Personalized nutrition management aims to tailor dietary guidance to an individual's intake and phenotype, but most existing systems handle food logging, nutrient analysis and recommendation separately. We present a next-generation mobile nutrition assistant that combines image based meal logging with an LLM driven multi agent controller to provide meal level closed loop support. The system coordinates vision, dialogue and state management agents to estimate nutrients from photos and update a daily intake budget. It then adapts the next meal plan to user preferences and dietary constraints. Experiments with SNAPMe meal images and simulated users show competitive nutrient estimation, personalized menus and efficient task plans. These findings demonstrate the feasibility of multi agent LLM control for personalized nutrition and reveal open challenges in micronutrient estimation from images and in large scale real world studies.
A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON
NutriGen: Personalized Meal Plan Generator Leveraging Large Language Models to Enhance Dietary and Nutritional Adherence
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information
Technical University of Munich（慕尼黑工业大学）；University Hospital Augsburg（奥格斯堡大学医院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Aalto University（阿尔托大学）
FinAgent: An Agentic AI Framework Integrating Personal Finance and Nutrition Planning
Faculty of Computer and Information System, Islamic University of Madinah, Saudi Arabia（麦加伊斯兰大学计算机与信息学院）；Department of Computer Science, Effat College of Engineering, Effat University, Saudi Arabia（Effat大学工程学院计算机科学系）；Arab Open University, Bahrain（巴林开放大学）
From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）
Agentic AI Framework for Individuals with Disabilities and Neurodivergence: A Multi-Agent System for Healthy Eating, Daily Routines, and Inclusive Well-Being
MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）
DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care
Institute of Data Science（数据科学研究所）；Saw Swee Hock School of Public Health, National University of Singapore, Singapore（Saw Swee Hock公共卫生学院，新加坡国立大学，新加坡）；National University Hospital, Singapore（新加坡国立医院）
Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院）；State Key Laboratory of AI Safety, Institute of Computing Technology（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning
East China University of Science and Technology（华东理工大学）；Shanghai Jiao Tong University Affiliated Sixth People’s Hospital（上海交通大学附属第六人民医院）；Shanghai Diabetes Institute（上海市糖尿病研究所）；Shanghai Clinical Center for Diabetes（上海市糖尿病临床医学中心）
