Author

Yin Li

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Jul 2026

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

This work introduces 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, and shows that TOFFEE can effectively generate scalable trajectory data for complex analytical tasks across heterogeneous environments.

Ziting Wang, Yin Li, Zuhao Yang et al. · arXiv.org · 0 citations

May 2026

When JSON Is Not Enough: Semantic Reliability of Schema-Constrained LLM Ordering Agents

OrderBench is introduced, a deterministic benchmark for restaurant ordering agents that separates syntactic validity, schema validity, status decisions, exact item semantics, constraint preservation, and unsafe acceptances.

Yin Li · arXiv.org · 0 citations