arXiv Machine Learning
Towards Near-Real-Time Telemetry-Aware Routing with Neural Routing Algorithms
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arXiv Machine Learning
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Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks
The paper introduces TELGEN, a traffic engineering algorithm that uses graph neural networks to predict an optimal TE algorithm rather than a direct solution. TELGEN generalizes across diverse network topologies and traffic patterns, achieving less than a 3% optimality gap on networks up to 5,000 nodes and 3.6 million links, while reducing solving time by up to 84% and training time by up to 79.6% compared to existing methods.
arXiv Machine Learning
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TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
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 AI
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Trajectory-Initialized Neural Double Q-Routing for Large-Scale Overhead Hoist Transport Systems
arXiv:2608.30512v1 Announce Type: cross Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersecti...
arXiv AI
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From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control
The paper proposes a deployment‑focused framework for deadline‑constrained network control, introducing the Effective Congestion (EC) metric family and Uniform Path Grouping (UPG) heuristic to better capture traffic urgency and balance load. It integrates these with a Multi‑Agent Deep Reinforcement Learning architecture (MADRL EC (p*)) that combines a distributed scheduler and a centralized RL router. A unified training objective merges live‑reward, pre‑collected‑reward, and policy‑imitation terms, leading to the Model‑Guided Annealed Reinforcement Learning (MGA‑RL) protocol built on DDPG, which generalizes offline‑to‑online learning for demonstration‑driven training.
arXiv Machine Learning
Aug 4
Learning Graph-Indexed Trajectory Patterns for Stochastic On-Time Arrival Routing
arXiv:2508. 17218v4 Announce Type: replace Abstract: Correlated link travel times create decision-relevant patterns in partial route histories.
arXiv Machine Learning
Jun 9
Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth
arXiv:2606. 09539v1 Announce Type: new Abstract: Spatio-temporal graph neural networks (STGNNs) have become the dominant approach for traffic prediction, yet their computational requirements pose challenges for practical deployment in intelligent transportation systems (ITS).