Data scarcity and unreliable self-reporting -- such as concealment or exaggeration -- pose fundamental challenges to psychiatric intake and assessment. We propose a multi-agent synthesis framework that explicitly models patient deception to generate high-fidelity, publicly releasable synthetic psychiatric intake records. Starting from DAIC-WOZ interviews, we construct enriched patient profiles and simulate a four-role workflow: a \emph{Patient} completes self-rated scales and participates in a semi-structured interview under a topic-dependent honesty state; an \emph{Assessor} selects instruments based on demographics and chief complaints; an \emph{Evaluator} conducts the interview grounded in rater-administered scales, tracks suspicion, and completes ratings; and a \emph{Diagnostician} integrates all evidence into a diagnostic summary. Each case links the patient profile, self-rated and rater-administered responses, interview transcript, diagnostic summary, and honesty state. We validate the framework through four complementary evaluations: diagnostic consistency and severity grading, chain-of-thought ablations, human evaluation of clinical realism and dishonesty modeling, and LLM-based comparative evaluation. The resulting corpus spans multiple disorders and severity levels, enabling controlled study of dishonesty-aware psychiatric assessment and the training and evaluation of adaptive dialogue agents.

翻译：数据稀缺与不可靠的自我报告——如隐瞒或夸大症状——对精神科接诊与评估构成了根本性挑战。本文提出一种多智能体合成框架，通过显式建模患者欺骗行为来生成高保真、可公开释放的合成精神科接诊记录。以DAIC-WOZ访谈为起点，我们构建了丰富的患者档案，并模拟包含四个角色的工作流程：\emph{患者}在特定主题相关的诚实状态下完成自评量表并参与半结构化访谈；\emph{评估员}根据人口统计学特征和主诉选择评估工具；\emph{访谈员}基于他评量表进行访谈、追踪可疑迹象并完成评分；\emph{诊断医师}整合所有证据形成诊断摘要。每个案例均关联患者档案、自评与他评应答、访谈转录、诊断摘要及诚实状态。我们通过四项互补性评估验证该框架：诊断一致性与严重程度分级、思维链消融实验、临床真实性与欺骗建模的人工评估，以及基于LLM的对比评估。最终构建的语料库涵盖多种障碍类型及严重程度，为开展受控的诚实感知精神科评估研究，以及训练与评估自适应对话智能体提供了数据基础。