Pith Number

pith:UMBJWU2R

pith:2026:UMBJWU2RYZTSBD7UJSSOYXSXM6

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An LLM-RAG Approach for Healthy Eating Index-Informed Personalized Food Recommendations

Azlan Zahid, Grace Melo Guerrero, Rodolfo M. Nayga Jr., Yanjie Yang, Yibin Wang

An LLM-RAG system anchored in national nutrition data improves simulated Healthy Eating Index scores by 6.45 points on average.

arxiv:2605.15213 v1 · 2026-05-11 · cs.IR · cs.AI

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Claims

C1strongest claim

The simulation results showed a mean HEI improvement of 6.45, with the proportion of users HEI over 50 increasing from 45.12 to 61.26. Our findings suggest that the proposed LLM-RAG-based AI systems can support more precise, explainable, and personalized nutrition guidance to improve diet quality.

C2weakest assumption

The simulation accurately models real-world user responses and dietary behaviors, and that the embedding space from FPED descriptions effectively captures nutritional relevance for retrieval and HEI impact estimation.

C3one line summary

An LLM-RAG framework anchored in NHANES and FPED databases generates personalized food recommendations that improve simulated HEI scores by an average of 6.45 points.

References

7 extracted · 7 resolved · 0 Pith anchors

[1] Adhikari, S., & McFadden, B. R. (2025). Bridging taste and health: The role of machine learning in consumer food selection. International Food and Agribusiness Management Review, 28(2), 441–456. https 2025 · doi:10.22434/ifamr1131

[2] https://doi.org/10.3390/nu13041046 Douze, M., Guzhva, A., Deng, C., Johnson, J., Szilvasy, G., Mazaré, P.-E., Lomeli, M., Hosseini, L., & Jégou, H. (2025). THE FAISS LIBRARY . IEEE Transactions on Big 2025 · doi:10.3390/nu13041046

[3] S., Ajay, A., Shams, R., & Dash, K 2023 · doi:10.1016/j.jand.2023.05.015

[4] https://doi.org/10.1186/s41110-025-00360-4 Silva, P., Araújo, R., Lopes, F., & Ray, S. (2023). Nutrition and Food Literacy: Framing the Challenges to Health Communication. Nutrients, 15(22), 2023 · doi:10.1186/s41110-025-00360-4

[5] K., Hu, X.-H., Singh, A 2024 · doi:10.3390/nu15224708

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:00:46.537088Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a3029b5351c667208ff44ca4ec5e5767ba27d4f18e2cccf9308fc6f5742919db

Aliases

arxiv: 2605.15213 · arxiv_version: 2605.15213v1 · doi: 10.48550/arxiv.2605.15213 · pith_short_12: UMBJWU2RYZTS · pith_short_16: UMBJWU2RYZTSBD7U · pith_short_8: UMBJWU2R

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UMBJWU2RYZTSBD7UJSSOYXSXM6 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a3029b5351c667208ff44ca4ec5e5767ba27d4f18e2cccf9308fc6f5742919db

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7d01138732b9ab22260880abb929db8c8ca59cdf6e20688e27b2faa34252ca40",
    "cross_cats_sorted": [
      "cs.AI"
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.IR",
    "submitted_at": "2026-05-11T03:40:17Z",
    "title_canon_sha256": "a453835240ef2fd95c34db836682ecfb05e8fab3e5d707b3bc1359e26a681d51"
  },
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  "source": {
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    "kind": "arxiv",
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}