  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.
