标题： 位置：重新思考基于后处理搜索的神经方法以解决大规模旅行商问题 显示英文标题

标题： Position: Rethinking Post-Hoc Search-Based Neural Approaches for Solving Large-Scale Traveling Salesman Problems

摘要： 近年来，解决大规模旅行商问题（TSP）的进展利用了基于热图引导的蒙特卡洛树搜索（MCTS）范式，其中机器学习（ML）模型生成热图，表示每条边成为最优解的概率分布，以指导MCTS寻找解决方案。 然而，我们的理论和实验分析对基于ML的热图生成的有效性提出了质疑。 作为支持，我们证明了一种简单的基线方法可以在热图生成中优于复杂的ML方法。 此外，我们质疑基于热图引导的MCTS范式的实际价值。 为了证实这一点，我们的研究结果表明，尽管该范式依赖于特定问题的手工策略，但其效果仍劣于LKH-3启发式方法。 对于未来的研究，我们建议关注开发更理论上有根据的热图生成方法，并探索用于组合问题的自主、可泛化的ML方法。 代码可供审查：https://github.com/xyfffff/rethink_mcts_for_tsp. 显示英文摘要

摘要： Recent advancements in solving large-scale traveling salesman problems (TSP) utilize the heatmap-guided Monte Carlo tree search (MCTS) paradigm, where machine learning (ML) models generate heatmaps, indicating the probability distribution of each edge being part of the optimal solution, to guide MCTS in solution finding. However, our theoretical and experimental analysis raises doubts about the effectiveness of ML-based heatmap generation. In support of this, we demonstrate that a simple baseline method can outperform complex ML approaches in heatmap generation. Furthermore, we question the practical value of the heatmap-guided MCTS paradigm. To substantiate this, our findings show its inferiority to the LKH-3 heuristic despite the paradigm's reliance on problem-specific, hand-crafted strategies. For the future, we suggest research directions focused on developing more theoretically sound heatmap generation methods and exploring autonomous, generalizable ML approaches for combinatorial problems. The code is available for review: https://github.com/xyfffff/rethink_mcts_for_tsp.