide any concrete access information or links to open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not describe empirical experiments involving datasets or training. Therefore, it does not provide information about publicly available datasets used for training. |
| Dataset Splits | No | This is a theoretical paper that does not describe empirical experiments involving datasets. Therefore, it does not provide information about training/validation/test dataset splits. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not report on empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes theoretical algorithms and does not report on empirical experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper describes theoretical algorithms and does not report on empirical experiments, thus no experimental setup details like hyperparameters or system-level training settings are provided. |