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This abstract outlines the challenges encountered in evaluating neural network-local search heuristics hybrids for combinatorial optimization. It discusses limitations in algorithm performance evaluation, generalizability, and the impact of instance selection bias, offering insights that challenge prevailing assumptions in the field.