@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://aclanthology.org/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 NOT-FOUND. 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."
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<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 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 NOT-FOUND. 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 REV-SPL to jointly evaluate room reaching, exploration coverage, and decision correctness. To address this challenge, we present 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.</abstract>
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%0 Conference Proceedings
%T VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions
%A Su, Hung-Ting
%A Wang, Ting-Jun
%A Yeh, Jia-Fong
%A Sun, Min
%A Hsu, Winston H.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F su-etal-2026-vln
%X 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 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 NOT-FOUND. 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 REV-SPL to jointly evaluate room reaching, exploration coverage, and decision correctness. To address this challenge, we present 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.
%U https://aclanthology.org/2026.acl-long.1152/
%P 25134-25150
Markdown (Informal)
[VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions](https://aclanthology.org/2026.acl-long.1152/) (Su et al., ACL 2026)
ACL