# A Closed-Loop Multi-Agent System Driven by LLMs for Meal-Level Personalized Nutrition Management

arXiv: https://arxiv.org/abs/2601.04491 · HTML: https://mlsanity.com/paper/2601.04491/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [HD-EPIC: A Highly-Detailed Egocentric Video Dataset](https://mlsanity.com/paper/2502.04144/) (2025)
- [Large Scale Visual Food Recognition](https://mlsanity.com/paper/2103.16107/) (2021)
- [MACRec: a Multi-Agent Collaboration Framework for Recommendation](https://mlsanity.com/paper/2402.15235/) (2024)
- [Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers and Self-supervised Learning](https://mlsanity.com/paper/2103.13061/) (2021)
- [Structure-Aware Generation Network for Recipe Generation from Images](https://mlsanity.com/paper/2009.00944/) (2020)
- [A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches](https://mlsanity.com/paper/2010.07051/) (2020)
- [Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis](https://mlsanity.com/paper/2312.08592/) (2023)

## Built on by (most cited first, 2 of 2)

- [FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis](https://mlsanity.com/paper/2605.05499/) (2026)
- [Nutri-ATLAS: Embodied Agent for Tabulated Lookup and Assistance for Smarter nutrition](https://mlsanity.com/paper/2609.32803/) (2026)

## Abstract (arXiv)

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.