@inproceedings{wang-etal-2026-trajectory2task,
    title = "{T}rajectory2{T}ask: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents",
    author = "Wang, Ziyi  and
      Lu, Yuxuan  and
      Zhang, Yimeng  and
      Chen, Pei  and
      Dong, Ziwei  and
      Huang, Jing  and
      Gesi, Jiri  and
      Tang, Xianfeng  and
      Luo, Chen  and
      Liu, Qun  and
      Sang, Yisi  and
      Lu, Hanqing  and
      Li, Manling  and
      Lai, Jin  and
      Wang, Dakuo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2037/",
    pages = "44021--44044",
    ISBN = "979-8-89176-390-6",
    abstract = "Tool-calling agents are increasingly deployed in real-world customer-facing workflows. Yet most studies on tool-calling agents focus on idealized settings with general, fixed, and well-specified tasks.In real-world applications, user requests are often (1) ambiguous, (2) changing over time, or (3) infeasible due to policy constraints, and training and evaluation data that cover these diverse, complex interaction patterns remain under-represented.To bridge the gap, we present Trajectory2Task a verifiable data generation pipeline for studying tool use at scale under three realistic user scenarios: ambiguous intent, changing intent, and infeasible intents.The pipeline first conducts multi-turn exploration to produce valid tool-call trajectories. It then converts these trajectories into user-facing tasks with controlled intent adaptations. This process yields verifiable task that support closed-loop evaluation and training. We benchmark several state-of-the-art LLMs on the generated complex user scenario tasks and observe frequent failures.Finally, using successful trajectories obtained from task rollouts, we fine-tune lightweight LLMs and find consistent improvements across all three conditions, along with better generalization to unseen tool-use domains, indicating stronger tool-calling ability."
}