arXiv:2605. 14879v2 Announce Type: replace-cross Abstract: Many intelligent computing and autonomous systems rely on multiple independent, often learning, agents repeatedly sharing a limited resource.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2603. 05789v5 Announce Type: replace-cross Abstract: Repeated multi-agent interactions require evaluation metrics that capture not only payoff distributions but also their temporal organization.
arXiv:2606. 03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control.
arXiv:2608. 05340v1 Announce Type: cross Abstract: Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as eXtended Reality (XR).
arXiv:2606. 00266v1 Announce Type: cross Abstract: A long-standing challenge in distributed wireless systems is ensuring efficient and fair random channel access.
arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.
arXiv:2608. 09366v1 Announce Type: new Abstract: Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency.