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REVIEW 3 major objections 5 minor 44 references

Embodied Domain Adaptation for Object Detection

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that an open-vocabulary object detector can adapt to new indoor scenes without source data by clustering detections across time, refining pseudo-labels, and training with contrastive learning.

desk verdict Useful new benchmark and a sensible method, but the main empirical claim is undercut by an ensemble confound visible in the paper's own ablation. read the letter →

arxiv 2506.21860 v1 pith:KFTCVF7A submitted 2025-06-27 cs.RO cs.CV

Xiangyu Shi , Yanyuan Qiao , Lingqiao Liu , Feras Dayoub This is my paper · ORCID
classification cs.ROcs.CV
keywords open-vocabularyobjectdetectionsource-freedomainadaptationtemporalinstanceclusteringmeanteachercontrastivelearningpseudo-labelrefinementindoorrobotperceptionshift
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper addresses a practical gap: a robot entering a new home or lab faces objects and lighting that its detector has not seen, and labelled source data may be unavailable. It claims that a pre-trained open-vocabulary detector can be adapted to such dynamic indoor environments using only the robot's own unlabelled frames, by tracking object instances across time and using those tracks to refine pseudo-labels and to train a contrastive student model. To evaluate this, the paper builds the EDAOD benchmark, which applies sequential changes in lighting, layout, and object content in simulated iTHOR rooms and real MP3D scenes. The reported experiments show consistent detection-accuracy gains (AP50) over four source-free detection baselines, including gains on next-trajectory and continual-learning settings that go beyond a one-off domain transfer.

What carries the argument

The load-bearing mechanism is temporal instance clustering. A similarity score combining overlap (IoU) and logit Euclidean distance between consecutive frames is fed to the Hungarian algorithm to link bounding boxes into clusters; clusters separated by occlusions are merged when the cosine similarity of their end features exceeds a threshold. Each cluster's logits are averaged to produce the refined pseudo-labels that supervise the student, and the clusters themselves define positive pairs for a multi-positive InfoNCE contrastive loss. Multi-scale threshold fusion runs several teacher models at different merge thresholds and averages their predictions, balancing precision and recall.

What would settle it

Run the original implementations of MemCLR, IRG-SFDA, and UDAc-SFDA on their native datasets and compare their published detection scores with the paper's re-implementations; if the re-implementations score well below the published numbers, the claimed gains over these baselines would not show superiority. Alternatively, on EDAOD, shuffle the frame order or disable temporal clustering and check whether the gains over the Mean-Teacher baseline disappear.

Watch

Extended reading notes

Core claim

The central claim is that temporal consistency in an embodied video stream is a usable free signal for source-free domain adaptation of open-vocabulary detectors. The authors propose matching teacher-model bounding boxes across frames, merging clusters across occlusions, and averaging cluster logits to form refined pseudo-labels, then using those clusters as positive pairs for an InfoNCE-style contrastive loss within a Mean Teacher framework. They report that this method outperforms MemCLR, IRG-SFDA, UDAc-SFDA, and plain Mean-Teacher on their EDAOD benchmark, and that in a real-robot low-light trial the average number of detections per category rose from 35.67 to 63.33 after adaptation.

Load-bearing premise

The reported superiority over existing methods assumes that the paper's re-implementations of the four baselines match the performance of the original methods, since their official code was not directly usable and one baseline had its data-acquisition component removed.

Editorial extensions

If this is right

  • An adapted model improves detection within the same room layout using only unlabelled frames from the robot's own trajectory.
  • The model transfers to the next layout configuration without additional training, so adaptation gains accumulate as the robot explores more of the environment.
  • The continual-learning evaluation indicates the model retains knowledge of earlier layouts rather than forgetting them after adapting to new ones.
  • On the real-world MP3D-A trajectory, the method is the only one of the five compared models that improves over the source-only detector.
  • In a real-robot low-light trial, the average number of detections per category increased from 35.67 to 63.33 after adaptation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the authors leave implicit is that the temporal clustering mechanism depends on re-observing the same objects; a trajectory with little frame-to-frame overlap would weaken the pseudo-label refinement, so trajectory design partly determines the reported gains.
  • A testable extension is to fuse pseudo-labels at the label level instead of averaging entire teacher models, which would isolate whether the multi-scale threshold gain comes from ensemble averaging or from label diversity.
  • The fixed cluster-merge thresholds could be made category-aware, since small deformable objects and large furniture are likely to need different association tolerances under occlusion and lighting change.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes a source-free domain adaptation (SFDA) method for open-vocabulary object detection in indoor embodied settings. The method combines a Mean-Teacher framework with temporal instance clustering of pseudo-labels, multi-scale threshold fusion across cluster-merging thresholds, and an InfoNCE-style contrastive loss built on the resulting clusters. The authors also introduce an EDAOD benchmark built from iTHOR and MP3D, with multiple layouts and lighting conditions per environment, and report AP50 results across three evaluation settings: same trajectory, next trajectory, and continual learning. The central claim is that EDAOD outperforms four existing SFDA object-detection methods without accessing source data.

Significance. If the results hold, the paper would make a useful contribution on two fronts: it is, to my knowledge, the first SFOD benchmark targeted specifically at open-vocabulary detection in dynamic indoor environments, and the temporal-instance-clustering idea is a natural and plausible way to improve pseudo-label quality for embodied agents that observe scenes from multiple viewpoints. The contrastive formulation that treats temporally linked boxes as positives is also reasonable. However, the evaluation as presented does not yet isolate the effect of the proposed adaptation components from a three-model inference ensemble, and the baseline comparisons rest on author re-implementations that are not validated. These issues are fixable and do not invalidate the underlying approach, but they must be resolved before the stated superiority claim can be accepted.

major comments (3)
  1. [§IV-B4, Eq. (6); Tables I and IV] The configuration labeled "Ours" in Tables I and II is not a single model but an inference-time average of n=3 models trained with different cluster-merging thresholds τ2 ∈ {0.8, 0.85, 0.9} (Eq. (6)). Every baseline is a single model. Table IV quantifies the fusion bonus on the same iTHOR rooms: e.g., Kitchen1 36.03 (fused) versus 34.82 (best single threshold) and Bathroom1 31.44 versus 30.31, i.e., up to about 1.6 AP. Several reported wins over baselines in Table I are no larger than this (Kitchen2: Ours 37.45 vs MemCLR 36.94; Bathroom2: Ours 36.06 vs UDAc-SFDA 35.32). The central superiority claim therefore conflates the proposed adaptation components with a 3-model ensemble. The paper should report single-threshold EDAOD against single-model baselines, or give baselines the same multi-model fusion; Table IV alone does not resolve the ambiguity.
  2. [§V-B and Tables I–II] All four baselines were re-implemented by the authors because the official code is "not directly applicable" to OVOD, and UDAc-SFDA was additionally modified by removing its data-acquisition component. No evidence is provided that these re-implementations match the original methods (e.g., by reproducing published numbers on a closed-set SFOD benchmark or by releasing the re-implemented code). Since the headline conclusion is relative superiority over these baselines, the re-implementations are load-bearing. The authors should either run official implementations where possible, report a fidelity check of their re-implementations, or release the code so the comparison can be audited.
  3. [§V-C and §V-E (Table IV)] The multi-scale fusion set τ2 = {0.8, 0.85, 0.9} is chosen on the basis of Table IV, which reports results on the same iTHOR test rooms used for the final evaluation; no validation split or nested protocol is described. Moreover, all tables report a single run without variance or significance measures, leaving small differences (e.g., Kitchen2 37.45 vs 36.94) indistinguishable from noise. To support the stated "significant gains," the evaluation needs either a held-out validation trajectory or room for hyperparameter selection, or error bars over multiple seeds and a statistical comparison.
minor comments (5)
  1. [§V-A] The dataset section would benefit from exact per-environment frame counts, a clear train/validation/test split for each trajectory, and an explicit statement about whether the benchmark and code will be released.
  2. [§IV-B4] The term "multi-scale threshold fusion" is misleading: the method trains several independent models and averages their predictions (Eq. (6)), rather than fusing thresholds within a single model. Consider renaming it, e.g., "threshold-ensemble fusion," or clarifying the design choice.
  3. [§V-F and Table V] The real-world evaluation reports raw detection counts rather than precision/recall or AP, and the per-category ground-truth counts vary substantially; the "Average Detections" metric therefore conflates precision and recall and should be supplemented with a detection-quality metric or qualitative error analysis.
  4. [References] References [30] and [41] appear to refer to the same CVPR 2023 paper (Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection) and should be merged; the author name "Vibashan" should be given in full in the reference list.
  5. [Eq. (3)] The logit feature vectors l_prev and l_curr are used in a Euclidean distance inside the similarity formula, but their dimensionality and normalization are not defined; please state the feature source and how they are extracted, since this affects the matching behavior.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the method and benchmark are empirical, and no prediction reduces by construction to an input or to a self-citation.

full rationale

The paper's contribution is an empirical adaptation method and benchmark. The mechanism equations (1)-(10) define losses and an inference-time ensemble; the reported gains are experimental results, not derivations whose outputs are equivalent to their inputs. The multi-scale threshold fusion in Eq. (6) is an explicit ensemble of n models; while this makes the comparison with single-model baselines potentially unfair and is a validity concern (and Table IV shows fusion adds up to about 1.6 AP), it is not circular: the fused prediction is defined as an average, not fitted to the evaluation metric. The 'Same Trajectory' evaluation adapts and tests on the same frames, which is transductive and may inflate absolute numbers, but this applies equally to all listed methods and does not make the central superiority claim reduce by construction. The self-citation to UDAc-SFDA [27] (Shi et al., same first author) is only as a baseline and is explicitly modified; it is not load-bearing. Baseline re-implementations and test-set hyperparameter selection are correctness and validity risks, not circularity. No step in the paper's derivation chain is equivalent to its input by definition.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim depends on several free hyperparameters and domain assumptions. Most notably, the multi-scale threshold values and confidence threshold are tuned on the evaluation set itself, and the benchmark validity is asserted rather than externally validated. The re-implementation of baselines is a key assumption that affects the comparison.

free parameters (5)
  • cluster merging thresholds tau_2 = [0.8, 0.85, 0.9]
    Selected based on AP50 on the test environments (Table IV); multi-scale fusion averages models trained with these values.
  • matching distance threshold tau_1 = 1.5
    Hand-set for Hungarian matching between bounding boxes across frames.
  • pseudo-label confidence threshold = 0.5
    Teacher predictions above 0.5 are used as pseudo-labels for student training.
  • EMA decay alpha_1 = 0.99
    Controls teacher update in Mean Teacher framework.
  • temperature beta = 0.1
    Temperature in InfoNCE contrastive loss.
assumptions (4)
  • domain assumption Detic's zero-shot predictions are sufficiently reliable to serve as initial pseudo-labels for adaptation
    The whole method initializes from Detic and uses its predictions as pseudo-labels without source data.
  • domain assumption Temporal clustering of bounding boxes across frames correctly associates the same physical object
    The method assumes IoU and logit similarity across consecutive frames correspond to persistent objects, even under occlusion.
  • domain assumption iTHOR and Matterport3D with synthetic lighting perturbations represent realistic indoor domain shifts
    The benchmark validity rests on these datasets capturing lighting, layout, and object diversity changes.
  • ad hoc to paper Re-implemented baselines are faithful to the original methods
    Section V-B states official code is not directly applicable, so all baselines were re-implemented by the authors.

