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..
SDDs Are Exponentially More Succinct than OBDDs
Authors: Simone Bova
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that SDDs are more succinct than OBDDs also in theory, by constructing a family of boolean functions where each member has polynomial SDD size but exponential OBDD size. |
| Researcher Affiliation | Academia | Simone Bova Technische Universit at Wien Favoritenstraße 9 11 1040 Wien (Austria) |
| Pseudocode | No | The paper contains mathematical definitions, propositions, theorems, and proofs, but no structured pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any statement or link for open-source code related to the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical evaluation on datasets. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical evaluation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper describes theoretical proofs and constructions, and does not report on empirical experiments requiring hardware specifications. |
| Software Dependencies | No | The paper describes theoretical proofs and constructions, and does not report on empirical experiments requiring specific software dependencies. |
| Experiment Setup | No | This is a theoretical paper focused on proofs and constructions, and does not include details about an experimental setup or hyperparameters. |