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GRALIS: Fusing Coalition and Gradient Attribution with Closed-Form Conservation Error and Finite-Sample Guarantees

This story is from 2026-08-24. It is preserved in the archive; the latest stories are on the live feed.

arXiv:2605.05480v3 Announce Type: replace-cross Abstract: The main post-hoc XAI methods for deep networks -- GradCAM, SHAP, LIME, Integrated Gradients -- originate from heterogeneous theoretical foundations and are not naturally comparable within a single representation. A recent benchmark also fin…

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  1. 2026-08-24 04:00 · arXiv stat.ML
    GRALIS: Fusing Coalition and Gradient Attribution with Closed-Form Conservation Error and Finite-Sample Guarantees

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