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This article delves into the evaluation of learned heuristics like S2V-DQN and ECO-DQN against traditional heuristics like Tabu Search in the context of Max-Cut optimization. It analyzes their performance, generalization to unseen graph types, and scalability to harder instances, shedding light on the efficacy of machine learning approaches in solving combinatorial optimization problems.