Traceable and Explainable Multimodal Large Language Models: An Information-Theoretic View
Authors: Zihan Huang
Junda Wu
Rohan Surana
Raghav Jain
Tong Yu
Raghavendra Addanki
David Arbour
Sungchul Kim
Julian McAuley
Venue: COLM 2025 (2025)
Keywords: multimodal LLM
information theory
TL;DR: We introduce an information-theoretic framework that uses mutual information, a Concept Bottleneck, and an InfoNCE mechanism to explain how multimodal models align and integrate visual and textual inputs.

Abstract: Existing multimodal large language models (MLLMs) often lack traceable and explainable mechanisms for visual-textual alignment, making it challenging to understand how textual instructions shape multimodal representations. To address this shortcoming, we propose an information-theoretic framework that clarifies how MLLMs handle and transform both text and visual inputs. In particular, we measure the visual information gain that arises from textual instructions and multimodal encodings, thereby illuminating how different modalities interact and contribute to the model’s overall processing.
Our framework decomposes the multimodal encoding process into layer-wise mutual information measures for better explainability, quantifying the visual contribution as the difference between unconditional and text-conditional mutual information. Specifically, inspired by the Information Bottleneck framework, we introduce a Concept Bottleneck that maps high-dimensional multimodal representations into an interpretable space, enabling tractable variational upper bounds on the mutual information between visual inputs and the model’s internal states. Furthermore, we quantify the contextual contribution introduced by textual cues via an InfoNCE mechanism that contrasts multimodal representations computed with and without text guidance. This dual perspective, facilitated by tractable variational upper bounds, provides insight into how visual information is encoded and filtered by textual instructions, while also highlighting the contextual information induced and enhanced by MLLMs. 
Empirical findings demonstrate underexplored dynamics of visual-textual interaction within MLLMs, 
underscoring how textual instructions distinctly shape visual representations and demonstrating how visual prompts, 
when effectively paired with instructions, enhance multimodal understanding.

Published as a conference paper at COLM 2025
Traceable and Explainable Multimodal Large Language Mod- els: An Information-Theorele View
Zihan Huang'/ Jaannng/Junde Junda Wul+, Rohan Surana', Raghav Jain', Tong Yu?, Raghavendra Addanki², David Arbour?, Sungchul Kim², Julian McAuley! UC San Diego Adobb Research {zih043, juw069, rsurana, r @eussd.ede edu raddanki, arbour, sukim) @adobe. com
Abstract
Existing multimodal large language models (MLLMs) often lack trace- able and explainable mechanisms for visual-textual alignment, making it challenging to understand how textual instructions shape multimodal representations. To address this shortcoming, we propose information- theoretic framework that clarifies how MILMs handle and transform both text and visual inputs. In particular, we measure the visual information gain that arises from textual instructions and multimodal encodings, thereby illuminating how different modalities interact and contribute to the model's overall processing. Our framework decomposes the multimodal encoding process into layer-wise mutual information measures for better explain- ability, quantifying the visual contribution as the difference between un- conditional and text-conditional mutual information. Specifically, inspired by the Information Bottleneck framework, we introduce Concept Bot- tleneck that maps high-dimensional multimodal representations into an interpretable space, enabling tractable variational upper bounds the mutual information between visual inputs and the model's internal states. Furthermore, we quantify the contextual contribution introduced by textual cues via an InfoNCE mechanism that contrasts multimodal representations computed with and without text guidance. This dual perspective, facil- itated by tractable variational upper bounds, provides insight into how visual information encoded and filtered by textual instructions, while also highlighting the contextual information induced and enhanced by MLLMs. Empirical findings demonstrate underexplored dynamics of visual-textual interaction within MLLMs, underscoring how textual instructions distinctly shape visual representations and demonstrating how visual prompts, when effectively paired with instructions, enhance multimodal understanding.
1 Introduction
Multimodal Large Language Models (MLLMs) have advanced in integrating multimodal information, achieving impressive results in tasks such as question answering (Zhang et al., 2023b; Wu et al., 2025c;b), captioning (Liu et al., 2025; Wu et al., 2024c), navigation (Wang et al., 2025; Nguyen et al., 2024) and multimodal retrieval (Bao et al., 2025; Li al., 2024; Wu et al., 2025a; Huang et al., 2025; Wu et al., 2024a). However, understanding how textual instructions shape multimodal representations within these models remains challenging (Zhao al., 2024; Wu al., 2024b; Wang et al., 2024b). Prior works have attempted to analyze multimodal alignment using attention-based visualization techniques (Yeh et al., 2024; DeRose et al., 2021; Jaunet et al., 2022; Wu et al., 2021) and perturbation- analyses (Lundberg & Lee, 2017; Ribeiro et al., 2016; Wu et al., 2025d).
sensitivity
While these methods provide insights into which visual features influence model outputs, they lack principled and quantifiable framework to distinguish the intrinsic contribution
"These authors contributed equally this work.

