Crystalis：用于协同多视图可视化生成的渐进式成核与语义退火
Crystalis: Progressive Nucleation and Semantic Annealing for Coordinated Multi-View Visualization Generation
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对大语言模型难以生成协同多视图可视化的问题，提出Crystalis框架，基于以查询为中心的CMV建模，通过渐进式成核和语义退火机制，在多任务基准测试中取得高成功率，且经用户研究验证了工作流程可用性。
AI中文摘要：
大语言模型（LLMs）可以生成单个图表，但共享数据流和跨视图交互的协同多视图可视化（CMVs）仍难以实现。数据转换、视觉编码和交互协调之间紧密的字段级耦合会导致一个组件中的错误使其他组件失效。我们提出了Crystalis框架，它基于以查询为中心的CMV建模，将CMV分解为跨越三种组件类型（数据、可视化、交互）和三个抽象层次（需求、规范、可执行对象）的依赖图上的结构化查询。通过渐进式成核从需求到对象垂直结晶每个查询，同时语义退火通过分层逻辑检查在每个层次上强制查询之间的水平一致性。在五个前沿LLMs的12个任务基准测试中，Crystalis实现了高达75%的端到端成功率，远超代理编码基线，用户研究也证实了其分解和迭代细化工作流程的可用性。
英文摘要：
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.
