As large language models (LLMs) are increasingly integrated into multi-agent and human-AI systems, understanding their awareness of both self-context and conversational partners is essential for ensuring reliable performance and robust safety. While prior work has extensively studied situational awareness which refers to an LLM’s ability to recognize its operating phase and constraints, it has largely overlooked the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner. In this paper, we formalize this latter capability as interlocutor awareness and present the first systematic evaluation of its emergence in contemporary LLMs. We examine interlocutor inference across three dimensions—reasoning patterns, linguistic style, and alignment preferences—and show that LLMs reliably identify same-family peers and certain prominent model families, such as GPT and Claude. To demonstrate its practical significance, we develop three case studies in which interlocutor awareness both enhances multi-LLM collaboration through prompt adaptation and introduces new alignment and safety vulnerabilities, including reward-hacking behaviors and increased jailbreak susceptibility. Our findings highlight the dual promise and peril of identity—sensitive behavior in LLMs, underscoring the need for further understanding of interlocutor awareness and new safeguards in multi-agent deployments.

When customers present ambiguous references, service staff typically need to clarify the customers’ specific intentions. To advance research in this area, we collected 1,000 real-world consumer dialogues with ambiguous references. This dataset will be used for subsequent studies to identify ambiguous references and generate responses. Our analysis of the dataset revealed common strategies employed by service staff, including directly asking clarification questions (CQ) and listing possible options before asking a clarification question (LCQ). However, we found that merely using CQ often fails to fully satisfy customers. In contrast, using LCQ, as well as recommending specific products after listing possible options, proved more effective in resolving ambiguous references and enhancing customer satisfaction.