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..
A Fast Algorithm for MaxSAT above Half Number of Clauses
Authors: Junqiang Peng, Mingyu Xiao
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we develop a new algorithm with runtime O (2.1479µ), significantly improving the previous best upper bound O (5.4064µ) for this important problem. Here, the O notation omits polynomial factors. |
| Researcher Affiliation | Academia | Junqiang Peng , Mingyu Xiao University of Electronic Science and Technology of China, Chengdu, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes various reduction and branching rules in textual form and through lemmas, but it does not present a unified pseudocode block or algorithm figure. |
| Open Source Code | No | The paper does not mention providing any open-source code for the methodology described. |
| Open Datasets | No | The paper describes a theoretical algorithm and its complexity analysis. There are no empirical experiments involving datasets or training. |
| Dataset Splits | No | The paper describes a theoretical algorithm and its complexity analysis. There are no empirical experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes a theoretical algorithm and its complexity. There are no empirical experiments mentioned that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical, presenting an algorithm and its complexity analysis. It does not mention any specific software dependencies with version numbers required for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical, presenting an algorithm and its complexity analysis. It does not describe any empirical experiment setup details such as hyperparameters or system-level training settings. |