Dual-Anchoring: Addressing State Drift in Vision-Language Navigation

Jianyi Liu; Jinjun Wang; Kailin Lyu; Kangyi Wu; Lin Zhao; Pengna Li; Qingrong He; Xi Lin

arxiv: 2604.17473 · v3 · pith:ZN7FNZKInew · submitted 2026-04-19 · 💻 cs.CV · cs.AI

Dual-Anchoring: Addressing State Drift in Vision-Language Navigation

Kangyi Wu , Pengna Li , Kailin Lyu , Xi Lin , Lin Zhao , Qingrong He , Jinjun Wang , Jianyi Liu This is my paper

Pith reviewed 2026-05-22 09:53 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords vision-language navigationstate driftdual anchoringprogress anchoringmemory anchoringlong-horizon navigationVideo-LLMs

0 comments

The pith

The Dual-Anchoring Framework prevents state drift in vision-language navigation by anchoring progress and memory representations.

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

Vision-language navigation agents lose track of instructions and past observations in long scenarios, causing aimless wandering. The paper traces this to two deficits: progress drift, where completed sub-goals blur with remaining ones, and memory drift, where history of visited landmarks fades. It counters both by supervising the agent to output structured progress text and by using retrospective prediction of landmark embeddings from a world model. If these anchors work, agents should complete more complex instructions over extended distances without degrading shorter tasks.

Core claim

State drift in VLN stems from progress drift and memory drift; the Dual-Anchoring Framework fixes it by having the agent generate explicit text tokens that separate completed from remaining sub-goals and by forcing retrospective prediction of object-centric embeddings of past landmarks through a Landmark-Centric World Model, thereby maintaining accurate internal state alignment with task execution.

What carries the argument

Dual-Anchoring Framework that uses Instruction Progress Anchoring for supervised sub-goal text generation and Memory Landmark Anchoring for retrospective verification of Segment Anything Model embeddings via a Landmark-Centric World Model.

If this is right

Overall success rate rises by 15.2 percent across navigation tasks.
Long-horizon trajectories see a 24.7 percent gain in completion.
Improvements appear in both simulated and real-world settings.
Training benefits from the two new large datasets of progress descriptions and grounded landmark observations.

Where Pith is reading between the lines

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

The anchoring idea may transfer to other language-guided sequential tasks such as manipulation or dialogue-based planning.
Explicit state supervision could help reduce drift in broader Video-LLM deployments beyond navigation.
Testing the same retrospective verification on even longer instruction sets would show whether gains continue to scale.

Load-bearing premise

That progress drift and memory drift are the dominant failure causes in long scenarios and that the new supervision signals and retrospective predictions will correct them without harming short-horizon performance or creating fresh failure modes.

What would settle it

Measure success rate on a standard VLN benchmark after applying the framework; if long-horizon gains disappear or short-horizon performance drops relative to baselines, the claim does not hold.

Figures

Figures reproduced from arXiv: 2604.17473 by Jianyi Liu, Jinjun Wang, Kailin Lyu, Kangyi Wu, Lin Zhao, Pengna Li, Qingrong He, Xi Lin.

**Figure 1.** Figure 1: The Challenge of State Drift. As the trajectory extends, the agent’s predicted path deviates from the ground truth due to internal state decoupling. This manifests as the Progress Drift (confusion about which instruction step is active) and Memory Drift (failure to remember visited landmarks). 1 Introduction Vision-Language Navigation (VLN) has emerged as a central challenge in embodied artificial intell… view at source ↗

**Figure 2.** Figure 2: Overview of the Dual-Anchoring Framework. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Overview of the data generation pipeline. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Qualitative Visualization in Simulated Environment. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗

**Figure 5.** Figure 5: Performance across different trajectory lengths. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗

**Figure 6.** Figure 6: Qualitative Visualization of Real-World Deployment. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

read the original abstract

Vision-Language Navigation(VLN) requires an agent to navigate through 3D environments by following natural language instructions. While recent Video Large Language Models(Video-LLMs) have largely advanced VLN, they remain highly susceptible to State Drift in long scenarios. In these cases, the agent's internal state drifts away from the true task execution state, leading to aimless wandering and failure to execute essential maneuvers in the instruction. We attribute this failure to two distinct cognitive deficits: Progress Drift, where the agent fails to distinguish completed sub-goals from remaining ones, and Memory Drift, where the agent's history representations degrade, making it lose track of visited landmarks. In this paper, we propose a Dual-Anchoring Framework that explicitly anchors the instruction progress and history representations. First, to address progress drift, we introduce Instruction Progress Anchoring, which supervises the agent to generate structured text tokens that delineate completed versus remaining sub-goals. Second, to mitigate memory drift, we propose Memory Landmark Anchoring, which utilizes a Landmark-Centric World Model to retrospectively predict object-centric embeddings extracted by the Segment Anything Model, compelling the agent to explicitly verify past observations and preserve distinct representations of visited landmarks. Facilitating this framework, we curate two extensive datasets: 3.6 million samples with explicit progress descriptions, and 937k grounded landmark data for retrospective verification. Extensive experiments in both simulation and real-world environments demonstrate the superiority of our method, achieving a 15.2% improvement in Success Rate and a remarkable 24.7% gain on long-horizon trajectories. To facilitate further research, we will release our code, data generation pipelines, and the collected datasets.

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

Dual-anchoring adds concrete supervision signals and new datasets to VLN but the paper does not quantify how much progress or memory drift actually drives failures.

read the letter

The paper introduces a Dual-Anchoring Framework for vision-language navigation. It uses structured progress tokens to keep track of completed versus remaining sub-goals and a Landmark-Centric World Model that retrospectively predicts SAM object embeddings to maintain history of visited places. They also release two sizable new datasets (3.6M progress samples and 937k landmark examples) and report a 15.2% success-rate lift overall plus 24.7% on long trajectories in both simulation and real-world tests. Releasing the data and pipelines is the clearest positive here; that kind of resource helps the field more than another incremental model tweak. The experiments appear to cover standard VLN benchmarks and some real-robot runs, which is better than pure sim-only work. The main soft spot is the attribution step. The authors state that Progress Drift and Memory Drift are the key problems in long scenarios, yet the provided text gives no breakdown of baseline error types or percentages. Without that, it is hard to know whether the gains come from the anchoring mechanisms themselves or simply from the extra curated supervision at scale. Short-horizon performance is claimed not to suffer, but again the details on that check are thin in what is visible. This is aimed at people already working on embodied agents and Video-LLMs for navigation. A reader who needs practical fixes for long-horizon drift or wants the new datasets would find it useful. The work is coherent enough on its own terms to deserve a serious referee, even if the failure-mode analysis needs strengthening in revision.

Referee Report

2 major / 1 minor

Summary. The paper claims that state drift in long-horizon Vision-Language Navigation arises from two cognitive deficits—Progress Drift (failure to track completed vs. remaining sub-goals) and Memory Drift (degraded history/landmark representations)—and proposes a Dual-Anchoring Framework to address them. Instruction Progress Anchoring supervises generation of structured text tokens delineating sub-goals, while Memory Landmark Anchoring uses a Landmark-Centric World Model for retrospective prediction of SAM object-centric embeddings. The approach is enabled by two new large-scale datasets (3.6M progress samples and 937k landmark instances) and reports a 15.2% Success Rate improvement overall plus 24.7% gain on long trajectories in both simulation and real-world tests.

Significance. If the performance gains hold after proper controls, the work would represent a meaningful step toward reliable long-horizon VLN with Video-LLMs by making progress and memory representations explicit. The release of code, data-generation pipelines, and the two curated datasets is a concrete positive that could support follow-on research. The focus on long-trajectory metrics is well-motivated given the problem domain.

major comments (2)

[Abstract] Abstract: The direct attribution of long-scenario failures primarily to Progress Drift and Memory Drift is not accompanied by any quantitative failure-mode breakdown of baseline agents (e.g., percentage of errors due to drift versus perception, planning, or actuation). Without this evidence the 15.2% SR and 24.7% long-horizon gains cannot be confidently ascribed to the anchoring mechanisms rather than the scale of the newly introduced 3.6 M and 937 k datasets.
[Experiments] Experiments section: No ablation isolates the contribution of the two anchoring formulations from the effect of training on the new curated datasets, and short-horizon performance is not reported relative to baselines to verify that the method does not degrade easy cases or introduce new failure modes.

minor comments (1)

[Abstract] Abstract: The acronym 'Video-LLMs' is introduced without a short parenthetical gloss or citation to the specific models employed in the VLN setting.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments raise valid points about strengthening the attribution of gains to the proposed mechanisms and providing more rigorous ablations. We address each major comment below and will revise the manuscript accordingly to improve clarity and experimental support.

read point-by-point responses

Referee: [Abstract] Abstract: The direct attribution of long-scenario failures primarily to Progress Drift and Memory Drift is not accompanied by any quantitative failure-mode breakdown of baseline agents (e.g., percentage of errors due to drift versus perception, planning, or actuation). Without this evidence the 15.2% SR and 24.7% long-horizon gains cannot be confidently ascribed to the anchoring mechanisms rather than the scale of the newly introduced 3.6 M and 937 k datasets.

Authors: We acknowledge that a quantitative failure-mode breakdown of baseline agents would provide stronger evidence for attributing improvements specifically to the Dual-Anchoring Framework rather than dataset scale alone. The manuscript currently motivates the two drift types through qualitative examples and long-horizon performance trends, but does not include such a breakdown. In the revised version, we will add a new analysis subsection that manually categorizes a representative sample of baseline failures on long trajectories into progress drift, memory drift, perception, planning, and actuation errors, reporting the resulting percentages. We will also clarify that the new datasets are not generic scale increases but are purpose-built to provide the supervision signals required by the anchoring objectives. revision: yes
Referee: [Experiments] Experiments section: No ablation isolates the contribution of the two anchoring formulations from the effect of training on the new curated datasets, and short-horizon performance is not reported relative to baselines to verify that the method does not degrade easy cases or introduce new failure modes.

Authors: We agree that isolating the anchoring formulations from the effect of the new datasets is important for validating the method. The current experiments compare the full Dual-Anchoring model against prior baselines but do not include a control that trains the base Video-LLM on the new data without the anchoring losses. In revision, we will add this ablation (training the baseline architecture on the 3.6M progress and 937k landmark samples using only standard VLN objectives) and report the resulting performance gap relative to the full model. We will also add short-horizon metrics (Success Rate and SPL on trajectories with fewer than five sub-goals) for both our method and the main baselines to confirm that performance on simpler cases is not degraded. revision: yes

Circularity Check

0 steps flagged

No circularity: claims rest on new supervision signals and datasets

full rationale

The paper introduces Instruction Progress Anchoring via structured text token supervision and Memory Landmark Anchoring via retrospective prediction in a Landmark-Centric World Model, supported by newly curated datasets of 3.6M progress samples and 937k landmark data. These are presented as explicit additions to address Progress Drift and Memory Drift, with performance gains measured empirically in simulation and real-world settings. No equations, fitted parameters, or self-citations are shown that would reduce the central claims to inputs by construction. The derivation chain is therefore self-contained through the proposed mechanisms and external validation rather than self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard VLN and LLM training assumptions plus the new curated datasets; no free parameters, invented physical entities, or ad-hoc axioms beyond domain-standard ones are introduced in the abstract.

axioms (1)

domain assumption Video-LLMs suffer from Progress Drift and Memory Drift as the primary causes of failure in long-horizon VLN scenarios.
Directly stated in the abstract as the attribution of observed failures.

pith-pipeline@v0.9.0 · 5852 in / 1249 out tokens · 33097 ms · 2026-05-22T09:53:01.440202+00:00 · methodology

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discussion (0)

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unclear
Relation between the paper passage and the cited Recognition theorem.

We attribute this failure to two distinct cognitive deficits: Progress Drift... and Memory Drift... Dual-Anchoring Framework that explicitly anchors the instruction progress and history representations.

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Forward citations

Cited by 1 Pith paper

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

SpaAct: Spatially-Activated Transition Learning with Curriculum Adaptation for Vision-Language Navigation
cs.CV 2026-04 unverdicted novelty 6.0

SpaAct activates spatial awareness in VLMs using action retrospection, future frame prediction, and progressive curriculum learning to reach SOTA on VLN-CE benchmarks.