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 [1].
Understanding EFX Allocations: Counting and Variants
Authors: Tzeh Yuan Neoh, Nicholas Teh
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we investigate the problem of determining the minimum number of EFX allocations for a given instance, arguing that this approach may yield valuable insights into the existence and computation of EFX allocations. ... Notably, we resolve open problems regarding WEFX by proving polynomial-time computability under binary additive valuations and establishing the first constant-factor approximation for two agents. |
| Researcher Affiliation | Academia | Tzeh Yuan Neoh1,2, Nicholas Teh3 1Institute of High-Performance Computing, Agency for Science, Technology and Research, Singapore 2Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore 3University of Oxford, UK EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Returns an EFX allocation when m = n + 2. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code, links to code repositories, or mention of code in supplementary materials. |
| Open Datasets | No | The paper discusses theoretical concepts related to fair division, valuations, and allocations (e.g., 'restricted instances', 'binary additive valuations', 'general monotone valuations') and does not describe or use any specific publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper does not utilize or refer to any specific datasets for empirical evaluation, therefore, there is no mention of dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis, proofs, and algorithms, and does not describe any experimental setup or hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and presents algorithms and proofs. It does not mention any specific software dependencies or their version numbers required to replicate experimental results. |
| Experiment Setup | No | The paper focuses on theoretical contributions, including proving computability and approximation factors, and does not describe an experimental setup with specific hyperparameters or training configurations. |