The deployment of large language models (LLMs) in automated negotiation has set a high performance benchmark, but their computational cost and data privacy requirements render them unsuitable for many privacy-sensitive, on-device applications such as mobile assistants, embodied AI agents or private client interactions. While small language models (SLMs) offer a practical alternative, they suffer from a significant performance gap compared to LLMs in playing emotionally charged complex personas, especially for credit negotiation. This paper introduces EQ-Negotiator, a novel framework that bridges this capability gap using emotional personas. Its core is a reasoning system that integrates game theory with a Hidden Markov Model(HMM) to learn and track debtor emotional states online, without pre-training. This allows EQ-Negotiator to equip SLMs with the strategic intelligence to counter manipulation while de-escalating conflict and upholding ethical standards. Through extensive agent-to-agent simulations across diverse credit negotiation scenarios, including adversarial debtor strategies like cheating, threatening, and playing the victim, we show that a 7B parameter language model with EQ-Negotiator achieves better debt recovery and negotiation efficiency than baseline LLMs more than 10 times its size. This work advances persona modeling from descriptive character profiles to dynamic emotional architectures that operate within privacy constraints. Besides, this paper establishes that strategic emotional intelligence, not raw model scale, is the critical factor for success in automated negotiation, paving the way for effective, ethical, and privacy-preserving AI negotiators that can operate on the edge.

翻译：摘要：大语言模型（LLMs）在自动化谈判中的部署树立了高绩效基准，但其计算成本与数据隐私要求使其难以适用于移动助手、具身AI代理或私人客户交互等众多隐私敏感的终端设备应用。尽管小语言模型（SLMs）提供了实用替代方案，但在处理情绪复杂的动态人格（尤其是信贷谈判场景）时，其性能与LLMs存在显著差距。本文提出EQ-Negotiator这一新型框架，通过情感人格弥合能力鸿沟。其核心是融合博弈论与隐马尔可夫模型（HMM）的推理系统，可在无需预训练的情况下在线学习并追踪债务人情绪状态。这使得EQ-Negotiator能够赋予SLMs战略智能：在缓和冲突、坚守伦理标准的同时反制操纵行为。通过涵盖欺诈、威胁、卖惨等对抗性债务人策略的多样化信贷谈判场景的智能体间仿真，我们证明搭载EQ-Negotiator的7B参数语言模型在债务回收率与谈判效率上均优于基线LLMs（其参数量超前者十倍以上）。该工作将人格建模从描述性角色档案推进至可在隐私约束下运行的动态情感架构。此外，本文证实战略情感智能（而非模型原始规模）才是自动化谈判成功的关键因素，为开发能在边缘设备运行的高效、合伦理且保护隐私的AI谈判者铺平道路。