Food Critic：可审计餐厅安全推理的智能体可视化系统
Food Critic: An Agentic Visualization System for Auditable Restaurant-Safety Reasoning
AI 导读
研究者提出 Food Critic，一个面向食客餐厅决策支持的智能体可视化系统，整合公共卫生检查记录与大众点评数据，并将智能体推理外化为可检视的可视化证据。系统提供三种接地方式：仅数据库的 ReAct 智能体、接入食品安全法规的检索增强智能体，以及产生结构化假设和类型化证据的本体引导智能体。研究在每种接地条件下用多个用例评估了 LLM 模型。
正文 · 原文
Abstract:Generative AI agents are increasingly being embedded into visualization systems that can search data, invoke tools, summarize evidence, and produce recommendations. However, when agents rely solely on their own knowledge, the visualization problem is not only whether the agent provides a useful answer, but also whether users can inspect how that answer is grounded. We present Food Critic, an agentic visualization system for diners' restaurant decision support that integrates public health inspection records with crowd review data. The system grounds agents under three conditions: a database-only ReAct agent, a retrieval- augmented agent with access to food-safety regulations, and an ontology-guided agent that produces structured hypotheses and typed evidence. Food Critic externalizes the agent's reasoning within these groundings as inspectable visual evidence. We evaluate LLM models with several use cases under each grounding of our agentic system. Our work positions restaurant choice as a concrete case for studying human-agent collaboration, visual provenance of agent reasoning, and the design of visual interfaces for grounded GenAI decision support.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2611.15843 [cs.AI] |
| (or arXiv:2611.15843v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15843 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ishrat Jahan Eliza [view email]
[v1]
Wed, 11 Nov 2026 18:57:07 UTC (5,625 KB)
来源：arXiv cs.AI · arxiv.org