Choosing suitable psychometric scales is an essential and difficult step in psychological consultation, which requires clinicians to integrate patient information, behaviors, and dynamic contextual information. Existing systems mainly use static pipelines to choose scale, or directly predict symptoms according to user inputs, limiting their ability to support dynamic assessment, risk management, and transparent decision-making. To address these limitations, we propose DySRec, a multi-agent conversational system for dynamic psychometric scale recommendation. DySRec operates as an interactive chatbot that engages users in multi-turn dialogue, models scale selection as a continuous conversational decision process, and coordinates specialized agents to maintain user context, recommend assessment scales, monitor psychological risk, and log decision trajectories. In this way, DySRec can integrate and capture heterogeneous signals, including semantic, interaction behaviors, assessment history, and content state, to dynamically update user representations and calculate scale-context compatibility score for recommending most matched scales. Moreover, DySRec incorporates a closed-loop refinement mechanism. Recommendation agent will feedback the missing or uncertain attributes and guide the conversation to elicit the targeted information. In this paper, we showcase the prototype design and architecture of DySRec, and this system has been verified in a real-world application.

翻译：选择合适的心理量表是心理咨询中关键且困难的步骤，要求临床医生整合患者信息、行为及动态情境信息。现有系统主要采用静态流水线选择量表，或根据用户输入直接预测症状，限制了其对动态评估、风险管理和透明决策的支持能力。为解决上述局限，我们提出DySRec——一个面向动态心理量表推荐的多智能体对话系统。DySRec作为交互式聊天机器人运行，通过多轮对话与用户互动，将量表选择建模为连续的对话决策过程，并协调各专业智能体维护用户情境、推荐评估量表、监控心理风险及记录决策轨迹。通过这种方式，DySRec能够整合并捕获包括语义信息、交互行为、评估历史及内容状态在内的异构信号，动态更新用户表征并计算量表-情境兼容性分数，以推荐最匹配的量表。此外，DySRec引入闭环优化机制：推荐智能体会反馈缺失或不确定的属性，并引导对话获取目标信息。本文展示了DySRec的原型设计与架构，该系统已在真实场景中得到验证。