@inproceedings{su-etal-2026-vln,
    title = "{VLN}-{NF}: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions",
    author = "Su, Hung-Ting  and
      Wang, Ting-Jun  and
      Yeh, Jia-Fong  and
      Sun, Min  and
      Hsu, Winston H.",
    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.1152/",
    pages = "25134--25150",
    ISBN = "979-8-89176-390-6",
    abstract = "Conventional Vision-and-Language Navigation (VLN) benchmarks assume instructions are feasible and the referenced target exists, leaving agents ill-equipped to handle false-premise goals. We introduce \textbf{VLN-NF}, a benchmark with false-premise instructions where the target is absent from the specified area and agents must navigate, gather evidence through in-room exploration, and explicitly output . VLN-NF is constructed via a scalable pipeline that rewrites VLN instructions using an LLM and verifies target absence with a VLM, producing plausible yet factually incorrect goals. We further propose \textbf{REV-SPL} to jointly evaluate room reaching, exploration coverage, and decision correctness. To address this challenge, we present \textbf{ROAM}, a two-stage hybrid that combines supervised room-level navigation with LLM/VLM-driven in-room exploration guided by a free-space clearance prior. ROAM achieves the best REV-SPL among compared methods, while baselines often under-explore and terminate prematurely under unreliable instructions. Code and data will be released upon acceptance."
}