@inproceedings{fang-etal-2025-preemptive,
    title = "Preemptive Detection and Correction of Misaligned Actions in {LLM} Agents",
    author = "Fang, Haishuo  and
      Zhu, Xiaodan  and
      Gurevych, Iryna",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.12/",
    pages = "222--244",
    ISBN = "979-8-89176-332-6",
    abstract = "Deploying LLM-based agents in real-life applications often faces a critical challenge: the misalignment between agents' behavior and user intent. Such misalignment may lead agents to unintentionally execute some critical actions that carry negative outcomes (e.g., accidentally triggering a $\textit{buy-now}$ in web shopping), resulting in undesirable or even irreversible consequences. Although addressing these issues is crucial, the preemptive detection and correction of misaligned actions remains relatively underexplored. To fill this gap, we introduce $\texttt{InferAct}$, a novel approach that leverages the belief reasoning ability of LLMs, grounded in Theory-of-Mind, to detect misaligned actions. Once the misalignment is detected, $\texttt{InferAct}$ alerts users for timely correction, preventing adverse outcomes and enhancing the reliability of LLM agents' decision-making processes. Experiments on three widely used tasks demonstrate $\texttt{InferAct}$ achieves up to 20{\%} improvements on Marco-F1 against baselines in misaligned action detection. An in-depth evaluation of misalignment correction further highlights $\texttt{InferAct}${`}s effectiveness in improving agent alignment."
}