Attribution via Distributional Paths for Information Revelation

• URL: http://arxiv.org/abs/2606.03885v1
• Date: Tue, 02 Jun 2026 16:50:28 GMT
• Title: Attribution via Distributional Paths for Information Revelation
• Abstract summary: Our method Reveal-IG lifts path attribution from input space to a space of structured probe distributions.<n>It produces stable, signed attributions -- leading to metrics that use attribution sign while remaining competitive on the rest.
• Score: 2.707154152696381
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Feature attribution methods explain predictions by assigning importance scores to input features. Path-based methods such as Integrated Gradients are especially appealing because they satisfy \textit{completeness}: attributions sum to the change in model output between a reference state and the input. Yet most path methods define this trajectory in input space, explaining a model through pointwise perturbed inputs along a chosen path. An input-space path integrates the model's raw response at each point it passes through, with no control over the resolution at which a feature is queried; the early, baseline-adjacent part of the trajectory contributes to the explanation on equal footing with the input itself. Here, we lift path attribution from input space to a space of structured probe distributions around the example of interest, and call our method Reveal-IG. Rather than traversing raw input values, Reveal-IG progressively reveals information about the input and attributes changes in the model's expected output along this distributional path. The result is a path-attribution framework that retains completeness with respect to the expected model response, and naturally accommodates multiscale image probes and feature-wise uncertainty in tabular data. Synthetic diagnostics show that Reveal-IG avoids path artifacts that affect input-space methods, and across ImageNet classification and tabular regression it produces stable, signed attributions -- leading on metrics that use attribution sign while remaining competitive on the rest.

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