Multi-Resolution Attribution from Adaptive Routing State

Abstract:Adaptive hierarchical systems accumulate routing state as they learn which components to select. We show that this state already defines a coherent attribution over the hierarchy. A leaf receives the product of the local routing weights on its path, while an internal node receives the corresponding prefix product. The same learned state can therefore be read consistently at group and component levels, and every finer readout sums exactly to its coarser counterpart. This attribution describes the preferences learned by the deployed router rather than an intrinsic or counterfactual value of a component. Across LLM, Census, agentic, and telecom-network hierarchies, the learned state contains meaningful structure at several levels, and the clearest organisation need not occur at the leaves. In the telecom study, Site- or Region-level readouts usually reveal clearer structure than Cell-level readouts. Comparison with Shapley attribution can then show whether the preferences learned in deployment match capabilities revealed by counterfactual coalitions. The result is a hierarchical explanation that requires no separate attribution model: the same routing state supports consistent explanations at several levels of the system.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2605.22866 [cs.AI]

(or arXiv:2605.22866v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.22866 Focus to learn more arXiv-issued DOI via DataCite

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