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..
Structured d-DNNF Is Not Closed under Negation
Authors: Harry Vinall-Smeeth
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our proof of Theorem 2 exploits a connection between knowledge compilation and communication complexity which has been widely deployed in recent years, see e.g. [Beame and Liew, 2015; Bova et al., 2016; Amarilli et al., 2020]. We start from the same piece of communication complexity as [G o os et al., 2022], where an analogous result for unambiguous finite automata (UFA) is obtained. However, while the size of UFAs is related to the fixed partition communication complexity model the size of structured d-DNNF is related to another model: the best partition communication complexity. We therefore adapt an ingenious construction from [Knop, 2017], which allows one to lift results from the fixed partition model to the best partition model. |
| Researcher Affiliation | Academia | Harry Vinall-Smeeth Technische Universit at Ilmenau, Germany EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement) for source code. |
| Open Datasets | No | The paper is theoretical and does not mention datasets, training, or data splits. |
| Dataset Splits | No | The paper is theoretical and does not mention datasets, validation, or data splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |