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..
On the Completeness of Best-First Search Variants That Use Random Exploration
Authors: Richard Valenzano, Fan Xie
AAAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,837 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,410 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 | In this paper, we provide a theoretical justification for this increased robustness by formally analyzing how these algorithms behave on infinite graphs. |
| Researcher Affiliation | Academia | Richard Valenzano University of Toronto Toronto, Canada EMAIL Fan Xie University of Alberta Edmonton, Canada EMAIL |
| Pseudocode | Yes | Algorithm 1 The OCL Algorithm Framework |
| Open Source Code | No | The paper is theoretical and focuses on formal analysis; it does not mention or provide access to any open-source code for an implementation of its concepts. |
| 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 discuss experimental validation using dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |