# Liquid-Graph Time-Constant Network for Multi-Agent Systems Control

arXiv: https://arxiv.org/abs/2404.13982 · HTML: https://mlsanity.com/paper/2404.13982/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2024.

## Built on by (most cited first, 4 of 4)

- [Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements](https://mlsanity.com/paper/2503.02437/) (2025)
- [Riemannian Liquid Spatio-Temporal Graph Network](https://mlsanity.com/paper/2601.14115/) (2026)
- [Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning](https://mlsanity.com/paper/2606.30893/) (2026)
- [Real-time fall detection based on vision for low-power edge platforms](https://mlsanity.com/paper/2607.12909/) (2026)

## Abstract (arXiv)

In this paper, we propose the Liquid-Graph Time-constant (LGTC) network, a continuous graph neural network(GNN) model for control of multi-agent systems based on therecent Liquid Time Constant (LTC) network. We analyse itsstability leveraging contraction analysis and propose a closed-form model that preserves the model contraction rate and doesnot require solving an ODE at each iteration. Compared todiscrete models like Graph Gated Neural Networks (GGNNs),the higher expressivity of the proposed model guaranteesremarkable performance while reducing the large amountof communicated variables normally required by GNNs. Weevaluate our model on a distributed multi-agent control casestudy (flocking) taking into account variable communicationrange and scalability under non-instantaneous communication