发表机构
Nanyang Technological University; Huawei; Industrial and Commercial Bank of China Limited（南洋理工大学; 华为; 中国工商银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有数据代理难以适应新环境的问题，提出TOFFEE系统，通过蒙特卡洛树搜索等方法，能从给定数据环境合成高质量数据代理轨迹，可用于监督微调与上下文学习，还展示了系统框架、界面及工作流程与应用场景。
AI 中文摘要
由大语言模型驱动的数据代理在数据驱动的决策中发挥着越来越重要的作用。然而，现有数据代理难以推广到未见过的数据环境和分析工作流程，特别是在异构企业环境中。这使得合成高质量数据代理轨迹的需求日益增长，这些轨迹可用于监督微调数据和上下文学习演示。因此，我们引入了TOFFEE系统，它通过蒙特卡洛树搜索、自适应模型选择和跨任务前缀重用，从给定数据环境中合成高质量数据代理轨迹。我们展示了TOFFEE能够为异构环境中的复杂分析任务有效生成可扩展的轨迹数据。在本演示中，我们介绍了TOFFEE的系统框架，包括任务池构建、轨迹探索器和学习成本模型。我们还介绍了TOFFEE的网络界面及其工作流程，并展示了两个端到端场景：数据代理微调的轨迹合成和演示增强的数据代理推理。
英文摘要
LLM-powered data agents are playing an increasingly important role in data-driven decision making. However, existing data agents struggle to generalize to unseen data environments and analytical workflows, especially in heterogeneous enterprise settings. This creates a growing need for synthesizing high-quality data agent trajectories that capture complex analytical workflows for given data environments. Such trajectories support two key downstream uses: they can serve as supervised finetuning (SFT) data that adapts data agent models to the target domain, and as in-context learning (ICL) demonstrations to guide general-purpose LLMs in unfamiliar data environments. Thus, we introduce TOFFEE, a system for synthesizing high-quality data agent trajectories from given data environments via Monte Carlo Tree Search (MCTS) with adaptive model selection and cross-task prefix reuse. We show that TOFFEE can effectively generate scalable trajectory data for complex analytical tasks across heterogeneous environments. In this demonstration, we present the system framework of TOFFEE, including its task pool construction, trajectory explorer, and learned cost model. We also introduce the web interface of TOFFEE and its workflow, and demonstrate two end-to-end scenarios: trajectory synthesis for data agent finetuning, and demonstration-augmented data agent reasoning.
CommentsAccepted to VLDB 2026