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Published as a conference paper at COLM 2025
of visual inputs from the modifications introduced by textual guidance in multimodal encoding. Consequently, they struggle to explain how visual information is retained, transformed, or compressed by textual instructions. Recent works propose to capture layer-wise information flow in text-only LLMs, detecting information deficiencies across layers (Kim et al., 2024a; Wu et al., 2022). However, existing techniques fail to disentangle retained visual content from text-driven refinements across different model layers, making it difficult trace the flow of multimodal information. Without rigorous framework to track these interactions, current explainability methods remain insufficient for systematically understanding MLLMs. As shown in Figure 1, conventional similarity metrics either exhibit monotonic trends or degenerate discriminative power in later layers, failing to reveal the nuanced processing stages. More critically, they lack theoretical grounding for interpreting multimodal interactions.
To address this limitation, we propose an information-theoretic framework to systemat- ically quantify the interplay between visual and textual modalities in MLLMs. Inspired by the Information Bottleneck principle (Zhu et al., 2025; Yang et al., 2024; Tishby et al., 2000b; Wu et al., 2023), our framework leverages Concept Bottleneck (Yamaguchi et al., 2025; Lai et al., 2024; Koh et al., 2020a) that maps complex multimodal representations into interpretable latent spaces, enabling a tractable measurement of visual information specifi- cally retained, transformed, or enhanced by textual instructions. Furthermore, we leverage an InfoNCE-based contrastive mechanism distinguish the contributions of textual in- structions explicitly, offering clearer insights into how textual contexts shape multimodal processing.
Combining the above theoretical probes, we conduct comprehensive empirical studies re- vealing underexplored dynamics of multimodal information processing within MLLMs (Luo & Specia, 2024; Zhao et al., 2024; Zhuang et al., 2022). We uncover that visual information evolves through multi-stage transformations shaped by textual instructions, concerning task-specific contexts (Awal et al., 2025; Hu et al., 2023) and visual complexities (Mu et al., 2024). In addition, we demonstrate that disruptive textual instructions significantly de- grade the effectiveness of visual representation processing, highlighting the vulnerability of MLLMs to linguistic distractions, which leads to potential hallucinations (Liu et al., 2024a). We reveal that visual prompting significantly enhances representation quality only when effectively paired with textual guidance (Nguyen et al., 2023; Wu et al., 2024d), emphasizing the critical synergy between textual and visual cues. Finally, our analysis illustrates how different types of instructions shape the multimodal interaction patterns within MLLMs, offering novel insights into designing more robust and explainable multimodal systems. We summarize our contributions as follows:
We introduce an information-theone neck principle to systematically quantify and analyze interactions between visual and textual modalíties across different layers of MLLMs.
framework based on the Information Bottle-
We leverage Concept Bottleneck to enable interpretable and traceable analyses of visual representations influenced by textual contexts, while an InfoNCE-based metric is proposed to explicitly measure the contextual contributions of instructions.
Our extensive empirical analyses provide. insights into multimodal information dynamics, revealing underexplored interactions between visual prompting, textual instructions, and their combined effects on representation processing in MLLMs.
2 Related Works
2.1 Explainable Multimodal Large Language Models
Yu & Ananiadou (2025) study Llava's VQA mechanism, showing visual embeddings encode semantic features and proposing tool to identify key image regions. Neo et al. (2024) use ablations to show LLaVA localizes object info and aligns visual tokens with vocabulary representations across layers. Zhang et al. (2024b) reveal that LLaVA integrates visual and textual info in two stages, general and targeted, before final prediction. Qi et al. (2025) find VLMMs lack spatial awareness due to large embedding norms and proposes normalization
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Cosine Similarity
Euclidean Distance
Cosine Similarity
Enclddan Distance
Value
Value
Layer Correlation
Layer
Pearson Correlation
1.033
Conreiaiinn
Pearson Comelation
Value
100
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Layer
Figure 1: Evaluation result of traditional diagnostic metrics.
to improve spatial reasoning. Zhang et al. (2024a) show shallow layers contain redundant image features and introduce truncation method enhance interpretability and accuracy. Unlike prior works that focus on attention patterns or token-level visual attribution, our approach provides a quantifiable, layer-wise decomposition visual information shaped by textual cues, offering deeper traceability of multimodal interactions.
2.2 Information-Theoretic Approaches in Multimodal Large Language Models
Kim et al. (2024a) propose a layer-wise information framework to detect hallucinations by tracking information flow across model layers. Wang (2024c) analyze semantic vs. linguistic input influence in NLP tasks, showing semantics dominate, especially in sentiment classification. Federici et (2021) present an info-theoretic view of distribution shifts, linking test error to data assumptions and generalization objectives. Ton et al. (2024) quantify information gain at each CoT step, enabling unsupervised detection of faulty reasoning in LLMs. Zhang et al. (2025) introduce Entro-duction, using entropy and its variance to dynamically guide reasoning depth in multi-step tasks. While existing methods use information- theoorttic tools to detect failures assess influence, framework uniquely quantifies visual information gain conditioned on textual instructions, enabling fine-grained analysis of how MLLMs encode, transform, and align modalities.
An Information-theerrtic View
central goal of this section to develop traceable and explainable mechanisms for analyzing how MLLMs integrate and process visual and textual information. To this end, we introduce framework that provides principled and quantifiable way to measure how visual signals are filtered, retained, transformed in the presence of textual instructions.
3.1 Multimodal Encoding in MLLMS
In multimodal large language model, given image input V, the text instruction T provides contextual guidance that influences the processing of the visual information. The image is first processed by an image encoder based Vision Transformer (ViT) and multimodal projector (Liu et al., 2023), which encode the input image as image tokens f(V). These image tokens subsequently concatenated with language tokens derived from the text input T, before input into the branch of the large language model backbone π,,
(x"),, 1
(x())
N = 8T (f)(
(1)
where 8n is the multi-layer Transformer that encodes multimodal inputs as intermediate multimodal representations (!) for each layer I. In addition, denote the encoded visual
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representations as x (((V)) E x(), while the multimodal representations as x I x()).
at the last token position of the image and the multimodal token sequences, respectively.
3.2 Multimodal Information Flow in MLLMs
Following the information bottleneck (IB) framework (Tishby et al., 2000a; Shwartz-Ziv & Tishby, 2017), we design the layer-wise measurement
FVP = (1
X (XiVizziV -1 -1 VVVViVV IT)
(2)
to capture the portion of visual information in the layer-/ representation, specifically ex- (f(v)) x
may encode rich set of visual details, not all of these details are pertinent once the textual context is
tracted in the presence of textual context. Intuitively, although
taken into account. By subtracting the conditional mutual information I(x (x(() ( IT)
that quantifies the extraneous visual information given the text (Tishby et al., 2000a), we isolate those visual features that are enhanced by the accompanying text. This metric thus reflects the model's ability to filter out irrelevant visual noise while preserving features that contribute meaningfully to multimodal understanding.
To formally track the quantities the mutual information and the prompt-conditional mutual information in equation we first propose to leverage Concept Bottleneck (Koh et al., 2020b) as a variational information bottleneck (in Lemma 3.1) to track each layer's 's multimodal representations in list of candidate visual concepts, which are unbiased to the MLLM. Then, we quantify the mutual information terms in equation 2 by deriving the variational upper bound in the visual conceptual space for the unconditional mutual information (in Lemma 3.2) and the prompt-conditional mutual information (in Lemma 3.3).
Lemma 3.1 (Concept Bottleneck for Mutual Information via Variational IB) Let x(I)
I(V))
be the layer-I representation obtained when both the visual imput V and the text instruction are provided. Define the intermediate concept vector by Ć =fcB (1) (f(V),)) where fcB(.) (-) is a concept wheree
bottleneck function (Koh al., 2020b; Kim al., 2018). A variational encoder q(C V) maps the visual input V to latent concept distribution, where such concept vector as its unbiased prior distribution r(C) over the concept space. In addition, the conditional prior r(C T) approximates the aggregated posterior of C conditioned on the text instruction T.
Then, the mutual information is approximated by the following variational upper bounds.
Lemma 3.2 (Variational Upper Bound for Mutual Information) Given the variational en- coder q(C è V) and prior r r(C), the mutual information between the concept representation and the visual input is upper-bounded by
(x (X S By (L
Lemma 3.3 (Variational Upper Bound for Conditional Mutual Information) Assume that samples can be grouped by text instruction, and let r(Ć1 T) denote variational approximation to the distribution of Ć given T. Then, the conditional mutual information upper-bounded as
(x V < Er [Evi [KL((g (9(C) 1V) IT)]]]
(4)
Thus, we define the aligned visual information by integrating equation 3 and equation 4:
D Ev V (LL(((C( I [I(C))] Er [KL(q(C| V) II | [))]]
(5)
By employing the concept bottleneck representation Ć within this variational framework, we obtain an interpretable estimate of both the mutual information I(C; V) and the conditional mutual information I(C V T), thereby quantifying the extent to which visual features are aligned with and enhanced by the textual instruction.
