Title: The Narrow Gate: Localized Image-Text Communication in Native Multimodal Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Computer Vision

Visual-Language Model

Interpretability

Native Multimodal Models
Abstract: Recent advances in multimodal training have significantly improved the integration of image understanding and generation within a unified model. This study investigates how vision-language models (VLMs) handle image-understanding tasks, focusing on how visual information is processed and transferred to the textual domain. We compare *native multimodal VLMs*, models trained from scratch on multimodal data to generate both text and images, and *non-native multimodal VLMs*, models adapted from pre-trained large language models or capable of generating only text, highlighting key differences in information flow. We find that in native multimodal VLMs, image and text embeddings are more separated within the residual stream. Moreover, VLMs differ in how visual information reaches text: non-native multimodal VLMs exhibit a distributed communication pattern, where information is exchanged through multiple image tokens, whereas models trained natively for joint image and text generation tend to rely on a single post-image token that acts as a *narrow gate* for visual information. We show that ablating this single token significantly deteriorates image-understanding performance, whereas targeted, token-level interventions reliably steer image semantics and downstream text with fine-grained control.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this submission, the authors perform a detailed experimental evaluation regarding the “communication mechanism” between different modalities (i.e., image and text) in pre-trained Vision-Language-Models (VLMs). Specifically, in their evaluation they compare text-only output models with models that are able to generate multimodal outputs. The authors argue that there is a clear difference between these two types of models when it comes to how information from the image modality flows to the textual modality. Specifically, they find that models trained to only produce textual outputs have more distributed attention patterns than those that are also able to generate images, which the authors find to mainly communicate through a “narrow gate” (the end-of-image EOI) token. \n\nThe authors provide a broad evaluation showcasing a difference in behavior between model types in general and with respect to the narrow gate in particular, including similarity metrics in embedding space, patching interventions, clustering analysis, and attention knockout experiments.\n\nSTRENGTHS AND WEAKNESSES:\n**Strenghts.**\n\n- First of all, I would like to commend the authors on the general quality of the submission — the information is well-presented, the writing and structure are clear, and the analyses and conclusions well presented.\n- The authors carefully design a multitude of experiments to showcase the behavioral differences between the models, employing adequate and state-of-the-art techniques for doing so. This allows them to distill a clear mechanistic difference between model types that can be tested through interventions.\n- While the analysis is quite *narrow* (focusing on a highly specific mechanism in the model, the *narrow gate*), the in-depth analysis and experimental evaluation yields reliable results for future research to build on.\n\n**Weaknesses.**\n\nWhile the evaluation and presentation of the observed effect are convincing, I have a couple of remaining questions and concerns.\n\n- **Relevance.** While the authors results convince me that there is a difference between models additionally trained for image generation and those that only produce text, the practical relevance of this finding is still a bit unclear to me. This is in particular the case as the results are also not overly surprising — if there is a loss on the image tokens to produce a very specific kind of information, it seems to be expected that these tokens will take a different role compared to when no loss is applied to the image tokens. Specifically, with no loss on the image tokens, there is no pressure on the EOI token to capture much semantics about the image, as the image tokens themselves can directly be used. Why is this finding of importance? \n- **Interpretation of results.**\n    - The authors argue that the results show that “the semantic content is more distributed [… and the models rely …] on multiple regions of the image” rather than a single token. It is unclear to me if, given the current results, much of a conclusion about the representation of information in the image tokens can be drawn (such as them representing different image regions in a specific fashion). Specifically, it seems to me that a very simple hypothesis could explain most of the respective results: the image tokens might just have a lot of redundant information (similar embeddings), which would be consistent with their pretraining paradigm, in which the spatial dimensions are averaged over to yield a single semantic embedding vector. As a result, deleting any single token or even larger subsets of tokens is unlikely to make a difference — importantly, highly similar embeddings would also induce the observed highly distributed attention maps. I would be curious to know the authors' take on this; image token deletion experiments or differently trained embedding models (e.g. DINO) could help better understand the underlying reason for the observed attention patterns. \n    - Secondly, I have difficulties with the argument that in Llava-7B (or other text-only models) the “image embeddings and text embeddings align increasingly throughout the layers”. Specifically, what does it mean to be an image embedding later in the network? As no loss is applied to the output at the position of image tokens, assigning any modality label to these tokens seems arbitrary. The model might just use the compute of those additional, potentially redundant tokens to improve performance (see also register tokens (Darcet et al, 2023) or pause tokens (Goyal et al., 2023).\n\nAdditional minor weaknesses.\n\n- Figure 1 (right) is cumbersome to read, as 3 dimensions are shown in 2 dimensions without the 3rd axis being clear.\n- The authors evaluate a custom version of Emu3 (fine-tuned from Emu3-Gen) — why is this relevant and how do results differ between models?\n- Minor typos: line 133 fig 2 is lowercase at the beginning of the sentence, in line 150 the word appendix is repeated, in line 169 there is a missing punctuation.\n\nQUESTIONS:\nPlease see the above weaknesses. In particular, I would highly appreciate if the authors could elaborate on the significance of their findings. Additionally, I would be curious to hear the authors' opinion on the stated concerns regarding the interpretation of the results.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates how visual information is transferred to textual outputs in vision-language models (VLMs), with a specific focus on the internal communication patterns between image and text tokens. The authors compare multimodal-output VLMs (e.g., Chameleon, Emu3) with unimodal-output VLMs (e.g., LLaVA, Pixtral), identifying a key difference: the former tend to use a single “narrow gate” token—EOI(end-of-image)—as the main conduit for visual information, while the latter employ distributed communication across many image tokens. Through a combination of attention analysis, semantic probing, ablation studies, and activation patching, the authors demonstrate that in narrow-gate models, blocking or modifying the EOI token drastically alters performance and output semantics. This highlights the architectural and functional distinction between these classes of models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The paper provides a detailed investigation of cross-modal communication mechanisms inside VLMs, using diverse interpretability tools such as attention knockout, cosine similarity, semantic probing (χ metric), and activation patching.\n\n2. The discovery that multimodal-output VLMs rely on a single token for image-to-text information transfer has broad implications for model control, interpretability, and robustness.\n\n3. The analysis is conducted across several strong open-source VLMs (Chameleon, Emu3, LLaVA, Janus, Pixtral), and the conclusions are supported by consistent evidence across different models and tasks (VQA, captioning, classification).\n\nWeaknesses\n\n1. The work is primarily an empirical analysis rather than a method or model contribution. While the findings are meaningful, the lack of algorithmic novelty may limit its perceived impact in NeurIPS.\n\n\n2. The paper omits discussion of prior studies[1,2,3,4,5,6] on communication patterns between image and text tokens, some findings are already observed in prior work.\n\n[1] Mitigating Multimodal Hallucination from an EOS Decision Perspective.\n\n[2] Mitigating Object Hallucination via Concentric Causal Attention.\n\n[3] Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation.\n\n[4] Controlmllm: Training-free visual prompt learning for multimodal large language models.\n\n[5] Attention Reallocation: Towards Zero-cost and Controllable Hallucination Mitigation of MLLMs.\n\n[6] An image is worth 1/2 tokens after layer 2: Plug-and-play inference acceleration for large vision-language models.\n\n\n3. The paper studies only limited models. It is unclear whether the phenomenon holds in other models (e.g. Qwen2.5-VL, InternVL2.5). Broader generalization is needed for the findings to have lasting significance.\n\n4. The paper appears somewhat rushed and underdeveloped in its experimental design. Several claims are made based on limited observations, without the breadth of analysis necessary to establish them as robust conclusions.\n\nQUESTIONS:\nsee weakness.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates how visual information is processed and transferred to the textual domain by several representative multimodal-output and unimodal-output VLMs. The work finds that multimodal-output VLMs more rely on a single token for visual information while unimodel-output VLMs exhibit a distributed communication pattern. Modifying the narrow gate token enables steering of the image semantics to control the output behavior.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper is well-written. It introduce the findings of narrow gate token for visual information in multimodal-output VLMs and distributed communication pattern in unimodel-output VLMs by extensive statistics across several VLMs.\n2. The work presents that editing narrow gate token can control the global visual information.\n\nWeaknesses:\n1. The conclusions presented in the Abstract and Introduction seem to conflict with those in Section 4. According to Figure 1, Janus stands out as an outlier among multimodal-output VLMs. It does not exhibit the narrow gate token pattern but instead aligns more closely with the distributed communication pattern typical of unimodal-output VLMs. Therefore, Section 4 concludes that the presence of narrow gate tokens depends on whether the model is jointly trained from scratch on vision and language data. However, at the beginning of the paper, the classification is based solely on the output modality—multimodal versus unimodal. I suggest revising the conclusions in the Abstract and Introduction to maintain consistency. Furthermore, given the substantial differences among models, this conclusion is difficult to verify. Communication patterns may be influenced not only by the training strategy (i.e., joint vision-language pretraining) but also by the architecture of the vision encoder.\n2. While I appreciate the paper’s careful and well-reasoned investigation, the contributions are somewhat difficult to assess. The narrow gate token, also known as a memory or pivot token, is a widely observed phenomenon in both LLMs and VLMs. After identifying the narrow gate token pattern, I believe the paper should go further by proposing ideas to mitigate the drawbacks of this pattern and support them with experimental validation—even small-scale experiments would be valuable. For example, in Section 4, the authors suggest that “a possible approach to encourage a more distributed communication can be masking the text-on-[EOI] attention during the last part of training,” but this idea lacks empirical support.\n3. The experiments in the paper are limited to the image classification task, namely: “⟨image⟩ This animal is a __.” I believe it is important to expand the exploration to include broader question-answering scenarios to enhance the generalizability of the findings.\n4. The paper focuses exclusively on the information flow from vision to text. Considering that the multimodal-output VLMs are introduced, I am curious whether the narrow gate token pattern also emerges during image generation tasks in multimodal-output VLMs. This would relate to the vision-text-to-vision communication paradigm, which warrants further exploration.\n\nQUESTIONS:\nMy main concerns are items 1–3 under Weaknesses. If these are addressed, I will consider raising the rating.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis is a study on the different representation patterns emerging in multimodal LLM (image + text) depending on how they are trained and their architecture. The findings from the authors draw a clear distinction between models trained to generate multimodal outputs compared to models trained to generate unimodal (text-only) outputs. The former maintain a more clear separation per modality in their token latent space and have special tokens, like $[EOI]$, that exhibit a significantly higher amount of attention from text tokens compared to the rest of the multimodal tokens. This does not hold for unimodal out models where instead tokens of different modality become more similar in the deep layer of the model and attention tends to be more scattered around all image tokens. Leveraging this observation the author shows how it is possible to modify the attention pattern towards the [EOI] token in a few MLLMs to destroy their performance.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n+ The finding that natively multimodal models keep almost orthogonal representation in their token space across modalities is interesting and somehow counterintuitive to me. I would have expected the natively multimodal training to actually help the two modality to converge into some form of shared representation. This result hints at the possibility of having better ways of mixing the two modalities when training natively multimodal out models.\n\n+ The experimental analysis is well structured and nicely self-contained, the work provides all the ingredients to understand its content.  \n\n## Weaknesses\n\na. **Ambiguity in how the cross-modal attention is computed**: I would like to challenge the way cross-modal attention is computed in Eq. 1. This is the main experimental tool used throughout the paper but I would argue that it does not consider few aspects: \n\n1. In my opinion it would make more sense to check the cross-attention pattern only among the predicted tokens belonging to the “answer” to a certain task (e.g., the class in the imagenet examples) rather than the whole text tokens. A lot of text tokens might belong to the task instructions and should naturally not interact much with visual tokens.\n\n2. Influence of text->image attention vs text->text attention when predicting the next token. All the analysis is performed only considering the “text->image” attention and attention scores are also reported normalizing only across cross-modal attention. It might be the case that in absolute terms the cross-modal attention is lower than the uni-modal attention between the current text token being predicted and the previous ones. This would be important to report to understand when the visual content really plays a role and when instead it does not. \n\n3. Attention experiments in Fig. 3 are for a specific “global” task, i.e., image classification. It would be interesting to verify whether exactly the same attention pattern emerges for other visual understanding tasks. Tab. 1 provides some evidence in that regard, but only partial. Same holds for the clustering experiments in Fig. 4 that are fundamental to identify which tokens matter and which do not. Do these hold for tasks different from imagenet? \n\nb. **Are attention patterns really predictive of which tokens act as narrow gates?** The answer the paper implies is yes, but to be honest the evidence from the paper is somehow not convincing.  