Pith. sign in

REVIEW 5 major objections 8 minor 3 cited by

Large Language Model-Based Semantic Communication System for Image Transmission

T0 review · 5 major / 8 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read This paper proposes transmitting images as short text captions generated by a vision-language model and regenerated by a diffusion model at the receiver, cutting transmitted data 4,250-fold while raising effective data rate.

desk verdict The 4250x compression claim is an artifact of comparing a caption to raw image bits; the system is an off-the-shelf integration whose evaluation doesn't support its headline numbers. read the letter →

arxiv 2501.12988 v1 pith:R3ZVZLGM submitted 2025-01-22 eess.SP

Soheyb Ribouh , Osama Saleem This is my paper · ORCID
classification eess.SP
keywords semanticcommunicationlargelanguagemodelsimagetransmissionOFDM6Gvision-languagediffusionUMachannel
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

This paper proposes an end-to-end semantic image-transmission system in which a vision-language model at the transmitter turns an image into a short text caption, and a diffusion model at the receiver turns the caption back into an image. Over a 3GPP urban-macrocell OFDM link, the system sends about 1.392 Kb instead of 5.916 Mb per image, a 4,250-fold reduction, and achieves an effective rate of 23.05 Mbps versus 19.34 Mbps for conventional transmission. The authors report that the reconstructed image remains recognizable even when the channel corrupts parts of the caption, because the receiver regenerates from meaning rather than from exact bits. The pay-off, if true, is a concrete route to the extremely low-overhead image delivery envisioned for 6G, at the price of replacing bit-exact fidelity with semantic fidelity.

What carries the argument

The load-bearing mechanism is the text prompt as a semantic bottleneck: Algorithm 1 compresses an image into caption tokens through an autoregressive vision-language model, and Algorithm 2 embeds those tokens with a CLIP text encoder and regenerates an image through a denoising diffusion process. This pair converts bit errors in the channel into semantic errors, so a corrupted token changes a few letters of the caption while the overall meaning remains recoverable. The channel is a full OFDM chain with LDPC coding, 4-QAM, pilots, channel estimation, and equalization over the UMa model, which is what makes the data-size and rate numbers physically meaningful.

What would settle it

Take a set of images containing small task-critical details such as street signs, license plates, or fine-grained species, run the described LLaVA-to-Stable-Diffusion pipeline over the UMa channel at high SNR, and compare a downstream classifier's accuracy on the original images with its accuracy on the reconstructed ones. If accuracy drops substantially even though SSIM stays high, then the caption bottleneck does not preserve the semantic content that the claimed 4,250-fold compression is supposed to carry.

Watch

Extended reading notes

Core claim

The paper's central claim is that semantic content, not raw pixels, is what needs to cross a wireless channel, and that modern vision-language and generative models make this practical. The proposed system uses LLaVA as the semantic encoder: its image encoder and aligned text decoder produce a token-by-token prompt such as "A brown and white bird perched on a wooden post." At the receiver, a CLIP text encoder embeds the received prompt and Stable Diffusion generates an image conditioned on that embedding. Evaluated over a 28 GHz UMa channel with 60–120 km/h mobility, the prompt-based system transmits 4,250 times less data and achieves a higher effective data rate than the conventional LDPC-coded QAM-OFDM baseline, because it sends only the text that describes the image. At low SNR the semantic system has higher SSIM than the conventional one; at high SNR the conventional system has higher SSIM, which the authors attribute to the unavoidable variation in generative reconstruction.

Load-bearing premise

The promised gain rests on the assumption that a short caption is a faithful enough stand-in for the image's meaning, so that an image regenerated from the caption is an acceptable substitute for the original; the paper never validates this against a task, a human reader, or an external semantic ground truth.

