Why do AI agents communicate in human language?

• URL: http://arxiv.org/abs/2506.02739v1
• Date: Tue, 03 Jun 2025 10:53:29 GMT
• Title: Why do AI agents communicate in human language?
• Authors: Pengcheng Zhou, Yinglun Feng, Halimulati Julaiti, Zhongliang Yang,
• Abstract summary: Large Language Models (LLMs) have become foundational to modern AI agent systems.<n>In most existing systems, inter-agent communication relies primarily on natural language.<n>We argue that this design introduces fundamental limitations in agent-to-agent coordination.
• Score: 6.784986834853486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have become foundational to modern AI agent systems, enabling autonomous agents to reason and plan. In most existing systems, inter-agent communication relies primarily on natural language. While this design supports interpretability and human oversight, we argue that it introduces fundamental limitations in agent-to-agent coordination. The semantic space of natural language is structurally misaligned with the high-dimensional vector spaces in which LLMs operate, resulting in information loss and behavioral drift. Beyond surface-level inefficiencies, we highlight a deeper architectural limitation: current LLMs were not trained with the objective of supporting agentic behavior. As such, they lack mechanisms for modeling role continuity, task boundaries, and multi-agent dependencies. The standard next-token prediction paradigm fails to support the structural alignment required for robust, scalable agent coordination. Based on this, we argue that two core questions deserve careful examination: first, given that AI agents fundamentally operate in high-dimensional vector spaces, should they rely on a language system originally designed for human cognition as their communication medium? Second, should we consider developing a new model construction paradigm that builds models from the ground up to natively support structured communication, shared intentionality, and task alignment in multi-role, multi-agent environments? This paper calls for a reconsideration not only of how agents should communicate, but also of what it fundamentally means to train a model that natively supports multi-agent coordination and communication.

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