arXiv subjects

Maya Swisa

Publications and source records attributed to Maya Swisa.

2 recordsLinked to original sources

arXiv2026

veriFIRE: an Industrial Case Study in Verifying Consistency Properties for a DNN-Based Wildfire Detection System

We present our ongoing work on the veriFIRE project: a collaboration between industry and academia, aimed at applying verification to increase the reliability of a real-world, safety-critical system. Specifically, we target an airborne platform for wildfire detection, which incorporates two deep neural networks. We present an end-to-end methodology for verifying \textit{consistency properties} in this system. Our approach encodes application-grounded requirements into solver-compatible queries for existing neural network verifiers. We study properties of interest over critical operational scenarios: (i) monotonicity of detector confidence as target intensity increases; and (ii) bounded detector response under physically plausible blur over the sensor. We instantiate these encodings using state-of-the-art neural network verification backends and evaluate them at scale on real background samples. For the first property, all verification queries are solved in under five minutes. For the second property, verification is substantially harder, highlighting key scalability challenges for richer, higher-dimensional specifications. Overall, the results demonstrate that meaningful, domain-specific guarantees can be obtained for industrial systems.

cs.LO↗

arXiv2025

Learning to Split: A Reinforcement-Learning-Guided Splitting Heuristic for Neural Network Verification

Modern neural network verifiers often encode neural network verification as constraint satisfaction problems. When dealing with standard piecewise-linear activation functions, such as ReLUs, verifiers typically employ branching heuristics that break a complex constraint satisfaction problem into multiple, simpler problems. The verifier's performance depends heavily on the order in which this branching is performed: a poor selection may give rise to exponentially many sub-problems, hampering scalability. Here, we focus on the setting in which many related verification queries must be solved for the same neural network. The core idea is to use past experience to make good branching decisions, expediting verification. We present a reinforcement-learning-based branching heuristic that achieves this, by applying Deep Q-learning from Demonstrations (DQfD). Our experimental evaluation demonstrates a substantial reduction in average verification time and in the average number of iterations required, compared to modern splitting heuristics. These results highlight the great potential of reinforcement learning in the context of neural network verification.

cs.LO↗