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.
Dividing Conflicting Items Fairly
Authors: Ayumi Igarashi, Pasin Manurangsi, Hirotaka Yoneda
IJCAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 15,641 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,764 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 significantly extend this result by establishing that a maximal EF1 allocation exists for any graph when the two agents have monotone valuations. To compute such an allocation, we present a polynomial-time algorithm for additive valuations, as well as a pseudo-polynomial time algorithm for monotone valuations. Moreover, we complement our findings by providing a counterexample demonstrating a maximal EF1 allocation may not exist for three agents with monotone valuations; further, we establish NP-hardness of determining the existence of such allocations for every fixed number n 3 of agents. All of our results for goods also apply to the allocation of chores. |
| Researcher Affiliation | Collaboration | 1University of Tokyo 2Google Research EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 CHAINEF1(S; G = (M, E), v) Algorithm 2 SWAPEF1(G = (M, E), v) |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper defines a theoretical problem instance with M = [m] goods and G = (M, E) as an undirected graph. It does not mention using or providing access information for any publicly available or open dataset for experimental purposes. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on a specific dataset, therefore, it does not provide dataset split information. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and proofs. It does not describe any experimental implementation or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not present an experimental setup with specific details like hyperparameters or training configurations. |