[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86061-en":3,"doc-seo-86061-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86061,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Toward Contemplative LLM A Modular Framework for Evaluating and Enhancing LLM Alignment in Mental Health","Large language models rapidly reshape human–AI interaction, but their movement toward more autonomous behavior raises urgent ethical-alignment risks. Drawing on contemplative traditions such as mindfulness and compassion, the work motivates a contemplative principle–informed approach to improve cooperation and reduce ethical violations in LLM outputs. A modular, reusable evaluation framework is proposed to integrate new models, metrics, and benchmarks, enabling standardized cross-evaluation in mental-health scenarios while bridging computational assessment with human-centered ethical reasoning.","Toward Contemplative LLM: A Modular Framework for Evaluating and Enhancing LLM Alignment in Mental Health  \nAsher Sprigler  \nElectrical and Computer Engineering Purdue University West Lafayette, IN, USA [asprigle@purdue.edu](asprigle@purdue.edu)  \nYang-Yang Feng  \nBiomedical Engineering Washington University in St. Louis St. Louis, MO, USA [yang-yang.feng@wustl.edu](yang-yang.feng@wustl.edu)  \nIftach Amir  \nPsychological & Brain Sciences Washington University in St. Louis St. Louis, MO, USA [amiri@wustl.edu](amiri@wustl.edu)  \nJonathan E. Bogard  \nOlin Business School Washington University in St. Louis St. Louis, MO, USA [bogard@wustl.edu](bogard@wustl.edu)  \nTodd S Braver  \nPsychological & Brain Sciences Washington University in St. Louis St. Louis, MO, USA[tbraver@wustl.edu](tbraver@wustl.edu)  \nYi Ding  \nElectrical and Computer Engineering Purdue University West Lafayette, IN, USA [yiding@purdue.edu](yiding@purdue.edu)  \narXiv :2607 . 1087 1v 1 [ cs .AI] 12 Jul 2026  \nDavid Kinney  \nDepartment of Philosophy Washington University in St Louis St. Louis, MO, USA [kinney@wustl.edu](kinney@wustl.edu)  \nYixue Zhao  \nInterdisciplinary Science Yixue Research Institute Washington, DC, USA [yixue@yixuezhao.com](yixue@yixuezhao.com)  \n1 Introduction  \nLarge language models (LLMs) are rapidly transforming human-AI interaction, enabling new capabilities in communication, decisionmaking, and knowledge generation. However, these advances introduce critical challenges in ethical alignment, as models evolve rapidly toward increasingly autonomous behavior that may surpass human-level intelligence [3, 7] . This trajectory underscores the urgency of proactively guiding AI development to ensure alignment.  \nContemplative traditions have long guided ethical behavior and prosocial interaction. In modern contexts, these traditions have been adapted into scientific paradigms for studying cognitive training, therapeutic intervention, and neuroscience investigations [2, 8] . Research on contemplative practices such as mindfulness and meditation shows effects on emotional regulation, cognition, and moral reasoning [4, 9, 11, 12] . Recent work further suggests that contemplative principles (e.g., mindfulness, compassion, non-dual reasoning), grounded in both ancient traditions and modern science, may offer a promising alternative paradigm for alignment. Early evidence indicates that incorporating such principles into prompting strategies can improve cooperation and reduce ethical violations in LLM outputs [5], suggesting the potential for a contemplatively based constitutional approach to ethical alignment.  \nHowever, as new models, evaluation metrics, and benchmarks emerge rapidly, it remains challenging to systematically assess whether and how contemplative principles enhance LLM alignment across diverse and evolving scenarios. In many cases, newly released models can render prior experimental findings obsolete almost immediately. Existing approaches are often developed asad hoc solutions, which lack the flexibility to adapt quickly to new  \nHARMONY: Human-centered AI Research for Mental Health, an Open Networking Symposium, Pittsburgh, PA, USA  \n2026. ACM ISBN 978-x-xxxx-xxxx-x/YYYY/MM  \n[https://doi.org/10.1145/nnnnnnn.nnnnnnn](https://doi.org/10.1145/nnnnnnn.nnnnnnn)  \nmodels, metrics, or benchmarks. Clear examples can be seen in current state-of-the-art work in the mental health domain where LLM alignment is crucial [1, 6, 13] . Thus, it is vital to develop generalizable approaches to quickly test new models against a wide range of potential benchmarks and metrics.  \nTo address this challenge and establish a strong foundation for future work on contemplative LLM, we have designed a modular framework that enables seamless integration of new models, metrics, and benchmarks through a reusable and customizable pipeline, initially targeted towards the mental health domain. Currently, our framework can reproduce existing state-of-the-art results [1, 6,","cbCaikEpQBdXxx7V","https://ap.wps.com/l/cbCaikEpQBdXxx7V","pdf",371108,1,2,"English","en",105,"# Introduction\n# Framework Core Features","[{\"question\":\"Why is ethical alignment a key challenge for rapidly advancing LLMs?\",\"answer\":\"As models evolve toward increasingly autonomous behavior, they may produce outputs that conflict with human ethical expectations, making proactive alignment guidance essential.\"},{\"question\":\"How do contemplative traditions relate to improving LLM alignment?\",\"answer\":\"Mindfulness, compassion, and related contemplative principles can support emotional regulation, cognition, and moral reasoning, and early results suggest they improve cooperation and reduce ethical violations through prompting strategies.\"},{\"question\":\"What core capabilities does the modular framework provide for evaluating LLM alignment?\",\"answer\":\"The framework offers a flexible reusable pipeline and cross-evaluation support by decoupling evaluation from specific tasks, allowing consistent application of metrics across models and 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is ethical alignment a key challenge for rapidly advancing LLMs?","Question",{"text":74,"@type":75},"As models evolve toward increasingly autonomous behavior, they may produce outputs that conflict with human ethical expectations, making proactive alignment guidance essential.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do contemplative traditions relate to improving LLM alignment?",{"text":79,"@type":75},"Mindfulness, compassion, and related contemplative principles can support emotional regulation, cognition, and moral reasoning, and early results suggest they improve cooperation and reduce ethical violations through prompting strategies.",{"name":81,"@type":72,"acceptedAnswer":82},"What core capabilities does the modular framework provide for evaluating LLM alignment?",{"text":83,"@type":75},"The framework offers a flexible reusable pipeline and cross-evaluation support by decoupling evaluation from specific tasks, allowing consistent application of metrics across 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