Pith. sign in

REVIEW 3 major objections 6 minor 25 references

Time-Contrastive Pretraining for In-Context Image and Video Segmentation

T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Time-contrastive prompts lift CT organ Dice by up to 14.9 points

desk verdict Plausible combination of time-contrastive retrieval and VOS-based ICL, but the headline gains are confounded by an imbalanced fine-tuning procedure and inconsistent arithmetic. read the letter →

arxiv 2506.17837 v1 pith:L7RR5VLG submitted 2025-06-21 cs.CV

Assefa Wahd , Jacob Jaremko , Abhilash Hareendranathan This is my paper · ORCID
classification cs.CV
keywords time-contrastivelearningvisualin-contextvideoobjectsegmentationpromptretrievalmedicalimageself-supervisedpretrainingCTorganfew-shot
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

Temporal, the method introduced here, claims that a prompt retriever pretrained with a time-contrastive objective—where nearby video frames count as matching examples and distant frames as non-matching—can choose the context images that make visual in-context learning work for organ segmentation. The paper casts in-context learning itself as a video object segmentation problem: selected context pairs plus the query are concatenated as frames of a synthetic video and passed to a VOS model (SAM2), which labels the query. On the MICCAI FLARE 2022 CT dataset it reports 90.95% Dice for image segmentation (10.64 points above its best baseline) and 92.45% Dice for video segmentation (14.88 points above baselines), with the largest jumps on organs like the pancreas. The deeper claim is that temporal proximity in unlabeled video is a sufficient signal for learning which examples to retrieve, removing the need for labeled retrieval training.

What carries the argument

The load-bearing mechanism is the multi-positive time-contrastive loss, a self-supervised objective that trains an encoder so that temporally adjacent frames are pulled together and distant frames pushed apart; it extends Time-Contrastive Networks by replacing the triplet loss with a multi-positive cross-entropy computed over a binary label matrix, allowing multiple positives per anchor and cross-view positives via augmentation. The second half of the machinery is the formulation of ICL as video object segmentation: context pairs and the query are arranged as frames in a synthetic video, and a pretrained VOS model (SAM2) segments the final frame, which permits arbitrarily many full-resolution context images. For video inputs, the same retriever scores each frame, picks diverse keyframes, and propagates their masks bidirectionally to the entire sequence.

What would settle it

Run the same fine-tuning protocol (100 epochs, SAM2-B+ backbone, synthetic videos built by each baseline's own retriever) on the VOS-based ICL baseline with CLIP, DINOv2, and MedCLIP retrieval as well as on SAM2 and Medical SAM2 with mask prompts, then re-evaluate on the same 10 FLARE test scans; if the gap narrows to a few Dice points, the time-contrastive objective is not the cause of the reported improvements.

Watch

Extended reading notes

Core claim

The central discovery is that the context-retrieval problem for visual in-context learning can be solved by self-supervision from video: frames that are close in time are treated as positive pairs, frames far apart as negative pairs, and a ResNet-50 encoder trained with a multi-positive cross-entropy loss on this signal produces embeddings in which semantically similar medical images cluster. When a query arrives, the top similar training images are retrieved, concatenated with the query as a synthetic video, and segmented by the SAM2 VOS model without any manual prompt. The same retriever also scores every frame of a full video, selects temporally diverse keyframes that receive high-confidence mask predictions, and propagates those masks bidirectionally to cover the sequence. The paper attributes the large gains on FLARE 2022, especially on the pancreas, to this combination of time-contrastive retrieval and VOS-based inference.

Load-bearing premise

The headline improvements compare fine-tuned Temporal against baselines that were not fine-tuned; if the same synthetic-video fine-tuning boosts the baselines to a similar extent, the gains attributed to time-contrastive retrieval would largely disappear.

Editorial extensions

If this is right

  • If the reported gains hold, in-context segmentation can run fully automatically on CT volumes, with no manual first-frame prompt for videos and only a handful of labeled slices for new organs.
  • The capacity for variable, full-resolution context sets removes a known ceiling of grid-based visual in-context learning, which should help tasks that require fine spatial detail.
  • Because the retriever learns from unlabeled video alone, the same pretraining signal is available in any temporally coherent domain, from ultrasound sweeps to endoscopic video.
  • The diversity-aware selection step indicates that context sets should be chosen for coverage as well as similarity, a design principle that transfers to any retrieval-based few-shot learner.

