发表机构
ModelsLive Inc.（ModelsLive 公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高风险咨询领域的两阶段结构，提出动态解耦框架ThuRunel，结合有限状态信念管理、思维链教师合成与生成适配器，在引导完整性和专家简报质量上优于十一个基线，并已部署为双语应用。
AI 中文摘要
医疗美容、法律咨询和教育规划等高风险的咨询领域呈现出两阶段结构。早期阶段需要共情式引导和情感支持，而后期阶段则需要权威的专家判断。完全自动化的智能体或人类初级顾问均无法在大规模上充分应对这一结构。我们将核心设计挑战形式化为动态解耦，探讨AI咨询智能体应如何决定询问什么、何时停止、自主解决什么以及将什么转交给专家。我们提出了ThuRunel，一种结合了有限状态信念管理框架、思维链教师合成协议和学习型生成适配器的咨询智能体。与十一个基线相比，ThuRunel在引导完整性和专家简报质量方面取得了持续改进。ThuRunel已作为双语网页应用公开部署，其中相同的解耦决策在客户端侧运行，并基于一个在每次回答中引用其来源的精选知识库。
英文摘要
High-stakes advisory domains such as medical aesthetics, legal consultation, and educational planning exhibit a two-phase structure. The early phase requires empathetic elicitation and emotional support, and the late phase requires authoritative specialist judgment. Neither fully automated agents nor human junior consultants adequately address this structure at scale. We formalize the core design challenge as dynamic decoupling, asking how an AI advisory agent should decide what to ask, when to stop, what to resolve autonomously, and what to forward to the specialist. We present ThuRunel, an advisory agent combining a finite-state belief management framework, a chain-of-thought teacher synthesis protocol, and learned generation adapters. Against eleven baselines, ThuRunel achieves consistent improvements in elicitation completeness and specialist brief quality. ThuRunel is publicly deployed as a bilingual web application in which the same decoupling decisions operate from the client's side, grounded in a curated knowledge base that cites its sources in every answer.
Comments14 pages, 22 figures, 8 tables
