mlsanityData to 14 Oct 2026

WONDER: A Radio World Model-based Negotiation Framework for Multi-Agent UAV Coverage Optimization

arXiv 2608.16955 ↗First posted 16 Aug 2026cs.MA cs.LGcited by 0

On the map: World models → World models for navigation

Abstract (arXiv)

Post-disaster damage to terrestrial infrastructure can disrupt wireless coverage,while Uncrewed Aerial Vehicle (UAV) swarms provide a promising solution for rapid restoration.However, due to the limitations in local geometry observations hidden radio impact,and inter-UAV communication,there exists a significant gap between locally visible movement choices and swarm-level coverage outcomes.To combat this gap,we propose a raido World-model-based Optimized Negotiation framework for Distributed UAV covERage (WONDER).Particularly, to tackle the unavailability of the future radio field from onboard observations, WONDER uses a Joint-Embedding Predictive Architecture (JEPA)-based radio world model to learn and predict the incremental radio effect of each candidate trajectory from deployment-available information.Multi-round negotiation in WONDER then coordinates ranked proposals by committing one trajectory at a time and re-evaluating the remaining proposals under the updated context. Our theoretical analyses further validate the effectiveness of such a world model-based framework. WONDER also adopts a Proximal Policy Optimization (PPO)-style Actor and alternates between updating the world model and the actor. Furthermore,we build RadioDynamics,a comprehensive simulation environment that integrates UAV mobility,radio propagation, inter-UAV communication modeling,and digital-twin geometry with ray-traced fields in 62 metropolitan scenes.Experiments on 11 testing scenes in RadioDynamics show that WONDER achieves the highest balanced score among seven evaluated methods,reaching 0.870 with a 0.162 coverage advantage over STACCA, while maintaining 100% connectivity between UAVs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in World models for navigation, one of 11 directions in World models. Builds on 25 of its 40 references that are in the map. First posted on arXiv 16 Aug 2026.

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25 of 40 references.

References · 25

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13 more
  1. 2025CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timelinesscited by 6
  2. 2025In-Context Reinforcement Learning via Communicative World Modelscited by 5
  3. 2025Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Modelscited by 4
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  13. 2026Action-Sufficient Goal Representationscited by 1

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  2. 2026Game-Theoretic Multi-Agent Reinforcement Learning for Swarm Trajectory Planning in Low-Altitude Wireless Networks
  3. 2025Green Emergency Communications in RIS- and MA-Assisted Multi-UAV SAGINs: A Partially Observable Reinforcement Learning Approach
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  6. 2025Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms
  7. 2025Optimizing UAV Aerial Base Station Flights Using DRL-based Proximal Policy Optimization
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