arXiv AI July 28, 2026 By James Izzard, Hassan Eshkiki, Fabio Caraffini

Statistically Supported LLM Ingredient and Recipe Data Collection in Computational Nutrition

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.

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.

llms

arXiv Computation and Language

Sep 22

A framework for recipe data structure with applications for culinary and nutritional insights

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...

By Mansi Goel, Sumit Bhagat, Saloni Srivastava, Malav Patel, Shlok Vinodkumar Mehroliya, Ganesh Bagler

llms rag

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

Sep 22

Quantifying Hidden Salt for Precision Healthcare: Sodium Assessment via Joint-Factor Retrieval and Chain-of-Thought Inference

arXiv:2609.22171v1 Announce Type: cross Abstract: Precision healthcare, particularly for conditions like hypertension and cardiovascular disease, necessitates monitoring of dietary sodium intake. How...

By Mingyu Huang, Weiqing Min, Yuehui Fang, Yuna He, Shuqiang Jiang

llms rag benchmarks

arXiv Machine Learning

Jun 3

Self-Soupervision: Cooking Model Soups without Labels

arXiv:2602. 02890v2 Announce Type: replace Abstract: Model soups are strange and strangely effective combinations of parameters.

By Anthony Fuller, James R. Green, Evan Shelhamer

fine-tuning

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 AI

Sep 1

Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge

arXiv:2608.29249v1 Announce Type: new Abstract: The online culinary ecosystem is increasingly populated by recipe content generated, modified, or summarized by Large Language Models (LLMs). While oft...

By Saransh Kumar Gupta, Armaan Shah, Lipika Dey, Partha Pratim Das, Ramesh Jain

llms

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A framework for recipe data structure with applications for culinary and nutritional insights

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

Quantifying Hidden Salt for Precision Healthcare: Sodium Assessment via Joint-Factor Retrieval and Chain-of-Thought Inference

Self-Soupervision: Cooking Model Soups without Labels

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge