dorsal/arxiv
View SchemaPediaMind-R1: A Temperament-Aware Language Model for Personalized Early Childhood Care Reasoning via Cognitive Modeling and Preference Alignment
| Authors | Zihe Zhang, Can Zhang, Yanheng Xu, Xin Hu, Jichao Leng |
|---|---|
| Categories | |
| ArXiv ID | 2601.08848vv1 |
| URL | https://arxiv.org/abs/2601.08848 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
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.
{
"annotation_id": "2df0a7fb-b9fe-41f9-a97e-dc9641515517",
"date_created": "2026-02-17T05:53:19.293000Z",
"date_modified": "2026-02-17T05:53:19.293000Z",
"file_hash": "73f24a9b8e04616039294430114d1aae5a4b13f67734bbf4acde25e7291fe143",
"private": false,
"record": {
"abstract": "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.",
"arxiv_id": "2601.08848",
"authors": [
"Zihe Zhang",
"Can Zhang",
"Yanheng Xu",
"Xin Hu",
"Jichao Leng"
],
"categories": [
"cs.CL",
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "PediaMind-R1: A Temperament-Aware Language Model for Personalized Early Childhood Care Reasoning via Cognitive Modeling and Preference Alignment",
"url": "https://arxiv.org/abs/2601.08848",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "4f6e77db-ca8b-49ee-b5b7-4063a86c9870",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}