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3.3 Induced Contextual Information from MLLMs
In multimodal large language models (MLLMs), the text instruction T does more than provide textual guidance to the visual encoder; it also induces higher-level task-related contextual information into the LLM's internal representations. To quantify this induced contextual information, we define the task-related mutual information as the difference between the mutual information computed over multimodal representations (obtained when the text present) and the mutual information computed over baseline representations (obtained without text). Formally, we write:
x(I) J ( (KIMET ((V))) -(x))) x (ivi))
(x())
where (1) ([((),TT and ((L) ((V), denote the layer-l and the final layer L representations com-
puted with the text instruction T, respectively, and x0) [(V]] and x denote the corre- (f(v) sponding representations when the textual guidance is absent. This formulation isolates
the additional contextual information that is induced by the LLM's language language processing
branch. To estimate these mutual information terms in practice, we adopt an InfoNCE-based approach. Recall that InfoNCE provides a tractable lower bound for mutual information by contrasting positive pairs against negative pairs. We define two losses for mini-batch of N samples.
Lemma 3.4 (InfoNCE Lower Bound for Task-Related Mutual Information) For a mini- batch of N samples, let
(() ((V))TT and (L)) denote the layer-I and layer-L representations
f(V).T]
computed with the text instruction T for the i-th sample, and similarly let x(Di (((V)) and x(L) denote the corresponding baseline representations computed without T. With a similarity function s(,,-) and temperature parameter > O, we define
s ((x()) (1)
X (L)
((x(0ss I(VI,T (
x (L)
(0 N
1
Σ
logexp
I(VI (VVV)) -log Σ exp
T
(6)
s(x x x()) If(V) F(VI))
((x (,) [((V))
s
1
((VV = N
Σ
log exp
log
exp
T
T
which provide lower bounds on the mutual information between the multimodal representations,
(X ((VI.) 2 log (l))
(L) Z log N -
F(VY.TY'
(8)
Finally, by the estimation of the above InfoNCE lower bounds (detailed in Appendix B), we provide an estimate for the task-related mutual information:
N log N |,mm
log N
(I)
-
(9)
This result quantifies the benefit of the contexts in injecting task-related information into the
model. In particular, lower (1) ( indicating higher mutual information in the presence
of T, relative to the baseline loss (1)
[[()) suggests that the text instruction effectively aligns
and enriches the representations, thereby enhancing the multimodal understanding of the task.
Experiments
To validate our framework, we instantiate the two core metrics, F(() and F((), introduced in Section 3. These metrics allow us to systematically probe how visual and textual modalities
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interact across layers in MLLMs. We conduct extensive experiments to investigate how these interactions manifest across different tasks, input settings, and instruction types, addressing the following research questions (RQs), each targeting specific aspect of traceability and explainability in multimodal processing:
RQ1: How do textual instructions shape visual information processing?
RQ2: How do textual instructions influence the multimodal information flow?
RQ3: How do visual prompts interact with textual instructions in MLLMs?
RQ4: How do multimodal representations vary across different instructions?
Datasets. We assess the behavioral differences of MILMs in processing various types of data based several image-to-texx datasets. Specifically, we employ the Visual Question Answering-v2 (VQA-v2) (Goyal et al., 2017) and AOKVQA (Schwenk et al., 2022) datasets to investigate MLLMs' performance in Visual QA challenges. Additionally, we image data from VQA (Antol et al., 2015) to implement and train our Fv and F7 evaluators. We leverage the validation set of COCO-caption (Chen et al., 2015) and the Fine-grained Hallucination Evaluation Framework (Hal-Eval) (Jiang et al., 2024) to explore MLLMs' behavior in Visual Captioning challenges.
Models. We use LLaVA-1.5 (Liu et al., 2024b) with 7B parameters and Qwen-2.5-VL (Bai et al., 2023) with 3B parameters to extract the visual hidden states information with dif- ferent settings on various datasets. we use YOLO11-large (Khanam & Hussain, 2024) to annotate object distribution prior detecting objects on the images. We further illustrate the implementation details in Appendix A.
4.1 (RQ1) How do textual instructions shape visual information processing?
