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].
Insight into Voting Problem Complexity Using Randomized Classes
Authors: Zack Fitzsimmons, Edith Hemaspaandra
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The first step in classifying the complexity of an NP problem is typically showing the problem in P or NP-complete... We show that this problem is equivalent to Exact Perfect Bipartite Matching, and so CCRV for First-Last can be determined in random polynomial time. |
| Researcher Affiliation | Academia | Zack Fitzsimmons1 and Edith Hemaspaandra2 1College of the Holy Cross 2Rochester Institute of Technology zfitzsim@holycross.edu, EMAIL |
| Pseudocode | No | The paper describes theoretical concepts and proofs with examples, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |