The Narrow Gate: Localized Image-Text Communication in Native Multimodal Models
Authors: Alessandro Pietro Serra
Francesco Ortu
Emanuele Panizon
Lucrezia Valeriani
Lorenzo Basile
Alessio ansuini
Diego Doimo
Alberto Cazzaniga
Venue: NeurIPS 2025 poster ()
Keywords: Computer Vision
Visual-Language Model
Interpretability
Native Multimodal Models
Primary area: deep_learning

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.

The Narrow Gate: Localized Image-Text Communication in Native Multimodal Models
Alessandro Pietro Serra 1.2+
Francesco Ortu 1.3+
Emanuele Panizon
Lucrezia Valeriani 1.3
Lorenzo Basile
Alessio Ansuini
Diego Doimo
Alberto Cazzaniga
1 Area Science Park, Trieste. Italy 2 SISSA, Trieste, Italy
University of Trieste, Trieste, Italy
fdiego. doimo, alberto.. cazzznigg/arrracccccccaa......
Abstract
Recent advances in multimodal training have significantly improved the integration of image understanding and generation within a unified model. This study inves- tigates 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 VL.Ms, models trained from scratch multimodal data to generate both text and images, and non-native multimodal VLMs, models adapted from pre-trained large language models capable of gener- ating 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 non-native multimodal VLMs exhibit distributed communication pattern, where information is exchanged through multiple image tokens, whereas models trained natively for joint image and text generation tend rely on single post-image token that acts narrow gale for visual information. We show that ablating this single token significantly deteriorates maag-understanding performance, whereas targeted, token-level interventions reliably steer image semantics and downstream text with fine- iineigrieeed control.
Introduction
The rise of foundation models [1] trained on vast amounts of text has transformed natural language processing (NLP), showing that a single large language model (LLM) [2] can handle many different linguistic tasks [3-5]. The rich set of features encoded in LLM embeddings has been then used an effective prior knowledge both for text-conditional image generation [6-8] and image understanding [9-13]. Recently, the availability of large open datasets [14, 15] and improved techniques align text and image embeddings [16] have also enabled the creation multimodal models that can understand and generate visual content within single architecture [17-20]. This unification allows a deeper understanding of the visual world, as generative tasks often require insight into the fundamental concepts and relationships within the data [21]. For example, model that generates images from text descriptions must grasp the semantic content of those images to ensure that they faithfully reflect the details and intent of the text [22, 23). As a result, research has rapidly progressed in
"Equal contribution
Equal supervision.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Localized Communication
Distributed Communication a.V,,, Pixtral, Janus, A-U
Impact of Ablation
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Figure 1: Image- Communication Vision Language Models. The figure compares how different VLM architectures handle image-text information flow. Left: Native multimodal models (Chameleon, Emu3) process visual and textual tokens separately with information transfer occurring primarily through single narrow gate the end-of-iman of-image token [[EOI]). Center: In non-native multimodal models (LLaVA, Pixtral, Janus, VILA-U), communication is distributed across many internal image tokens; visual tokens in deeper layers align more strongly with text tokens. Right: The figure shows the relative performance on MS-COCO image captioning after performing different ablations, compared to ablation (see section 3.4). The results show that for narrow gate models, removing attention to the [EOI] token (y-axis) impacts performance more significantly than removing attention to all image tokens (x-axis).
integrating multiple modalities into a unified framework with an increasingly deeper multimodal fusion. Early approaches used cross-attention modules between modality-specinf encoders [17]. Further advancements highlighted the importance of using a pre-trained LLM backbone combined with lightweight projection layers, often fine-tuning the LLM to refine multimodal representations [19, 24, 25]. More recently, research has shown success in training vision language models (VLM) from scratch [20, 26], achieving performance similar to text-only output VLMs on visual understanding tasks. In this work, on both text and image modalities, without relying on a pretrained LLM backbone and CLIP-based text-aligned vision encoder, and capable of generating both images and text.
the term native multimodal to refer VLMs that are trained from scratch
Although recent studies have examined the internal mechanisms of unimodal output VLMs [27-30], remains largely unknown how modalities interact in the hidden representations of native multimodal models. In this work, we focus on some of the latest native VL.Ms, Chameleon [20] and Emu3 [26], and compare them with range of non-native ones, including both text-only VLMs such as LLaVA [11] and multimodal generators that rely on a pretrained language backbone like VILA-U [31], on how they transfer information from the visual domain to the textual domain in various image understanding tasks.
We observe that in native multimodal models (Chameleon and Emu3), image and text representations remain well-separated in different clusters from input to output, while for non-native models they tend to mix in late layers (section 3. 1). Additionally, in this second class of models, visual information flows to text through multiple tokens. In contrast, native multimodal VLMs channel the global image information into a single token placed immediately after the image, the aud-of-image token, denoted by [EOI] (section 3.2). The [EDI] token behaves like a memory token [32] or narrow gate through which the image information must pass to guide text generation.
We establish the functional role of by showing that (1) blocking attention from text tokens to [EOI] causes sharp drop in performance across classification, VQA, and captioning tasks (section 3.4) and that (2) editing the representation of [EOI] the semantic content of the generated text (section 4.1), confirming its causal role in mediating image semantics. These properties do not appear in non-native multimodal VLMs, where the information between modalities flows through many distributed image tokens. Finally, we propose a fine-tuning strategy to remove the narrow gate native multimodal models by redistributing visual-to-text transfer across many image tokens (section 4.2).

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2 Background and Methods
Model architectures. We analyze six VLMs differing in their training objectives and multimodal integration. Two of them, Chameleon [20] and Emu3 [26], are native multimodal models trained from scratch jointy generate images and text using a unified discrete tokenizer with VQ-GAN image encoder. Since the released Emu3 variants were specialized either for image generation (Emu3-Gen) or for understanding (Emu3- Chatt,, we fine-tune the Emu3- Gen checkpoint on balanced mixture of image-ten mexge-text datasets so that it can handle both tasks within a single model (see appendix B.3). This enables direct comparison with Chameleon, which natively supports the joint generation of images and text. As non-native multimodal VLMs, we analyze two text-output VL.Ms: L.La VA-onevision- 7B [33] (LLavVA) and Pixtral-12B [13] (Pixtral), fine-tuned on Qwen2-7B and Mistral Nemo 12B, respectively. Additionally, we also analyze Janus-1.33 [34] (Janus) and VILA-U [31]. which are both multimodal in output and fine-tuned on a pre-trained LLM, DeepSeck-LLM-LL
[35] and LLaMA-2-7B, respectively [36]. Janus employs SigLIP vision encoder and separate pathways for image understanding and generation, while VILA-U combines CLIP-based visual features with a VQ-VAE discretization stage, yielding semantically rich visual embeddings. All the models are decoder-only and trained with next-token prediction loss. In Chameleon, Emu3, Janus, and VILA-U the loss is computed both image and text tokens, while in LLaVA and Pixtral, it applies only to text tokens. In all the cases, a special end-of-image token ( [EOI]) marks the transition from image to text. Additional architectural details are provided in table A2, and related multimodal training strategies are discussed in appendix A.
Datasets. We study visual understanding tasks where the model is asked to answer questions from VQAv2 [37]. generate image captions for Flickr30k [38] and MS-COCO [39]. and complete simple prompts about ImageNet images [40]. We provide the experimental setup, including dataset composition, prompt usage, and data selection criteria, in the appendix B.
2.1 Analytical Tools
Analyzing information flow in VLMs. To understand how VLMs process information, we an- alyze both attention maps and token representations across layers. We denote the residual stream representation of the token at position at layer as zi. In our setup, images are typically followed by textual instructions. To study how these modalities interact, we focus on the cross-modal attention: let [Eor be the position of the end-of-image token, and Ai,j the attention from token i to token j. We define A A,, with N [Eo1) <is N. jjj < [(Oo] which captures how much text tokens attend to visual tokens in the sequence.
Quantifying cross-modal attention. We construct a metric that quantifies the average attention that all the text tokens give to a token at position j within the image part of the prompt, which we assume to span the first N [eorr tokens. Formally, we define the (relative) cross-modal attention f3 as
fー - - Σ
Σ
A A
NEW
N
Where j = 1,.,,.... 1, such that Σ N S3 =1
WID. I, identify, respectively, the layer and head. C is normalization factor
Blocking Cross-Modal communication with Attention Knockout. We use the attention knockout [41] to selectively block attention between tokens. Given a set of source tokens S and a set of target tokens T, we zero out the attention weights A. where E S and j E across all heads in selected layers to prevent the target tokens from attending to the source tokens.
Probing the semantic information with Neighborhood Overlap. To evaluate how well the residual stream encodes an abstract property of the image, like the identity of the main object represented (ImageNet) or the important parts of the scene (MS-COCO), we use the neighborhood overlap [42]. The neighborhood overlap measures the consistency between the k-nearest neighbors of a datapoint in the residual stream at layer denoted by (i) and the nearest neighbors in reference data representation, .a (f), which encodes the abstract property of interest. the case of ImageNet, all the points belonging to the same class are considered nearest neighbors [42] for the
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purpose of computing the reference then written as:
(i). The neighborhood overlap, denoted by Lot can be
Lot 1 Σ Xk
(2)
nk
where denotes the cardinality of a set, and n. the dataset size. In our experiments, we set A: 30.
Modifying the semantic information with localized interventions. To evaluate the influence of specific components on model outputs, we use activation patching [43, 44]. This approach involves two forward passes on two different inputs: We first collect the activations i from a target input, then replace the corresponding activations z; in base input during a second forward pass. The impact is
assessed by comparing the resulting output distribution Iese to the target output distribution Purget-
patched
patched and Ptargeti We use a variant of the Jaccard index [45] quantify the similarity between Trase
Similarity (
(Thuse PPaargtt)
patcheed
Σ
min(q:, pt)
The score ranges from 0 (no overlap) to (identical distributions), measuring how much the patched model mimics the target behavior.
Reproducibility. We used the HuggingFace implementations of Chameleon [46, 47]. Emu3-Ger [48], LLaVA [49], Pixtral [50], VILA-U [31] and Janus [51] models. We run all the experiments on a single NVIDIA A100 GPUs with 40GB VRAM. For the Chameleon 34B, used two 40GB GPUs. The code needed to reproduce the experiments is available at: itarrasscienceparkgitthie githuubib/bbbbrrro io/Narrow gate.
3 Results
3.1 Modality Gap in Native Multimodal VLMs
Native multimodal VLMs generate both image and text tokens using shared transformer backbone, enabling unified next-token prediction across modalities within a common representation space. In contrast, text-output VLMs produce only textual tokens, using image tokens solely as contextual input. Although both VLM types encode image and text tokens in the residual stream, their role in the generation processes differ significantly. This motivates our first analysis of whether native multimodal models develop mppelit subspaces in the residual stream that separate image and text representations.
Cross-modal angular separation in VLMs. To address this question, we randomly select 10. 000 image-caption pairs from the Flickr30k dataset and extract the residual stream representations of all hidden layers at randomly chosen text token position (see appendix B for the complete prompt structure). To compare the geometric organization of the different modalities, we first measure the median cosine similarity between representations of image and text at each hidden layer.
As shown in figure 2-1eft, this analysis reveals difffrrnnt trends across architectures. In the Chameleon- 7B (blue) and Chameleon-34B (green), the representative vectors of image and text tokens remain nearly orthogonal throughout the hidden layers, with median cosine similarity values consistently below 0.10. The cosine similarity in LLaVA (yellow) and Emu3 (green) is higher, starting at 0.2 in the initial layers of the network. But while in Emu3, the cosine similarity decreases 0 in the last layers of the network, in VA it rises to 0.5. This implies that in Emu3, the two modalities remain separated late layers, and in LLava, they increasingly mix. Similarly, other non-native multimodal models (Janus, Pixtral, VILA-U) show the same qualitative behavior of LLaVA and are reported to appendix C.I.
Clustering of visual and textual embeddings. To characterize the degree of mixture of the modalities from different perspective, taking into account the norm of the embeddings, we cluster the embeddings with the Advanced Density Peaks algorithm [52] (see appendix B,4 for brief introduction) and analyze the composition of each cluster. Figure 2-right shows the homogeneity score [53] of the clusters, measuring how the clustering of tokens aligns with their modality. In

