MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration

2026-04-24

AI Summary

This study addresses the challenges knowledge workers face in efficiently integrating information across multiple documents and constructing structured knowledge under high cognitive load. To this end, the authors propose an interactive visual system that enables users and large language models (LLMs) to collaboratively and iteratively build dynamic knowledge graphs within a shared visual environment, synergistically combining intelligent retrieval with human-driven organization. The system integrates LLM-powered document understanding, dynamic graph generation, an interactive interface, and a human-AI co-editing mechanism. A user study (N=12) demonstrates that, compared to a pure retrieval baseline, the proposed approach significantly improves the quality and coverage of organized knowledge while effectively reducing users’ cognitive load.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• knowledge synthesis
• interactive knowledge organization
• human-AI collaboration
• structured understanding
• conceptual modeling

Innovation

• interactive knowledge co-creation
• dynamic knowledge graph
• AI-human collaboration
• visual exploration
• structured conceptual understanding