Attribution via Distributional Paths for Information Revelation

Feature attribution methods often either compare predictions under different subsets of observed feature information, as in SHAP-style methods, or integrate gradients along a path from a baseline to the input, as in Integrated Gradients. We introduce Reveal-IG, which combines these perspectives by defining a continuous path of distributions that progressively reveal information about an input. At each point along this path, the model is evaluated in expectation over partially revealed inputs, and attribution integrates the resulting change in expected prediction. This replaces IG's trajectory through individual input values with a trajectory through information states, while retaining a completeness property: feature attributions sum to the change in expected model output across the path. The framework naturally supports structured perturbations, including variable-resolution image probes and feature-wise uncertainty in tabular data. On synthetic diagnostics, Reveal-IG reduces path artifacts compared to pointwise input-space paths. Across ImageNet classification and tabular regression, Reveal-IG produces stable, signed attributions, leading on sign-sensitive image benchmarks while remaining competitive across standard attribution metrics.