Reading between the lines

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

  • One testable extension is to ablate the retriever: substituting random keyframes on the same fine-tuned VOS backbone would isolate how much of the video Dice gain comes from retrieval rather than propagation.
  • The paper's own design suggests the fine-tuning stage may be the dominant contributor; fine-tuning each baseline with the identical synthetic-video protocol would separate the retriever's contribution from the fine-tuning's.
  • The time-contrastive objective could generalize to other ICL modalities, such as using temporal order in surgical or ultrasound sequences to retrieve prompts for detection or registration.
  • An implicit consequence is that the retriever might transfer across datasets without retraining, since embeddings trained on FLARE videos could retrieve context for other CT tasks; the paper does not test this.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The paper proposes Temporal, a self-supervised time-contrastive objective for pretraining a prompt retriever for visual in-context segmentation. Context images are arranged as frames of a synthetic video and segmented by a video object segmentation (VOS) model, SAM2. For image segmentation the retriever selects top-K annotated contexts; for video segmentation it selects keyframes, predicts keyframe masks with the same pipeline, and propagates them bidirectionally. The method is evaluated on MICCAI FLARE 2022 CT organ segmentation using 40 scans for context/pretraining and 10 scans for testing. The paper reports 90.95% Dice for image segmentation and 92.45% Dice for video segmentation after fine-tuning, claiming gains of 10.64 and 14.88 percentage points over non-fine-tuned baselines.

Significance. If the central comparisons were properly controlled, the contribution would be meaningful for medical few-shot segmentation: a domain-specific self-supervised retriever that supports variable context size and full-resolution images, a unified image/video inference pipeline, and a plausible pretraining objective with a multipositive contrastive loss. The no-fine-tune image result (83.55% vs. 80.31% Dice) is a positive but modest signal, and the authors provide a code link and a coherent method description. However, the headline gains are not established because the fine-tuned comparisons are uncontrolled and the reported gain arithmetic is internally inconsistent.

major comments (3)
  1. [§2.3 (Fine-tuning), §3.1, Tables 1–2] The headline improvements compare a fine-tuned Temporal against baselines that are not fine-tuned. The only controlled comparison is the no-fine-tune image row, where Temporal achieves 83.55% versus 80.31% for Foster et al. with DINOv2, a 3.24-point gap. The fine-tuned 90.95% result cannot be attributed to the time-contrastive retriever unless the same fine-tuning recipe is applied to the VOS-based baselines, because the procedure trains SAM2 on synthetic videos constructed by the same retriever and could improve any VOS-based ICL pipeline regardless of the retriever. Please add fine-tuned versions of the baselines and report the controlled comparison.
  2. [Abstract, §4, Table 2] The reported improvement arithmetic is inconsistent. For image segmentation, 90.95 − 80.31 = 10.64 percentage points, but the conclusion states a 9.23% increase. For video segmentation, 92.45 − 77.83 = 14.62, while 92.45 − 77.57 = 14.88; the abstract's 14.88% is only against the weaker SAM-2 baseline, and the row reaching 92.45% uses a manual mask prompt, whereas the automated fine-tuned row is 92.23% (14.4% above Medical SAM-2). The paper should specify the exact baseline for every reported gain and use 'percentage points' rather than '% improvement'.
  3. [§3.1, Tables 1–2] No error bars, confidence intervals, or significance tests are reported for any result, despite the test set consisting of only 10 scans (922 slices). The 3.24-point no-fine-tune advantage and the organ-level differences (e.g., Pancreas 79.53% vs. 49.10%) may be within patient-level or run-to-run variability. Please report per-scan variances, bootstrap intervals, or repeated-run statistics, and clarify what the '5 evals' for the random baseline actually are.
minor comments (6)
  1. [Eq. (5)] The negative-sum term in the denominator should be exp(ẑ_i^T ẑ_k / τ), not exp(ẑ_k^T ẑ_j / τ); as written, the loss does not match the stated contrastive objective.
  2. [Table 1] The Zhang et al. grid-based row is reported as N/A, while the text states that the grid-based method achieves 'around 5%' Dice. Please either include the actual numbers or remove the row and adjust the text.
  3. [Abstract and §4] The expressions '10.64% improvement' and '14.88% improvement' describe percentage-point differences, not relative improvements; use 'percentage points' to avoid overstating the gains.
  4. [§2.3, video segmentation] The confidence score c_q used in confidence-based keyframe filtering is not defined. Please specify how the VOS model produces a confidence value for each keyframe prediction.
  5. [§3.1, Diversity-Aware Context Selection] The claimed ↑2.1% improvement for context size 5 is not shown in Table 1; only the size-10 comparison (89.65% vs. 90.95%) appears. Also, the validation protocol for setting λ=0.7 is not described, so it is unclear whether any of the 10 test scans were used for model selection.
  6. [Introduction] The word 'presever' appears to be a typo for 'preserve'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the reported gains rest on standard train/test evaluation, though the headline comparison is confounded by an asymmetric fine-tuning control.

full rationale

Temporal's derivation chain is empirical and self-contained. The retriever f_theta is pretrained with a multipositive time-contrastive loss on unlabeled video frames (Section 2.2, Eq. 5); the VOS backbone V_phi is fine-tuned on synthetic videos assembled from training-set images and their ground-truth masks (Section 2.3, 'Fine-tuning'); and all scores in Tables 1 and 2 are measured on a held-out 10-scan FLARE 2022 test set. No quantity in the method is defined in terms of the test Dice it is supposed to predict, no fitted parameter is renamed as a prediction, and no load-bearing claim rests on a self-citation by the authors. The reported 90.95% and 92.45% Dice values are therefore not circular by construction. The legitimate concerns are experimental-control and reporting issues: the abstract's 10.64% and 14.88% improvements compare a fine-tuned Temporal against baselines that were not given the same fine-tuning recipe (Section 3.1, Tables 1 and 2), so the marginal contribution of the retriever is not isolated; the conclusion's '9.23%' image-improvement figure is arithmetically inconsistent with the 10.64 percentage points shown in Table 1; and no error bars are reported for the 10-scan test set. These affect how convincing the empirical claim is, but they do not make the derivation equivalent to its inputs.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The central claim rests on the choice of temporal positive pairs, the transferability of a pretrained VOS model, and the fairness of the evaluation. The free parameters are standard contrastive and retrieval hyperparameters selected by validation; they are listed because the headline numbers depend on them. No new physical or conceptual entities are invented.

free parameters (5)
  • M, number of sampled temporal positives per anchor = 2
    The implementation states that two adjacent frames are selected as positives per anchor; this controls the number of positives in the contrastive loss and is not derived.
  • temperature tau = 0.1
    Selected via grid search over {0.1, 0.5, 1.0}; it sets the sharpness of the contrastive loss and affects all learned embeddings.
  • context size K = 10 for image segmentation, 20 keyframes for video
    The authors state that 10 context images work best and that 20 keyframes are sampled for video; the headline results depend on these choices.
  • diversity penalty lambda = 0.7
    Used in the diversity-aware context selection score and determined through validation rather than derived.
  • confidence threshold gamma = not specified
    Used to filter keyframe predictions in video inference; no value or selection procedure is reported, leaving a gap in the protocol.
assumptions (3)
  • domain assumption Temporally adjacent frames in CT volume videos are semantic positives and distant frames are negatives for organ segmentation context.
    Used to build the label matrix in Equation 3; if temporal adjacency does not track organ-level relevance, the retriever learns the wrong invariances.
  • domain assumption SAM2 can segment a query image when context pairs are fed as a synthetic video, and this transfers to medical CT after fine-tuning.
    Equation 2 rests on this transfer; the paper relies on SAM2 as the VOS model and then fine-tunes it, but the transfer is assumed before the fine-tuning step.
  • domain assumption The 40 training scans and 10 test scans are representative and are split without leakage from the unlabeled pretraining data.
    The dataset paragraph says unlabeled data is used for pretraining and 40 scans form the context set, but the exact overlap between pretraining, context, and test scans is not stated explicitly.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Time-Contrastive Pretraining for In-Context Image and Video Segmentation." pith.science (2026). https://pith.science/paper/L7RR5VLG

@misc{pith2026250617837,
  author       = {Pith},
  title        = {Pith review of: Time-Contrastive Pretraining for In-Context Image and Video Segmentation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/L7RR5VLG}},
  note         = {Machine review of arXiv:2506.17837}
}
read the original abstract

In-context learning (ICL) enables generalization to new tasks with minimal labeled data. However, mainstream ICL approaches rely on a gridding strategy, which lacks the flexibility required for vision applications. We introduce Temporal, a time-contrastive self-supervised objective that pretrains a prompt retriever for visual ICL, and formulate ICL as a video object segmentation (VOS) task. Temporal addresses key limitations of grid-based methods that restrict the number and resolution of context images. By reframing ICL as a VOS problem, our approach supports a variable number of context images while preserving their full resolution. To address the challenge of selecting optimal context sets for queries, we pretrain a prompt retriever on videos via self-supervised learning, where adjacent frames serve as positives and distant frames as negatives. For image segmentation, the prompt retriever selects relevant sequences that, when combined with the query, form coherent videos for VOS processing. For video segmentation, it identifies keyframes, predicts their masks using our ICL pipeline, and propagates them throughout the sequence. When evaluated on MICCAI FLARE 2022, our method achieves substantial improvements over baselines: 90.95% Dice score for image segmentation (10.64% improvement) and 92.45% Dice for video segmentation (14.88% improvement).

Figures

Figures reproduced from arXiv: 2506.17837 by the authors.

Figure 1
Figure 1. Model architecture. (a) Visual in-context learning via VOS: The retriever fθ selects the optimal context set P for a query image xq. The query image xq is appended to the context to create a video sequence, and the VOS model predicts the segmentation mask for xq using this sequence. (b) Video segmentation with ICL: 1) Select keyframes (Section 2.3), 2) Predict masks for keyframes using step (a), 3) Propagate masks a… view at source ↗
Figure 2
Figure 2. Multi-positive sampling strategy. For each anchor image in a clip, we sample M temporally adjacent frames as positives and all other frames in the clip as negatives. Shaded regions are frames that were not sampled in a minibatch. (the query image). This allows handling full-resolution images and variable-sized context sets. However, they relied on pre-trained CLIP embeddings for con￾text retrieval, which may not be … view at source ↗
Figure 3
Figure 3. Qualitative segmentation results on Pancreas. Our approach (right) produces more accurate organ segmentations and tracking compared to baselines. The baselines fail to track the organ after t = 32. 4. Bidirectional Propagation: Since keyframes may occur anywhere in the video, we feed prompts P into the VOS model Vϕ and propagate masks bidirec￾tionally (forward and backward) to generate segmentations for all frames: … view at source ↗

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

25 extracted references · 11 canonical work pages

  1. [1]

    https://doi.org/10.48550/arXiv.2301.08243

    Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., LeCun, Y., Ballas, N.: Self-Supervised Learning from Images with a Joint-Embedding Pre- dictive Architecture (Apr 2023). https://doi.org/10.48550/arXiv.2301.08243

    arXiv doi:10.48550/arxiv.2301.08243 2023

  2. [2]

    https://doi.org/ 10.48550/arXiv.2304.12210

    Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., Tian, Y., Schwarzschild, A., Wilson, A.G., Geiping, J., Garrido, Q., Fernandez, P., Bar, A., Pirsiavash, H., LeCun, Y., Gold- blum, M.: A Cookbook of Self-Supervised Learning (Jun 2023). https://doi.org/ 10.48550/arXiv.2304.12210

    arXiv doi:10.48550/arxiv.2304.12210 2023

  3. [3]

    Bar, A., Gandelsman, Y., Darrell, T., Globerson, A., Efros, A.A.: Visual Prompting via Image Inpainting

    work page

  4. [4]

    https://doi.org/10.48550/arXiv.2005.14165

    Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Nee- lakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D.M., Wu, J., Winter, C.,Hesse,C.,Chen,M.,Sigler,E.,Litwin,M.,Gray,S.,Chess,B.,Clark,J.,Berner, C., McCandlish, S., Radford, A., Sutskever, ...

    arXiv doi:10.48550/arxiv.2005.14165 2020

  5. [5]

    https://doi.org/10.48550/ arXiv.2002.05709

    Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A Simple Framework for Con- trastive Learning of Visual Representations (Jun 2020). https://doi.org/10.48550/ arXiv.2002.05709

    arXiv doi:10.48550/arxiv.2002.05709 2020

  6. [6]

    XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

    Cheng, H.K., Schwing, A.G.: XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model (Jul 2022). https://doi.org/10.48550/arXiv. 2207.07115

    work page Pith review arXiv doi:10.48550/arxiv.2207.07115 2022

  7. [7]

    Denner, S., Bujotzek, M., Bounias, D., Zimmerer, D., Stock, R., Jäger, P.F., Maier- Hein, K.: Visual Prompt Engineering for Medical Vision Language Models in Ra- diology (Aug 2024)

    work page 2024

  8. [8]

    Flexible visual prompts for in-context learning in computer vision

    Foster, T., Croitoru, I., Dorfman, R., Edlund, C., Varsavsky, T., Almazán, J.: Flexible visual prompts for in-context learning in computer vision (Dec 2023). https://doi.org/10.48550/arXiv.2312.06592

    work page Pith review arXiv doi:10.48550/arxiv.2312.06592 2023

Show all 25 references
  1. [9]

    https://arxiv.org/abs/2111.06377v3 (Nov 2021)

