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This paper presents the Semantic Signal-Assisted Decision Support (SSADS) framework, which converts return notes into condition factors and signal-quality scores to optimize inspection and recovery allocation in reverse logistics, showing improvements in net recovery value in synthetic benchmark scenarios.