For example Fig. 3 Chameleon-7B has disproportionally high attention over the “last image” token, significantly higher than the attention to [EOI] token for 2/3 of the model. However the results in Tab. A2 shows that masking the “last image token” has a negative effect way lower than masking [EOI]. Same holds for the 32nd token. This in my opinion points out to the fact that the attention analysis is not enough to identify these narrow gates into the model and something on top is missing. The neighborhood analysis in Fig. 4 might help there, but as mentioned above is only limited to a specific task and it’s not clear how it could be generalized to several. \n\nc. **Actionability of the findings** Building on weakness B, it is not clear to me how the findings of this paper can help us to design better or more robust models going forward. This discussion is currently missing from the paper, reducing the contribution to a detailed experimental report. This alone would not be a major issue, but combined with the doubts on the experimental procedure I raised in the previous two weaknesses, it makes me question the completeness of the work. \n\nd. [minor]**Influence of the visual encoder vs the training loss**: the authors did touch upon this discussion in the paper, but only partially. A side effect of the models chosen for this evaluation is that models trained to be unimodal output have a strong vision encoder trained already for image understanding (Siglip or Pixtral-ViT), while models trained for multimodal generation have typically VQVAE which have never been trained for being powerful standalone image feature extractor. The results on Janus seem to suggest that this is the main differentiation, rather than the training objective of the MLLM. A model like [1] could be included in the discussion to either support or deny this claim as similar to Janus uses a SigLIP encoder and has been trained for both text and image output, but without having a separate visual encoder used for generation. \n\n## References \n\n1. [Tong, Shengbang, et al. \"Metamorph: Multimodal understanding and generation via instruction tuning.\" arXiv preprint arXiv:2412.14164 (2024). ](https://arxiv.org/pdf/2412.14164)\n\nQUESTIONS:\n* How would Fig. 2 look like for [EOI]? I.e., is the [EOI] token more aligned to text representations wrt to the average image token?\n\n* Have you analized the attention pattern of [EOI] wrt the rest of the image tokens for the different models? Is [EOI] in chameleon somehow attending tot he whoel image, while in the other models is not?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates how multimodal-output VLMs that both generate images and text differ from text-only output VLMs in the way they process and transfer visual information into the textual domain. It shows that multimodal-output VLMs (e.g., Chameleon, Emu3) maintain largely separate image and text embeddings and rely on a single [EOI] token as a *narrow gate* through which all visual semantics must pass. By analyzing cross-modal attention (Sec. 3.2), the authors demonstrate that [EOI] token captures a disproportionately large share of text-on-image attention and retains the bulk of semantic content beyond early layers. Ablation (Sec. 3.3) and steering (Sec. 3.4) experiments confirm the causal importance of the [EOI] gate—blocking its connections drastically degrades performance on VQA, captioning, and classification tasks, whereas ablating all other visual tokens has a smaller effect.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\nTo the best of my knowledge, this paper is the first to compare how multimodal-output VLMs and text-only VLMs exchange information across modalities. By identifying a single *narrow gate* token (e.g., [EOI]) through which visual semantics flow into the text pipeline, it lays the groundwork for future interpretability studies of unified vision–language models. As such, it offers a novel framework that researchers can build on to better understand and diagnose multimodal architectures.\n\n## Weaknesses\n- Limited scope of findings: the paper compares multimodal‐output and text‐only VLMs only at the token level, centering its analysis on the “narrow gate” token. It focuses almost entirely on demonstrating the gate’s existence and lacks any further substantive or surprising conclusions.\n- Questionable validity of the zero‐ablation method: the zero‐ablation approach may distort the model’s representation distribution, undermining the reliability of the results presented in Table 1.\n- No causal analysis of narrow‐gate emergence: although the study highlights the narrow gate and argues that training differences give rise to it, it does not rigorously investigate or empirically validate the mechanisms by which this bottleneck forms, leaving its origin unexplained.\n\nQUESTIONS:\n- At lines 288–290, you claim that visual information is passed through the last token. To substantiate this, one must examine the value of the last token’s \\(\\mathcal{X}^{l,\\text{text\\_gt}}\\). Have you checked this? Do the results actually support your claim?\n- The notation \\(A_{\\text{img}\\rightarrow\\text{text}}\\) on line 88 is ambiguous. Is it meant to represent a set, a matrix, or something else? How is this symbol defined and used in the main text? It appears not to be referenced elsewhere in the paper.\n- In the Chameleon model, what is your hypothesis for why the 32nd token serves as the narrow gate? What underlying mechanism or training dynamic leads this specific position to assume gating behavior?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}