LLM-assisted writing has seen rapid adoption in interpersonal communication, yet current systems often fail to capture the subtle tones essential for effectiveness. Email writing exemplifies this challenge: effective messages require careful alignment with intent, relationship, and context beyond mere fluency. Through formative studies, we identified three key challenges: articulating nuanced communicative intent, making modifications at multiple levels of granularity, and reusing effective tone strategies across messages. We developed PersonaMail, a system that addresses these gaps through structured communication factor exploration, granular editing controls, and adaptive reuse of successful strategies. Our evaluation compared PersonaMail against standard LLM interfaces, and showed improved efficiency in both immediate and repeated use, alongside higher user satisfaction. We contribute design implications for AI-assisted communication systems that prioritize interpersonal nuance over generic text generation.

翻译：LLM辅助写作在人际交流中已迅速普及，但现有系统往往难以捕捉对沟通有效性至关重要的微妙语气。邮件撰写尤其体现了这一挑战：有效的消息不仅需要流畅性，更需与意图、关系和情境进行精细匹配。通过形成性研究，我们识别出三个关键挑战：表达细微的沟通意图、进行多粒度修改，以及在跨消息场景中复用有效的语气策略。我们开发了PersonaMail系统，通过结构化沟通要素探索、细粒度编辑控制和成功策略的自适应复用机制来解决这些不足。评估实验将PersonaMail与标准LLM界面进行对比，结果表明该系统在即时使用和重复使用中均提升了效率，同时获得了更高的用户满意度。我们提出了优先考虑人际沟通细微差异而非通用文本生成的AI辅助通信系统设计启示。