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.
On the Complexity of Chore Division
Authors: Alireza Farhadi, MohammadTaghi Hajiaghayi
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,636 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,587 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 | In this paper, we show that chore division and cake cutting problems are closely related to each other and provide a tight lower bound for proportional chore division. |
| Researcher Affiliation | Academia | Alireza Farhadi1 , Mohammad Taghi Hajiaghayi1 , 1 University of Maryland, College Park EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. No information about publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not discuss hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |