Neural asymmetric routing models increasingly encode directionality through matrix representations and asymmetry-aware attention. The final routing action, however, is not a node in isolation but a directed transition chosen under the current partial route. This creates a representation--decision mismatch: pairwise cost information may be encoded upstream while the final candidate logit is still largely parameterized as context--node compatibility. We propose a decoder-design principle for neural asymmetric routing: the final score should explicitly expose transition-level quantities suggested by the problem's cost-to-go structure. We instantiate this principle with an edge-aware decoder that adds candidate-specific terms for the current directed edge, return-to-start closure, and static lightweight lookahead, while keeping the representation backbone fixed. On a controlled SVD/Sinkhorn asymmetric backbone, the decoder improves over the RADAR reference when trained on ATSP-100 and evaluated zero-shot on ATSP-100/200/500/1000, reducing the ATSP-1000 gap from $4.13\%$ to $2.73\%$. On ACVRP, the same score-level modification shows the same qualitative trend under a richer routing state. ATSP ablations and directed-transition diagnostics sharpen the mechanism: the strongest evidence concerns sensitivity to the current directed edge, while closure and static lookahead act as heuristic continuation cues. The results support a mechanism study: a key decoder-side signal in neural asymmetric routing is decision-time exposure of transition-level edge information.
From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing
School of Artificial Intelligence, Nanjing University（南京大学人工智能学院）；National Key Laboratory for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；Hong Kong University of Science and Technology（香港科学与技术大学）；Frontier Robotics（前沿机器人）
SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solver
School of Automation and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen, China（自动化与智能制造学院，南方科技大学，深圳，中国）；Pengcheng Laboratory, Shenzhen, China（鹏城实验室，深圳，中国）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology, Shenzhen, China（广东省全驱动系统控制理论与技术重点实验室，南方科技大学，深圳，中国）；College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China（计算机科学与软件工程学院，深圳大学，深圳，中国）
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Intel（英特尔）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·拉希德人工智能大学）；Rutgers University（罗格斯大学）
LMask: Learn to Solve Constrained Routing Problems with Lazy Masking
Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Department of Statistics and Data Science, University of Pennsylvania（宾夕法尼亚大学统计与数据科学系）
Beyond Solving: Prescriptive Probing for Neural Routing Solvers
Foster School of Business, University of Washington, Seattle, WA, USA；Paul G.\ Allen School of Computer Science \& Engineering, University of Washington, Seattle, WA, USA
Seoul National University（首尔大学）；Samsung Research（三星研究院）
胶囊网络（CapsNets）最近作为一种新的神经结构受到关注。本文介绍了顺序路由框架，我们认为这是第一种方法，以适应CapsNet的结构，序列到序列的识别。输入序列被封装，然后按窗口大小切片。通过迭代路由机制，每个切片在相应的时间被分类为标签。之后，损失通过连接主义时间分类（CTC）计算。在路由过程中，所需的参数数量可以通过在切片上共享可学习的权重来控制，而不管序列的长度如何。我们还提出了一个顺序的动态路由算法，以取代传统的动态路由。所提出的技术可以最小化由路由迭代引起的解码速度下降，因为它可以以非迭代方式操作而不降低精度。与基于双向长短期记忆的CTC网络相比，该方法在《华尔街日报》语料库上的单词错误率为16.9%，降低了1.1%。在TIMIT语料库上，与基于卷积神经网络的CTC网络相比，它的电话错误率降低了0.7%，为17.5%（Zhang et al.，2016）。
Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology（广东省全驱动系统控制理论与技术重点实验室，南方科技大学）；Pengcheng Laboratory（鹏城实验室）
Adversarial Generative Flow Network for Solving Vehicle Routing Problems
Singapore Management University（新加坡管理大学）；Singapore Institute of Manufacturing Technology (SIMTech), Agency of Science, Technology and Research (A*STAR)（新加坡制造技术研究院（新加坡科技研究局下属机构））
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy（创新医学工程系，威尼斯大学，意大利）；School of Computer Science and Engineering, Beihang University, China（计算机科学与工程学院，北航大学，中国）；Dept. of Computer Science, Reykjavik University, Iceland（计算机科学系，雷克雅未克大学，冰岛）
