推理时技能增强提供了一种轻量级的方法，通过注入可重用的程序知识而无需更新模型参数来改进数据分析代理。然而，, 发现有效的数据分析技能仍然具有挑战性,，因为可靠的监督成本高昂，而且不同分析格式的成功标准各不相同。这就提出了一个关键问题：如何仅通过未标记的探索来发现可重用的数据分析技能。我们提出 DataCOPE, 是一种用于数据分析代理的无监督验证者引导的技能发现框架。 DataCOPE 从探索轨迹中获取验证器信号，并使用它们来表征轨迹之间的相对质量或一致性。它迭代地协调用于轨迹生成,的数据分析代理、用于信号提取,的无监督验证器和用于对比技能蒸馏的技能管理器。对于报告式分析,，我们将验证器实例化为自适应检查表验证器，该验证器通过可验证的覆盖率, 派生特定于任务的标准, 对报告进行评分，并迭代地细化检查表。对于推理式分析,，我们将其实例化为答案一致性验证器，该验证器按答案一致性对轨迹进行分组，并使用自洽性作为辅助信号。我们根据 Deep Data Research 的报告式分析和 DABStep 的推理式分析来评估 DataCOPE。在这两种设置中，, DataCOPE 始终比基线提高了保留性能。四种模型设置的平均值, DataCOPE 在报告型和推理型任务上的平均得分分别提高了 9.71% 和 32.30%。

Inference-time skill augmentation provides a lightweight way to improve data-analytic agents by injecting reusable procedural knowledge without updating model parameters. However, discovering effective skills for data analysis remains challenging, as reliable supervision is expensive and success criteria vary across analytical formats. This raises the key question of how to discover reusable data-analysis skills from unlabeled exploration alone. We propose DataCOPE, an unsupervised verifier-guided skill discovery framework for data-analytic agents. DataCOPE derives verifier signals from the exploration trajectories and uses them to characterize relative quality or aggreement among trajectories. It iteratively coordinates a Data-Analytic Agent for trajectory generation, an Unsupervised Verifier for signal extraction, and a Skill Manager for contrastive skill distillation. For report-style analysis, we instantiate the verifier as an Adaptive Checklist Verifier that derives task-specific criteria, scores reports by verifiable coverage, and iteratively refines the checklist. For reasoning-style analysis, we instantiate it as an Answer Agreement Verifier that groups trajectories by answer agreement and uses self-consistency as an auxiliary signal. We evaluate DataCOPE on report-style analysis from Deep Data Research and reasoning-style analysis from DABStep. Across both settings, DataCOPE consistently improves held-out performance over baselines. Averaged across four model settings, DataCOPE improves the mean score by 9.71% and 32.30% on report-style and reasoning-style tasks respectively.

科目: 人工智能 (cs.AI); 计算和语言 (cs.CL); 机器学习 (cs.LG); 多代理系统 (cs.MA)

Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multiagent Systems (cs.MA)