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..
Complexity Results for Compressing Optimal Paths
Authors: Adi Botea, Ben Strasser, Daniel Harabor
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results give a first theoretical underpinning to the problem of creating space-efficient CPDs using RLE. |
| Researcher Affiliation | Collaboration | Adi Botea IBM Research Ireland Ben Strasser Karlsruhe Institute of Technology Germany Daniel Harabor NICTA Australia |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper is theoretical and does not mention any release of source code. |
| Open Datasets | No | The paper is theoretical and does not involve datasets or training. |
| Dataset Splits | No | The paper is theoretical and does not involve datasets or validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details or hyperparameters. |