Towards High Semantic Fidelity: Hyperdimensional Symbolic Messages in Multi-Agent Communication

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-Hyperdimensional Symbolic Messages (HSM), and its corresponding communication framework 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 operation. Experiments exhibit that Herm outperforms baselines across various noisy scenarios while maintaining superior structural readability.