    He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked Autoencoders Are Scalable Vision Learners. https://arxiv.org/abs/2111.06377v3 (Nov 2021)

    2021 arXiv

  2. [10]

    He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum Contrast for Unsuper- vised Visual Representation Learning (Mar 2020)

    2020

  3. [11]

    https://doi.org/10.48550/arXiv.1512.03385

    He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (Dec 2015). https://doi.org/10.48550/arXiv.1512.03385

    2015 doi arXiv

  4. [12]

    https://doi.org/10.48550/arXiv.1711.05101

    Loshchilov, I., Hutter, F.: Decoupled Weight Decay Regularization (Jan 2019). https://doi.org/10.48550/arXiv.1711.05101

    2019 doi arXiv

  5. [13]

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W.,Howes,R.,Huang,P.Y.,Li,S.W.,Misra,I.,Rabbat,M.,Sharma,V.,Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P....

    2024

  6. [14]

    https://doi.org/ 10.48550/arXiv.2103.00020

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning Transferable Visual Models From Natural Language Supervision (Feb 2021). https://doi.org/ 10.48550/arXiv.2103.00020

    2021 doi arXiv

  7. [15]

    Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., Mintun, E., Pan, J., Alwala, V., Carion, N., Wu, C.Y., Girshick, R., Dollár, P., Feichtenhofer, C.: SAM 2: Segment Anything in Images and Videos 10 Wahd et al

  8. [16]

    https://doi.org/10.48550/arXiv.1704.06888

    Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., Levine, S.: Time-Contrastive Networks: Self-Supervised Learning from Video (Mar 2018). https://doi.org/10.48550/arXiv.1704.06888

    2018 doi arXiv

  9. [17]

    https://doi.org/10.48550/arXiv

    Sun, Y., Chen, Q., Wang, J., Wang, J., Li, Z.: Exploring Effective Factors for Im- proving Visual In-Context Learning (Apr 2023). https://doi.org/10.48550/arXiv. 2304.04748

    2023 doi

  10. [18]

    Machine Learning109(2), 373–440 (Feb 2020)

    van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Machine Learning109(2), 373–440 (Feb 2020). https://doi.org/10.1007/ s10994-019-05855-6

    2020

  11. [19]

    https://doi.org/10

    Wang, X., Wang, W., Cao, Y., Shen, C., Huang, T.: Images Speak in Images: A Generalist Painter for In-Context Visual Learning (Mar 2023). https://doi.org/10. 48550/arXiv.2212.02499

    2023 doi arXiv

  12. [20]

    https://doi.org/10.48550/arXiv

    Wang, Z., Wu, Z., Agarwal, D., Sun, J.: MedCLIP: Contrastive Learning from Unpaired Medical Images and Text (Oct 2022). https://doi.org/10.48550/arXiv. 2210.10163

    2022 doi arXiv

  13. [21]

    Journal of Big Data3(1), 9 (May 2016)

    Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. Journal of Big Data3(1), 9 (May 2016). https://doi.org/10.1186/s40537-016-0043-6

    2016 doi

  14. [22]

    ACM Computing Surveys56(12), 1–38 (Dec 2024)

    Yu, B.X., Chang, J., Wang, H., Liu, L., Wang, S., Wang, Z., Lin, J., Xie, L., Li, H., Lin, Z., Tian, Q., Chen, C.W.: Visual Tuning. ACM Computing Surveys56(12), 1–38 (Dec 2024). https://doi.org/10.1145/3657632

    2024 doi

  15. [23]

    https://doi.org/ 10.48550/arXiv.2311.03648

    Zhang, J., Wang, B., Li, L., Nakashima, Y., Nagahara, H.: Instruct Me More! Random Prompting for Visual In-Context Learning (Nov 2023). https://doi.org/ 10.48550/arXiv.2311.03648

    2023 doi arXiv

  16. [24]

    https://doi.org/10.48550/arXiv.2301.13670

    Zhang, Y., Zhou, K., Liu, Z.: What Makes Good Examples for Visual In-Context Learning? (Feb 2023). https://doi.org/10.48550/arXiv.2301.13670

    2023 doi arXiv

  17. [25]

    https://doi.org/10

    Zhu, J., Hamdi, A., Qi, Y., Jin, Y., Wu, J.: Medical SAM 2: Segment medical images as video via Segment Anything Model 2 (Dec 2024). https://doi.org/10. 48550/arXiv.2408.00874

    2024 doi arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.