arxiv: 2604.10533 · v2 · submitted 2026-04-12 · 💻 cs.RO · cs.CL· cs.CV

Recognition: unknown

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

Hung-Ting Su , Ting-Jun Wang , Jia-Fong Yeh , Min Sun , Winston H. Hsu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:28 UTC · model grok-4.3

classification 💻 cs.RO cs.CLcs.CV

keywords vision-and-language navigationfalse-premise instructionsfeasibility-aware navigationVLN-NF benchmarkROAM agentREV-SPL metricLLM VLM pipelineNOT-FOUND decision

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The pith

Vision-language navigation agents must explore rooms and output NOT-FOUND when targets are absent under false-premise instructions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Standard VLN benchmarks assume every instruction describes a reachable, existing target, leaving agents unprepared for commands that rest on false premises. The paper builds VLN-NF by rewriting existing instructions with an LLM to create plausible but incorrect goals and then using a VLM to confirm the target is missing from the named room. Agents must therefore reach the room, explore it, and explicitly declare the target absent rather than guessing or stopping early. The work also defines REV-SPL, a metric that scores room reaching, exploration coverage, and decision correctness together, and introduces the ROAM agent that outperforms baselines on this measure.

Core claim

VLN-NF is a benchmark consisting of false-premise instructions in which the referenced target does not exist in the specified room. Agents are required to navigate to the room, perform in-room exploration to gather evidence, and output NOT-FOUND when the evidence shows the target is absent. The benchmark is generated through a scalable pipeline that rewrites instructions via LLM and verifies target absence via VLM. ROAM, a two-stage hybrid agent, first performs supervised room-level navigation and then uses LLM/VLM-driven exploration guided by a free-space clearance prior; it records the highest REV-SPL while baselines under-explore and terminate prematurely.

What carries the argument

ROAM, the two-stage hybrid that pairs supervised room-level navigation with LLM/VLM-guided in-room exploration using a free-space clearance prior to produce correct NOT-FOUND decisions.

If this is right

Agents must reach the correct room, gather evidence through exploration, and decide explicitly whether to output NOT-FOUND.
Baselines that terminate early without adequate exploration receive low REV-SPL scores under unreliable instructions.
The REV-SPL metric jointly penalizes poor room reaching, insufficient coverage, and incorrect termination decisions.
The LLM-VLM rewriting pipeline enables automatic scaling of false-premise benchmarks without manual labeling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same evidence-gathering loop could be added to other embodied tasks where instructions may be outdated or incorrect.
In home environments, explicit NOT-FOUND outputs would prevent robots from repeatedly searching for non-existent items.
Replacing the free-space prior with learned occupancy models might further reduce unnecessary exploration steps.

Load-bearing premise

The LLM rewriting and VLM verification pipeline produces plausible yet factually incorrect goals that accurately reflect real-world false-premise scenarios.

What would settle it

Physical robot trials in which an object is removed from the instructed room after the command is given, followed by measurement of whether the agent explores sufficiently and correctly outputs NOT-FOUND.

Figures

Figures reproduced from arXiv: 2604.10533 by Hung-Ting Su, Jia-Fong Yeh, Min Sun, Ting-Jun Wang, Winston H. Hsu.

**Figure 1.** Figure 1: A toy illustration of failure modes under unreliable instructions. For compactness, we visualize two separate single-target queries in the same scene (cup: feasible; plate: infeasible/absent in the kitchen). Left: Standard VLN (DUET+VLN) lacks an explicit NOT-FOUND output (hence “?” for plate). Middle: Adding NOT-FOUND to action space (DUET+VLN-NF) can lead to premature abstention. Right: Our proposed ROA… view at source ↗

**Figure 2.** Figure 2: We design a scalable pipeline to rewrite VLN instructions and generate NF ( [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of our two-stage framework, ROAM (Sec. 4). Left (Sec. 4.1): a room-level navigator trained with room labels only guides the agent to enter the target room. Right (Sec. 4.2): an LLM-based in-room explorer selects the next viewpoint using VLM captions as semantic context and a geometric coverage prior from FREE (Sec. 4.3) to favor headings that lead to larger unsearched regions. 4.2 In-room Explorer… view at source ↗

**Figure 4.** Figure 4: FREE (Sec. 4.3) segments navigable floor regions from the current view using an open-vocabulary visual model and uses depth-based raycasting to estimate a free-space clearance dfree for each candidate direction. The resulting clearance cues are appended to the LLM prompt to encourage coverage-oriented exploration. is modified to include an explicit NOT-FOUND action in its action space/prompt, allowing t… view at source ↗

**Figure 5.** Figure 5: Most occurred objects for NOT-FOUND instructions. Observation : the result of the action ... ( this Thought / Action / Action Input / Observation can repeat N times ) Thought : I found my target object , but I should check whether any other objects may be hidden . or Thought : I checked that no objects are hidden , I can stop . Final Answer : Not found ! ---- Begin ! Instruction : { action_plan } Initial … view at source ↗

**Figure 6.** Figure 6: On the left, Grounding DINO detects the door [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

read the original 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 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/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

VLN-NF adds a benchmark for false-premise instructions in VLN and a hybrid method that explores before saying not-found, but the performance edge over baselines looks sensitive to how those baselines were adapted to the new task.

read the letter

The main point here is a benchmark that forces VLN agents to deal with instructions whose premise is false—the target is simply not in the room—and to explore enough to conclude that and output NOT-FOUND. That matches a real failure mode when humans give noisy or outdated commands. The authors build VLN-NF by rewriting standard instructions with an LLM and checking target absence with a VLM, which gives a scalable way to generate the data. They also define REV-SPL to score room arrival, exploration coverage, and the final decision together. ROAM runs supervised room navigation first, then switches to LLM/VLM-driven search inside the room using a free-space prior to avoid getting stuck. This hybrid feels like a practical way to combine learned policies with on-the-fly reasoning. The construction pipeline and the metric are the clearest additions over prior VLN work. The soft spot is the comparison. The claim that ROAM wins because baselines under-explore and terminate early only holds if the baselines were given the same freedom to keep searching rooms and the same requirement to emit NOT-FOUND. If they were run with unmodified code that assumes a target always exists, their poor numbers are expected by design and do not isolate the value of ROAM's two-stage structure. The abstract does not spell out the adaptation steps, so that part needs checking in the full experiments. This work is aimed at people building embodied agents that must stay robust when instructions are imperfect. It is worth sending to peer review because the benchmark idea is concrete and the problem is worth solving, provided the authors clarify the baseline setup and show the raw numbers with error bars.

Referee Report

2 major / 2 minor

Summary. The paper introduces VLN-NF, a benchmark extending vision-and-language navigation to false-premise instructions in which the referenced target is absent from the specified room. Agents must navigate to the room, perform in-room exploration to gather evidence, and explicitly output NOT-FOUND. The benchmark is built via an LLM-based instruction rewriting pipeline followed by VLM verification of target absence. The authors define a new REV-SPL metric that jointly scores room reaching, exploration coverage, and decision correctness. They propose ROAM, a two-stage hybrid agent that performs supervised room-level navigation followed by LLM/VLM-driven in-room search guided by a free-space clearance prior, and report that ROAM attains the highest REV-SPL while baselines under-explore and terminate prematurely.

Significance. If the evaluation holds, the work addresses a practically relevant gap in VLN: existing agents and benchmarks assume feasible instructions and existing targets, which limits deployment in real environments where instructions can be erroneous. The scalable benchmark-construction pipeline, the REV-SPL metric, and the hybrid ROAM architecture could provide useful tools and baselines for feasibility-aware navigation research.

major comments (2)

[Evaluation] Evaluation section: the central claim that ROAM outperforms baselines on REV-SPL because baselines 'under-explore and terminate prematurely' is load-bearing. It is unclear whether the baselines were modified to support explicit NOT-FOUND output and in-room search; if they were run with unmodified VLN code that assumes a target always exists, their poor performance follows by construction and does not demonstrate superiority of ROAM's hybrid design. Please provide the exact adaptation protocol, output format, and termination criteria used for each baseline.
[Benchmark Construction] Benchmark construction pipeline (Section 3): the claim that the LLM-rewriting + VLM-verification procedure produces 'plausible yet factually incorrect goals that accurately reflect real-world false-premise scenarios' requires stronger validation. No human study or error analysis is reported on the realism or diversity of the generated false-premise instructions; without this, the benchmark's ecological validity remains unverified.

minor comments (2)

[Abstract] Abstract: reports 'empirical gains on REV-SPL' without any numerical values, error bars, or number of runs; a one-sentence quantitative summary would improve readability.
[Figures and Tables] Figure captions and tables: ensure all axes, legends, and metric definitions (especially REV-SPL components) are fully self-contained so readers can interpret results without returning to the text.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive feedback on our work introducing the VLN-NF benchmark and ROAM agent. We address each major comment point by point below, with planned revisions indicated.

read point-by-point responses

Referee: [Evaluation] Evaluation section: the central claim that ROAM outperforms baselines on REV-SPL because baselines 'under-explore and terminate prematurely' is load-bearing. It is unclear whether the baselines were modified to support explicit NOT-FOUND output and in-room search; if they were run with unmodified VLN code that assumes a target always exists, their poor performance follows by construction and does not demonstrate superiority of ROAM's hybrid design. Please provide the exact adaptation protocol, output format, and termination criteria used for each baseline.

