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..
Pandora’s Problem with Deadlines
Authors: Ben Berger, Tomer Ezra, Michal Feldman, Federico Fusco
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is an efficient thresholdbased strategy that achieves a constant approximation relative to the performance of the optimal strategy for the deadlines setting. The paper also contains extensive proofs and lemmas, indicating theoretical work. The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24) |
| Researcher Affiliation | Collaboration | 1 Offchain Labs, Inc. 2 Simons Laufer Mathematical Sciences Institute 3Tel Aviv University 4 Microsoft ILDC 5Department of Computer, Control and Management Engineering Antonio Ruberti , Sapienza University of Rome |
| Pseudocode | No | The paper describes the algorithmic steps and logic verbally and through mathematical formulations (e.g., 'In this section we present our main result: an efficient strategy... in three steps'), but it does not include formal pseudocode blocks or figures. |
| Open Source Code | No | The paper does not provide any information about open-source code availability, specific repository links, or statements about code release in supplementary materials. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets. It discusses 'box distributions' and 'random variables' in a theoretical context, but does not mention specific datasets or their public availability. |
| Dataset Splits | No | As the paper is theoretical and does not conduct empirical experiments, there is no discussion of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments or hardware used. No specific hardware details (like GPU/CPU models or memory) are provided. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific solvers). |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup, hyperparameters, or training configurations, as it does not describe empirical experiments. |