LINC：将局部后果评分与隐藏匹配解耦以实现构造性神经路由
LINC: Decoupling Local Consequence Scoring from Hidden Matching in Constructive Neural Routing
AI总结：
LINC通过显式计算局部后果，解耦局部评分与隐藏匹配，提升构造性神经路由在CVRPTW、TSP和CVRP中的性能，缩小与基准算法的差距。
摘要：
构造性神经路由求解器通常通过将解码器上下文与候选嵌入进行匹配来评分下一步动作，隐藏了确定性的一步后果，如旅行、等待、松弛和容量变化。我们提出LINC（通过规范比较的局部推断），是一种解码器侧的候选决策架构，显式计算这些后果。LINC根据其决策角色使用这些后果：以共享线性局部评分器比较中心相对后果，而可行集摘要调节解码器上下文。这保留了标准的全局匹配，并减轻了隐藏状态重新发现转移算术的负担。具有时间窗的车辆路径问题（CVRPTW）作为主要的受约束路由压力测试；相同的接口扩展到车辆路径问题（CVRP）和旅行商问题（TSP）。特别是，对于CVRPTW，LINC将PolyNet的Solomon/Homberger差距从13.83\%/38.15\%降至7.26\%/14.71\%；对于TSP和CVRP，它也改进了外部基准差距。
英文摘要：
Constructive neural routing solvers usually score the next action by matching a decoder context against candidate embeddings computed once per instance from static node features such as coordinates, demands, and time windows; deterministic one-step consequences such as travel, waiting, slack, and capacity changes are not provided as inputs and must be inferred implicitly by the model. We propose LINC, a decoder-side candidate decision architecture that decouples computing these consequences from learning how much they matter. The environment computes the consequences explicitly at each step; candidate-level consequences are scored by a state-conditioned shared linear comparator, while feasible-set summaries modulate the decoder context. This keeps the cached embeddings and standard global matching while giving the scoring function direct access to deterministic transition quantities. The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) serves as the main constrained-routing testbed, and the same interface extends to the Capacitated Vehicle Routing Problem (CVRP) and Traveling Salesman Problem (TSP). Across external benchmarks and no-retraining scale-transfer settings, LINC consistently improves strong neural baselines, with the advantage becoming more pronounced as test size moves further beyond the training scale, especially on constrained routing problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
