MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866908993199276032 |
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| author | Li, Xiang Li, Cara Kuang, Emily Liu, Can Zhao, Jian |
| author_facet | Li, Xiang Li, Cara Kuang, Emily Liu, Can Zhao, Jian |
| contents | Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding. This process is inherently iterative: users explore content, identify relationships between concepts, and continuously reorganize their mental models. However, current approaches offer limited support. LLM-based systems let users query information but not shape how knowledge is organized; manual tools like mind maps support structure creation but lack intelligent assistance. This leaves an open opportunity: supporting collaborative construction where users and AI jointly develop an evolving knowledge representation. We present MindTrellis, an interactive visual system where users and AI collaboratively build a dynamic knowledge graph. Users can query the graph to retrieve document-grounded information, and contribute by introducing new concepts, modifying relationships, and reorganizing the hierarchy to reflect their developing understanding. In a user study where 12 participants created slide decks, MindTrellis outperformed retrieval-only baselines in knowledge organization and cognitive load, as measured by expert ratings of content coverage and structural quality. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_23129 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration Li, Xiang Li, Cara Kuang, Emily Liu, Can Zhao, Jian Human-Computer Interaction Artificial Intelligence Information Retrieval Multiagent Systems Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding. This process is inherently iterative: users explore content, identify relationships between concepts, and continuously reorganize their mental models. However, current approaches offer limited support. LLM-based systems let users query information but not shape how knowledge is organized; manual tools like mind maps support structure creation but lack intelligent assistance. This leaves an open opportunity: supporting collaborative construction where users and AI jointly develop an evolving knowledge representation. We present MindTrellis, an interactive visual system where users and AI collaboratively build a dynamic knowledge graph. Users can query the graph to retrieve document-grounded information, and contribute by introducing new concepts, modifying relationships, and reorganizing the hierarchy to reflect their developing understanding. In a user study where 12 participants created slide decks, MindTrellis outperformed retrieval-only baselines in knowledge organization and cognitive load, as measured by expert ratings of content coverage and structural quality. |
| title | MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration |
| topic | Human-Computer Interaction Artificial Intelligence Information Retrieval Multiagent Systems |
| url | https://arxiv.org/abs/2604.23129 |