MPBMC: Multi-Property Bounded Model Checking with GNN-guided Clustering
#MPBMC #bounded model checking #Graph Neural Networks #clustering #multi-property verification #formal methods #GNN
📌 Key Takeaways
- MPBMC introduces a novel approach to bounded model checking by handling multiple properties simultaneously.
- It utilizes Graph Neural Networks (GNNs) to guide clustering of properties, improving efficiency.
- The method aims to enhance verification scalability for complex systems by reducing redundant computations.
- This technique integrates machine learning with formal verification to optimize property checking processes.
📖 Full Retelling
arXiv:2603.04450v1 Announce Type: cross
Abstract: Formal verification of designs with multiple properties has been a long-standing challenge for the verification research community. The task of coming up with an effective strategy that can efficiently cluster properties to be solved together has inspired a number of proposals, ranging from structural clustering based on the property cone of influence (COI) to leverage runtime design and verification statistics. In this paper, we present an atte
🏷️ Themes
Formal Verification, Machine Learning
📚 Related People & Topics
Graph neural network
Class of artificial neural networks
Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. One prominent example is molecular drug design. Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form the...
Entity Intersection Graph
Connections for Graph neural network:
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Artificial intelligence
2 shared
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Mixture of experts
1 shared
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LUMINA
1 shared
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Interpretability
1 shared
Mentioned Entities
Original Source
--> Computer Science > Logic in Computer Science arXiv:2603.04450 [Submitted on 26 Feb 2026] Title: MPBMC: Multi-Property Bounded Model Checking with GNN-guided Clustering Authors: Soumik Guha Roy , Sumana Ghosh , Ansuman Banerjee , Raj Kumar Gajavelly , Sudhakar Surendran View a PDF of the paper titled MPBMC: Multi-Property Bounded Model Checking with GNN-guided Clustering, by Soumik Guha Roy and 3 other authors View PDF Abstract: Formal verification of designs with multiple properties has been a long-standing challenge for the verification research community. The task of coming up with an effective strategy that can efficiently cluster properties to be solved together has inspired a number of proposals, ranging from structural clustering based on the property cone of influence to leverage runtime design and verification statistics. In this paper, we present an attempt towards functional clustering of properties utilizing graph neural network embeddings for creating effective property clusters. We propose a hybrid approach that can exploit neural functional representations of hardware circuits and runtime design statistics to speed up the performance of Bounded Model Checking in the context of multi-property verification . Our method intelligently groups properties based on their functional embedding and design statistics, resulting in speedup in verification results. Experimental results on the HWMCC benchmarks show the efficacy of our proposal with respect to the state-of-the-art. Comments: 6 pages, 5 figures Subjects: Logic in Computer Science (cs.LO) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE) Cite as: arXiv:2603.04450 [cs.LO] (or arXiv:2603.04450v1 [cs.LO] for this version) https://doi.org/10.48550/arXiv.2603.04450 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Soumik Guha Roy [ view email ] [v1] Thu, 26 Feb 2026 19:56:52 UTC (359 KB) Full-text links: Access Paper:...
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