arXiv AI July 7, 2026 By Enrique Adrian Villarrubia-Martin, David Mu\~noz-Valero, Luis Rodriguez-Benitez, Giovanni Montana, Luis Jimenez-Linares

Relational Multi-Agent Reinforcement Learning for Dynamic Pricing in High-Speed Railway Markets

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arXiv:2607. 05179v1 Announce Type: cross Abstract: In liberalised railway systems, operators must set prices dynamically in an environment with partial observability, as they retain private information about their objectives and performance, where regulatory constraints prohibit communication or direct information exchange between competitors to prevent explicit collusion.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures

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GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets

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Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing

arXiv:2607. 19985v1 Announce Type: new Abstract: Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations.

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arXiv AI

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LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems

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Agentic Graph Token Reasoning

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Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks

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