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Figure 2: Modality Gap in VLMs. (left) Cosine similarity between text and image token embeddings shows that LLa achieves increasing alignment with depth, while Emu3 shows little alignment and Chameleon maintains orthogonality. Points represent median cosine similarity, with shaded areas indicating the inter-quartile range. (right) Homogeneity score assesses how well token clusters (via Advanced Density Peaks) correspond to their original modality.
Chameleon and Emu3 models, the homogeneity score is always 1, meaning that each cluster contains embeddings from single modality. In contrast, LLaVA's homogeneity score decreases from almost 1 to approximately 0.6 in the later layers of the network, indicating tendency for the text and image embeddings to mix. In the appendix C.I, we show that, similarly to LLaVA, also in Janus, Pixtral, and VILA-U, the clusters of late layers contain embeddings from both modalities.
We conclude that textual and visual representation spaces are largely separated in the Chamelcon and Emu3 models, which are the only ones trained from scratch multimodal data and can generate both images and textual samples. This brings forth the problem of finding how and where communication between the two modalities is performed.
3.2 Analysis of Cross-Modal Attention
As shown in section 3, hidden representations visual and textual tokens mix in the late layers of non-native multimodal models, whereas they occupy well-separated regions of the residual stream native multimodal models. Despite this structural difference, all model families achieve strong performance image-understanding tasks, suggesting that cross-modal attention effectively transfers semantic information from visual to textual tokens. this process, attention matrices play a central role: while enabling information exchange across modalities, they must bridge the modality gap, which is particularly pronounced native multimodal models such as Chameleon and Emu3. These observations motivate the definition of two key properties for tokens that mediate cross-modal communication: (i) having a high weight in text-to-image attention and (ii) encoding rich semantic information about the visual input. In the following, examine whether Chameleon-7BB Emu3, and LLaVA (the latter representing non-native multimodal models) contain tokens that satisfy both criteria, identifying candidates that act as communication gales between modalities. Corresponding analyses for other models are provided in figure A3.
Cross-mode attention patterns. To quantify the role of each token in image-to-text semantic communication, we use the ImageNet dataset and select 100 images per class for 100 animal classes [40]. We construct 10, 000 prompts of images followed by text of the form "(image) This animal is a " The sentence was chosen to guide the model towards generating a class-relevant response.
Figure illustrates the relative text-on-image attention, as defined in section 2. at different token positions for Chameleon-77 (left), Emu3 (center), and LLaVA (right). We single out tokens with an average value larger than 1%. The remaining tokens are aggregated as internal image. Both Chameleon and Emu, the special [EDI] token captures a large amount of cross-modal attention (shown in green in the figure). In Chameleon, this token alone receives 40% to 50% of the total attention from the textual tokens between layer 2 and 6, remaining above 15 to 20% the second half of the network. Similarly, in Emu3 [EOI] receives more than 30 to 40% of text-on-image attention after layer 10. Other highly attended tokens are, in Chameleon, the 32nd image token from layer to layer 10 (orange) and the last token of the image receiving between 66% and 86% in the middle and
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Figure 3: Cross-Modal Attention Contributions of Image Tokens. Contribution of different image token positions to the total text-on-image attention across layers in Chameleon (left), Emu (center), and LLaVA (right), computed on ImageNet data. Tokens with average contribution larger than 1% are singled out. The remaining tokens are aggregated as "internal image".
final layers (shown in pink). In Emu instead, small group of tokens describing the size of the image receive from 10 40% of the total cross-modal attention before layer 17 and after layer 30 (shown in dark orange). The remaining attention is distributed among the other 1024 image tokens. In the right panel, we present the distribution of average attention among tokens in LLaVA The [EOI] token accounts for approximately 10 20% of the attention between layers and 13, and less than 10% for the remaining layers. All the remaining attention is distributed across the other image tokens. These results suggest the emergence of two distinct communication regimes. Native multimodal models tend to concentrate text-on- on-image attention on few single tokens (such [EOI]), ), which may act as privileged channel for visual information. In contrast, non not-vetive multimodal models allocate little attention to [EOI], with attention instead dominated by internal image tokens, suggesting a more diffuse and distributed communication between modalities.
3.3 Probing the Semantic Content Visual Tokens
To determine whether the tokens that receive the highest text-on-image attention also encode abstract information about the visual input the second condition for serving as communication channels we probe the semantic content of the hidden representations. For this purpose, use the neighborhood overlap (x section 2.1) with different reference ground truths: the labels of the ImageNet dataset and the caption embeddings of a Brren2-BB-GTE text encoder for the MS-COCO.
Aligmment ImageNet class labels. First, we measure the neighborhood overlap X between maage-token embeddings and ImageNet class labels (section 2. I), averaging across positions when [EOT] in Chameleon-7B and Emu3 exhibits a sharp rise in X after the first few layers, exceeding 0.4 and 0.25 respectively, and retaining high values through the model's depth. In contrast, the 32nd, final, and size tokens maintain values near zero, indicating that they attract attention but do not encode meaningful semantic content. The internal image tokens (blue curves) initially capture visual semantics in early layers but gradually lose this information beyond layer 10, leaving [EOI] as the dominant semantic carrier. This pattern holds across scales: in Chamelcon- 34B, [EOI] exceeds 1.tt > 0.4 after layer 16, while other tokens remain below 0.25 (figure A3). For LLaVA, 1ast for [EDI] starts around 0.2 and decreases below 0.1 in later layers, whereas internal image tokens maintain high and stable overlap values (>> 0.4). A similar trend is observed in other non-native multimodal models, is shown in figure A3 models such as Janus, Pixtral, and VILA-U: internal image tokens retain the highest X while [EOI] contributes little semantic signal. These results suggest two distinct encoding regimes. In native multimodal models (Chameleon, Emu3), the [EDI] token both attracts majority text-on-image attention and carries the richest visual semantics, fulfilling the conditions for narrow communication gule. In non-native multimodal models, semantic content remains broadly distributed across internal image tokens, indicating diffuse, multi-token communication pattern.
multiple tokens are analyzed. As shown figure 4,

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Figure 4: Localization of Visual Semantic Information. The figure compares how visual semantic information localized in Chameleon, Emu3, and LLaVA, by measuring the neighborhood overlap between image tokens and ImageNet labels. The blue curves show the average overlap across all image tokens, excluding the 32nd token for the case of Chameleon.
Alignment to the embedding image captions. To verify that this behavior is not specific to classification, we repeated the analysis on MS-COCO captioning data. We compute the neighborhood overlap between the visual-token representations and their corresponding captions. To define metric in the "space of captions", we use their semantic embeddings through a Qwen2-7B-GTB text encoder (see appendix C.2). The results (figures A2 and A3) mirror those ImageNet: in native multimodal models, [EOI] maintains the strongest alignment with caption embeddings, whereas in non-native multimodal models, semantic information remains distributed across internal image tokens.
3.4 Ablation Experiments: The Effect of Localized Communication Downstream Tasks
Building on the previous analyses, where we identified the tokens most involved in visual-textual communication, we now test their functional importance through attention ablations. Specifically, we evaluate how blocking communication at selected token positions affects performance on three standard lssion-lanuuae benchmarks: VQAv2 [37], Flickr30k [38], and MS-COCO [39]. Details on evaluation metrics are provided in appendix B.
Ablation setup. For each model, randomly sample 2,000 examples per dataset and measure baseline performance before applying targeted attention knockouts. The performance for visual question answering VQAv2 is evaluated with the VQA metric [54], for the captioning benchamarks instead we used CIDEr [55]. We block (?) communication between the [EOI] and text tokens by zeroing A [Eor] and (ii) all text-on-image attention by zeroing out Aeex--img (section 2.1). This isolates the impact of the [EOI] token relative to the broader visual stream. Results for Chameleon, Emu3, and LL are summarized in table additional models (Pixtral, Janus, VILA-U) and further token positions are reported in appendix C.4.
Table 1: Effect of Attention Knockout on Image Understanding Tasks. Performance of the models on VQAv2, Flickr-30k. MS-COCO, and ImageNet under different ablation settings. Numbers bold mark the worst performance for each model and task.
Model
Ahlation
VQAv2
MS-COCO
Flickr
ImageNet
0.51
0.48
0.34
0.46
Chameleon-PP
[EO1]
0.25
0.04
0.04
img
0.40
0.27
0.17
0.47
0.57
0.63
0.29
0.35
Emu3
text
[601]
0.48
0.33
0.13
0.24
text
img
0.42
0.54
0.21
0.30
0.80
0.98
0.70
0.5
LLaVA
text
[E0I]
0.80
0.97
0.71
0.45
text
img
0.00
0.01
0.02
0.05

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Results. In native multimodal models, ablating the [EOI] token causes a substantial performance collapse across all tasks. For instance, in Chameleon-7B. accuracy drops from 0.51 to 0.25 on VQAv2 and from 0.34 to 0.04 on Flickr30k, with similar reductions exceeding 90% on MS-COCO and near-zero overlap on ImageNet. Emu3-8B shows comparable degradation, with performance falling by roughly 50% across tasks. In both models, blocking all text-on-image attention (Axxt-iigg) produces smaller declines than ablating the single [EOI] token, confirming its dominant role. Similar trends hold for Chameleon-34B (appendix C.4). In contrast, non-native multimodal models rely on distributed communication. In LLaVA, performance is unaffected by the ablation of [EOI] but collapses entirely when all rext-on-image attention is removed (0.00 on VQAv2, 0.02 on Flickr30k, 0.01 on MS- MS-COCO, 0.05 on ImageNet). Pixtral, Janus, and VILA-U exhibit the same pattern, indicating that their cross modal information flow is mediated by many internal image tokens rather than a single position. These ablation results reinforce the distinction between communication regimes. Native multimodal models (Chameleon, Emu3) depend [ЕOI] token-for transferring visual semantics to text. By contrast, non-native multimodal models (LLaVA, Pixtral, Janus, VILA-U) exhibit distributed communication: information flows through numerous image tokens and cannot be disrupted by a localized ablation.
localized, token-level bottleneck
Implication of Narrow Gate for Model Editing and Robustness
4.1 Steering Image Semantics Through Activation Patching
The previous analyses showed that in native multimodal models (Chameleon, Emu3), the [EOI] token encodes global visual semantics (section 3.2) and acts as the main communication channel to text (section 3.4). We now test whether this localized structure also enables constructive interventions, specifically, whether modifying the representation at [EOI] can steer the model's interpretation of an image toward desired semantic class.
Activation patching setup. do so, we select 20 ImageNet animal classes and sample 100 images per class, forming 10 class pairs. For each pair, we perform activation patching (section 2.1) with the following procedure: (i) extract the [EOI] representation at each layer from the target class images ( ([EOI] Laaget) (ii) inject this representation into the residual stream the base class images by replacing [EDI] 1" base with [EOI]* !" stagee at a chosen layer 1"; and (iii) evaluate the model's output distribution for the final text token in the prompt "(image) This animal is -- We measure the similarity between output probabilities (see equation (3)) and the fraction of cases where the target class probability exceeds that of the base class, For further details on experimental setup see appendix B.
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Figure 5: Impact of Activation Patching at [EOI]. Results of patching the [EOI] repressntttooooooonn from a target class onto the [EOI] representnnninaonnn on a base class. The experiment is performed at each layer. Two metrics are evaluated: (left) The similarity measure (defined in equation (3)) quantifies how the probability distribution over the vocabulary of the patched image aligns with that of the target. (right) The accuracy, defined as the fraction of patched images where the probability of the target class token is larger than the probability of the base class token.
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Results. Figure 5-left reports the similarity measure as function of the patched layer for Chameleon-7B, Chameleon-
Emu3, and LLaVA. In native multimodal models, patching [EOI] representations from the target class induces strong semantic steering: the similarity rises sharply after a relative depth of 0.3. peaking at 0.86 Chameleon-7B/34B and 0.70 in Emu3. The right panel quantifies the success rate of semantic transfer: patching changes the predicted class from base to target approximately 90% of cases for Chameleon and 75% for Emu3. In contrast, LLaVA, representative of non-native multimodal models, shows no measurable effect. [EOI] patching leaves both similarity and class probabilities unchanged: the base class consistently dominates the output, indicating that cross cross-modal information is distributed across multiple image tokens rather than lo- calized single position. These results demonstrate that in native multimodal models, the [EOI] token provides causal handle for controlling image semanties: altering its representation is sufficient to steer model predictions. In non-native multimodal models, by contrast, semantic information is distributed throughout the image representation, rendering localized interventions ineffective.
4.2 Improving Model Robustness with Localized Fine-tuning
The presence of narrow gate in native multimodal models can constrain how the model integrates visual and linguistic features, making its representations overly dependent on specific internal pathway. To promote a richer and more distributed exchange between modalities, in this section, we show fine-tuning strategy that masks the token during training, forcing the model encode and communicate visual information stored in [EOI] through alternative internal tokens. Details about the training setup are reported in appendix B.3.
Masked fine-tuning, Figure shows how model performance evolves during training on 2000 test samples from the MS-COCO and VQAv2 tasks. Solid lines report fine-tuning while dashed lines correspond to standard fine-tuning where all input tokens are attended. Purple profiles show the performance when [EOI] is ablated, while green profiles show the test performance when all tokens are visible. At the beginning of training, ablating [EOI] causes large performance drop, especially for the COCO captioning task (see section 3.4). However, when [EOI] masked during fine-tuning, performance on both MS-COCO and VQAv2 (solid-purple profiles) increases and approaches the "unablated" performance (green profiles), which remains approximately constant during training. In contrast, when [EOI] is not masked during training (dashed purple profiles), the performance gap and the narrow gate phenomenon remains, since the model continues to rely mostly on [E to store and communicate visual information.
with [EOI] masked,
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Masking the [EOI] token during fine-tuning reduces the model's reliance on a single narrow gate and encourages a more distributed communication between image and text. Solid lines indicate runs where [EOI] masked; dashed lines correspond to standard fine-tuning. Green curves report test accuracy with all tokens visible, while purple curves show performance when [EDI] is ablated. When trained with masked [EOI], the performance under ablation (solid purple) progressively recovers and approaches the unablated baseline (green), indicating improved robustness and reduced dependence on [EOI].
Figure 6: Performance Dynamics During Masked [EOI] Fine-tuning.

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Discussion and Conclusion
The Role of [EDI] in Cross-Modal Communication. In this work, we analyzed information flow within transformer-basee VLMs using geometric and mechanistic interpretability techniques. Our study focused on native multimodal models those trained from scratch to generate both images and text- filling a gap in the existing literature, mainly focused on text-output or fine-tuned multimodal systems [56].
Our analyses reveal a narrow gale communication mechanism in native multimodal VLMs. These models exhibit a pronounced modality gap: visual and textual representations remain well separated throughout the network (section 3. yet semantic transfer occurs effectively through a single token, the end-of-image ([EOI]). The [EOI] token accumulates visual semantics in early and mid layers and subsequently channels this information to textual tokens (section 3.2). Ablation (section 3.4) and crtiaation-pacciing (section 4.1) experiments demonstrate that disrupting or modifying this token has a direct causal impact on model predictions, confirming that [EOI] serves as the central conduit for image-to- to-tett communication.
In contrast, non-natinn non-native multimodal models (e.g., LLaVA, Janus, Pixtral, VILA-U) display a more distributed communication pattern, in which visual semanties are shared across many image tokens. These results suggest that the emergence of localized, token-level communication is a specific property of native multimodal architectures and training regimes.
The Role of Architecture in the Emergence of the Narrow Gate The presence of a narrow gate is linked the modality gap between image and text representations. Based we hypothesized that three architectural and training factors jointly influence its emergence. The multimodal output objective: training objective that requires generating both image and text tokens, encourages the model to maintain distinct representational pathways for each modality, enforcing their separation. The training history: models trained from scratch on multimodal data, develop different subspaces for each modality, while models derived from pre- pre-trained text-only LLMs and later fine-tuned for multimodal use (Janus, VILA-U) retain a text-aligned internal geometry and exhibit distributed communication. The type of image encoder: low-level visual tokenizers such as VQ-GAN (used in Chameleon), represent images through local features rather than high-level semantic embeddings. This increases the abstraction gap between image and text tokens and promotes the formation of distinct communication interface centered on [EDI]. Taken together, these factors show that the narrow gate is not an incidental artifact but structural consequence of jointly training a multimodal generator with low-level image tokenization.
results,
Implications and Future Directions The localized communication via [EOI] introduces both limitations and opportunities. On one hand, the reduced communication bandwidth may constrain multimodal reasoning and expose the model to potential vulnerabilities. For example small perturba- tions or targeted manipulations of a single token can strongly influence output semantics. Encouraging a more distributed information flow, for instance by masking text-on- [ЕOI] attention during late-stage fine-tuning, could mitigate these issues. On the other hand, the narrow gate mechanism provides clear interpretability and practical benefits. It enables controlled steering of visual semantics, simplifies tracing how visual information shapes text generation, and facilitates efficient fine-tuning or alignment by localizing cross-modal transfer to single token. Moreover, the [EOI] token acts natural memory token [32], summarizing global image information and allowing early image tokens to be discarded thereby reducing memory and computation costs [57, 58].
Overall, our study identifies the architectural and representational conditions under which narrow gate communication emerges in multimodal transformers. By bridging geometric analysis, causal interventions, and training design, our findings highlight how architectural choices determine whether visual-textual communication is concentrated through a single token distributed across many, clarifying in this way an essential dimension of modern languaee modeling.
Limitations
Our analysis is limited to the image-to-text direction and to subset of possible VLM architectures. We do not evaluate the communication in the text-to-image direction, or native VLMs that rely on higher-level, continuous encoding, and models with diffusion-based decoders. Additionally, our
10

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study focuses only on native vision-language models. Extending these analyses to native multimodal models trained across a broader range of modalities remains an important open direction for future research.
Acknowledgments
The authors acknowledge the AREA Science Park supercomputing platform ORFEO made available for conducting the research reported in this paper, and the technical support of the Laboratory of Data Engineering staff. Alessandro Pietro Serra, Francesco Ortu, Emanuele Panizon, Lorenzo Basile, Alessio Ansuini, Diego Doimo and Alberto Cazzaniga were supported by the project "Supporto alla diagnosi di malattie rare tramite l'intelligenza artificiale" CUP: FS3C22001 770002 and "Valutazione automatica delle immagini diagnostiche tramite I' l'intelligenza artificiale", CUP: 113322001780000 Alessio Ansuini and Alberto Cazzaniga were supported by the European Union NextGenerationEU within the project PNRR "PRP@ CERIC" IR0000028 Mission 4 Component 2 Investment 3.1 Action 3.1.1. Lucrezia Valeriani was supported by the project "QuB Quantum Behavior in Bio- logical Function" " CUP: 10022100200001.. Lorenzo Basile was supported by the European Union NextGeneraai within the project PNRR "Finanziamento di progetti presentati da giovani ricercatori" Mission Component 2 Investment 1.2, CUP: J93C25000440001.
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A Related Works
Special Tokens, Memory Tokens, Registers. The importance of special tokens to store and redistribute global information was emphasized by Burtsev et al. [32]. In their work, they add these special tokens the beginning of the sequence to serve as memory units accessible through the self-attention mechanism, improving performance on various language processing tasks. In vision transformers, Darcet et al. [57] found that high-norm tokens in low-informative areas of the images are used to store global information while discarding the local one. Adding learnable "register tokens" dedicated to global information processing helped climinate the high-norm artifacts, improving performance. Similarly, Wen et al. [29] used learnable registers summarize salient visual information and remove the image tokens altogether, improving the efficiency of vision-land asionnlanguage models.
Localization of information in Text-Only VLMs. In text-only VLMs, this kind of summarization particularly useful since much of the attention to image tokens is concentrated in the initial layers, likely on a few "anchor tokens" [28, 59]. Basu et al. [28] demonstrated with multimodal causal tracing that, in VQA tasks, subset of strongly attended late image tokens in early layers transfer information to the text. However, subsequent studies have shown that important visual information is localized in heads of deeper layers of the network [60, 61], or within tokens corresponding to the spatial positions of objects in the image [27]. Interestingly, although these tokens have different statistical properties from those of textual tokens [62], they encode, in deeper layers, vocabulary words that describe the objects to which they correspond.
Multimodal Vision Language Models. Many approaches have been proposed to adapt decoder- only LLMs trained with autoregressive loss for image understanding [10-13, 63, 64) and multimodal generation tasks [16, 19-21, 24-26, 31, 34, 65, 66]. These models consist of an LLM, an image encoder, an image decoder (for multimodal-output VLMs), and adapters between the LLM and visual components. The success of diffusion- image generation has led to the widespread use of stable diffusion as the decoding architecture in multimodal-ouppe For the image encoding part, most architectures use a vision transformer (ViT), often that of CLIP [16, 19, 21, 24, 25, 34, 65, 66]. due to the effectiveness of VIT embeddings in encoding image semantics [67]. The outputs from the visual encoder are either directly projected into the LLM embedding space [21, 24, 25, 67] or, in the case of multimodal generation, often mapped to discrete set of tokens [16, 19, 65]. A drawback of these methods is the complexity of the encoding architecture. Moreover, the image-text contrastive loss make the image representations resemble textual ones excessively [64], with text often favored as the preferred modality [17, 19, 25]. Alternative approaches use VQ-VAEs [68, 69] encode and decode the images and train the LLMs from scratch on multimodal datasets [20, 26]. This native multimodal approach fuses the visual and textual representations early in training instead of using complex pipelines bridge the modality gap [63, 67, 70, 71] and eliminates the need for adapters, greatly simplifying the architecture design.
models [16, 19, 21, 24, 25, 66).
B Experimental Setup
B.1 Models Architectures
Table A2 shows the detailed configurations of each model employed in the experiments.
Parameters
Chameleon-788
Chameleon-Sa 34H
Emu3--8
LLLVV-78
Pixtml-128
Aanus-1,38
VILA-U1-7
N. Parameters
7B
3:B
SB
7B
12B
1.3B
7B
Backbone
Qwen2-7B
Mistral Nemo 12B
DeepSeck LLM--13B
2-BB
Visual Incoder
VQNGAN
VONGAN
VQ-GAN
SigLJP
Pixtral-ViT ViT
VQ
RQ SIP4
Image Generation
Yes
Yes
Yes
No
No
Yes
Yes
N. Layers
32
48
32
28
40
24
32
N. Attention Hends
Hidden Size
4096
8192
4096
3584
5120
2045
4096
Table A2: Comparison of Model Architectures. Detailed configurations of each model, including parameters, visual encoder generation capabilities, layer counts, attention heads, and hidden sizes.

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B.2 Datasets details
Captioning prompt constructed from Flickr30K and MSCOCO. The Flickr-30k [38] dataset and the MSCOCO dataset [39] contain respectively 30,000 and 330,000 images. Each sample of the first and approximately 200, 000 of the latter is associated with five captions. For the captioning benchmarks presented in tables I and A3 constructed the prompts with the following structure: "(image) Provide a one-sentence caption for the provided image.
Image-text prompt constructed from Flickr-30k. To generate prompts used section 3.1 that alternate between text-first and image-first formats, structured the inputs by concatenating all five captions for each image, either preceding or following the image itself, depending on the intended sequence.
Question answering prompt constructed from VQAv2. The dataset VQAv2 [37] consists of 204, 721 images from the MSCOCO dataset each associated with variable number multiple choice question answering. For this benchmark presented in tables and A3, we constructed prompts concatenating the image, the first of the associated question, and the instruction "Answer the question using single word or phrase."
Image-text prompt constructed from ImageNet. For the experiment described in sections 3.2 and 3.4 we use the following prompt construction "<image> This animal is a". For the activa- tion patching experiment described section 4.1 we use the same prompt for Chameleon-7B. For Chameleon-34B, we adopt slightly modified prompt: "<image> Answer the question using a single word, number, or short phrase. This animal is a" as Chameleon- re- fused to respond to the original prompt.
For the activation patching experiment, described section 4.1, we manually select 20 classes from the list above to ensure semantic diversity in the animal represented, and then we random sample 100 images for each class, obtaining a total of 2000 images. The selected pairs are:
aalpiicannlliggttorrrooroooo n. 01, arabian_cameer .n.1....1.. 01)
Shald_eagle.r 01, barn_spider.n..c .n..p)
(bee_eater 01, theetah.n.011
n.0.i great.grey_ool.n...
(green_mamba.n) m. 01, grey_whale.n.01)
(hippopotamus.nug 01. jaguar.n.01.
(king.penguin..n.01 kit_fox.n...
(1ionfish.n.01, macaw.n.01)
(proboscis_monkey.. .n..mkkey...... 01.
.1)
(tailed_frog.nt) 010, trilobite. 01)
B.3 Finetuning details
Emu3-Gen finetuning. We fine-tuned Emu3 on a mixture of datasets using 37.5k samples from the VQAv2 [37] and MS-COCO-2014 [39] training and 150k samples the LLa LLaV---nstruet-- 150K using the Hugging Face implementation. We fine-tuned Emu3 for one epoch with batch size of 64 using LoRA with a rank of 64, an alpha of 16, Adam optimizer without weight decay, and cosine annealing scheduler starting with learning rate of 5e-5.
Removing the narrow gate with fine-tuning. We fine-tuned Chameleon-77 7b and Emu3 on a dataset of 15k samples, 7.5k samples from the LLaVA-instruct-IN training set. We fine-tuned the models for one epoch with a batch size of 128, using LoRA with rank of 128, alpha of 256, Adam optimizer with weight decay 0.1, cosine annealing learning scheduler with starting learning rate of 2e-5.
training set, and 7.5k from the VQAv2

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B.4 Advanced density peaks clustering.
The Advanced Density Peaks (ADP) clustering [52] is a mode-seeking, density- based clustering algorithm that finds the modes of the probability density the data's 's oow-dimensional data without performing any explicit dimensional reduction. We summarize the main steps below and refer the interested reader to the original paper [52].
The algorithm consists of three steps: the estimation of the data's intrinsic dimension, the estimation of the local density around each point, and final density-basee clustering of the data. Following previous works [72-74]. we estimate the intrinsic dimension with Gride, a neighbor- based intrinsic dimension estimator, setting the rank of the nearest neighbor involved in the estimate 16 (see [74] for more details). We then measure the local density around each data point with a ANN approach: Pi,k - .V.. Here, N the number of data points, and V the volume of the ball, which has a radius equal to the distance between the point i and its hdhh nearest neighbor. Importantly, we measure the volume on the intrinsic manifold using the intrinsic dimension value estimated in the first step.
The third step is the density-based clustering. With the knowledge of the pi we find a collection of density peaks C {c² {c",...c") assign the data points around them, and find the density قaن of saddle points between a pair of clusters Cg with the procedure described in [52]. The statistical reliability of the peaks is assessed with t-test on log p log pa where pa is the maximum density of peak Co. and pa.. the density of the saddle point between co and cg. Once the confidence level Z is fixed, all the clusters that do not pass the t-test are merged since the value of their density peaks are considered indistinguishable from the nearby saddle point. The process is repeated until all the peaks satisfy the t-test and are statistically robust with confidence Z [52]. In the analysis reported in section 3.1, we remove clusters with size lower than 50.
C Additional Results
C.1 Modality Gap in VLMs
We extend the results of section 3.1 to Janus, Pixtral, and VILA-U. As illustrated in Figure Al, all of the additional models exhibit an alignment between modalities that grows along the model depth, similar to the behavior observed in LLaVA. Figure A Al-left confirms this alignment across modalities for all the models. Figure A -rigght reveals that the homogeneity within modality clusters is lower compared the Chameleon models and Emu3. This indicates that LLaVA, Janus, Pixtral, and VILA-U have a smaller modality gap.
0.8
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cosine simllarity
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VILA-U-7B
Figure A1: Modality Gap in VLMs. (left) Cosine similarity between text and image token em- beddings as a function of model depth reflects the orthogonality of modalities Janus, Pixtral, and VILA-U models. Points represent median cosine similarity, with shaded areas indicating the interquartile range. (right) Homogeneity score of token clusters generated via Advanced Density Peaks with respect their original modality.
3

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Chameleon-78
Emu3-8B
LLaVA-7B
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Size Tokens
Figure A2: Localization of Visual Semantic Information. Neighborhood overlap between visual token embeddings and caption embeddings on the MS-COCO dataset for the models Chameleon-7B, Emu3-8B and LLaVA-7B The results closely resemble those of figure 4
C.2 A comparison of the semantic content of MS-COCO images in [EOI] and Qwen-GTE captions embeddings.
In section 3.3 we investigated the alignment between token representations across layers and the classes of ImageNet. In this section, will now extend those findings to the MS-COCO captioning task to investigate the results generalize to tasks different from classification. We will proceed as follows: first, for each image in the MS-COCO dataset, visual tokens at each layer of every model analyzed in our study; Concurrently, we embedded the captions for these images using the Qwen2- -- 7B-GTE text encoder [75]. To measure the aligmment between these two sets of representations we used neighborhood overlap described at section 2. where in this context, the reference data representations other words for each sample i, we computed the fraction of shared k-nearest neighbors between the visual token embedding and the caption embedding according equation (2). This approach allowed us to perform a direct comparison of the semantic alignment across all models and token types.
extract the activations of the relevant
the embeddings from the text encoder. In
The results for the models of figure 4 are presented in figure A2, while the profiles for the remaining models are depicted in figure A3-right. We can notice that both group plots closely mirror the findings from our initial ImageNet experiments. This strong correspondence indicates that the localization of semantic information within specific tokens is a consistent characteristic of models like Chameleon and Emu, and that this phenomenon is not task-dependent. Furthermore, the high degree of alignment between the representations of these specialized visual tokens and the caption embeddings suggests that they encode high-level semantic abstractions that comparable those captured by sophisticated text-based sentence transformers.
C.3 Cross-Modal Attention and Semantic Content of Visual Tokens
The results for Chameleon-34B closely mirror those of Chameleon-7BB and Emu3, discussed in section 3.2. Figure A3-left visualizes the cross-modal attention contributions of image tokens, where tokens contributing more than 1% individually are shown explicitly, while the rest are grouped as "internal image". In Chameleon-34B, cross-modal attention is primarily concentrated on three tokens-(ENN) [EOI], the first image token, and the last. In contrast, Janus, Pixtral, and VILA-U exhibit pattern similar to LLaVA (section 3.2), where attention to special tokens remains low. At the same time, significant portion of the information is retained within internal image tokens. Figure A3-center further reveals that in Chameleon- 34B as in the other native multimodal VLMs, [EDI] consistently
maintains high Xx Lgtt values across layers, indicating its dominant role encoding visual information. Meanwhile, all other tokens exhibit near-zero overlap values, suggesting minimal contribution meaningful visual representation. For Janus, both [EOI] and internal image tokens carry strong information from the earliest layers, implying that visual encoding may be directly influencing their representations. Similarly in Pixtral, both the [EOI] and the last image token exhibit a X than that of LLaVA, although still below Janus. However, as shown in appendix C.4, the information encoded in these special tokens do not play significant role in information flow within the backbone models. Among the non-native multimodal VLMs, VILA-U most closely resembles LLaVA in terms
I.9t higher

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of semantic alignment, as most of the information is concentrated within the internal image tokens, while all other special tokens achieve negligible overlap scores.
Chamelcon-34B
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coit-on-megge maagee attertion
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text on inage altenson
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Figure A3: Localization of Information in Different Models. Each row corresponds model. The left panel shows the contribution image token positions to total text-on- text-on-image attention. The center panel presents the neighborhood overlap between selected image tokens and ImageNet labels. The right panel finally presents the neighborhood overlap between visual token embeddings and caption embeddings the MS-COCO dataset.
5

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C.4 Effect of attention knockout on vision vision-language tasks
Table A3 extends the results of table showing the ablation effect on all tokens identified for their strong eross-modal attention. The analysis further confirms that none of the special tokens, except for [EDI], play significant role, as their ablation does noticeably affect performance on any benchmarks.
Model
Ablation
VQAv2
Flickr
MS-COCO
ImageNet (Xa outitt
0.51
0.34
0l.48
01.46
text + [601]
0.25
0.04
0.04
0.01
Chameleon-7B
text - Last- Last-Image
0.42
0.20
0.32
0.46
text 32nd
0.51
0.34
0.49
0.46
0.59
0.41
0.47
0.43
text [EDI]
0.39
0.01
0.01
0.04
Chameleon-3ee 34B
text Last-Image
0.58
0.39
0.46
0.41
text - First- Image
0.57
0.38
0.46
0.42
0.57
0.29
0.63
0.35
Emu3
text + [EOI]
0.48
0.13
0.32
0.24
text img
0.42
0.22
0.54
0.30
0.70
0.98
0.5
LLaVA
text + [EDI]
0.71
0.97
0.45
text + img
0.00
0.00
0.01
0.05
0.79
0.59
0.68
0.63
text [00]]
0.77
0.62
0.68
0.61
Pixtral
text [EOL]s
0.76
0.54
0.61
0.61
text Last-Image
0.77
0.57
0.68
0.62
1025
0.77
0.55
0.62
0.61
img
0.37
0.00
0.01
0.06
0.51
0.32
0.43
0.65
Janus
text + [EDI] 0.51
0.32
0.42
0.65
text + img
0.00
0.01
0.01
0.06
0.71
0.42
0.61
0.64
VILA-U
text
[601]
0.72
0.43
0.61
0.65
Last-Image
0.72
0.42
0.61
0.64
text
img
0.39
0.01
0.02
0.08
Table A3: Effect of Attention Knockout on Image Understanding Tasks. Performance of the models on visual question answering (VQAv2), image captioning (Flickr-30k and MS-COCO), and image classification (ImageNet) under different ablation settings. We ablated the special tokens with high cross-modality attention by removing their communication with the text tokens. Numbers in bold mark the worst performance for each model and task.

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Reviews:
[{"review_id": "SKuGjIh06d", "reviewer": "Reviewer_7J47", "summary": "This 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.", "questions": "- 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?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "This paper investigates how multimodal-output VLMs and text-output VLMs exchange information across modalities. By identifying the narrow gate token, the authors demonstrate how visual semantics propagate differently depending on the model type (i.e., multi-modal output VLMs versus text-only output VLMs).\n\nMy initial major concern was the limited novelty of the work, as the original submission focused primarily on establishing the existence of the narrow gate without offering deeper or unexpected insights.\n\nWhile I still believe that the paper does not fully explain the underlying mechanism behind the narrow gate phenomenon, my main concern regarding limited novelty has been sufficiently addressed. The additional experiments presented in the revised version highlight the importance of the narrow gate and point to meaningful directions for future research. Accordingly, I find a final score of 5 to be appropriate.", "rebuttal": "We thank the reviewer for their recognition of the originality of our findings and the possible future applications for a better understanding of MMLMs. Below, we address your concerns.\n\n### Weaknesses\n> *Limited scope of findings: the paper [...] focuses almost entirely on demonstrating the gate’s existence and lacks any further substantive or surprising conclusions.*\n\nWe believe that our paper goes beyond demonstrating the existence of the *“narrow gate”* at the [EOI] token. \nFirst of all, this finding is tightly related to the general, and counterintuitive, observation that native multimodal-output models maintain a large modality gap across all layers (Fig. 3), a property generally considered to be a drawback for cross-modal reasoning [1,2]. \n\nWe support our findings by analyzing the impact of diverse architectures and training protocols on the narrow gate phenomenon. These insights are of practical importance. Localizing and characterizing how these models develop an effective communication mechanism despite the modality gap is a finding central and timely in the multimodality community,  since this class of models is being employed in several state-of-the-art multimodal training pipelines [3,4], motivated by advances in autoregressive image generation [5].\n\nWe discuss the potential applications where the narrow gate can be exploited proactively in Sec. 3.4 (e.g., semantic steering via activation patching) and propose how to use it to reduce memory and computing usage. We also suggest that a possible way to make the models more robust and *“encourage a more distributed communication can be masking the text-on-[EOI] attention during the last part of training”* (lines 325-326). \n\nTo validate experimentally the last claim and address the reviewer's concern, we fine-tuned Chameleon and Emu3, masking the [EOI] tokens on an instruction dataset composed of 10k samples from the LLaVA training set and 5k samples from the VQAv2 training set. In this case, we can significantly reduce the detrimental effect of [EOI] ablation on performance. Results are summarized below:\n- **Chameleon**:\n  - VQAv2 accuracy improved from 50% to 62% (base). With ablation of [EOI], it improves from 0.05% to 52%.\n  - COCO-CIDER score increased from 0.48 to 0.72 (base). With ablation of [EOI], the score increased from 0.06 to 0.66.\n\n- **Emu3**:\n  - VQAv2 accuracy improved from 44% to 50% (base). With ablation of [EOI], it improves from 35% to 46%.\n  - COCO-CIDER score increased from 0.73 to 0.87 (base). With ablation of [EOI], the score increased from 0.40 to 0.82.\n\nTo validate the importance of keeping [EOI] masked during training, we performed a standard fine-tuning run without masking [EOI]. Then, we repeat the ablation experiment in Tab. I and we measured the effect of ablation [EOI] after the fine-tuning.\n- **Chameleon**: Final VQAv2 accuracy was 53% (base) and 35% with [EOI] ablation. COCO-CIDER was 0.75 (base) and 0.05 with [EOI] ablation.\n\n- **Emu3**: Final VQAv2 accuracy was 45% (base) and 43% with [EOI] ablation, and COCO-CIDER was 0.82 (base)  and 0.48 with [EOI] ablation.\n\nThese results show that masking [EOI] during training is crucial for removing almost completely the gating mechanism, distributing the communication of visual information across multiple tokens, and removing the issues associated with a constrained information flow (see lines 324-327). Notably, it can also improve performance versus standard fine-tuning, except for COCO-CIDER in Chameleon. \n\nOur results, therefore, provide two main contributions. First, a new empirical finding and a mechanistic explanation for how native multimodal training impacts information flow, advancing the understanding of VLM internals. Secondly, they suggest practical, actionable strategies for exploiting or mitigating the issues associated with the presence of the narrow gate.\n\n> *Questionable validity of the zero‐ablation method [...].*\n\nThe zero-ablation approach may distort the model's internal representations. However, for all the models, the zero-ablation on any single token (besides [EOI] in Chameleon and Emu3) does not cause a performance drop despite the distribution shift caused by the ablation (see Tables 1 and A2). This suggests that the performance drop observed in Emu3 and Chameleon when [EOI] is zero-ablated is not due to OOD effects, but instead to the gating mechanism of  [EOI].\n\nWe agree with the reviewer that milder ablation techniques exist in the literature, the mean-ablation being one of the most common alternatives [6]. In this case, the ablated token is substituted with its mean value taken over the dataset. We applied the mean ablation to [EOI] in the COCO dataset in Chameleon-7b, and also in this case, we observed a degradation in CIDER from 0.46 to 0.08.\n\n> *No causal analysis of narrow‐gate emergence [...].*\n\nIn our study, we analyzed VLMs composed of a different set of architectures (visual encoders, connectors) and trained with different pipelines (from scratch or fine-tuned on existing LLM backbones). \nThis wide variety of models allowed us to pin down the conditions to observe a narrow gate: the model must be trained from scratch on multimodal data, both for understanding and generation tasks, and must possess a low-level vision encoder (VQ-VAE). Studying how the narrow gate forms during training is beyond the scope of our work due to the lack of intermediate training checkpoints for Chameleon and Emu3. \n\n\n### Questions\n> *At lines 288–290, [...] one must examine the value of the last token’s ($\\chi^{l,gt}$). Have you checked this?*\n\nWe agree with the reviewer on this observation.  We measured $\\chi^{l,gt}$   also on the last token, and its value grows from 0 to 0.45 between layer 0 and 24 in Chameleon-7b, from 0 to 0.58 between layer 0 and 44 in Chameleon-34, and from 0 to 0.37 between layer 0 and 11 in Emu3. In all cases, the growth of the  $\\chi^{l,gt}$ in the last token comes after the one on [EOI]. When [EOI] is ablated, also the $\\chi^{l,gt}$ in the last token drops (see Table 1). Therefore, the claim at lines 288-290 is fully supported by the experiment. \n\n> *The notation ($A_{\\text{img}\\rightarrow\\text{text}}$) on line 88 is ambiguous. [...]*\n\n$A_{i,j}$ is a matrix which we introduce in line 87 as the “attention from token i to token j”. Given that in our setup the image comes before the text (line 85), if we set “$N_{[EOI]} < i ≤ N, 0 < j < N_{[EOI]}$”, $A_{\\text{img}\\rightarrow\\text{text}}$ is the rectangular block of the attention matrix in which the text tokens at positions $N_{[EOI]} < i ≤ N$ attend to the image tokens $0 < j < N_{[EOI]}$. We denote this block of the attention matrix as cross-modal attention (see end of line 86) and analyze it in Sec. 3.2. \nWe use $A_{\\text{img}\\rightarrow\\text{text}}$ in Sec. 3.4 at lines 233 - 250 - 255 to reference the part of the attention matrix set to zero when we ablate the communication between image and text tokens. \n\n> *In the Chameleon model, what is your hypothesis for why the 32nd token serves as the narrow gate? [...]*\n\nThe 32nd token in chameleon-7b is not a narrow gate. In lines (165, 166), we write that a token must “*(i) have a large weight in the text-image attention, and ii) have a rich semantic knowledge of the image*”. The analysis of Sec. 3.3 shows that condition (ii) is not met for token 32nd, which has a $\\chi^{l,gt}$ with the imagenet concepts close to zero in all the layers (see Fig. 4 left). \nIn a side experiment, not reported in the paper, we verified that the norm of the token 32 at layers $5-12$ is $\\times100$ larger than all the other image tokens. Massively activated tokens in LLMs can occur as artifacts in LLMs, and their origin is the subject of current research [7].\n\n[1] W. Lieng et al., Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning. \n\n[2] Q. Huang et al., Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate.\n\n[3] https://openai.com/index/introducing-4o-image-generation/\n\n[4] M. Shukor et al., Scaling Laws for Native Multimodal Models\n\n[5] P. Sun et al, Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation\n\n[6] Wang,  et al. \"Interpretability in the wild: a circuit for indirect object identification in GPT -2 small.\"\n\n[7] Sun, M. et al, Massive Activations in Large Language Models\n\n---\n\nThank you again for your comments and suggestions. If we manage to address your main concerns, we politely ask you to consider raising your score. Please don’t hesitate to let us know if you have further questions. We are happy to answer your questions during this rebuttal discussion period."}, {"review_id": "BE5xVddE5V", "reviewer": "Reviewer_9UB8", "summary": "This 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.", "questions": "* 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?", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## 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)", "was_revised": true, "final_justification": "The author clarified a lot of things that were not clear in the main paper and answered most of my doubts, hence I'm raising my score. The extent of additional experiments provided in the rebuttal is great but it's what could have made the submission truly interesting if executed with more time. If this was a journal my rating would have been a major revision, considering it's a conference and that's not possible I would consider this work really borderline. \nI don't have major issues with the work being accepted if all the additional results are integrated in a cohesive way, but at the same time the original submission was in my opinion very weak and it would be interesting to re-evaluate a version of the work with the additional material included in a cohesive story. The finetuning experiments in the rebuttal are in my opinion very counter-intuitive and go against the intuition in the paper. I didn't find the explenation of the authors very convincing but to their credits they only had the extremelly limited time of the rebuttal phase to run experiments and interpret the results.\n\nConsidering all the points above I would lean towards keeping a borderline negative rating (3).", "rebuttal": "We thank the reviewer for their comments that our results and analysis are both well structured and surprising, and that they could help design better ways to train natively multimodal models. In the section below, we will address your concerns.\n\n###  Weaknesses\n\n> *a.  Ambiguity cross-modal attention is computed:*\n\n- 1: The cross-attention pattern reported in Fig. 3 remains quantitatively consistent if we analyze only the answer tokens as the reviewer suggests. To answer the reviewer's concern, we repeated the experiment described in Sec. 3.2 on Chameleon-7b, measuring the attention patterns only on the token generated after the prompt “ `<image>` This animal is a _”. \nAlso in this setup, the highly attended image tokens are the 32nd, [EOI], and last image ones, with [EOI] receiving 44% on the total cross modal attention between layers 1 and 8, the 32nd receiving 54% of the cross modal attention between layer 5 and 12 and the last token 73 % of the total cross modal attention after layer 12. \nTherefore, the highly attended tokens do not change if we restrict the analysis to the answer tokens.\n\n- 2: In Llava and Chameleon-7b, the unimodal “text->text” attention makes up from 12% to 24% of the total attention throughout the model, much smaller than the cross-modal part. The general patterns of the cross-modal part remain unchanged when we include the unimodal one. \nWe’d like to highlight, however, that the primary focus of our work is on the communication of information between image and text in Vision-Language Models. The answers can be grounded on the visual part of the input only through the cross-modal part of the attention, regardless of how strong it is relative to the unimodal attention. For this reason, in Fig.3, we report where the information flows in the cross-modal attention block.\n\n- 3: We performed additional experiments to verify that the patterns of Fig. 3 are task agnostic and remain qualitatively unchanged in the captioning COCO, Flickr, and visual understanding VQAv2 tasks. \nThe experiment depicted in Figure 4 requires the selection of a set of representative concepts associated with a dataset. This selection is simple for Imagenet, where the meaningful semantics is given by the classes. It is less trivial in Flickr30k (or VQAv2). To address the reviewer's concern, we extend the experiments to a subset of 10000 samples taken from the Flickr30k dataset. To extract the important concepts contained in the captions, we used a gte-Qwen1.5-7B-instruct[1] (a Sentence Transformer [2]) to map each caption to a vector encoding its semantics. We then measure the similarity between the hidden representations of [EOI] and the Flickr30k embeddings from the Sentence Transformer. We compute the representation similarity with the Neighborhood Overlap. The result of this experiment is consistent with the one on ImageNet: the similarity between [EOI] and the captions embedding increases from 0.0 to 0.16 between layers 5 to 9, and it remains approximately 0.16 for all the remaining layers. For the average image embedding, and all the other image embeddings, it remains close to zero. \\\nWe also extended the analysis to two other classification tasks: Caltech101[3], comprising 101 classes of different objects, and DTD Dataset [4] with 47 classes of textures inspired by human perception. Also in these cases, the overlap profiles exhibit trends consistent with COCO captions and ImageNet. \n\n> *b. Are attention patterns predictive*\n\nAs we write in lines (165, 166),  a candidate's narrow gates must *\"(i) have a large weight in the text-image attention, and ii) have a rich semantic knowledge of the image\"*. Therefore, **we never implied that attention patterns alone are predictive of tokens acting as narrow gates. The analysis of Sec. 3.2 is aimed at discussing both these two necessary conditions**, with the section paragraph *“Probing the semantic content of visual tokens”* showing that in Emu3 and Chameleon, only [EOI] retains visual semantic information and satisfies condition (ii) and can thus be considered a candidate narrow gate. The final evidence that [EOI] acts as a narrow gate is given by the ablation results shown in Table 1. By addressing your previous concern a) we also showed that the analysis of Sec. 3.2 generalizes across datasets (Caltech101, DTD) and tasks (COCO-captioning). \n\n> *c. Actionability of the findings* \n\nBetween lines 323 and 333, we discuss several potential practical implications of our findings, suggesting that a possible way to make the models more robust and *“encourage a more distributed communication can be masking the text-on-[EOI] attention during the last part of training”* (lines 325-326). \nTo validate experimentally this claim and address the reviewer's concern, we fine-tuned Chameleon-7b and Emu3 on a dataset composed of 10k samples from the LLaVA training set and 5k samples from the VQAv2 training set, masking the attention to [EOI] during training. \nTo evaluate performance, we used 50 validation samples each from VQAv2 and COCO. Results are summarized below:\n- **Chameleon**:\n    - VQAv2 accuracy improved from 50% to 62% (base). With ablation of [EOI], it improves from 0.05% to 52%.\n    - COCO-CIDER score increased from 0.48 to 0.72 (base). With ablation of [EOI], the score increased from 0.06 to 0.66.\n\n- **Emu3**:\n    - VQAv2 accuracy improved from 44% to 50% (base). With ablation of [EOI], it improves from 35% to 46%.\n    - COCO-CIDER score increased from 0.73 to 0.87 (base). With ablation of [EOI], the score increased from 0.40 to 0.82.\n  \nTo validate the importance of keeping [EOI] masked during training, we performed a standard fine-tuning run without masking [EOI]. Then, we repeat the ablation experiment in Tab. I and we measured the effect of ablation [EOI] after the fine-tuning.\n- **Chameleon**: Final VQAv2 accuracy was 53% (base) and 35% with [EOI] ablation. COCO-CIDER was 0.75 (base) and 0.05 with [EOI] ablation.\n\n- **Emu3**: Final VQAv2 accuracy was 45% (base) and 43% with [EOI] ablation and COCO-CIDER was 0.82 (base) and 0.48  with [EOI] ablation.\n\nThese results show that the training with [EOI] masked is an effective strategy to remove almost completely the gating mechanism of [EOI]. Notably, it can also improve performance versus standard fine-tuning except for COCO-CIDER in Chameleon. The masked-finetuning, targeted on [EOI], is an example of how the findings of this work can be used to improve the model's performance and robustness. \n\n> *d.  Influence of the visual encoder*\n\nWe agree with the reviewer's observation that the models analyzed can not fully disentangle the impact of the vision encoder from that of the training protocol. To shed light on this point, in an experiment we performed after the submission deadline, we analyzed LIQUID [5]. This model is unified multimodal in output, differing from Chameleon only because its LLM backbone **was finetuned from Gemma-7B** and rather than being trained from scratch. In particular, **LIQUID encodes the images with the same VQ-VAE as Chameleon**. In the table below, we report the ablation analysis for the VQAv2 task. The results indicate that LIQUID behaves similarly to Janus and the other unimodal-output models: removing the [EOI] token does not affect performance, whereas removing the internal image tokens does degrade it. This experiment shows that having a VQ-VAE encoder is not sufficient to produce the narrow gate, suggesting that the natively multimodal training process and objective function play crucial roles.\n\n| *Liquid*      | *VQA (2000 pt)* |\n|---------------|-----------------|\n| *Baseline*    | 0.576           |\n| *EOI*         | 0.543           |\n| *Last*        | 0.549           |\n| *img->text*   | **0.177**           |\n\n\n### Questions\n\n> *How would Fig. 2 look like for [EOI]?...*\n\nWe have measured the median cosine similarity between the [EOI] embeddings and i) random text token embeddings and ii) random image token embeddings chosen from 200 random samples of the Flickr dataset. For the Chameleon 7b model, both profiles start similarly, with an initial peak at 0.10 at layer 2. After layer 10, the cosine similarity with image embeddings drops to 0.01, and the cosine similarity with text remains constant at 0.10. Both these values are well below the average text-text and image-image similarity.\n\n> *Have you analyzed the attention pattern of [EOI] wrt the rest of the image tokens for the different models?...*\n\nThis seems not to be the case. To evaluate the sparsity of the attention pattern of the [EOI] tokens into the image, we calculated the (normalized) entropy of the [EOI] row of the attention pattern. We have done so for the Chameleon 7b and Llava models. \nIn the former - even if we exclude the anomalous contribution from the 32nd token  - Snorm fluctuates between 0.5 and 0.82.\nHowever, Snorm is much higher in the Llava model, with values fluctuating between 0.73 and 0.99, indicating that in some layers the attention from [EOI] in Llava is almost uniform.\n\n### Paper formatting concerns\n> *Fig. 3 and Fig. 4 change the legend...*\n\nThank you, we will update the legend in the camera-ready version.\n\n> *What is  in Eq. 2?...*\n\nThank you for raising this clarity issue. We will add an example in the method section of the paper after Eq.2 is defined.\n\n[1] huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct\n\n[2] Reimers et al., Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\n\n[3] Li et al, Caltech 101, doi.org/10.22002/D1.20086\n\n[4] M. Cimpoi, et al. Describing Textures in the Wild\n\n[5] Junfeng, et al. \"Liquid: Language models are scalable and unified multi-modal generators.\" \n\n---\n\nThank you again for your detailed suggestions. If we manage to address your main concerns, we politely ask you to consider raising your score. If, on the other hand, there are some remaining questions, we are happy to address them during this rebuttal discussion period."}, {"review_id": "JmiayiUw0P", "reviewer": "Reviewer_R1ay", "summary": "This 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.", "questions": "My main concerns are items 1–3 under Weaknesses. If these are addressed, I will consider raising the rating.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "This paper reveals the \"narrow gate\" phenomenon in the image-text transmission process of multimodal large models through extensive experiments. My primary concern lies in the practical significance of this finding; however, given that the authors provided a downstream application case by masking attentions to the narrow gate token when training during the rebuttal stage, I decide to raise the rating from 3 to 4.", "rebuttal": "Thank you for your valuable feedback and the thorough analysis of our manuscript. We will use your suggestions to improve the quality of our work. We address the concerns you raised below.\n\n### Weaknesses\n1. > The conclusions presented in the Abstract and Introduction seem to conflict with those in Section 4. [...]\n\nWe agree with the reviewer. We will modify the Abstract and Introduction to be more consistent with our discussion and conclusions. Indeed, there is an impact of the diversity of the architectures (visual encoder) and training protocols (training from scratch vs fine-tuning of the LLM backbone) on the narrow gate phenomenon. \n\nTo shed light on this point, in an experiment we performed after the submission deadline, we analyzed LIQUID [1]. LIQUID is a unified multimodal-output model that differs from Chameleon only because its LLM backbone was fine-tuned from Gemma-7B, rather than being trained from scratch. In particular, LIQUID encodes the images with the same VQ-VAE as Chameleon. In the table below, we report the ablation analysis for the VQAv2 task.\n\n| Liquid | VQA (2000pt) |\n|:-------:|:------------:|\n|Baseline| 0.576 |\n|EOI | 0.543 |\n|Last | 0.549 |\n|img->text | **0.177**|\n\nThis experiment shows that having a VQ-VAE encoder is not sufficient to produce the narrow gate, suggesting that the natively multimodal training process and objective function play crucial roles.\n\n2. > *[...] the contributions are somewhat difficult to assess. The narrow gate token [...] is a widely observed phenomenon in both LLMs and VLMs.* \n   \nWe discuss the memory tokens in lines 912-921 and briefly cite the analogy with the narrow gate in lines 331-333. \nWhile both the narrow gate and memory tokens are known to store global input information, our study goes further. Specifically, we show that the [EOI] token in Chameleon and Emu not only stores global information but also emerges as a **unique** cross-modal communication channel in these multimodal models. Previous studies focusing on registers [2] only addressed VLMs trained for image understanding, where **several register tokens** store global information. The phenomenon we describe in this work is novel both in the model we analyze and in the qualitative picture that emerges on such multimodal-output VLMs.  \nSecondly, unlike memory tokens that are often manually introduced to capture global information, either through explicit design [3] or training objectives [4], the [EOI] token becomes a *“narrow\"* gate for the information flow naturally during the autoregressive training, without targeted intervention. To our knowledge, this emergent role of [EOI] has not been found in existing work in either unimodal or multimodal output VLMs. If the reviewer can suggest relevant literature, we will be happy to compare and discuss it further in the camera-ready version of the work.\n\n> *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. [...]*\n\nFollowing the reviewer’s suggestion, we experimentally validated our proposed mitigation strategy by fine-tuning both Chameleon-7b and Emu3. We trained the models on a dataset composed of 10k samples from the LLaVA training set and 5k samples from the VQAv2 training set. We fine-tuned the model for one epoch using LoRA with a batch size of 128, weight decay of 0.1, and a cosine-annealed learning rate starting at 2e-5. During training, we masked the attention to the [EOI] token. \n\nTo evaluate performance, we used 50 validation samples each from VQAv2 and COCO. Results are summarized below:\n- **Chameleon**:\n    - VQAv2 accuracy improved from 50% to 62% (base). With ablation of [EOI], it improves from 0.05% to 52%.\n    - COCO-CIDER score increased from 0.48 to 0.72 (base). With ablation of [EOI], the score increased from 0.06 to 0.66.\n\n- **Emu3**:\n    - VQAv2 accuracy improved from 44% to 50% (base). With ablation of [EOI], it improves from 35% to 46%.\n    - COCO-CIDER score increased from 0.73 to 0.87 (base). With ablation of [EOI], the score increased from 0.40 to 0.82.\n  \nTo validate the importance of keeping [EOI] masked during training, we performed a standard fine-tuning run without masking [EOI]. Then, we repeat the ablation experiment in Tab. I and we measured the effect of ablation [EOI] after the fine-tuning.\n- **Chameleon**: Final VQAv2 accuracy was 53% (base) and 35% with [EOI] ablation. COCO-CIDER was 0.75 (base) and 0.05 with [EOI] ablation.\n\n- **Emu3**: Final VQAv2 accuracy was 45% (base) and 43% with [EOI] ablation and COCO-CIDER was 0.82 (base) and 0.48  with [EOI] ablation.\n  \nThese results show that the training with [EOI] masked is an effective strategy to remove almost completely the gating mechanism of [EOI]. Notably, it can also improve performance versus standard finetuning except for COCO-CIDER in Chameleon. \n\n3. > *The experiments in the paper are limited to the image classification task [...]*\n\nThe main findings of the paper are summarized in Table I of Section. 3.3 and support the central role of the narrow-gate ([EOI]-token) in the Chameleon and Emu models across diverse tasks: ImageNet classification, visual question answering (VQAv2), and caption generation (Flicker-30k and MS-COCO). Therefore, our experiments are not limited to image classification tasks. \n\nTo extend the experimental evidence given for ImageNet in Sec. 3.2, we analyzed the attention patterns of Fig. 3 for visual question answering (VQAv2) and captioning (Flickr-30k), finding the same characteristic attention pattern.  \nThe experiment depicted in Figure 4 requires instead the selection of a set of representative concepts associated with a dataset. This selection was simple for Imagenet, where the classes give the meaningful semantics, but it is less trivial in Flickr30k (or VQAv2). \n\nTo address the reviewer's concern, we extend the experiments to a subset of 10000 samples taken from the Flickr30k dataset, analyzing the Chameleon-7b representations. \nTo extract the essential concepts contained in the captions, we used a gte-Qwen1.5-7B-instruct [5] (a Sentence Transformer [6]) to map each caption to a vector encoding its semantics. We then measure the similarity between the hidden representations of [EOI] and the Flickr30k embeddings from the Sentence Transformer with the Neighborhood Overlap [7]. If an internal representation of Chameleon is similar to the embeddings from the gte-Qwen1.5-7B-instruct this means that these representations encode similar abstractions of the image. \nThe result of this experiment is consistent with the one on ImageNet: the similarity between [EOI] and the captions embedding increases from 0.0 to 0.16 between layers 5 and 9, and it remains approximately 0.16 for all the remaining layers. For the average image embedding, and all the other image embeddings, it remains close to zero. \n\nIn addition, to further validate the generality of our results within the classification domain itself, we have introduced two additional benchmarks: Caltech101 [8] and Describable Textures Dataset [9]. Also these cases exhibit trends consistent with those shown in the paper in Fig. 3 for ImageNet. \n\n4. > *The paper focuses exclusively on the information flow from vision to text. [...]