NutriOrion: A Hierarchical Multi-Agent Framework for Personalized Nutrition Intervention Grounded in Clinical Guidelines

TL;DR

NutriOrion is introduced, a hierarchical multi-agent framework with a parallel-then-sequential reasoning topology that decomposes nutrition planning into specialized domain agents with isolated contexts to mitigate anchoring bias and demonstrates strong personalization with negative correlations.

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 constraints during synthesis, ensuring clinical validity by construction rather than post-hoc filtering. For clinical interoperability, NutriOrion maps synthesized insights into the ADIME standard and FHIR R4 resources. Evaluated on 330 stroke patients with multimorbidity, NutriOrion outperforms multiple baselines, including GPT-4.1 and alternative multi-agent architectures. It achieves a 12.1 percent drug-food interaction violation rate, demonstrates strong personalization with negative correlations (-0.26 to -0.35) between patient biomarkers and recommended risk nutrients, and yields clinically meaningful dietary improvements, including a 167 percent increase in fiber and a 27 percent increase in potassium, alongside reductions in sodium (9 percent) and sugars (12 percent).

Figures (6)

• Figure 1: Conceptual comparison of reasoning paradigms for high-stakes decision making. (a) Monolithic LLM Approach: Standard single-agent models process heterogeneous data in a unified context window. This often leads to information entanglement, where conflicting constraints are hallucinated or ignored due to attention dilution. (b) NutriOrion (Ours): A hierarchical modular framework that decouples reasoning into specialized streams. By isolating domain contexts and enforcing deterministic safety gates, NutriOrion synthesizes complex inputs into precise, verifiable, and structured interventions without reasoning collapse.
• Figure 2: The NutriOrion System Pipeline. The framework integrates three modular components: (Left) Retrieval-Augmented Grounding ($\mathcal{R}$): Ingestion of patient profiles and vectorization of clinical guidelines (e.g., DASH, ADA); (Center) Hierarchical Agent Reasoning ($\mathcal{H}$): A three-stage orchestration consisting of parallel diagnostic analysis (Stage 1), sequential dietary refinement (Stage 2), and safety-aware synthesis (Stage 3); (Right) Output Standardization ($\mathcal{O}$): Projection of synthesized ADIME-structured insights into interoperable FHIR R4 clinical resources.
• Figure 3: Personalization Heatmap. Pearson correlations between patient biomarkers and nutrient intake. Blue (Negative) indicates effective personalization (e.g., High BP $\rightarrow$ Low Sodium), while Red (Positive) implies unsafe recommendations. NutriOrion consistently achieves the strongest protective correlations across all five metrics, significantly outperforming baselines.
• Figure 4: Multi-dimensional Performance (Bubble Chart).X-axis: Actionability; Y-axis: Food Compass Score 2.0 (Nutritional Quality); Bubble Size: Dietary Diversity; Color: NutriScore Health Gradient (Red$\rightarrow$Green). NutriOrion (top-right) achieves the optimal balance: high quality, actionable output, and high diversity. Gray labels indicate unstructured baselines, as detailed in Table \ref{['tab:ncpquest']}.
• Figure 5: RD Clinical Appropriateness Evaluation. Mean scores (1–5 scale) across six dimensions for NutriOrion outputs