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..
Participatory Budgeting with Donations and Diversity Constraints
Authors: Jiehua Chen, Martin Lackner, Jan Maly9323-9330
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 17,875 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,287 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 | To sum up, our work provides a ο¬rst axiomatic and computational analysis of PB with donations and diversity constraints, in the form of both upper and lower bounds. We discuss features and pitfalls of this idea, propose methods to handle donations, and analyze their computational demands. |
| Researcher Affiliation | Academia | TU Wien, Vienna, Austria |
| Pseudocode | Yes | Algorithm 1: Sequential-R(I) |
| Open Source Code | No | The paper states, "Due to space limits, most proofs are deferred to (Chen, Lackner, and Maly 2021)," referencing a technical report on arXiv. This is not an explicit statement of releasing source code for the methodology or a direct link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve experiments with datasets, thus no dataset is mentioned as publicly available or open for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments or data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments or their setup, therefore, no hyperparameters or system-level training settings are provided. |