Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
New Compilation Languages Based on Restricted Weak Decomposability
Authors: Petr Illner
AAAI 2025 | Venue PDF | LLM Run Details | Input Tokens: 17,752 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,032 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that nw DNNF circuits are suitable for computing MPEs in two-layer Bayesian networks (BNs) with large domains. |
| Researcher Affiliation | Academia | Petr Illner Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics Charles University, Czech Republic EMAIL |
| Pseudocode | No | We refer the curious reader to (Illner and Kuˇcera 2024) for a more detailed description of Bella and the pseudocode, where the methods compute Components and compute New Cut were changed as described below. |
| Open Source Code | Yes | 1https://github.com/Illner/Bella Compiler |
| Open Datasets | No | We implemented Bels2 to (randomly) generate and convert such BNs into CNF formulae using the encodings mentioned in this paper. |
| Dataset Splits | No | Ten instances were created for each density. Since the compilers are randomised, each instance was compiled three times, and the given results are averages. |
| Hardware Specification | Yes | The experiments3 were performed on a Linux machine (Debian 11) using an AMD EPYC 7543 2.8GHz processor and 512 Gi B of RAM. |
| Software Dependencies | No | The experiments3 were performed on a Linux machine (Debian 11) using an AMD EPYC 7543 2.8GHz processor and 512 Gi B of RAM. The time-out (resp. memory-out) was set to two/six hours (resp. 16 GB). |
| Experiment Setup | Yes | The time-out (resp. memory-out) was set to two/six hours (resp. 16 GB). The following compilers were considered: Bella, D44 (the randomised variant introduced by Illner and Kuˇcera (2024) was used), C2D5, and Sharp SAT-TD6. Ten instances were created for each density. Since the compilers are randomised, each instance was compiled three times, and the given results are averages. |