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Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models
작성자
Haebom
Author
Younwoo Choi, Changling Li, Yongjin Yang, Zhijing Jin
Outline
This paper highlights the imperative for understanding LLMs' awareness of both their own context and conversational partners to ensure reliable performance and robust security as large-scale language models (LLMs) are integrated into multi-agent and human-AI systems. While prior research has focused on context awareness—the ability to recognize the LLM's operational stages and constraints—interactor awareness, which identifies and adapts to the identity and characteristics of conversational partners, has been relatively overlooked. In this paper, we formalize this interactor awareness capability and present the first systematic evaluation of its emergence in modern LLMs. By examining interactor inference across three dimensions—inference patterns, linguistic style, and alignment preferences—we demonstrate that LLMs reliably identify peers within the same family and specific key model families, such as GPT and Claude. To demonstrate its practical significance, we develop three case studies demonstrating how interactor awareness enhances multi-LLM collaboration through prompt adaptation and introduces novel alignment and security vulnerabilities, including increased reward hacking behavior and jailbreak vulnerabilities. These findings highlight the dual promises and risks of identity-sensitive behavior in LLMs, highlighting the need for further understanding of interactant awareness and novel safeguards in multi-agent deployments. Code published under https://github.com/younwoochoi/InterlocutorAwarenessLLM .
