Neural-guided Ant Colony Optimization (ACO) suffers from a fundamental training-inference misalignment: policies are typically trained to generate static priors (e.g., heatmaps), yet deployed to guide iterative, long-horizon search processes. In this paper, we present DyNACO, a novel framework that achieves dynamic neural guidance by periodically observing the pheromone distribution and the incumbent solution. To make DyNACO tractable at scale, we pair the policy with a perturbation-based ACO backend and a scope-restricted refinement mechanism that jointly ensure efficacy and stable credit assignment. On TSP, DyNACO scales to 100,000-node instances and outperforms neural baselines while often reducing total runtime compared to the unguided solver. We extend DyNACO to CVRP via a capacity-aware backend, consistently improving the unguided baseline with less than 1% neural overhead. We further provide in-depth analysis validating the model's generalization capabilities and elucidating why dynamic guidance outperforms static priors. Our work underscores the necessity of aligning neural training with iterative search dynamics in learning-guided optimization. The code is available at https://github.com/shoraaa/DyNACO.

翻译：神经引导的蚁群优化（ACO）面临一个根本性的训练-推理错配问题：策略通常被训练用于生成静态先验（例如热力图），却被部署用于指导迭代的、长程的搜索过程。本文提出了DyNACO，一个通过周期性观测信息素分布和当前最优解来实现动态神经引导的新型框架。为使DyNACO在大规模场景下可行，我们将该策略与基于扰动的ACO后端以及范围受限的精化机制配对，二者共同确保有效性与稳定的信用分配。在TSP问题上，DyNACO可扩展至10万节点规模实例，优于神经基线方法，同时相比无引导求解器通常能减少总运行时间。我们通过引入容量感知后端将DyNACO扩展到CVRP问题，以低于1%的神经开销持续改进了无引导基线。我们进一步提供了深入分析，验证了模型的泛化能力，并阐明了为什么动态引导优于静态先验。本工作强调了在基于学习的优化中，使神经训练与迭代搜索动态保持对齐的必要性。代码开源在 https://github.com/shoraaa/DyNACO。