ProToMEx: Rapid, Interpretable Explanations via Structured Representations

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On September 7, 2026, arXiv cs.LG published the paper ProToMEx, which proposes a fast and interpretable model explanation method based on structured representations. This research aims to address the blackbox problem of deep learning models by enhancing the transparency and interpretability of models through the construction of structured intermediate representations, while maintaining推理 efficiency.

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A arXiv cs.LG en 2026-09-07 12:00

ProToMEx proposes a new paradigm based on probabilistic topic models, aiming to provide fast and interpretable machine learning classifier explanations through structured representations. This framework learns potential “topics” that represent higher-level causes of classification, going beyond the importance of individual features to reveal underlying semantic structures, and can provide both global and local explanations. Empirical results show that the explanations generated by ProToMEx have a fidelity comparable to SHAP and LIME, but reduce the cost of local explanation generation on standard tabular datasets and synthetic datasets by approximately 30-40 times, making it suitable for real-time applications.