arXiv:2607. 22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.
arXiv:2604.02927v3 Announce Type: replace Abstract: Routing algorithms are crucial for efficient computer network operations, and in many settings they must be able to react to traffic bursts within...
arXiv:2609.08232v1 Announce Type: cross Abstract: Detailed routing remains a dominant runtime bottleneck in physical design due to increasing complexity of design rules. Modern routers can struggle t...
arXiv:2609.22951v1 Announce Type: cross Abstract: Enterprise agentic systems that route every trajectory step to a frontier model waste 60-80% of their inference budget on subtasks that smaller model...
The paper introduces a history‑aware offline reinforcement learning policy that predicts iterative cost weights for routing in dense integrated circuit designs. By incorporating a lightweight LSTM and additional router features, the policy retains sequence context and improves convergence across various placement densities and guide qualities. Integrated into any cost‑based router with minimal changes, the approach reduces design rule violations by an average of 92% and cuts runtime by 10%.
arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.
arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.
The paper investigates why attention-based Pointer Networks, though size‑agnostic, lose performance when applied to different sized Capacitated Vehicle Routing Problems (CVRP). It identifies three causes—node‑density shifts, attention dispersion, and diluted graph context—and evaluates architectural changes on both encoder and decoder sides, including a learned permutation‑invariant graph‑context aggregation. The authors show that entropy‑based attention scaling improves large‑to‑small transfer, while the new aggregation methods best aid small‑to‑large transfer, and they combine these insights into a selector that chooses the optimal model for each train‑test size pair, reducing costs by up to 3.2% across seen and many unseen sizes.
arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.
arXiv:2609.08189v1 Announce Type: new Abstract: Dynamic layer routing reduces the inference cost of Large Language Models (LLMs) by learning to skip layers for individual tokens. Existing methods, ho...
arXiv:2610.27936v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) enable fast, flexible last-mile and emergency delivery, but their routing remains challenging as the number of clients...
arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.