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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Semi-random Likelihood of Doctrinal Paradoxes
Authors: Ao Liu, Lirong Xia5124-5132
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic data in the next Section confirm our theoretical results. We conduct numerical experiments to verify the results in Theorem 1. |
| Researcher Affiliation | Academia | Ao Liu and Lirong Xia Department of Computer Science, Rensselaer Polytechnic Institute 110 8th St, Troy, NY 12180, USA EMAIL, EMAIL |
| Pseudocode | No | The paper discusses proof techniques but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions "synthetic data" but does not provide any specific access information (link, DOI, citation) for a publicly available dataset used for training. |
| Dataset Splits | No | The paper mentions conducting "numerical experiments" on "synthetic data" but does not provide specific details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions "numerical experiments" but does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |