Data-driven reports communicate decision-relevant insights by tightly interleaving narrative text with charts grounded in underlying tables. However, current LLM-based systems typically generate narratives and visualizations in staged pipelines, following either a text-first-graph-second or a graph-first-text-second paradigm. These designs often lead to chart-text inconsistency and insight freezing, where the intermediate evidence space becomes fixed and the model can no longer retrieve or construct new visual evidence as the narrative evolves, resulting in shallow and predefined analysis. To address the limitations, we propose \textbf{EvidFuse}, a training-free multi-agent framework that enables writing-time text-chart interleaved generation for data-driven reports. EvidFuse decouples visualization analysis from long-form drafting via two collaborating components: a \textbf{Data-Augmented Analysis Agent}, equipped with Exploratory Data Analysis (EDA)-derived knowledge and access to raw tables, and a \textbf{Real-Time Evidence Construction Writer} that plans an outline and drafts the report while intermittently issuing fine-grained analysis requests. This design allows visual evidence to be constructed and incorporated exactly when the narrative requires it, directly constraining subsequent claims and enabling on-demand expansion of the evidence space. Experiments demonstrate that EvidFuse attains the top rank in both LLM-as-a-judge and human evaluations on chart quality, chart-text alignment, and report-level usefulness.

翻译：数据驱动报告通过将叙述性文本与基于底层表格的图表紧密交织，来传达与决策相关的洞察。然而，当前基于大语言模型的系统通常在分阶段流水线中生成叙述和可视化，遵循"文本优先-图表后置"或"图表优先-文本后置"的范式。这些设计常常导致图表与文本不一致以及"洞察冻结"问题，即中间证据空间变得固定，模型无法在叙述演进过程中检索或构建新的视觉证据，从而导致分析流于表面且预先定义。为应对这些局限，我们提出了\textbf{EvidFuse}，一种免训练的多智能体框架，能够实现数据驱动报告的写作时文本-图表交织生成。EvidFuse通过两个协作组件将可视化分析与长篇草拟解耦：一个配备探索性数据分析知识并能够访问原始表格的\textbf{数据增强分析智能体}，以及一个规划大纲并起草报告、同时间歇性发出细粒度分析请求的\textbf{实时证据构建撰写器}。该设计使得视觉证据能够在叙述需要时被精确构建和整合，直接约束后续论断，并实现证据空间的按需扩展。实验表明，EvidFuse在图表质量、图表-文本对齐以及报告级实用性方面，于大语言模型即法官评估和人工评估中均获得最高排名。