用知识图谱指导多智能体协作，提升营养问答准确率
NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering
- 将智能体融入异构知识图谱，通过图神经网络学习任务导向的路由策略
- 在多个基准上表现优于单智能体与集成基线，显著提升多跳推理能力
- 适合需要复杂推理的健康领域问答系统研发者参考
饮食对人类健康至关重要，营养问答（Nutrition QA）为个性化膳食指导和预防饮食相关慢性病提供了可行路径。然而，现有方法面临两大挑战：单智能体推理能力有限，以及设计高效多智能体架构的复杂性，同时上下文过载也影响决策准确性。本文提出营养图路由器（NG-Router），将营养问答建模为受监督、基于知识图谱引导的多智能体协作问题。该框架将智能体节点嵌入异构知识图谱，利用图神经网络学习任务感知的智能体路由分布，其软监督信号来自实际智能体性能。为缓解上下文过载，进一步提出基于梯度的子图检索机制，在训练中识别关键证据，增强多跳与关系推理能力。在多个基准与骨干模型上的大量实验表明，NG-Router持续优于单智能体及集成基线，为复杂营养健康任务提供了一种原则性的领域感知多智能体推理方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。