arXiv AI July 29, 2026 By Itai Zilberstein, Pranav Rajbhandari, Steve Chien, Tuomas Sandholm

Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling

arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.

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

benchmarks

arXiv AI

Jun 9

Dynamic Distributed Constraint Optimization and Metareasoning for Continual, Large-Scale Satellite Operations

arXiv:2601. 06188v3 Announce Type: replace Abstract: As Earth-observing satellite constellations grow in size and capability, distributed onboard control offers a pathway to novel responses and time-sensitive measurements.

By Itai Zilberstein, Steve Chien

agents

arXiv Machine Learning

Jun 2

Distributed GNEP Algorithms without Multiplier Sharing and Applications to Multi-Robot Coordination and Contextual Bandit-Based Active Learning

arXiv:2606. 00759v1 Announce Type: new Abstract: Recent advances in artificial intelligence have expanded the focus from classical optimization to include equilibrium analysis in noncooperative games.

By Shao-An Yin

agents reinforcement-learning robotics safety

arXiv Machine Learning

Aug 10

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

arXiv:2608. 06563v1 Announce Type: new Abstract: Machine learning and optimization have advanced together, with practical demands motivating new theory and theoretical breakthroughs enabling new applications.

By Grigory Malinovsky

fine-tuning

arXiv AI

Jun 17

Online LLM Selection via Constrained Bandits with Time-Varying Demand

arXiv:2606. 17489v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles.

By Yin Huang, Qingsong Liu, Jie Xu

llms reinforcement-learning benchmarks

arXiv AI

Jul 15

Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination

arXiv:2607. 12763v1 Announce Type: cross Abstract: Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior.

By Usman Haider, Karl Mason

reinforcement-learning benchmarks safety

arXiv AI

Jun 11

Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling

arXiv:2501. 12942v2 Announce Type: replace Abstract: Effective multi-user delay-constrained scheduling is crucial in various real-world applications, including embodied AI, instant messaging, live streaming, and data center management, where efficient resource allocation is required among users with diverse delay sensitivities.

By Zhuoran Li, Ruishuo Chen, Hai Zhong, Longbo Huang

diffusion reinforcement-learning robotics

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Dynamic Distributed Constraint Optimization and Metareasoning for Continual, Large-Scale Satellite Operations

Distributed GNEP Algorithms without Multiplier Sharing and Applications to Multi-Robot Coordination and Contextual Bandit-Based Active Learning

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization

Online LLM Selection via Constrained Bandits with Time-Varying Demand

Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination

Offline Diffusion Policy for Multi-User Delay-Constrained Scheduling