Bound Propagation meets Constraint Simplification: Improving Logic-based XAI for Neural Networks

• URL: http://arxiv.org/abs/2603.01923v1
• Date: Mon, 02 Mar 2026 14:36:31 GMT
• Title: Bound Propagation meets Constraint Simplification: Improving Logic-based XAI for Neural Networks
• Abstract summary: Logic-based methods for explaining neural network decisions often suffer from high computational costs.<n>In this work, we improve the efficiency of such methods by combining bound propagation with constraint simplification.<n>Our experiments suggest that combining these techniques reduces explanation time by up to 89.26%, particularly for larger neural networks.
• Score: 1.0499611180329804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Logic-based methods for explaining neural network decisions offer formal guarantees of correctness and non-redundancy, but they often suffer from high computational costs, especially for large networks. In this work, we improve the efficiency of such methods by combining bound propagation with constraint simplification. These simplifications, derived from the propagation, tighten neuron bounds and eliminate unnecessary binary variables, making the explanation process more efficient. Our experiments suggest that combining these techniques reduces explanation time by up to 89.26\%, particularly for larger neural networks.

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