Editorial extensions

If this is right

  • If the caption bottleneck is accepted, image delivery over 6G links can be planned in terms of hundreds of bytes per image rather than megabytes, shifting bandwidth budgets from source coding to semantic coding.
  • The system's robustness at low SNR means a corrupted caption still yields a recognizable image, so link-budget margins for image traffic could be relaxed compared with bit-exact transmission.
  • Because the semantic decoder always regenerates something, there is an inherent ceiling on pixel-level fidelity at high SNR; applications that need exact images would keep conventional transmission, while meaning-oriented applications would use the semantic pipeline.
  • The paper positions the same LLM-based semantic coding as extendable to other data modalities, making language the common representation across future intelligent communicating nodes.

Reading between the lines

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

  • Beyond the paper's claims, the 4,250-fold ratio compares a lossy semantic description against a bit-exact source; a fair engineering comparison would also count the bits an application actually consumes, so the ratio may be smaller for tasks that need fine-grained details such as text inside the image.
  • A reader might test the semantic-fidelity claim by replacing SSIM with task-level metrics: if the caption omits a stop sign's text or a vehicle's license plate, a downstream detector on the reconstructed image would fail while SSIM looks acceptable.
  • The prompt itself is a high-level description, so the scheme implicitly converts image transmission into a text-understanding problem; this suggests a possible extension where the same channel carries editing instructions such as "same scene at night" rather than the original caption, turning the link into a generative image-editing channel.
  • Because the receiver's diffusion model is fixed and pretrained, the system's low-SNR robustness is partly a property of the generative prior rather than of the wireless chain; a different receiver prior would shift the SNR at which the semantic and conventional curves cross.
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

5 major / 8 minor

Summary. The manuscript proposes an end-to-end OFDM-based semantic communication system for image transmission. At the transmitter, a LLaVA-based semantic encoder converts an image into a short text prompt; after LDPC coding, QAM modulation, and OFDM transmission over a 3GPP Urban Macro-cell channel model, the receiver decodes the prompt and uses a CLIP text encoder together with Stable Diffusion to regenerate an image. The authors report SSIM and BLEU results versus SNR and claim a 4250-fold reduction in transmitted data size (5.916 Mb to 1.392 Kb) together with a higher effective data rate (23.05 Mbps vs. 19.34 Mbps) than a conventional communication method.

Significance. If the claims were supported, the work would be a potentially interesting demonstration that LLM-generated captions can serve as the semantic source representation for image transmission over realistic channels, achieving compression far beyond conventional source coding. The use of a 3GPP UMa channel model rather than only AWGN, the end-to-end integration of a full physical-layer chain, and the reliance on public tools (Sionna, LLaVA, Stable Diffusion) are all strengths that make the system reproducible in principle. However, the current evidence does not establish the central claims: the evaluation rests on a single illustrative image, no test corpus or error bars are reported, the conventional baseline is not defined, and the adopted metrics (SSIM, and BLEU measured against the encoder's own prompt) do not measure semantic fidelity or task utility. The headline compression ratio conflates source substitution (sending a caption instead of pixels) with compression, so the quantitative claims are not yet credible.

