arXiv AI August 26, 2026 By Ying Jin, Weiqing Min, Mingyu Huang, Shuqiang Jiang

Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling

The paper introduces a personalized sustainable diet recommendation system that uses a constraint‑aware decision‑making model to align individual food choices with four pillars of sustainability: nutrition, affordability, cultural acceptability, and environmental respect. It presents a new dataset, SusDiet, containing roughly 150,000 recipes annotated with sustainability indicators, and demonstrates that the model encourages more sustainable selections while preserving user preferences. The study offers a quantitative framework intended to guide future interventions and policy‑making for sustainable development.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

1d ago

ShanLiangRen: A Nutrition Agent for Personalized Daily Meal Planning

ShanLiangRen is a nutrition agent designed to generate personalized daily meal plans that satisfy both user constraints and multidimensional nutritional goals. It transforms dietary specifications, nutrient data, user attributes, and natural language requirements into a constrained planning instance, then uses a retrieval‑augmented generation approach to narrow the candidate set from a large ingredient and recipe space. Finally, it refines plans via Pareto‑guided iterative revisions with an LLM, producing fully quantified meal plans with explicit ingredients, portion sizes, and compliance reports.

By Miao Xie, Xiao Zhang, Yuan Wang, Ruixin Zhu, Chunli Lv

llms rag agents

arXiv AI

Jun 10

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.

By Asiful Arefeen, Carol Johnston, Hassan Ghasemzadeh

llms rag multimodal safety

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

Aug 17

Designing Sustainable Federated Learning as a Service using Neural Architecture Search

arXiv:2608. 14359v1 Announce Type: new Abstract: The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments.

By Keya Patel, Sajib Mistry, Sheik Fattah, Deepak Kanneganti, Aneesh Krishna, Mufti Mahmud, Monowar Bhuyan

arXiv AI

Jul 29

Nudging Sustainable Choices through LLM-Generated Recommendation Explanations

arXiv:2607. 25726v1 Announce Type: new Abstract: Recommender systems mediate everyday consumption, offering a promising channel for encouraging sustainable choices.

By Haya Halimeh, Dietmar Jannach, Oliver M\"uller

llms

One email a morning, machine-written

Related stories

ShanLiangRen: A Nutrition Agent for Personalized Daily Meal Planning

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

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

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

Designing Sustainable Federated Learning as a Service using Neural Architecture Search

Nudging Sustainable Choices through LLM-Generated Recommendation Explanations