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This paper explores using a model's semantic ID hierarchy for off-policy evaluation in generative recommenders, showing that coarsening to code-prefix clusters improves estimation accuracy under production logging constraints.
Proposes ACE, a plug-and-play method that adaptively enhances coarsening-based GNN training on heterophilic graphs by reconstructing node features and applying anisotropic regularization, achieving consistent gains on heterophilic benchmarks with minimal overhead.