Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas
Rifat Mehreen Amin, Alperen Adatepe, Daniela Fernandes, Daniel Buschek, Andreas Butz

TL;DR
This paper introduces CanvasConvo, a spatial, non-linear conversational interface for large language models that enhances exploration and management of complex, branching interactions.
Contribution
The paper presents CanvasConvo, a novel spatial interface that transforms linear chats into interactive, visualized branching conversation trees for improved exploration.
Findings
Non-linear structures support exploratory workflows.
Users can manage multiple conversation branches effectively.
The interface enhances interaction flexibility and continuity.
Abstract
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…
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