ChatGraPhT：一种支持智能大语言模型多路径反思的可视化对话界面 (ChatGraPhT: A Visual Conversation Interface for Multi-Path Reflection with Agentic LLM Support)

Large Language Models (LLMs) are increasingly used in complex knowledge work, yet linear transcript interfaces limit support for reflection. Schon's Reflective Practice distinguishes between reflection-in-action (during a task) and reflection-on-action (after a task), both benefiting from non-linear, revisitable representations of dialogue. ChatGraPhT is an interactive tool that shows dialogue as a visual map, allowing users to branch and merge ideas, edit past messages, and receive guidance that prompts deeper reflection. It supports non-linear, multi-path dialogue, while two agentic LLM assistants provide moment-to-moment and higher-level guidance. Our inquiry suggests that keeping the conversation structure visible, allowing branching and merging, and suggesting patterns or ways to combine ideas deepened user reflective engagement. Contributions are: (1) the design of a node-link, agentic LLM interface for reflective dialogue, and (2) transferable design knowledge on balancing structure and AI support to sustain reflection in complex, open-ended tasks.

翻译：大语言模型（LLMs）在复杂知识工作中应用日益广泛，然而线性的文本交互界面限制了其对反思过程的支持。Schon的反思实践理论区分了行动中反思（任务执行期间）与行动后反思（任务完成后），二者均受益于非线性、可回溯的对话表征形式。ChatGraPhT是一款将对话以可视化图谱形式呈现的交互工具，允许用户对观点进行分支与合并、编辑历史消息，并通过系统引导促进深度反思。该系统支持非线性多路径对话，同时配备两个智能大语言模型助手，分别提供实时操作指导与高层策略引导。研究表明：保持对话结构可见性、允许分支合并操作、建议观点整合模式等方法能有效提升用户的反思参与度。本文贡献包括：（1）提出面向反思性对话的节点-链接式智能大语言模型交互界面设计；（2）提炼出可迁移的设计知识，阐明如何在复杂开放任务中通过平衡结构约束与AI支持来维持反思过程。