major comments (5)
  1. [Section IV, Table II] The evaluation reports no dataset, no number of test images, no confidence intervals, and no description of how the SSIM and BLEU curves in Figs. 2 and 3 are averaged. The only concrete example is a single bird image in Fig. 4. Without a defined test corpus and protocol, the central quantitative claims (4250x data reduction and 23.05 vs. 19.34 Mbps) are not reproducible, and the reported curves cannot be meaningfully assessed.
  2. [Section IV, Table II and Fig. 2] The 'conventional communication' baseline is never specified. If it transmits the raw uncompressed image, then the 4250x ratio compares a text caption to raw pixels and is not a source-compression ratio; if it uses a compressed format or a learned codec, that must be stated and the comparison must be made at equal end-to-end rate. The manuscript also does not state the SNR at which the effective data rates in Table II are computed, even though the success-rate factor S = Bs/Bt presumably depends on the operating SNR.
  3. [Section IV, Fig. 3] The BLEU score is computed between the received prompt and the prompt generated by the semantic encoder at the transmitter, which is part of the system under test rather than an external ground truth. A high BLEU score therefore only shows that the wireless channel delivered the transmitted caption with few errors; it says nothing about whether the caption faithfully represents the image content. A task-oriented metric (e.g., retrieval, classification, caption-based QA) or human judgment of semantic preservation is needed to validate the semantic representation.
  4. [Section IV, Figs. 2 and 4] SSIM is used as the main reconstruction metric, but the decoder deliberately generates a new image from a text description, so pixel-level similarity to the original is not the appropriate notion of fidelity. The authors acknowledge this variation, yet the claimed 'superior' low-SNR performance is an artifact of comparing a generated image with the original using a metric that the pipeline is structurally unable to maximize. The example in Fig. 4 at SNR=3.3 dB actually shows loss of the wooden post in both the prompt and the reconstructed image, which is a semantic content error; treating this as a successful reconstruction is not justified.
  5. [Section IV, Effective Data Rate] The definitions of Bs, Bt, N, and Tsymbol are not tied to the numerical values in Table II. The table reports data sizes of 1.392 Kb and 5.916 Mb and rates of 23.05 and 19.34 Mbps, but the manuscript does not show how the success rates and the total transmission time T were obtained for each system, nor how many OFDM symbols were used for the conventional transmission. Without these details, the comparison in Table II cannot be verified.
minor comments (8)
  1. [Abstract and Section III-B] The paper repeatedly describes the receiver as an 'LLM-based semantic decoder', but Section III-B shows that the decoder is a CLIP text encoder plus Stable Diffusion; the wording should be corrected for consistency.
  2. [Section II-A, Eq. (4)] The quantity Hse(X̂) is introduced as the entropy of the reconstructed data, but the notation and the surrounding explanation are not sufficient to connect it to the semantic channel capacity formula; moreover, this theoretical framework is not used in the experimental section.
  3. [Section IV, Fig. 3] The figure label says 'BLUE score' instead of 'BLEU score'.
  4. [Section IV] There are numerous typos and grammar errors, including 'recontacted image quality', 'the semantic information prompt was successfully recovered', 'wnoden p st', 'provinding', and 'by by'; the manuscript should be carefully proofread.
  5. [Algorithm 1] The functions EnI, Enp, Align, and Autoreg are not specified; since the implementation is based on LLaVA, the manuscript should state the exact model version, prompt template, and alignment mechanism used.
  6. [Table I] The table gives subcarrier spacing and the number of OFDM symbols but not the total transmission time T; stating T would make the data-rate calculation in Table II directly verifiable.
  7. [Related Work and Section IV] The paper cites several semantic communication systems but provides no quantitative comparison against any of them (e.g., DeepJSCC or other image-oriented semantic communication baselines); a comparison table would help position the contribution.
  8. [Fig. 4] The reconstructed images referenced as ①, ②, ③, and ④ in the text are not clearly identified in the figure caption, and the caption does not state which panel corresponds to which SNR; please clarify.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the compression and data-rate figures are arithmetic, and the image-quality metric uses an external reference.

full rationale

The paper's central claims are arithmetic or externally benchmarked, not derived from their own outputs. The 4250x data-size reduction is a ratio of the measured bit counts in Table II (1.392 Kb vs 5.916 Mb) and is therefore a definitional accounting of the two payloads, not a fitted prediction. The SSIM evaluation in Fig. 2 compares the reconstructed image to the original transmitted image, an external reference outside the system's own encoder output. The BLEU comparison in Fig. 3 measures how faithfully the OFDM/LDPC channel delivered the semantic encoder's prompt; although this does not validate the caption as a sufficient semantic representation, it is a standard channel-fidelity check analogous to BER, not a circular derivation. No parameter is fitted to the test data, no uniqueness theorem is imported from the authors' prior work, and no load-bearing premise is justified solely by self-citation; the self-citations [3, 5, 8, 9] appear only as background. The paper's genuine weakness, namely that the semantic sufficiency of a LLaVA caption is asserted rather than validated against task-oriented or human ground truth, is a validity and correctness concern, not a circularity of the derivation chain.

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

The system introduces no new free parameters or invented entities. It relies entirely on pre-trained models and a loosely defined baseline. The main hidden assumptions are the sufficiency of the text prompt as a semantic representation, the validity of SSIM as a fidelity metric, and the choice of uncompressed transmission as the only baseline.

assumptions (4)
  • domain assumption Semantic entropy and semantic channel capacity formulas (Eqs. 1-4) from [20] and [22] are assumed correct.
    The paper quotes these formulas as background but does not derive or use them; the experimental claims do not depend on them.
  • domain assumption Pre-trained LLaVA, CLIP, and Stable Diffusion models perform the semantic encoding and decoding functions as claimed.
    Section III states these models are used without fine-tuning; the entire system performance is inherited from these models.
  • domain assumption The conventional communication baseline transmits the full image without source compression.
    Section IV compares 'conventional communication' with a data size of 5.916 Mb, implying no compression, but this is never stated or justified.
  • ad hoc to paper SSIM between the original and generated image is an appropriate measure of semantic communication quality.
    Section IV acknowledges that variation is inherent and not a flaw, yet still uses SSIM to claim superiority at low SNR.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Large Language Model-Based Semantic Communication System for Image Transmission." pith.science (2026). https://pith.science/paper/R3ZVZLGM

@misc{pith2026250112988,
  author       = {Pith},
  title        = {Pith review of: Large Language Model-Based Semantic Communication System for Image Transmission},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R3ZVZLGM}},
  note         = {Machine review of arXiv:2501.12988}
}
read the original abstract

The remarkable success of Large Language Models (LLMs) in understanding and generating various data types, such as images and text, has demonstrated their ability to process and extract semantic information across diverse domains. This transformative capability lays the foundation for semantic communications, enabling highly efficient and intelligent communication systems. In this work, we present a novel OFDM-based semantic communication framework for image transmission. We propose an innovative semantic encoder design that leverages the ability of LLMs to extract the meaning of transmitted data rather than focusing on its raw representation. On the receiver side, we design an LLM-based semantic decoder capable of comprehending context and generating the most appropriate representation to fit the given context. We evaluate our proposed system under different scenarios, including Urban Macro-cell environments with varying speed ranges. The evaluation metrics demonstrate that our proposed system reduces the data size 4250 times, while achieving a higher data rate compared to conventional communication methods. This approach offers a robust and scalable solution to unlock the full potential of 6G connectivity.

Figures

Figures reproduced from arXiv: 2501.12988 by the authors.

Figure 1
Figure 1. System model clude a semantic source coding, which compresses a text prompt into its key headwords at the transmitter and a semantic knowledge distillation technique for semantic decoding at the receiver, which generates customized prompts by learning the language style through in-context learning. A Scene Graph-based Generative Semantic Communication (SG2SC) framework is introduced in [12], where they propose a sem… view at source ↗
Figure 2
Figure 2. SSIM vs. SNR for semantic and conventional commu [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. BLEU score vs. SNR for semantic prompts. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Illustration of the transmission and reconstruction process. The transmitted image and its corresponding semantic [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. Large-Scale Model Enabled Semantic Communication Based on Robust Knowledge Distillation

    cs.LG 2025-08 conditional novelty 6.0 of 10

    A framework that combines neural architecture search and knowledge distillation to compress a ViT-B/16 teacher into a compact, channel-robust semantic encoder for image classification.

  2. AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives

    cs.NI 2025-09 conditional novelty 4.0 of 10

    A survey that organizes LLM and AI reasoning methods into a taxonomy and maps them onto the physical, link, network, transport, and application layers of wireless networks.

  3. From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications

    cs.AI 2025-05 conditional novelty 2.0 of 10

    This paper is a broad tutorial on applying LAMs and agentic AI to 6G, largely restating existing research rather than introducing new results.

Reference graph

Works this paper leans on

28 extracted references · 21 canonical work pages · cited by 3 Pith papers

  1. [1]

    & Chen, M

    Saad, W., Bennis, M. & Chen, M. A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network. 34, 134-142 (2019)

    work page 2019

  2. [2]

    & Others On the road to 6G: Visions, requirements, key technologies, and testbeds

    Wang, C., You, X., Gao, X., Zhu, X., Li, Z., Zhang, C., Wang, H., Huang, Y ., Chen, Y ., Haas, H. & Others On the road to 6G: Visions, requirements, key technologies, and testbeds. IEEE Communications Surveys & Tutorials . 25, 905-974 (2023)

    work page 2023

  3. [3]

    & Hadid, A

    Ribouh, S., Sadli, R., Elhillali, Y ., Rivenq, A. & Hadid, A. Vehicular Environment Identification Based on Channel State Information and Deep Learning. Sensors. 22, 9018 (2022)

    work page 2022

  4. [4]

    Study on Artificial Intelligence (AI)/Machine Learn- ing (ML) for NR Air Interface RAN,

    3GPP Release 18, “Study on Artificial Intelligence (AI)/Machine Learn- ing (ML) for NR Air Interface RAN,” Meeting #112, Athens, Greece, Tech. Rep., 27th February – 3rd March 2023

    2023

  5. [5]

    & Rivenq, A

    Ribouh, S., Elhillali, Y . & Rivenq, A. Multiple sequential constraint removal algorithm for channel estimation in vehicular environment. 2020 International Symposium On Networks, Computers And Communications (ISNCC). pp. 1-7 (2020)

    work page 2020

  6. [6]

    & Poor, H

    Chaccour, C., Saad, W., Debbah, M., Han, Z. & Poor, H. Less data, more knowledge: Building next generation semantic communication networks. IEEE Communications Surveys & Tutorials . (2024)

    work page 2024

  7. [7]

    & Miao, C

    Yang, W., Du, H., Liew, Z., Lim, W., Xiong, Z., Niyato, D., Chi, X., Shen, X. & Miao, C. Semantic communications for future internet: Fun- damentals, applications, and challenges. IEEE Communications Surveys & Tutorials. 25, 213-250 (2022)

    work page 2022

  8. [8]

    & Hadid, A

    Ribouh, S. & Hadid, A. Is Semantic Communication for Autonomous Driving Secured against Adversarial Attacks?. 2024 IEEE 6th Interna- tional Conference On AI Circuits And Systems (AICAS) . pp. 139-143 (2024)

    2024

Show all 28 references
  1. [9]

    & Hadid, A

    Ribouh, S. & Hadid, A. SEECAD: Semantic End-to-End Communication for Autonomous Driving. 2024 IEEE Intelligent V ehicles Symposium (IV). pp. 1808-1813 (2024)

    2024

  2. [10]

    & You, X

    Jiang, F., Peng, Y ., Dong, L., Wang, K., Yang, K., Pan, C. & You, X. Large ai model-based semantic communications. IEEE Wireless Commu- nications. 31, 68-75 (2024)

    2024

  3. [11]

    & Kim, S

    Nam, H., Park, J., Choi, J., Bennis, M. & Kim, S. Language-oriented communication with semantic coding and knowledge distillation for text- to-image generation. ICASSP 2024-2024 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP) . pp. 13506-13510 (2024)

    2024

  4. [12]

    & Shi, G

    Yang, M., Gao, D., Xie, F., Li, J., Song, X. & Shi, G. SG2SC: A generative semantic communication framework for scene understanding- oriented image transmission. ICASSP 2024-2024 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP) . pp. 13486-13490 (2024)

    2024

  5. [13]

