arXiv AI June 10, 2026 By Asiful Arefeen, Carol Johnston, Hassan Ghasemzadeh

MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention

arXiv:2606. 10120v1 Announce Type: cross Abstract: Postprandial hyperglycemia is a key risk factor for metabolic disorders; however, existing dietary guidance is often static, impractical, and insufficiently personalized, providing recommendations that are difficult to follow or not impactful.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 12

LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data

arXiv:2606. 12699v1 Announce Type: cross Abstract: Type 2 Diabetes (T2D) poses an increasing global health threat, demanding effective glycemic assessment to support personalized and improved diabetes care.

By Yifan Gao, Yanmin Gong, Yun Shi, Yuanxiong Guo

llms benchmarks

arXiv AI

2d ago

From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent

arXiv:2608. 13581v1 Announce Type: cross Abstract: Personalized glucose regulation remains a central yet unresolved challenge in precision nutrition, as postprandial glucose response varies substantially across individuals.

By Mingyu Huang, Weiqing Min, Ying Jin, Yilin Wang, Shuqiang Jiang

llms agents

arXiv AI

Jul 10

OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

arXiv:2607. 08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management.

By Qian Jiang, Zhecheng Shi, Jingpu Yang, Zirui Song, Miao Fang

llms agents multimodal benchmarks safety

arXiv Machine Learning

Jun 26

MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS

arXiv:2606. 23603v2 Announce Type: replace Abstract: Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize user preference without considering nutritional health.

By Aarya Vasantlal, Joshua Zolla, Chuxu Zhang

rag reinforcement-learning benchmarks safety

arXiv AI

Jun 9

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

arXiv:2606. 08948v1 Announce Type: cross Abstract: Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large multimodal datasets linking diverse foods to complete nutrient profiles.

By Runze Yan, Minxiao Wang, Jiaying Lu, Darren Liu, Xiao Hu, Hanqi Luo

llms diffusion fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Jul 9

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

arXiv:2601. 14590v3 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction.

By Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter, Melanie Hingle, Hassan Ghasemzadeh

llms fine-tuning multimodal safety

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LLM-Powered Personalized Glycemic Assessment in Type 2 Diabetes with Wearable Sensor Data

From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent

OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

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