将长篇大论的AI回答转为可点击的互动图像，方便快速定位和追问。
ChatImage: Navigating Long-Form LLM Answers through Interactive Images
- 把文本答案拆解成结构化视觉模块，生成布局一致的可视化图像。
- 通过视觉定位模型识别可交互区域，支持点击后展开细节问答。
- 适合需要精读复杂回答的研究者或产品经理，提升信息检索效率。
大型语言模型（LLM）能生成详尽复杂的回答，但通常以密集线性文本呈现，导致细粒度检查、导航和回溯困难。我们提出 ChatImage，一个将长篇 LLM 回答转化为交互式视觉图像的系统。给定文本答案后，ChatImage 首先将其内容规范化为结构化视觉模块，规划视觉布局并渲染出连贯图像；随后利用视觉定位模型（如 LocateAnything、MiMo-Vision），结合 SAM 式掩码优化，对渲染图像进行二次定位，识别应支持交互的可视区域；基于这些定位结果，在图像上叠加透明可点击热点。每个热点可打开详情面板与局部问答线程，让用户无需重读全文即可深入查询特定部分。不同于直接使用预设坐标作为交互几何，ChatImage 将其作为先验，并在渲染后重新接地，显著提升视觉内容与可交互区域的一致性。我们发布了参考实现，并引入一个包含30个问题的基准测试，涵盖信息图、地图和场景类回答格式。通过配置外部模型评估，报告了交互循环完成率、严格的视觉对齐阈值及基于SAM的掩码完整度诊断。
原文摘要 · Abstract (English)
Large Language Models (LLMs) can produce detailed answers to complex queries, but these answers are typically presented as dense linear text, which makes fine-grained inspection, navigation, and return visits difficult. We present ChatImage, a system that converts long-form LLM answers into interactive visual images. Given a textual answer, ChatImage first normalizes its content into structured visual modules, plans a visual layout, and renders a coherent image. It then applies a second grounding pass to the rendered image with vision grounding models such as LocateAnything and MiMo-Vision, with optional SAM-style mask refinement, to identify the visible regions that should support interaction. From these grounded regions, ChatImage overlays transparent clickable hotspots on the image. Each hotspot opens a detail panel and a region-scoped follow-up thread, allowing the user to inspect and query a specific part of the answer without re-reading the full response. Instead of treating planned coordinates as the final interaction geometry, ChatImage uses them as priors and grounds the interaction targets after rendering, which improves consistency between visual content and clickable regions. We release a reference implementation and introduce a 30-question benchmark covering infographic, map, and scene-based answer formats. Evaluation with configured external models reports interaction-loop completion, a strict visual-alignment gate, and a SAM-based mask-completeness diagnostic.
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