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.

翻译：基于大语言模型（LLM）的对话界面被广泛用于构思与分析，但其线性结构限制了对替代方案的探索以及对长时间交互的管理。我们提出CanvasConvo这一对话界面概念，它将线性对话转化为嵌入空间画布中的分支对话树。CanvasConvo允许用户通过直接从对话内容中创建分支来探索假设性场景，支持替代方向的并行发展。这些分支在画布上可视化呈现，同时与熟悉的聊天界面保持集成，使用户能够在线性和非线性交互之间切换。基于时间线的导航、自动标记与摘要、以及上下文感知控制（如目标设定、可复用提示）等功能，支持结构化的交互与连续性。我们通过一项为期5-7天的实地研究，对CanvasConvo进行了评估，共有24名参与者。研究结果揭示了非线性对话结构如何支持探索性工作流程以及基于LLM的工作中的不同交互模式。