让数据智能体把图表当作认知动作：新方法提升分析准确率
Charts as Epistemic Actions for Experience-Driven Agentic Data Analysis
这篇论文挺有意思：给智能体配上画图能力还不够，它教智能体什么时候该画图看趋势、什么时候读文本更准，参数都不用动。
arXiv 论文研究了图表能否作为智能体推理循环中的内部认知动作。作者构建了 200 个受控任务组成的测试集，发现智能体从自生成图表中获得的读取分数与数值输出相当，但仅有绘图权限并不能带来稳定使用。提出的方法从轨迹回放中蒸馏两类经验：一类记录如何选择和验证分析操作，另一类记录何时调用文本、图表或两者并用，并用轨迹裁判打分和验证门控筛选。在 3 个留出基准、4 个 LLM 骨干模型上，该方法提升了分析准确率与视觉证据质量，且完全不改模型参数。
Charts as Epistemic Actions for Experience-Driven Agentic Data Analysis
Data agents built on ReAct-style loops now execute complex analytical workflows over structured data, yet their intermediate reasoning still runs almost entirely through text. Human analysts instead move between numbers, tables, and charts, reading visual evidence to expose trends and anomalies that text summaries drop. We ask whether charts can serve as internal epistemic actions inside a data agent's reasoning loop, and how to teach an agent to use them. We first build \probe{}, a suite of 200 controlled tasks under four matched interface conditions, and find a persistent gap between what agents can read and what they choose to use. Agents score as well from self-generated charts as from numeric output, yet plotting access alone does not translate into consistent use. Task slices show that charts lead on temporal, pattern, and anomaly reasoning, while text leads on precise reading and conditional verification. We then propose \method{}, which distils two families of experience from conditioned trajectory rollouts. One family records how to select and verify analytical operations, and the other records when to invoke text, charts, or both. A trajectory judge scores outcomes and labels intermediate steps, and a validation gate admits only experience that improves held-out performance. On three held-out benchmarks with four LLM backbones, \method{} raises analytical accuracy and visual evidence quality over matched baselines, and backbones that never drew charts begin using them, all without touching model parameters.