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Cite this review

Pith. "Pith review of Embodied Domain Adaptation for Object Detection." pith.science (2026). https://pith.science/paper/KFTCVF7A

@misc{pith2026250621860,
  author       = {Pith},
  title        = {Pith review of: Embodied Domain Adaptation for Object Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KFTCVF7A}},
  note         = {Machine review of arXiv:2506.21860}
}
read the original abstract

Mobile robots rely on object detectors for perception and object localization in indoor environments. However, standard closed-set methods struggle to handle the diverse objects and dynamic conditions encountered in real homes and labs. Open-vocabulary object detection (OVOD), driven by Vision Language Models (VLMs), extends beyond fixed labels but still struggles with domain shifts in indoor environments. We introduce a Source-Free Domain Adaptation (SFDA) approach that adapts a pre-trained model without accessing source data. We refine pseudo labels via temporal clustering, employ multi-scale threshold fusion, and apply a Mean Teacher framework with contrastive learning. Our Embodied Domain Adaptation for Object Detection (EDAOD) benchmark evaluates adaptation under sequential changes in lighting, layout, and object diversity. Our experiments show significant gains in zero-shot detection performance and flexible adaptation to dynamic indoor conditions.

Figures

Figures reproduced from arXiv: 2506.21860 by the authors.

Figure 1
Figure 1. Top: Traditional Source-Free Domain Adaptation (SFDA) focuses on adapting closed-form detectors to shared categories. Bottom: Our approach uses OVOD to adapt the model for dynamic indoor environments with dynamic content and lighting conditions. The zero-shot capabilities of OVOD enable adaptation to diverse categories in these environments. for SFOD are mainly related to autonomous driving, such as Cityscapes [12],… view at source ↗
Figure 2
Figure 2. For each indoor scene layout, pseudo labels are first generated using the Instance Clusters algorithm to link the same instances with multiple [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The top row shows the predictions for the first four frames in our lab [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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Reference graph

Works this paper leans on

44 extracted references · 38 canonical work pages

  1. [1]

    Microsoft COCO: common objects in context,

    T. Lin et al., “Microsoft COCO: common objects in context,” inECCV, vol. 8693. Springer, 2014, pp. 740–755

    work page 2014

  2. [2]

    Detecting twenty-thousand classes using image-level supervision,

    X. Zhou et al., “Detecting twenty-thousand classes using image-level supervision,” in ECCV, 2022

    work page 2022

  3. [3]

    Yolo-world: Real-time open-vocabulary object de- tection,

    T. Cheng et al. , “Yolo-world: Real-time open-vocabulary object de- tection,” in CVPR, 2024

    work page 2024

  4. [4]

    Grounding DINO: marrying DINO with grounded pre- training for open-set object detection,

    S. Liu et al., “Grounding DINO: marrying DINO with grounded pre- training for open-set object detection,” CoRR, vol. abs/2303.05499, 2023

    arXiv 2023

  5. [5]

    Domain adaptive faster r-cnn for object detection in the wild,

    Y . Chen et al., “Domain adaptive faster r-cnn for object detection in the wild,” in CVPR, 2018, pp. 3339–3348

    work page 2018

  6. [6]

    Strong-weak distribution alignment for adaptive object detection,

    K. Saito et al., “Strong-weak distribution alignment for adaptive object detection,” in CVPR, 2019, pp. 6956–6965

    work page 2019

  7. [7]

    Prior-based domain adaptive object detection for hazy and rainy conditions,

    V . A. Sindagi et al., “Prior-based domain adaptive object detection for hazy and rainy conditions,” in ECCV. Springer, 2020, pp. 763–780

    work page 2020

  8. [8]

    Confmix: Unsupervised domain adaptation for object detection via confidence-based mixing,

    G. Mattolin et al. , “Confmix: Unsupervised domain adaptation for object detection via confidence-based mixing,” in WACV. IEEE, 2023, pp. 423–433

    work page 2023

Show all 44 references
  1. [9]

    Towards online domain adaptive object detection,

    V . VS et al. , “Towards online domain adaptive object detection,” in CVPR, 2023, pp. 478–488

