The Luna Bound Propagator for Formal Analysis of Neural Networks
arXiv:2603.23878v1 Announce Type: new Abstract: The parameterized CROWN analysis, a.k.a., alpha-CROWN, has emerged as a practically successful bound propagation method for neural network verification. However, existing implementations of alpha-CROWN are limited to Python, which complicates integration into existing DNN verifiers and long-term production-level systems. We introduce Luna, a new bound propagator implemented in C++. Luna supports Interval Bound Propagation, the CROWN analysis, and the alpha-CROWN analysis over a general computational graph. We describe the architecture of Luna and show that it is competitive with the state-of-the-art alpha-CROWN implementation in terms of both bound tightness and computational efficiency on benchmarks from VNN-COMP 2025.
arXiv:2603.23878v1 Announce Type: new Abstract: The parameterized CROWN analysis, a.k.a., alpha-CROWN, has emerged as a practically successful bound propagation method for neural network verification. However, existing implementations of alpha-CROWN are limited to Python, which complicates integration into existing DNN verifiers and long-term production-level systems. We introduce Luna, a new bound propagator implemented in C++. Luna supports Interval Bound Propagation, the CROWN analysis, and the alpha-CROWN analysis over a general computational graph. We describe the architecture of Luna and show that it is competitive with the state-of-the-art alpha-CROWN implementation in terms of both bound tightness and computational efficiency on benchmarks from VNN-COMP 2025.
Executive Summary
This article introduces Luna, a new bound propagator implemented in C++ for formal analysis of neural networks. Luna supports Interval Bound Propagation, the CROWN analysis, and the alpha-CROWN analysis over a general computational graph, addressing the limitations of existing Python-based alpha-CROWN implementations. The authors demonstrate Luna's competitiveness with state-of-the-art alpha-CROWN implementations on benchmarks from VNN-COMP 2025 in terms of both bound tightness and computational efficiency. This work has significant implications for the development of more efficient and scalable neural network verifiers.
Key Points
- ▸ Luna is a new bound propagator implemented in C++ for neural network verification
- ▸ Luna supports Interval Bound Propagation, the CROWN analysis, and the alpha-CROWN analysis
- ▸ Luna is competitive with state-of-the-art alpha-CROWN implementations on VNN-COMP 2025 benchmarks
Merits
Strength in Scalability
Luna's C++ implementation enables efficient integration with existing DNN verifiers and production-level systems.
Competitive Performance
Luna achieves competitive bound tightness and computational efficiency on VNN-COMP 2025 benchmarks.
Demerits
Limited Availability
As a new implementation, Luna's adoption may be hindered by the need for developers to transition from existing Python-based alpha-CROWN implementations.
Dependence on Benchmarks
The evaluation of Luna's performance is limited to the VNN-COMP 2025 benchmarks, and its effectiveness on other datasets is uncertain.
Expert Commentary
The introduction of Luna represents a significant step forward in the development of efficient neural network verification methods. By providing a C++ implementation of the alpha-CROWN analysis, Luna addresses the limitations of existing Python-based implementations and demonstrates competitive performance on VNN-COMP 2025 benchmarks. However, the limited availability of Luna and its dependence on specific benchmarks are noteworthy limitations that will need to be addressed in future work. Nevertheless, the authors' focus on scalability and competitiveness makes Luna a valuable contribution to the field of neural network verification.
Recommendations
- ✓ Future work should prioritize the development of C++ implementations for other neural network verification methods to facilitate more widespread adoption and integration with production-level systems.
- ✓ The authors should investigate the applicability of Luna to a broader range of datasets and verification tasks to further establish its effectiveness and competitiveness.
Sources
Original: arXiv - cs.LG
