A survey of 55 agentic VA systems proposes a co-evolutionary framework defining four agent roles (PLANNER, CREATOR, REVIEWER, CONTEXT MANAGER) mapped to visual analytics pipeline stages along with design guidelines.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 6roles
background 2polarities
background 2representative citing papers
BONSAI introduces a four-layer architecture and four-phase workflow for human-AI co-development of visual analytics applications, shown in case studies to enable efficient novel tool creation and reconstruction from paper descriptions.
DataSway supports creation of semantically aligned animations for metaphoric data visualizations by generating clips via VLMs and coordinating timelines based on entity order, attributes, layout, or randomness.
General-purpose coding agents achieve highest success on SciVis tasks but cost more compute, while domain-specific agents are efficient yet less flexible and computer-use agents falter on long workflows.
YAC is a prototype system that uses a tool-calling multi-agent architecture to translate natural language into linked interactive visualizations and filters for biomedical data, with user-adjustable structured output and a domain-expert user study.
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.
citing papers explorer
-
Exploring Agentic Visual Analytics: A Co-Evolutionary Framework of Roles and Workflows
A survey of 55 agentic VA systems proposes a co-evolutionary framework defining four agent roles (PLANNER, CREATOR, REVIEWER, CONTEXT MANAGER) mapped to visual analytics pipeline stages along with design guidelines.
-
BONSAI: A Mixed-Initiative Workspace for Human-AI Co-Development of Visual Analytics Applications
BONSAI introduces a four-layer architecture and four-phase workflow for human-AI co-development of visual analytics applications, shown in case studies to enable efficient novel tool creation and reconstruction from paper descriptions.
-
DataSway: Vivifying Metaphoric Visualization with Animation Clip Generation and Coordination
DataSway supports creation of semantically aligned animations for metaphoric data visualizations by generating clips via VLMs and coordinating timelines based on entity order, attributes, layout, or randomness.
-
Exploring Interaction Paradigms for LLM Agents in Scientific Visualization
General-purpose coding agents achieve highest success on SciVis tasks but cost more compute, while domain-specific agents are efficient yet less flexible and computer-use agents falter on long workflows.
-
YAC: Bridging Natural Language and Interactive Visual Exploration with Generative AI for Biomedical Data Discovery
YAC is a prototype system that uses a tool-calling multi-agent architecture to translate natural language into linked interactive visualizations and filters for biomedical data, with user-adjustable structured output and a domain-expert user study.
-
VIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents
VIDEE introduces a human-in-the-loop system using Monte-Carlo Tree Search for task decomposition, executable pipeline generation, and LLM-based evaluation with visualizations to support non-expert text analytics.