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.
arXiv:2607. 23273v1 Announce Type: cross Abstract: Computational nutrition needs precise ingredient data, but current databases are incomplete, inconsistent, and built for human reference rather than automated reasoning.
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.
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.
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.
arXiv:2609.22099v1 Announce Type: new Abstract: Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain larg...
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.
The paper examines how large language models (LLMs) can assess whether recipes are suitable for people with diabetes. It introduces a benchmark of 7,607 recipes, split evenly between suitable and unsuitable, and tests three prompting strategies that embed varying levels of diabetes dietary guidelines. Results show that LLMs tend to be cautious in labeling recipes as suitable, and those that can reason with the guidelines—particularly Mistral‑7B and Llama‑70B—perform best.
The paper investigates how well large language models (LLMs) can assess whether recipes are suitable for people with diabetes. It introduces a benchmark of 7,607 recipes, split evenly between suitable and unsuitable, and tests three prompting strategies that vary in how much diabetes dietary guidance they provide. Results show that LLMs tend to be cautious in labeling recipes as suitable, and those that can reason with dietary guidelines—particularly Mistral‑7B and Llama‑70B—perform best.
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.
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.
arXiv:2608. 10363v1 Announce Type: new Abstract: AI agents can accelerate nutrition research, but their analyses inherit the identity, semantic, and release ambiguities of the underlying data.
arXiv:2608. 06917v1 Announce Type: new Abstract: Recent Large Multimodal Models (LMMs) have achieved impressive performance in recipe generation from food images.