# VLN-NF: Feasibility-Aware Vision-and-Language Navigation with False-Premise Instructions

arXiv: https://arxiv.org/abs/2604.10533 · HTML: https://mlsanity.com/paper/2604.10533/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 69 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Builds on (most cited first, 30 of 30)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2004.02857/) (2020)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [History Aware Multimodal Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2110.13309/) (2021)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [Episodic Transformer for Vision-and-Language Navigation](https://mlsanity.com/paper/2105.06453/) (2021)
- [Discuss Before Moving: Visual Language Navigation via Multi-expert Discussions](https://mlsanity.com/paper/2309.11382/) (2023)
- [NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning](https://mlsanity.com/paper/2403.07376/) (2024)
- [A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility](https://mlsanity.com/paper/2202.02312/) (2022)
- [A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning](https://mlsanity.com/paper/2210.03112/) (2022)
- [LangNav: Language as a Perceptual Representation for Navigation](https://mlsanity.com/paper/2310.07889/) (2023)
- [Lana: A Language-Capable Navigator for Instruction Following and Generation](https://mlsanity.com/paper/2303.08409/) (2023)
- [Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning](https://mlsanity.com/paper/2402.17768/) (2024)
- [VLN-Trans: Translator for the Vision and Language Navigation Agent](https://mlsanity.com/paper/2302.09230/) (2023)
- [Controllable Navigation Instruction Generation with Chain of Thought Prompting](https://mlsanity.com/paper/2407.07433/) (2024)
- [Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation](https://mlsanity.com/paper/2009.13112/) (2020)
- [NavHint: Vision and Language Navigation Agent with a Hint Generator](https://mlsanity.com/paper/2402.02559/) (2024)
- [Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation](https://mlsanity.com/paper/2403.10700/) (2024)
- [Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments](https://mlsanity.com/paper/2407.21452/) (2024)
- [NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries](https://mlsanity.com/paper/2412.10726/) (2024)
- [When Robots Should Say "I Don't Know": Benchmarking Abstention in Embodied Question Answering](https://mlsanity.com/paper/2512.04597/) (2025)
- [Do What? Teaching Vision-Language-Action Models to Reject the Impossible](https://mlsanity.com/paper/2508.16292/) (2025)
- [HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models](https://mlsanity.com/paper/2506.15065/) (2025)
- [Which way is `right'?: Uncovering limitations of Vision-and-Language Navigation model](https://mlsanity.com/paper/2312.00151/) (2023)
- [Context-Aware Replanning with Pre-explored Semantic Map for Object Navigation](https://mlsanity.com/paper/2409.04837/) (2024)

## Built on by (most cited first, 3 of 3)

- [The Yes-Man Syndrome: Benchmarking Abstention in Embodied Robotic Agents](https://mlsanity.com/paper/2605.20544/) (2026)
- [ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints](https://mlsanity.com/paper/2604.14902/) (2026)
- [A Persian Vision-Language-Action Dataset and Benchmark for Ambiguity- and Infeasibility-Aware Instruction-to-Plan Grounding](https://mlsanity.com/paper/2610.15666/) (2026)

## Abstract (arXiv)

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 room 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. VLN-NF project page can be found at https://vln-nf.github.io/.