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Nutri-ATLAS：面向表格查询与智能营养辅助的具身智能体
该论文提出 Nutri-ATLAS，一个面向真实场景营养辅助的具身智能体，整合图结构营养推理、硬件感知的 LLM 选择与基于机器人的证据采集。系统基于 USDA FoodData Central 与 FoodKG 构建统一的食物-营养知识图谱，学习 64 维 GATv2 食物与食谱嵌入，并通过共享的混合图-文本打分机制支持营养提取、营养缺口填补、替代品检索与食谱级配餐；LLM 引导的技能接口负责导航、更新饮食上下文与食物可及性记忆。评估覆盖营养估计、替代检索、食谱推荐、患者画像依从性、边缘部署与真实具身执行：在 HealthyFoodSubs 上混合检索器取得 37.9% MAP、80.7% RR@5、90.1% RR@10；在 PFoodReQ 上达到 78.8% MAP、83.0% MAR、77.5% F1；患者画像研究显示所有入选案例均满足过敏与健康目标约束。
AI推荐理由 arXiv 预印本，作者与摘要信息完整，方法（Food-Nutrient 知识图谱、GATv2 64 维嵌入、混合图-文本检索、硬件感知 LLM 选择、机器人证据采集）与多组基准结果（HealthyFoodSubs 37.9% MAP、PFoodReQ 78.8% MAP 等）均有明确报告，信息密度较高。局限在于仅为预印本、未经同行评审，且摘要未给出与基线方法的对比细节，实际部署效果尚待验证。对具身 Agent、知识图谱增强 LLM 与边缘部署方向有参考价值。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.32803 (cs)
[Submitted on 26 Sep 2026]
Title:Nutri-ATLAS: Embodied Agent for Tabulated Lookup and Assistance for Smarter nutrition
Authors:Uttej Kallakuri, Boxun Hu, Ankur A. Butala, Najim Dehak, Tinoosh Mohsenin
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Abstract:Generative and Agentic IoT systems offer a promising foundation for digital healthcare applications that combine sensing, personalized reasoning, and autonomous interaction in real-world environments. Nutrition assistance is a natural use case, but existing Large Language Model (LLM)-based systems are often limited to passive text interaction and static context, making them unreliable when food descriptions are ambiguous or nutritional evidence is missing. We propose Nutri-ATLAS, an Embodied Agent for Tabulated Lookup and Assistance for smarter nutrition in the real world. It integrates graph-grounded nutrition reasoning, hardware-aware LLM selection, and robot-based evidence acquisition. Nutri-ATLAS builds a unified Food-Nutrient knowledge graph from USDA FoodData Central and FoodKG and learns 64-dimensional GATv2 food and recipe embeddings. A shared hybrid graph-text scoring mechanism supports food nutrition extraction, nutritional gap filling, substitute retrieval, and recipe-level meal composition, while an LLM-guided skill interface navigates landmarks, updates dietary-context and food-accessibility memory, and grounds recommendations in observed food availability. We evaluate Nutri-ATLAS across nutrient estimation, substitution retrieval, recipe recommendation, patient-profile adherence, edge deployment, and real-world embodied execution. On HealthyFoodSubs, the hybrid retriever achieves 37.9% MAP, 80.7% RR@5, and 90.1% RR@10. On NutriBench v2, Dense+GAT retrieval grounds nutrient estimation across nine quantized Qwen3.5-9B configurations. On PFoodReQ, Nutri-ATLAS reaches 78.8% MAP, 83.0% MAR, and 77.5% F1. A patient-profile study shows adherence to allergy and healthy-target constraints for all selected cases.
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Artificial Intelligence (cs.AI)
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arXiv:2609.32803 [cs.AI]
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arXiv:2609.32803v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.32803
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arXiv-issued DOI via DataCite (pending registration)
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From: Boxun Hu [view email]
[v1]
Sat, 26 Sep 2026 17:26:12 UTC (33,599 KB)
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