Value-Relevant Errors under Source-Free Structural Adaptation
Abstract
Graph adaptation concerns transferable structure under an information restriction, while value-aware learning concerns errors influencing a policy. A combined evaluation should preserve graph relations, behavior support, and independent action outcomes. Neither a large target graph nor a low average model loss establishes that the representation used for policy values transfers appropriately. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.
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Published
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