arXiv:2601. 17944v2 Announce Type: replace-cross Abstract: We study repeated allocation of shared resources among agents with time-varying demands and capped linear utilities.
By Seyed Majid Zahedi, Rupert Freeman
arXiv:2608. 07532v1 Announce Type: new Abstract: Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast.
By Mojtaba Eslami
arXiv:2608.22152v1 Announce Type: new
Abstract: Multi-agent systems built from large language models are deployed widely, yet how much performance is lost when two LLMs must coordinate rather than ac...
By Weixiang Sun, Zehong Wang, Hong Huang, Colby Nelson, Yanfang Ye
The paper introduces Hierarchical Reinforcement and Collective Learning (HRCL), a framework that combines multi‑agent reinforcement learning (MARL) with decentralized coordination. HRCL uses MARL at a high level to generate strategic guidance that limits the decision space for low‑level agents, enabling efficient short‑term coordination while considering long‑term effects. Experiments on synthetic, energy‑management, and drone‑swarm scenarios demonstrate faster convergence and significant reductions in system‑wide and individual costs compared to standalone MARL.
By Chuhao Qin, Evangelos Pournaras
arXiv:2606. 10472v1 Announce Type: cross Abstract: Dynamic multi-resource allocation is a central problem in shared computing environments, where users' demands arrive sequentially and resources must be distributed fairly without knowledge of future demands.
By Kaiqi Jiang, Karim El Husseini, Wenzhe Fan, Xinhua Zhang
The paper introduces DMFL-SQ, a decentralized multi-task learning algorithm that integrates graph-based personalization, agnostic fairness, and compressed event-triggered communication. It provides convergence guarantees for non-convex objectives, achieving an ≠O(T^{-1/2}) stationarity rate despite sparse, quantized, and event-triggered communication, and offers PAC-Bayes generalization bounds for the fairness objective. Experiments on CIFAR-10 and the MUSMET EEG dataset show that DMFL-SQ reduces communication while preserving predictive performance and improving fairness across clients.
By Krishnendu S. Tharakan, Carlo Fischione