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

arXiv: https://arxiv.org/abs/2608.16955 · HTML: https://mlsanity.com/paper/2608.16955/

## 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.

## Placed in

- World models / [World models for navigation](https://mlsanity.com/wm/world-models-for-navigation/)

## Builds on (most cited first, 25 of 25)

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- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics](https://mlsanity.com/paper/2511.08544/) (2025)
- [TarMAC: Targeted Multi-Agent Communication](https://mlsanity.com/paper/1810.11187/) (2018)
- [Multi-Agent Reinforcement Learning is a Sequence Modeling Problem](https://mlsanity.com/paper/2205.14953/) (2022)
- [Graph Convolutional Reinforcement Learning](https://mlsanity.com/paper/1810.09202/) (2018)
- [Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography](https://mlsanity.com/paper/2202.03269/) (2022)
- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [Context-aware Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2312.15600/) (2023)
- [Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective](https://mlsanity.com/paper/2505.20922/) (2025)
- [UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization](https://mlsanity.com/paper/2008.00218/) (2020)
- [CoDe: Communication Delay-Tolerant Multi-Agent Collaboration via Dual Alignment of Intent and Timeliness](https://mlsanity.com/paper/2501.05207/) (2025)
- [In-Context Reinforcement Learning via Communicative World Models](https://mlsanity.com/paper/2508.06659/) (2025)
- [Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models](https://mlsanity.com/paper/2505.08448/) (2025)
- [A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction](https://mlsanity.com/paper/2507.14186/) (2025)
- [Multi-Robot Active Information Gathering with Periodic Communication](https://mlsanity.com/paper/1703.02610/) (2017)
- [Connectivity Maintenance: Global and Optimized approach through Control Barrier Functions](https://mlsanity.com/paper/2003.10178/) (2020)
- [Online Connectivity-aware Dynamic Deployment for Heterogeneous Multi-Robot Systems](https://mlsanity.com/paper/2012.10008/) (2020)
- [UAV-Assisted Communication in Remote Disaster Areas using Imitation Learning](https://mlsanity.com/paper/2105.12823/) (2021)
- [Aerial Base Station Placement via Propagation Radio Maps](https://mlsanity.com/paper/2301.04966/) (2023)
- [Simultaneous Intermittent Communication Control and Path Optimization in Networks of Mobile Robots](https://mlsanity.com/paper/1609.07038/) (2016)
- [Aerial Base Station Placement: A Tutorial Introduction](https://mlsanity.com/paper/2110.01399/) (2021)
- [Transformer-Based Multi-Agent Reinforcement Learning for Networked Systems with Long-Range Interactions](https://mlsanity.com/paper/2511.13103/) (2025)
- [Action-Sufficient Goal Representations](https://mlsanity.com/paper/2601.22496/) (2026)

## 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.