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[2602.18650] NutriOrion: A Hierarchical Multi-Agent Framework for Personalized Nutrition Intervention Grounded in Clinical Guidelines
Summary
NutriOrion presents a hierarchical multi-agent framework for personalized nutrition interventions, addressing the complexities of multimorbidity in patients by integrating clinical guidelines and dietary needs.
Why It Matters
This research is significant as it tackles the challenge of personalized nutrition in patients with multiple health conditions, a growing concern in healthcare. By utilizing a multi-agent approach, NutriOrion enhances the accuracy and effectiveness of dietary recommendations, potentially leading to improved health outcomes and better management of chronic diseases.
Key Takeaways
- NutriOrion employs a hierarchical multi-agent framework to manage complex dietary needs.
- The framework reduces context overload by using specialized agents for different dietary aspects.
- Clinical evaluations show NutriOrion outperforms existing models, including GPT-4.1.
- It ensures clinical validity by incorporating safety constraints directly into the dietary synthesis process.
- The framework achieved significant dietary improvements in stroke patients, highlighting its practical application.
Computer Science > Multiagent Systems arXiv:2602.18650 (cs) [Submitted on 20 Feb 2026] Title:NutriOrion: A Hierarchical Multi-Agent Framework for Personalized Nutrition Intervention Grounded in Clinical Guidelines Authors:Junwei Wu, Runze Yan, Hanqi Luo, Darren Liu, Minxiao Wang, Kimberly L. Townsend, Lydia S. Hartwig, Derek Milketinas, Xiao Hu, Carl Yang View a PDF of the paper titled NutriOrion: A Hierarchical Multi-Agent Framework for Personalized Nutrition Intervention Grounded in Clinical Guidelines, by Junwei Wu and 9 other authors View PDF HTML (experimental) Abstract:Personalized nutrition intervention for patients with multimorbidity is critical for improving health outcomes, yet remains challenging because it requires the simultaneous integration of heterogeneous clinical conditions, medications, and dietary guidelines. Single-agent large language models (LLMs) often suffer from context overload and attention dilution when processing such high-dimensional patient profiles. We introduce NutriOrion, a hierarchical multi-agent framework with a parallel-then-sequential reasoning topology. NutriOrion decomposes nutrition planning into specialized domain agents with isolated contexts to mitigate anchoring bias, followed by a conditional refinement stage. The framework includes a multi-objective prioritization algorithm to resolve conflicting dietary requirements and a safety constraint mechanism that injects pharmacological contraindications as hard negative constraint...
