让大模型对话变分支树，支持并行探索与空间化交互。
Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas
- 将线性对话转为可分支的树状结构，嵌入空间画布中。
- 24人参与7天实地测试，验证其在探索性任务中的有效性。
- 适合需要多路径思考与长期协作的研究者和设计师。
由大语言模型驱动的对话界面广泛用于创意构思与分析，但其线性结构限制了对多种可能性的探索及长时交互管理。我们提出CanvasConvo，一种将线性聊天转化为嵌入空间画布的分支对话树的对话界面概念。用户可直接从对话内容分叉，探索假设性场景，实现多方向并行推进。分支在画布上可视化，同时保持与熟悉聊天界面的集成，支持线性与非线性交互的自由切换。通过基于时间线的导航、自动标签与摘要、以及上下文感知控制（如目标设定、可复用提示）等功能，支持结构化交互与连续性。我们在24名参与者中进行了为期5-7天的实地研究，结果表明非线性对话结构有效支撑探索性工作流，并促进多样化的人机交互方式。
原文摘要 · Abstract (English)
Conversational interfaces powered by large language models (LLMs) are widely used for ideation and analysis, yet their linear structure limits exploration of alternatives and management of long-running interactions. We present CanvasConvo, a conversational interface concept that transforms linear chat into a branching conversation tree embedded in a spatial canvas. CanvasConvo enables users to explore what-if scenarios by branching directly from conversational content, supporting parallel development of alternative directions. These branches are visualized on a canvas while remaining integrated with a familiar chat interface, allowing users to switch between linear and non-linear interaction. Features such as timeline-based navigation, automatic tagging and summarization, and context-aware controls (e.g., goals, reusable prompts) support structured interaction and continuity. We evaluated CanvasConvo in a 5-7 day field study with 24 participants. Our findings highlight how non-linear conversational structures support exploratory workflows and different interactions in LLM-based work.
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