Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation
This paper presents Crystalis, a framework that enables large language models to generate structurally correct coordinated multi-view visualizations (CMVs). It decomposes CMVs into structured queries over a dependency graph using query-centric modeling, and employs progressive nucleation and semantic annealing to ensure vertical and horizontal consistency. On a 12-task benchmark across five LLMs, Crystalis achieves 75% end-to-end success, far outperforming a baseline of 8.3%. A user study with 12 practitioners confirms the usability of the decomposition and iterative refinement workflow.
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[Submitted on 7 Jun 2026]
Title:Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation
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Abstract:Large language models (LLMs) can generate individual charts, but coordinated multi-view visualizations (CMVs), where views share data flows and cross-view interactions, remain out of reach. Tight field-level coupling among data transformations, visual encodings, and interaction coordinations causes errors in one component to silently invalidate others. Rather than pursuing end-to-end analytical quality, which depends on model capability, domain knowledge, and user expertise, we target a foundational question: can LLMs reliably produce structurally correct CMVs, and what abstractions make this possible? We present Crystalis, a framework built on query-centric CMV modeling that decomposes a CMV into structured queries over a dependency graph spanning three component types (Data, Visualization, Interaction) and three abstraction levels (requirement, specification, executable object). Two complementary mechanisms operate over this structure: progressive nucleation crystallizes each query vertically from requirement to object along the dependency order, while semantic annealing enforces horizontal consistency across queries at each level through layered logical checks. On a 12-task benchmark across five frontier LLMs, Crystalis achieves up to 75% end-to-end success, substantially outperforming an agentic coding baseline (8.3% E2E with the same foundation model), and a user study with 12 practitioners confirms the usability of the decomposition and iterative refinement workflow.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.24766 [cs.AI]
(or arXiv:2607.24766v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.24766
arXiv-issued DOI via DataCite
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From: Dazhen Deng [view email] [v1] Sun, 7 Jun 2026 09:38:44 UTC (2,384 KB)
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