Abstract: Multi-agent neural implicit mapping allows robots to collaboratively capture and reconstruct complex environments with high fidelity. However, existing approaches often rely on synchronous communication, which is impractical in real-world scenarios with limited bandwidth and potential communication interruptions. This paper introduces RAMEN: Real-time Asynchronous Multi-agEnt Neural implicit mapping, a novel approach designed to address this challenge. RAMEN employs a weighted multi-agent consensus optimization algorithm that explicitly accounts for communication disruptions. Using update information (i.e., update frequency), we quantify the uncertainty associated with each parameter of the neural network. Two neural networks are brought to consensus on the basis of their levels of uncertainty, with consensus biased towards network parameters with lower uncertainty. To achieve this, we derive a weighted variant of the decentralized consensus alternating direction method of multipliers (C-ADMM) algorithm, facilitating robust and efficient collaboration among agents. Through extensive evaluations on real-world datasets and real-robot hardware experiments, we demonstrate RAMEN’s superior mapping performance under challenging communication conditions.