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..
The Hardness of Reasoning about Probabilities and Causality
Authors: Benito van der Zander, Markus Bläser, Maciej Liśkiewicz
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution of this work is establishing the exact computational complexity of these satisfiability problems. We introduce a new natural complexity class, named succ R, which can be viewed as a succinct variant of the well-studied class R, and show that these problems are complete for succ R. |
| Researcher Affiliation | Academia | 1Institute of Theoretical Computer Science, University of L ubeck, Germany 2Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention any open-source code for the described methodology. |
| Open Datasets | No | The paper describes theoretical work and does not mention the use of any datasets for training. |
| Dataset Splits | No | The paper describes theoretical work and does not mention the use of any datasets for validation. |
| Hardware Specification | No | The paper describes theoretical work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not mention any specific software dependencies or versions for experimental setup. |
| Experiment Setup | No | The paper describes theoretical work and does not include details about an experimental setup, such as hyperparameters or training settings. |