PediaMind-R1: 一种基于气质的个性化婴幼儿护理推理语言模型：通过认知建模与偏好对齐
PediaMind-R1: A Temperament-Aware Language Model for Personalized Early Childhood Care Reasoning via Cognitive Modeling and Preference Alignment
AI总结：
PediaMind-R1通过整合认知建模与偏好对齐，实现基于婴幼儿气质的个性化护理推理。
摘要：
本文提出了PediaMind-R1，一种专门领域的大语言模型，旨在实现智能育儿场景中的主动个性化。与传统系统提供通用建议不同，PediaMind-R1借鉴了发展心理学的见解。它引入了Thomas-Chess框架中的气质理论，并为婴儿和幼儿（0-3岁）构建了气质知识图谱。我们的两阶段训练流程首先使用监督微调来教授结构化的链式推理，然后应用基于GRPO的对齐阶段以加强逻辑一致性、领域专业知识和同理心护理策略。我们进一步设计了一个包含气质敏感的多项选择测试和人类评估的评估框架。结果表明，PediaMind-R1能够准确解读婴幼儿气质特征并主动进行个性化推理。这项工作强调了将垂直领域建模与心理学理论相结合的价值。它提供了一种开发以用户为中心的LLM的新方法，推动了敏感护理情境中主动个性化的实践。
英文摘要：
This paper presents PediaMind-R1, a domain-specialized large language model designed to achieve active personalization in intelligent parenting scenarios. Unlike conventional systems that provide generic suggestions, PediaMind-R1 draws on insights from developmental psychology. It introduces temperament theory from the Thomas-Chess framework and builds a temperament knowledge graph for infants and toddlers (0-3 years). Our two-stage training pipeline first uses supervised fine-tuning to teach structured chain-of-thought reasoning, and then applies a GRPO-based alignment stage to reinforce logical consistency, domain expertise, and empathetic caregiving strategies. We further design an evaluation framework comprising temperament-sensitive multiple-choice tests and human assessments. The results demonstrate that PediaMind-R1 can accurately interpret early childhood temperament profiles and proactively engage in individualized reasoning. This work highlights the value of integrating vertical-domain modeling with psychological theory. It offers a novel approach to developing user-centered LLMs that advance the practice of active personalization in sensitive caregiving contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
