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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Achieving Envy-Freeness with Limited Subsidies under Dichotomous Valuations
Authors: Siddharth Barman, Anand Krishna, Yadati Narahari, Soumyarup Sadhukhan
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,956 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,478 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 prove that, under dichotomous valuations, there exists an allocation that achieves envy-freeness with a per-agent subsidy of either 0 or 1. Furthermore, such an envy-free solution can be computed ef๏ฌciently in the standard value-oracle model. Notably, our results hold for general dichotomous valuations and, in particular, do not require the (dichotomous) valuations to be additive, submodular, or even subadditive. Also, our subsidy bounds are tight and provide a linear (in the number of agents) factor improvement over the bounds known for general monotone valuations. |
| Researcher Affiliation | Academia | 1Indian Institute of Science, Bengaluru 2Indian Institute of Technology, Kanpur |
| Pseudocode | Yes | Algorithm 1 ALG, Algorithm 2 EXTEND, Algorithm 3 FINDSINK |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup with hyperparameters or training configurations. |