    2023

  2. [10]

    Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results,

    A. Tarvainen et al. , “Mean teachers are better role models: Weight- averaged consistency targets improve semi-supervised deep learning results,” NeurIPS, vol. 30, 2017

    2017

  3. [11]

    Source-free unsupervised domain adaptation with hypothesis consolidation of prediction rationale,

    Y . Shu et al. , “Source-free unsupervised domain adaptation with hypothesis consolidation of prediction rationale,” CoRR, 2024

    2024

  4. [12]

    The cityscapes dataset for semantic urban scene understanding,

    M. Cordts et al. , “The cityscapes dataset for semantic urban scene understanding,” in CVPR, 2016, pp. 3213–3223

    2016

  5. [13]

    Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?

    M. Johnson-Roberson et al., “Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?” arXiv preprint arXiv:1610.01983, 2016

    2016 arXiv

  6. [14]

    SHIFT: a synthetic driving dataset for continuous multi- task domain adaptation,

    T. Sun et al., “SHIFT: a synthetic driving dataset for continuous multi- task domain adaptation,” in CVPR, June 2022, pp. 21 371–21 382

    2022

  7. [15]

    The pascal visual object classes (VOC) challenge,

    M. Everingham et al. , “The pascal visual object classes (VOC) challenge,” IJCV, 2010

    2010

  8. [16]

    AI2-THOR: an interactive 3d environment for visual AI,

    E. Kolve et al., “AI2-THOR: an interactive 3d environment for visual AI,” CoRR, vol. abs/1712.05474, 2017

    2017 arXiv

  9. [17]

    Matterport3d: Learning from rgb-d data in indoor environments,

    A. Chang et al. , “Matterport3d: Learning from rgb-d data in indoor environments,” 3DV, 2017

    2017

  10. [18]

    Enhancing embodied object detection through language-image pre-training and implicit object memory,

    N. H. Chapman et al., “Enhancing embodied object detection through language-image pre-training and implicit object memory,” CoRR, vol. abs/2402.03721, 2024

    2024 arXiv

  11. [19]

    Interactron: Embodied adaptive object detection,

    K. Kotar et al., “Interactron: Embodied adaptive object detection,” in CVPR. IEEE, 2022, pp. 14 840–14 849

    2022

  12. [20]

    Evidential active recognition: Intelligent and prudent open-world embodied perception,

    L. Fan et al. , “Evidential active recognition: Intelligent and prudent open-world embodied perception,” in CVPR, June 2024, pp. 16 351– 16 361

    2024

  13. [21]

    Active open-vocabulary recognition: Let intelligent moving mitigate clip limitations,

    L. Fan and et al., “Active open-vocabulary recognition: Let intelligent moving mitigate clip limitations,” CVPR, pp. 16 394–16 403, June 2024

    2024

  14. [22]

    Move to see better: Self-improving embodied object detection,

    Z. Fang et al., “Move to see better: Self-improving embodied object detection,” in BMVC. BMV A Press, 2021, p. 270

    2021

  15. [23]

    Learning transferable features with deep adaptation networks,

    M. Long et al. , “Learning transferable features with deep adaptation networks,” in ICML, 2015

    2015

  16. [24]

    Domain-adversarial training of neural networks,

    Y . Ganin et al. , “Domain-adversarial training of neural networks,” JMLR, vol. 17, pp. 59:1–59:35, 2016

    2016

  17. [25]

    Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data,

    J. Huang et al. , “Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data,” in NeurIPS, 2021, pp. 3635–3649

    2021

  18. [26]

    A free lunch for unsupervised domain adaptive object detection without source data,

    X. Li et al. , “A free lunch for unsupervised domain adaptive object detection without source data,” in AAAI, vol. 35, no. 10, 2021, pp. 8474–8481

    2021

  19. [27]

    Improving online source-free domain adaptation for object detection by unsupervised data acquisition,

    X. Shi et al. , “Improving online source-free domain adaptation for object detection by unsupervised data acquisition,” in ECCVW, 2024, p. abs/2310.19258

