mlsanityData to 7 Oct 2026

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

arXiv 2604.23129 ↗First posted 25 Apr 2026cs.HC cs.AI cs.IRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 76 references that are in the map. First posted on arXiv 25 Apr 2026.

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References · 18

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  4. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  5. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  6. 2020Knowledge Graphscited by 619
  7. 2024Executable Code Actions Elicit Better LLM Agentscited by 537
  8. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 519
  9. 2023Theory of Mind for Multi-Agent Collaboration via Large Language Modelscited by 129
  10. 2023Graphologue: Exploring Large Language Model Responses with Interactive Diagramscited by 65
  11. 2023Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creationcited by 63
  12. 2023Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Modelscited by 48
6 more
  1. 2024LLM Theory of Mind and Alignment: Opportunities and Riskscited by 15
  2. 2021Differentiable 3D CAD Programs for Bidirectional Editingcited by 13
  3. 2025InteractComp: Evaluating Search Agents With Ambiguous Queriescited by 12
  4. 2023Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Modelscited by 10
  5. 2019Sketch-n-Sketch: Output-Directed Programming for SVGcited by 4
  6. 2025Natural Language Interaction for Editing Visual Knowledge Graphscited by 1

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