Mitigating Semantic Drift in Multi-Agent Communication: A Dynamic Neuro-Symbolic Approach

Abstract: The emergence of multi-agent systems (MAS) based on large language models (LLMs) has enabled autonomous collaboration on complex, goal-oriented tasks. However, effective interaction is frequently hindered by semantic drift, a phenomenon where heterogeneous agents assign conflicting meanings to shared terminology due to differing internal prompts or domain knowledge. Existing communication paradigms either rely on unconstrained natural language, which suffers from structural vagueness, or rigid symbolic schemas that fail to adapt to emergent concepts. To address this gap, we propose DOA, a novel dynamic ontology alignment framework that serves as a semantic media‑ tion layer for MAS. DOA integrates a proactive semantic prober to detect conceptual mismatches and a neuro-symbolic aligner that reconciles local semantic structures in real time. By grounding fluid natural language dialogues in an evolving shared ontology, our framework ensures deterministic mutual understanding over long-horizon tasks. Empirical evaluations in cross-domain supply chain and healthcare coordination scenarios demonstrate that DOA improves task success rates by an average of 31.5% and reduces communication overhead (token consump‑ tion) by 50% compared to state-of-the-art baselines. Our results provide a robust and scalable foundation for semantic consistency in next- generation industrial-grade AI systems.

Keywords: DOA; multi-agent systems; neuro-symbolic AI; semantic drift; large language models