    2024 arXiv

  20. [28]

    Simplifying source-free domain adaptation for object detection: Effective self-training strategies and performance insights,

    Y . Hao et al. , “Simplifying source-free domain adaptation for object detection: Effective self-training strategies and performance insights,” in ECCV 2024, 2024

    2024

  21. [29]

    A simple framework for contrastive learning of visual representations,

    T. Chen et al., “A simple framework for contrastive learning of visual representations,” in ICML, ser. Proceedings of Machine Learning Research, 2020

    2020

  22. [30]

    Instance relation graph guided source-free domain adaptive object detection,

    V . VS et al. , “Instance relation graph guided source-free domain adaptive object detection,” CVPR, 2023

    2023

  23. [31]

    Unbiased teacher for semi-supervised object detection,

    Y . Liu et al., “Unbiased teacher for semi-supervised object detection,” in ICLR, 2021

    2021

  24. [32]

    Adaptive batch normalization for practical domain adaptation,

    Y . Li et al. , “Adaptive batch normalization for practical domain adaptation,” PR, vol. 80, pp. 109–117, 2018

    2018

  25. [33]

    Domain adaptation for large-vocabulary object detec- tors,

    K. Jiang et al., “Domain adaptation for large-vocabulary object detec- tors,” CoRR, vol. abs/2401.06969, 2024

    2024 arXiv

  26. [34]

    Cross-domain weakly-supervised object detection through progressive domain adaptation,

    N. Inoue et al. , “Cross-domain weakly-supervised object detection through progressive domain adaptation,” in CVPR, 2018, pp. 5001– 5009

    2018

  27. [35]

    Semantic foggy scene understanding with syn- thetic data,

    C. Sakaridis et al. , “Semantic foggy scene understanding with syn- thetic data,” IJCV, vol. 126, pp. 973–992, 2018

    2018

  28. [36]

    Pushing the limits of unconstrained face detection: a challenge dataset and baseline results,

    H. Nada et al. , “Pushing the limits of unconstrained face detection: a challenge dataset and baseline results,” in BTAS. IEEE, 2018, pp. 1–10

    2018

  29. [37]

    Cascade R-CNN: delving into high quality object detection,

    Z. Cai et al. , “Cascade R-CNN: delving into high quality object detection,” in CVPR, 2018, pp. 6154–6162

    2018

  30. [38]

    Representation learning with contrastive predictive coding,

    A. van den Oord et al. , “Representation learning with contrastive predictive coding,” CoRR, vol. abs/1807.03748, 2018

    2018 arXiv

  31. [39]

    Semantic mapnet: Building allocentric semantic maps and representations from egocentric views,

    V . Cartillier et al. , “Semantic mapnet: Building allocentric semantic maps and representations from egocentric views,” in AAAI, 2021, pp. 964–972

    2021

  32. [40]

    Benchmarking robustness in object detec- tion: Autonomous driving when winter is coming,

    C. Michaelis et al. , “Benchmarking robustness in object detec- tion: Autonomous driving when winter is coming,” arXiv preprint arXiv:1907.07484, 2019

    1907 arXiv

  33. [41]

    Instance relation graph guided source-free domain adap- tive object detection,

    V . VS et al., “Instance relation graph guided source-free domain adap- tive object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 3520–3530

    2023

  34. [42]

    Probabilistic two-stage detection,

    X. Zhou et al. , “Probabilistic two-stage detection,” CoRR, vol. abs/2103.07461, 2021

    2021 arXiv

  35. [43]

    Deep residual learning for image recognition,

    K. He et al., “Deep residual learning for image recognition,” in CVPR, 2016, pp. 770–778

    2016

  36. [44]

    Adam: A method for stochastic optimization,

    D. P. Kingma et al. , “Adam: A method for stochastic optimization,” in ICLR, 2015

    2015

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Reviewed August 6, 2026 · model on record in the stance chip above.