*\n\nThis is a relevant and interesting question. However, exploring the narrow gate in image generation tasks involves significant additional experimentation and analysis. It would be best addressed in a separate, future study.\n\n\n[1] Wu, Junfeng, et al. \"Liquid: Language models are scalable and unified multi-modal generators.\"\n\n[2] Neo et al. Towards Interpreting Visual Information Processing in Vision-Language Models.\n\n[3] Wen et al. Efficient vision-language models by summarizing visual tokens into compact registers.\n\n[4] Burtsev et al. Memory transformer.\n\n[5] huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct\n\n[6] Reimers et al., Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\n\n[7] Doimo et al., Hierarchical nucleation in deep neural networks\n\n[8] Li et al, Caltech 101,  doi.org/10.22002/D1.20086\n\n[9] M. Cimpoi, et al. Describing Textures in the Wild\n\n---\nThank you again for your detailed suggestions. If we manage to address your main concerns, we politely ask you to consider raising your score. If, on the other hand, there are some remaining questions, we are happy to address them during this rebuttal discussion period."}, {"review_id": "XuDvG7rhjj", "reviewer": "Reviewer_mkQ5", "summary": "This 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.", "questions": "see weakness.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "My issues were resolved, I agree raising my score.", "rebuttal": "Thank you for your feedback and the analysis of our manuscript. We will address below the concerns you raised.\n\n### Weaknesses\n\n> *1. The work is primarily an empirical analysis...*\n\nWe respectfully disagree with the characterization that empirical contributions lack value at NeurIPS. The NeurIPS call for papers explicitly states: *'We also encourage in-depth analysis of existing methods that provide new insights in terms of their limitations or behaviour beyond the scope of the original work.\"*  While our contribution is empirical, we address a well-recognized problem of characterizing the differences in information flow across modalities in VLMs. By exposing a single-token bottleneck that can both steer and break multimodal models, our study provides actionable knowledge for robustness, safety, and efficiency research. \nWe believe empirical contributions are fundamental to scientific progress and provide essential foundations for future algorithmic developments.\n\n> *2. The paper omits discussion of prior studies...*\n\nWe thank the reviewer for the suggestions. We will add the relevant literature in the related work section. However, all of these papers deal with VLMs that output only text. The main finding of our paper is about VLMs, which can perform jointly in image understanding and image generation tasks and, as such, remain entirely novel. \n\n> *3. The paper studies only limited models*\n\nIn our work, we explore the cross-modal communication in a diverse set of models. We study i) VLMs which output images and text (Emu3, Chameleon, Janus) and output only text (Pixtral, Llava)  ii) VLMs trained from scratch (Emu3, Chameleon) and fine-tuned on pre-trained LLM backbones (Pixtral, Llava, Janus), and iii) models with discrete, low-level VQ-VAE vision encoders (Chameleon, Emu3) and high-level, text-aligned CLIP-like encoders (Janus, Pixtral, Llava). \nBoth Qwen2.5-VL and InternVL2.5 are multimodal-in-input but unimodal in output, like Llava and Pixtral. As we write in lines 54-55 and 319-321, our findings show that the narrow gate allows for selective ablation and steering, but *“none of these properties hold for Llava and other unimodal-output VLMs”*, and the ability to *“produce multimodal responses is crucial”* for the emergence of this phenomenon. For these reasons, based on the experiments we performed on LLava and Pixtral, we do not expect these models to present a narrow gate. \n\n> *4. The paper appears somewhat rushed and underdeveloped...*\n\nThe reviewer previously remarked that *“the conclusions are supported by consistent evidence across different models and tasks”* and this point of view is also shared by reviewer R1ay, who recognises that we base our claims on extensive statistics across several VLMs.\nOur conclusions stem organically from the experimental results. 1) The high attention weight and large semantic content indicate [EOI] to be a possible narrow gate for Chameleon and Emu, and 2) the selective ablation proves this to be correct, since the removal of a single token effectively destroys performance over a diverse set of downstream tasks. \n\n---\nIf the reviewer has more specific concerns about any of these points, we would be happy to provide clarification in the author-reviewers discussion phase. Otherwise, we politely ask them to consider raising their score."}, {"review_id": "bCK29ZAMvt", "reviewer": "Reviewer_gNmN", "summary": "In 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.", "questions": "Please 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.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "Overall, I think this submission discusses an interesting phenomenon that is quite relevant given the current interest in multi-modal large language models the community. While I agree with reviewer 9UB8 that the _utility_ of this finding is not fully clear, the finding in itself is interesting and the experiments well-executed. I view this as an interesting addition in the context of explainable and interpretable AI, in the sense that it sheds light on the inner workings of highly relevant models. I therefore raise my score to accept.", "rebuttal": "Thank you for your thoughtful and detailed review. We appreciate the time and care you took in evaluating our work, as well as your recognition of its key strengths. We address your main concerns point by point below.\n\n**Relevance**\n> *The practical relevance of this finding is still a bit unclear to me.*\n\nBetween lines 323 and 333, we discuss several potential practical implications of our findings, showing applications where the narrow gate can be exploited proactively (e.g., steering via patching) and propose how to use it to reduce memory and computing usage. \\\nWe also suggest that a possible way to make the models more robust and “*encourage a more distributed communication can be masking the text-on-[EOI] attention during the last part of training*” (lines 325-326). \nTo validate experimentally the last claim and address the reviewer's concern, we fine-tuned Chameleon and Emu3, masking the [EOI] tokens on an instruction dataset composed of 10k samples from the LLaVA training set and 5k samples from the VQAv2 training set. In this case, we can significantly reduce the detrimental effect of [EOI] ablation on performance.  \nThe results evaluated on 50 validation samples each from VQAv2 and COCO are summarized below:\n\n- *Chameleon*:\n    - VQAv2 accuracy improved from 50% to 62% (base). With ablation of [EOI], it improves from 0.05% to 52%.\n    - COCO-CIDER score increased from 0.48 to 0.72 (base). With ablation of [EOI], the score increased from 0.06 to 0.66.\n\n- *Emu3*:\n    - VQAv2 accuracy improved from 44 to 50% (base). With ablation of [EOI], it improves from 35% to 46%.\n    - COCO-CIDER score increased from 0.73 to 0.87 (base). With ablation of [EOI], the score increased from 0.40 to 0.82.\n\nTo validate the importance of keeping [EOI] masked during training, we performed a standard fine-tuning run without masking [EOI]. Then, we repeat the ablation experiment in Tab. I and we measured the effect of ablation [EOI] after the fine-tuning.\n- *Chameleon*: Final VQAv2 accuracy was 53% (base) and 35% with [EOI] ablation. COCO-CIDER was 0.75\n (base) and 0.05 with [EOI] ablation.\n\n- *Emu3*: Final VQAv2 accuracy was 45% (base) and 43% with [EOI] ablation and COCO-CIDER was 0.82 (base)  and 0.48 with [EOI] ablation.\n\nThese results show that the training with [EOI] masked is an effective strategy to remove almost completely the gating mechanism of [EOI]. Notably, masked fine-tuning also shows improved performance wrt the non-masked standard, except for COCO-CIDER in Chameleon.\nThe masked-finetuning, targeted on [EOI], is an example of how the findings of this work can be used to improve the model's performance and robustness. \n\n> *This is in particular the case as the results are also not overly surprising [...] 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\nThrough our experiments, we show that having multimodal output, i.e., having a loss on the image tokens, is not sufficient to present a narrow gate: the Janus model (and the Liquid model we have integrated during the rebuttal phase) are multimodal in output, but they do not present a narrow gate. Our results not only show the (hitherto unreported) existence of a multimodal narrow gate, but also shed light on its possible causes.\n\nThe fact that native multimodal-output models, as Chameleon and Emu3, construct an efficient communication strategy despite exhibiting a wide modality gap across all the layers (Fig. 3) is rather surprising. Indeed, the modality gap can create challenges for performance in multimodal tasks [1] and can be used to quantify the quality of the training in VLMs [2]. Understanding and characterizing how native multimodal-output models develop an effective bottleneck-style communication strategy is an important insight for future studies in multimodal models, especially given their recent implementation in several state-of-the-art multimodal training pipelines [3,4].\n\n\n**Interpretation of results.**\n>  *The authors argue that the results show that...*\n\nThe reviewer offers an interesting interpretation of why semantic content appears diffuse in models that do not exhibit a narrow-gating mechanism (e.g., Llava and Pixtral). \n\nHowever, our work specifically focuses on models like Chameleon and Emu3, which show the opposite behavior, where semantics are localized in a single token (the [EOI] token). In these models, we observe a significant embedding gap between image and text modalities throughout the hidden layers (see Fig. 1), much larger than in models where the semantic content is distributed, which suggests that this [EOI] may function as a modality-bridging messenger token. \nThis hypothesis can be motivated by the very low cosine similarity between [EOI] with both text and image embeddings (<0.1), indicating it doesn't belong to either modality. Instead, it seems to act as an intermediary to enable cross-modal communication.  \nThe structural difference, diffuse versus localized communication, might stem from how the models handle the alignment between modalities, and we agree that further experiments (e.g., token ablations or alternative pretraining approaches like DINO) could provide deeper insight.\n\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”*\n\nOur definition of image embedding is adopted following the convention in the literature [e.g. 5]. We simply refer to the embeddings of the tokens from the image. \nMoreover, previous work on the Llava-style models [5] showed that  “*activations in the late layers at each visual token position correspond to token embeddings that describe its original patch object in its corresponding image token*”. Image embeddings at late layers, therefore, do preserve interpretable visual information of the input, even when no loss is applied to those positions' output.  \n\n\n### Additional 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\nWe are not sure we understand the reviewer’s comment.  Figure 1 (right) shows how different models perform after ablation of the two possible communication channels: image-text (x-axis) and [eoi]-text (y-axis). There isn’t a third axis; the colors of the markers simply denote different models. \n\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\nWe fine-tuned the Emu3-Gen model since we wanted a model capable of both generating images and text.\n\n> *Minor typos [...]*\n\nWe thank the reviewer. These typos will be corrected in the final version.\n\n\n[1] W. Lieng et al., Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning. NeurIPS, 2022 \n\n[2]  Q. Huang et al., Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate, ICCV, 2025 \n\n[3] openai.com/index/introducing-4o-image-generation/  \n\n[4] M. Shukor et al., Scaling Laws for Native Multimodal Models, 2025, arXiv:2504.07951 \n\n[5] Neo et al. Towards Interpreting Visual Information Processing in Vision-Language Models.\n\n---\n\nThank you again for your comments and suggestions. If we manage to address your main concerns, we politely ask you to consider raising your score. Please don’t hesitate to let us know if you have further questions. We are happy to answer your questions during this rebuttal discussion period."}]

Author rebuttal:

Meta-review: The paper shows that multimodal-output VLMs concentrate image/text communication through a single token (narrow gate), while unimodal-output VLMs communicate in a distributed way. Ablating the EOI token cripples performance and editing it changes semantics. The rebuttal adds broader evidence and a small SFT-masking study.

The clear strength of this paper is that it presents is an increased understanding of how information flows in the investigated models. Reviews praised a careful analysis that links attention to causal effects with multiple probes. After the discussion, there was also a broadened validation across tasks/datasets, added training intervention with promising outcomes.

One reviewer remains borderline-reject desiring improved actionability and narrative. The exact mechanism behind the gate formation could also benefit from further investigation. There were some issues regarding clarity or formatting issues.

In the discussion two reviewers increased their scores while one reviewer stayed slightly negative but not arguing strongly against acceptance.

Overall, I recommend to **accept** this paper to NeurIPS. It analyses an interesting topic in a way that would be very suitable for presentation in my opinion.
Decision: Accept (poster)