NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909837082755072 |
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| author | Shi, Kaiwen Zhang, Zheyuan Yuan, Zhengqing Murugesan, Keerthiram Galass, Vincent Zhang, Chuxu Ye, Yanfang |
| author_facet | Shi, Kaiwen Zhang, Zheyuan Yuan, Zhengqing Murugesan, Keerthiram Galass, Vincent Zhang, Chuxu Ye, Yanfang |
| contents | Diet plays a central role in human health, and Nutrition Question Answering (QA) offers a promising path toward personalized dietary guidance and the prevention of diet-related chronic diseases. However, existing methods face two fundamental challenges: the limited reasoning capacity of single-agent systems and the complexity of designing effective multi-agent architectures, as well as contextual overload that hinders accurate decision-making. We introduce Nutritional-Graph Router (NG-Router), a novel framework that formulates nutritional QA as a supervised, knowledge-graph-guided multi-agent collaboration problem. NG-Router integrates agent nodes into heterogeneous knowledge graphs and employs a graph neural network to learn task-aware routing distributions over agents, leveraging soft supervision derived from empirical agent performance. To further address contextual overload, we propose a gradient-based subgraph retrieval mechanism that identifies salient evidence during training, thereby enhancing multi-hop and relational reasoning. Extensive experiments across multiple benchmarks and backbone models demonstrate that NG-Router consistently outperforms both single-agent and ensemble baselines, offering a principled approach to domain-aware multi-agent reasoning for complex nutritional health tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering Shi, Kaiwen Zhang, Zheyuan Yuan, Zhengqing Murugesan, Keerthiram Galass, Vincent Zhang, Chuxu Ye, Yanfang Computation and Language Diet plays a central role in human health, and Nutrition Question Answering (QA) offers a promising path toward personalized dietary guidance and the prevention of diet-related chronic diseases. However, existing methods face two fundamental challenges: the limited reasoning capacity of single-agent systems and the complexity of designing effective multi-agent architectures, as well as contextual overload that hinders accurate decision-making. We introduce Nutritional-Graph Router (NG-Router), a novel framework that formulates nutritional QA as a supervised, knowledge-graph-guided multi-agent collaboration problem. NG-Router integrates agent nodes into heterogeneous knowledge graphs and employs a graph neural network to learn task-aware routing distributions over agents, leveraging soft supervision derived from empirical agent performance. To further address contextual overload, we propose a gradient-based subgraph retrieval mechanism that identifies salient evidence during training, thereby enhancing multi-hop and relational reasoning. Extensive experiments across multiple benchmarks and backbone models demonstrate that NG-Router consistently outperforms both single-agent and ensemble baselines, offering a principled approach to domain-aware multi-agent reasoning for complex nutritional health tasks. |
| title | NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.09854 |