    & Cai, K

    Zheng, C. & Cai, K. GeNet: A Graph Neural Network-based Anti- noise Task-Oriented Semantic Communication Paradigm. ArXiv Preprint arXiv:2403.18296. (2024)

    2024 arXiv

  6. [14]

    & Yin, C

    Tong, H., Yang, Z., Wang, S., Hu, Y ., Saad, W. & Yin, C. Federated learning based audio semantic communication over wireless networks. 2021 IEEE Global Communications Conference (GLOBECOM) . pp. 1-6 (2021)

    2021

  7. [15]

    & Qin, Z

    Xie, H. & Qin, Z. A lite distributed semantic communication system for Internet of Things. IEEE Journal On Selected Areas In Communications . 39, 142-153 (2020)

    2020

  8. [16]

    & Qin, Z

    Weng, Z. & Qin, Z. Semantic communication systems for speech transmission. IEEE Journal On Selected Areas In Communications . 39, 2434-2444 (2021)

    2021

  9. [17]

    & Leung, V

    Zhang, W., Bai, K., Zeadally, S., Zhang, H., Shao, H., Ma, H. & Leung, V . Deepma: End-to-end deep multiple access for wireless image trans- mission in semantic communication. IEEE Transactions On Cognitive Communications And Networking . (2023)

    2023

  10. [18]

    & Fernando, A

    Lokumarambage, M., Gowrisetty, V ., Rezaei, H., Sivalingam, T., Ra- jatheva, N. & Fernando, A. Wireless end-to-end image transmission system using semantic communications. IEEE Access . (2023)

    2023

  11. [19]

    https://www.atis.org/wp-content/uploads/3gpp docu- ments/Rel15/ATIS.3GPP.37.885.V1530.pdf

  12. [20]

    Qin, Z., Tao, X., Lu, J., Tong, W. & Li, G. Semantic communications: Principles and challenges. ArXiv Preprint arXiv:2201.01389 . (2021)

    2021 arXiv

  13. [21]

    Sionna: An Open-Source Library for Next-Generation Physical Layer Research,

    Hoydis, J., Cammerer, S., Ait Aoudia, F., Vem, A., Binder, N., Marcus, G., and Keller, A., “Sionna: An Open-Source Library for Next-Generation Physical Layer Research,” arxiv preprint arXiv:2203.11854, 2022

    2022 arXiv

  14. [22]

    & Others An outline of a theory of semantic information

    Carnap, R., Bar-Hillel, Y . & Others An outline of a theory of semantic information. (Research Laboratory of Electronics, Massachusetts Institute of Technology,1952)

    1952

  15. [23]

    & Lee, Y

    Liu, H., Li, C., Wu, Q. & Lee, Y . Visual instruction tuning. Advances In Neural Information Processing Systems . 36 (2024)

    2024

  16. [24]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Dosovitskiy, A. An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv Preprint arXiv:2010.11929 . (2020)

    2020 arXiv

  17. [25]

    & Others Llama: Open and efficient foundation language models

    Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F. & Others Llama: Open and efficient foundation language models. arXiv 2023. ArXiv Preprint arXiv:2302.13971. 10 (2023)

    2023 arXiv

  18. [26]

    & Others Learning trans- ferable visual models from natural language supervision

    Radford, A., Kim, J., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J. & Others Learning trans- ferable visual models from natural language supervision. International Conference On Machine Learning . pp. 8748-8763 (2021)

    2021

  19. [27]

    & Ommer, B

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High- resolution image synthesis with latent diffusion models. Proceedings Of The IEEE/CVF Conference On Computer Vision And Pattern Recognition . pp. 10684-10695 (2022)

    2022

  20. [28]

    & Abbeel, P

    Ho, J., Jain, A. & Abbeel, P. Denoising diffusion probabilistic models. Advances In Neural Information Processing Systems . 33 pp. 6840-6851 (2020)

    2020

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.