arXiv:2610. 29261v1 Announce Type: cross Abstract: We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk.
arXiv:2603. 02462v2 Announce Type: replace-cross Abstract: A key challenge in developing unified neural solvers for combinatorial optimization (CO) is the efficient generalization of models from a given set of tasks to new tasks unseen during initial training.
arXiv:2609.14953v1 Announce Type: cross Abstract: This paper aims to develop new and efficient distributed algorithms for solving a class of monotone inclusions, $0 \in \sum_{i=1}^n (G_ix + T_ix)$, o...
We study group distributionally robust optimization (GDRO) over decentralized networks, where workers collaborate to learn robust models by minimizing the maximum local risk. Existing methods optimize...
arXiv:2607. 12172v1 Announce Type: cross Abstract: Decentralized gradient descent (DGD) is widely used for solving distributed optimization problems over networks of agents.
arXiv:2606. 07496v1 Announce Type: new Abstract: Decentralized stochastic optimization is a fundamental paradigm for large-scale learning over networks, where agents communicate only with their neighbors and no central coordinator is required.
The paper studies Multiagent Path Finding with limited communication ranges, aiming to minimize makespan while preserving a communication backbone. It presents three exact algorithms that are efficient for networks with tree or bounded‑treewidth topologies when the communication range and number of agents are given as input. The authors also prove that efficient algorithms are unlikely when the number of agents is part of the input, even for small makespan and communication range values.
arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.
arXiv:2608. 13616v1 Announce Type: cross Abstract: We consider a heterogeneous mobile-agent network composed of uncontrolled task agents and controllable communication agents.
arXiv:2307. 07113v2 Announce Type: replace-cross Abstract: In this paper, we consider the decentralized, stochastic nonconvex strongly-concave (NCSC) minimax problem with nonsmooth regularization terms on both primal and dual variables, wherein a network of $m$ computing agents collaborate via peer-to-peer communications.
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.
arXiv:2510. 01377v2 Announce Type: replace-cross Abstract: In this paper, we propose DeMuon, a method for decentralized matrix optimization over a given communication topology.