Authors: We agree that the baseline adaptation details are essential for validating the central claim and avoiding any perception of unfair comparison. The original submission did not elaborate sufficiently on this protocol. We will revise the Evaluation section to include a dedicated paragraph specifying the exact adaptation protocol (including any extensions to support NOT-FOUND output), the output format required for each baseline, and the termination criteria applied in the VLN-NF setting. This addition will make transparent how the baselines were configured and allow readers to evaluate whether performance gaps arise from ROAM's hybrid design rather than baseline limitations. revision: yes
Referee: [Benchmark Construction] Benchmark construction pipeline (Section 3): the claim that the LLM-rewriting + VLM-verification procedure produces 'plausible yet factually incorrect goals that accurately reflect real-world false-premise scenarios' requires stronger validation. No human study or error analysis is reported on the realism or diversity of the generated false-premise instructions; without this, the benchmark's ecological validity remains unverified.

Authors: We acknowledge that the absence of human validation or error analysis leaves the ecological validity claim under-supported. The pipeline derives instructions from existing VLN datasets via LLM rewriting and uses VLM verification to confirm target absence, which enforces factual incorrectness while aiming for linguistic plausibility. In the revised manuscript we will add an error analysis subsection in Section 3, reporting verification success rates and providing qualitative examples of generated instructions to illustrate diversity. A full human study on realism is not included in the current work. revision: partial

standing simulated objections not resolved

The request for a human study validating the realism and diversity of the generated false-premise instructions, as this would require new data collection and participant evaluation beyond the scope of the current revision.

Circularity Check

0 steps flagged

No significant circularity; empirical benchmark and method comparison

full rationale

The paper constructs VLN-NF via an LLM rewriting + VLM verification pipeline and introduces ROAM as a two-stage hybrid agent. No equations, fitted parameters, or first-principles derivations appear. REV-SPL is explicitly defined as a new joint metric rather than derived from prior results. Claims rest on empirical comparisons, not self-referential reductions or self-citation chains that bear the central load. Baseline adaptation details are not provided in the abstract, but this is an experimental fairness issue, not a circularity in any derivation chain. The work is self-contained as benchmark construction plus evaluation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is entirely empirical and introduces no mathematical axioms, free parameters, or new physical entities; all components rely on existing LLMs, VLMs, and navigation policies.

pith-pipeline@v0.9.0 · 5500 in / 1115 out tokens · 38707 ms · 2026-05-10T16:28:38.688142+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints
cs.AI 2026-04 unverdicted novelty 7.0

ADAPT augments planners with affordance reasoning to raise task success in environments with unspecified and time-varying object affordances, and a LoRA-finetuned VLM backend beats GPT-4o on the new DynAfford benchmark.

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · cited by 1 Pith paper · 2 internal anchors

[1]

ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints

Adapt: Benchmarking commonsense planning under unspecified affordance constraints.Preprint, arXiv:2604.14902. Shizhe Chen, Pierre-Louis Guhur, Cordelia Schmid, and Ivan Laptev. 2021. History aware multimodal transformer for vision-and-language navigation.Ad- vances in neural information processing systems, 34:5834–5847. Shizhe Chen, Pierre-Louis Guhur, Ma...

work page internal anchor Pith review Pith/arXiv arXiv 2021
[2]

InProceedings of the IEEE International Conference on Robotics and Automation (ICRA)

Discuss before moving: Visual language navi- gation via multi-expert discussions. InProceedings of the IEEE International Conference on Robotics and Automation (ICRA). IEEE. OpenAI. 2023. Gpt-4 technical report. Accessed: 2025- 05-18. Bowen Pan, Rameswar Panda, SouYoung Jin, Rogerio Feris, Aude Oliva, Phillip Isola, and Yoon Kim. 2023. Langnav: Language a...

work page arXiv 2023
[3]

Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Episodic transformer for vision-and-language navigation. InProceedings of the IEEE/CVF Interna- tional Conference on Computer Vision, pages 15942– 15952. Yuankai Qi, Qi Wu, Peter Anderson, Xin Wang, William Yang Wang, Chunhua Shen, and Anton van den Hengel. 2020. Reverie: Remote embodied visual referring expression in real indoor environ- ments. InProceed...

work page internal anchor Pith review arXiv 2020
[4]

Viewpoint ID

Learning to stop: A simple yet effective ap- proach to urban vision-language navigation. InFind- ings of the Association for Computational Linguistics: EMNLP 2020, pages 699–707, Online. Association for Computational Linguistics. He Yan, Xinyao Hu, Xiangpeng Wan, Chengyu Huang, Kai Zou, and Shiqi Xu. 2023. Inherent limitations of gpt=4 regarding spatial i...

work page arXiv 2020