Multi-agent communication improves message clarity using hyperdimensional symbols
Towards High Semantic Fidelity: Hyperdimensional Symbolic Messages in Multi-Agent Communication
Multiagent Systems
Summary
Communicating clearly between multiple agents is hard when messages get noisy or changed along the way. The paper introduces a new way to send messages using high-dimensional coded symbols that keep important details intact. This method helps reduce misunderstandings by structuring messages into parts and carefully decoding them back into meaning. Tests show that these messages stay clearer than existing methods, even with noise.
What this means in practice
- •For robotics developers: Improve communication protocols between robots working in noisy environments by using structured, hyperdimensional-coded messages to maintain meaning accuracy.
- •For distributed system engineers: Enhance message fidelity in networked multi-agent systems by applying the proposed symbolic message format to reduce semantic errors under transmission noise.
Authors
Shoucheng Song, Chang Yao, Hao Wu, Youfang Lin, Sheng Han, Kai Lv
Abstract
Semantic fidelity is essential for multi-agent communication under non-ideal channels, comprising transmission fidelity and conversion fidelity. Yet existing numerical and neural message formats cannot simultaneously achieve both. To address this, we propose a novel message format-\textbf{H}yperdimensional \textbf{S}ymbolic \textbf{M}essages (HSM) and its corresponding communication framework \textbf{Herm}. For transmission fidelity, Herm suppresses semantic entanglement via an entity-attribute-value structured encoding pipeline. For conversion fidelity, Herm first leverages a receiver-centric ego-binding mechanism to align semantics. Then, a similarity-aware dual-branch fusion module is proposed to prevent critical semantic dilution. To realize explicit message-semantic conversion, we devise an iterative decoding scheme via algebraic inverse operations. Experiments exhibit that Herm outperforms baselines across various noisy scenarios while maintaining superior structural readability.