Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale

• URL: http://arxiv.org/abs/2607.06233v2
• Date: Tue, 14 Jul 2026 02:46:45 GMT
• Title: Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
• Abstract summary: Existing data agents struggle to generalize to unseen data environments and analytical, especially in heterogeneous enterprise settings.<n>We introduce TOFFEE, a system for synthesizing high-quality data agent trajectories from given data environments via Monte Carlo Tree Search.<n>We show that TOFFEE can effectively generate scalable trajectory data for complex analytical tasks across heterogeneous environments.
• Score: 15.09465040407523
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Environment-free Synthetic Data Generation for API-Calling Agents [68.95520739708304]
  Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories.<n>We propose an environment-free synthetic data generation approach that leverages LLMs as on-the-fly digital world models.<n>We evaluate our approach on the challenging AppWorld and OfficeBench benchmarks, which include both information-retrieval and state-changing tasks.
  arXiv  Detail & Related papers  (2026-07-18T17:36:44Z)
• Exploring Autonomous Agentic Data Engineering for Model Specialization [37.35310287793494]
  Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains.<n>We formalize textbfAutonomous Agentic Data Engineering, a novel task designed to evaluate LLMs as autonomous data engineers.<n> Experiments show that autonomous LLM data engineers yield substantial gains, as GPT-5.2 constructs a training curriculum that improves a student model by textbf57.29%.
  arXiv  Detail & Related papers  (2026-05-28T17:50:10Z)
• Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs [81.43302841109349]
  Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs)<n>We propose an automated multi-agent framework to reconstruct the evolutionary graph of dataset development.
  arXiv  Detail & Related papers  (2026-04-12T06:24:07Z)
• Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs [66.63911043019294]
  Data preparation aims to denoise raw datasets, uncover cross-dataset relationships, and extract valuable insights from them.<n>This paper focuses on the use of LLM techniques to prepare data for diverse downstream tasks.<n>We introduce a task-centric taxonomy that organizes the field into three major tasks: data cleaning, standardization, error processing, imputation, data integration, and data enrichment.
  arXiv  Detail & Related papers  (2026-01-22T12:02:45Z)
• Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents [85.02904078131682]
  We introduce the agent data protocol (ADP), a light-weight representation language that serves as an "interlingua" between agent datasets.<n> ADP is expressive enough to capture a large variety of tasks, including API/tool use, browsing, coding, software engineering, and general agentic.<n>All code and data are released publicly, in the hope that ADP could help lower the barrier to standardized, scalable, and reproducible agent training.
  arXiv  Detail & Related papers  (2025-10-28T17:53:13Z)
• SyGra: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data [0.0]
  We present a comprehensive synthetic data generation framework for large language models (LLMs)<n>Our approach employs a modular and configuration-based pipeline capable of modeling complex dialogue flows with minimal manual intervention.<n>The resulting datasets are structured under a flexible schema supporting both SFT and DPO use cases, enabling seamless integration into diverse training.
  arXiv  Detail & Related papers  (2025-08-21T10:35:41Z)
• Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems [8.816332263275305]
  Traditional Data+AI systems rely heavily on human experts to orchestrate system pipelines.<n>Existing Data+AI systems have limited capabilities in semantic understanding, reasoning, and planning.<n>We propose the concept of a 'Data Agent' - a comprehensive architecture designed to orchestrate Data+AI ecosystems.
  arXiv  Detail & Related papers  (2025-07-02T11:04:49Z)
• AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning [98.26836657967162]
  textbfAgentOhana aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. textbfxLAM-v0.1, a large action model tailored for AI agents, demonstrates exceptional performance across various benchmarks.
  arXiv  Detail & Related papers  (2024-02-23T18:56:26Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.