Always Keep Your Promises: A Model-Agnostic Attribution Algorithm for Neural Networks

Inability to precisely understand neural network outputs is one of the most severe issues limiting the use of AI in multiple domains, from science and medicine to high-stakes decision and regulatory models. Layer-wise Relevance Propagation (LRP) is an established explainability method that addresses some of these limitations, but widespread adoption has not been possible because existing implementations must be coupled with individual model architectures, rendering them impractical for a rapidly evolving model space. Our algorithm, DynamicLRP, is a lightweight and flexible method for performing LRP on any neural network with provably minimal overhead. To achieve this, we introduce a novel graph search mechanism called the ``Promise System'' which repurposes deep learning computation graphs for non-gradient computations and is implemented at the primitive tensor operation level using standard automatic differentiation libraries. We demonstrate that DynamicLRP matches or surpasses specialised implementations in attribution quality across vision and language tasks and remains efficient for models at the billion-parameter scale. Notably, our implementation achieved 99.99\% operation coverage across 31,465 computation nodes from 15 diverse architectures, without any architecture-specific modifications. This is the first truly model-agnostic LRP solution, enabling high-quality neural network attribution across the full spectrum of modern AI architectures.