Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
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) demon
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- Linked via arxiv authorZiting Wang →
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
- Linked via arxiv authorYin Li →
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
- Linked via arxiv authorZuhao Yang →
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
- Linked via arxiv authorXiuchang Li →
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
- Linked via arxiv authorJiale Bai →
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
- Linked via arxiv authorGao Cong →
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale