Large language models (LLMs) are increasingly deployed as autonomous agents that make consumption decisions on behalf of users. This shift raises fundamental questions for consumer theory, which has traditionally modeled humans as the primary decision-makers. In this paper, we introduce LLM Consumer Behavior Theory, a new field of study concerned with analyzing consumer behavior in agentic markets. Drawing on classical and behavioral economics alongside recent advances in Natural Language Processing, we formalize how human preferences are reflected and acted upon by LLM-based agents, and how agent-level decisions aggregate into market demand. We unify previously fragmented literature on LLM decision-making, human behavior simulation, and preference elicitation under a common economic lens, highlighting where assumptions, such as rationality and heterogeneity, may fail in agentic markets. Rather than providing empirical validation, this paper outlines the scope of LLM consumer behavior and identifies open research questions related to alignment, preference representation, and market dynamics.
PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language
School of Computer Science Institute for Research in Fundamental Sciences（计算机科学学院基础科学研究研究所）；School of Electrical and Computer Engineering University of Tehran（电气与计算机工程学院塔里班大学）；Department of Computer Science Faculty of Mathematical Sciences Alzahra University（计算机科学系数学科学学院阿扎赫拉大学）
Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs
Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University（约翰霍普金斯大学）；University of California, Berkeley（加州大学伯克利分校）；Toronto General Hospital, University Health Network（多伦多综合医院，大学健康网络）；McGill University（麦吉尔大学）；University of Toronto（多伦多大学）；Independent Researcher（独立研究者）；Rutgers University（罗格斯大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）
FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs
Hong Kong Baptist University（香港浸会大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Northeastern University（东北大学）；National University of Singapore（新加坡国立大学）；XGRIDS；HKUST(GZ)（香港科技大学（广州））
A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System
Polytechnic University of Bari（巴里理工大学）；Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核子研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）
Enhancing Large Language Models with Domain-specific Retrieval Augment Generation: A Case Study on Long-form Consumer Health Question Answering in Ophthalmology
Yale University（耶鲁大学）；National Eye Institute, National Institutes of Health（美国国立卫生研究院国家眼科研究所）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Centre（新加坡国家眼科中心）；National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；Rice University（莱斯大学）；National University of Singapore（新加坡国立大学）
Can Large Language Models Reinvent Foundational Algorithms?
Xiongan AI Institute（雄安人工智能研究院）；Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute（上海期智研究院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）
Uncertainty Quantification in LLM Agents: Foundations, Emerging Challenges, and Opportunities
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Carnegie Mellon University（卡内基梅隆大学）；Nanyang Technological University（南洋理工大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Southern California（南加州大学）；University of California, Berkeley（加州大学伯克利分校）
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
School of Computer Science, Nanjing University of Information Science and Technology（南京信息工程大学计算机科学学院）；State Key Laboratory for Novel Software Technology, Nanjing University（南京大学软件新技术国家重点实验室）；National Institute of Healthcare Data Science, Nanjing University（南京大学健康数据科学国家研究院）；Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology（南京信息工程大学教育部长江数字取证工程研究中心）
Paying for Honesty Without Knowing the Truth: Reputation-Penalty Design for LLM Marketplace Agents
Univ. of Illinois Chicago（伊利诺伊大学芝加哥分校）；Springbrand Inc.（春品牌公司）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beihang University（北京航空航天大学）；Hangzhou Innovation Institute of BUAA（北京航空航天大学杭州创新研究院）；Microsoft（微软公司）
