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 [1].
Symbolic Functional Decomposition: A Reconfiguration Approach
Authors: Mateus de Oliveira Oliveira, Wim Van Den Broeck
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we study functional decomposition by leveraging on the notion of functional reconfiguration. In this setting, constraints are imposed not only on the factor functions f1, . . . , fk but also on the intermediate functions arising during the composition process. We introduce a symbolic framework to address functional reconfiguration and decomposition problems. In our framework, functions arising during the reconfiguration process are represented symbolically, using ordered binary decision diagrams (OBDDs). The function g used to specify the reconfiguration process is represented by a Boolean circuit C. Finally, the function class F is represented by a second-order finite automaton A. Our main result states that functional reconfiguration, and hence functional decomposition, can be solved in fixed-parameter linear time when parameterized by the width of the input OBDD, by structural parameters associated with the reconfiguration circuit C, and by the size of the secondorder finite automaton A. |
| Researcher Affiliation | Academia | Mateus de Oliveira Oliveira1,2, Wim Van den Broeck2 1Department of Computer and Systems Sciences, Stockholm University, Sweden 2Department of Informatics, University of Bergen, Norway EMAIL, EMAIL |
| Pseudocode | No | The paper describes theoretical concepts, definitions, theorems, lemmas, and proofs. It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to source code repositories. |
| Open Datasets | No | The paper is theoretical and does not use or evaluate on any specific publicly available datasets. It discusses abstract Boolean functions and languages, such as "finite language L {0, 1}n" or "regular language L over a given alphabet Σ", but these are theoretical constructs rather than empirical datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments using datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental results or the hardware used to obtain them. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies or versions used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |