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..
Searching for and Avoiding Hidden Sets Using Queries with Local Feedback
Authors: Tomasz Jurdzinski, Dariusz R. Kowalski
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present the first polynomial-time constructions of efficient (n, k)-Loc S... We complement our constructive results by proving almost-matching lower bounds... |
| Researcher Affiliation | Academia | 1University of Wroclaw, Poland 2Augusta University, USA |
| Pseudocode | Yes | 3 Local Selectors The following polynomial-time algorithm produces an (n, k)-Loc S of length... 4 Local Avoiding Selectors ... The following polynomial-time algorithm produces an (n, k, ℓ)-Loc AS of length... |
| Open Source Code | No | The paper does not provide any concrete access information for source code, such as a repository link, an explicit code release statement, or code in supplementary materials. |
| Open Datasets | No | The paper does not mention the use of any specific datasets for experimental evaluation, as it focuses on theoretical constructions and lower bounds for group testing. |
| Dataset Splits | No | As the paper is theoretical and does not conduct experiments on specific datasets, there is no mention of dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm constructions and lower bounds; therefore, it does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not detail experimental implementations; hence, it does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper focuses on theoretical constructions and mathematical proofs, and therefore does not describe an experimental setup, hyperparameters, or training configurations. |