We examine the internal layer-wise processing of MLLMs, specifically focusing on how textual guidance shapes visual representations (Wu et al., 2024e; Niu et al., 2024). Using explainable metrics (Fv, Fr), we analyze the transformation of these representations across layers and investigate variations influenced by task type, visual complexity, and textual precision (Awal et al., 2025; Hu al., 2023; Mu et al., 2024). Intuitively, an increase Fv indicates that visual information is being injected actively processed by the MLLMs, causing the representations to differ significantly from their initial margin-based states. The metric Fv measures relevant visual information retained after considering textual context. A decrease indicates effective filtering of irrelevant visual details. The metric Fr reflects task-related contextual information injected by textual instructions. While filtering visual information, the model integrates task-specific contextual information from the text, enhancing multinodal representations and increasing Fr. The interplay between Fy and Fr represents a balancing act. As the model filters out irrelevant visual complexity (lowering Fy), it simultaneously injects relevant textual information, increasing Fr. This dynamic maintains effective multimodal understanding, optimizing task performance.
Finding 1. Multimodal encoding process presents 4 stages. When examining the layer- to-layer representation transformation, we observe that the visual representation is not processed in straightforward manner with monotonous trend. Instead, the information undergoes a complex processing sequence across multiple stages, which we categorize as follows: (1) Information Pre- Pre-processing: an increase in Fv and a stable Fr; (2) Information Filtering: a decrease Fy and stable or decrease Fr; (3) Contextual Information Injection and Multimodal Mixture: an increase in Fy and increase stable Fri (4) Information Compression: a decrease in Fy and increase Fr, as shown in Figure 2c, 2d, 3.
We further validate this staged processing through targeted interventions. As shown in Figure 4, masking image tokens causes the characteristic dynamics Stages 1-3 to collapse into monotonic trends, while Stage 4 remains unaffected. This demonstrates that: (1) The first three stages are indeed visual-dependent processes, and (2) The final compression stage operates primarily on textual representations, consistent with its role in response generation.

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Camax Atswer Test, RA ni 1 Wronn Answer Text, (Diin 71:
Wrong (DKjen V)
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Figure 2: Fv and Fr across different layers in LLaV VA-115---
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(a) Fy across layers.
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Figure
Qwen Fy across layers after masking images.
Figure 3: Fv and Fx across layers Qwen-2.5-VL-3b.
Finding 2. In captioning tasks, multimodal information processing and compression dynamically interplay across various layers of MLLMs. As shown in Figure 2b, 3a, detect two peaks in the metric Fy: around layer 12 and 22 in LLaVA or around 15 and 25 in Qwen. On COCO-caption which excludes imprecise answers, Fv increases before layer 12 in LLaVA and layer 15 in Qwen, as is on Figure 2b, 3a, while Fy remains stable or decreases, as on Figure 2d, 3b. This indicates that the MLLMs are processing visual information from images rather than injecting information to align it with the output results. Between layer 12 to 18 in LLaVA or layer 15 to 21 in Qwen, both Fy and Fr decrease, suggesting an information filtering process by the MILMs, as the visual representation becomes more concrete (Fv) while the injected information (Fr) does not contribute towards generating text response. From layers 19 to 22 in LLaVA and 22 to 25 in Qwen, both Fv increase and Fr increase or remain stable, indicating information injection or multimodal mixture by the MLLMs, which alters the visual representation while incorporating more contextual information for output generation. Finally, the decrease in Fy and the increase in Fy suggest information compression, solidifying the visual information towards the textual output.
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In QA tasks, MLLMs show extended Information Pre-processing Stage compared to captioning tasks. We observe two less evident or delayed peaks compared to captioning tasks: around layers 18 and 29 in LLaVA and layer 16 and 25 in Qwen, as shown in Figure 2b, 3a. The delayed decrease in Fv, especially for LLaVA, suggests an extended Information Pre-processing Stage, indicating an extension of information processing to generate a specific response.
Finding 3.
Finding 4. LLaVA exhibit greater reliance on injected contextual information in QA tasks than more advanced Qwen. In QA tasks, the overall increasing trend of LLaVA's Fv, as illustrated in Figure 2b, suggests that the visual representation increasingly diverges from its original state. This elevated Fv, coupled with reduced and delayed information compression, indicates more extensive injection of contextual information and common sense from the MLLM, as well corresponding decrease in response certainty. In contrast, Qwen maintains a more stable four-stage trend, as depicted in Figure 3a, highlighting its superior effectiveness information injection and greater certainty in responses.
Finding 5. Complex visual and inconsistent textual inputs influence MLLMs' efficiencies of information processing in certain stages. HAL-Eval and AOKVQA exhibit higher visual complexity than COCO-caption and VQA, and they provide imprecise answer can- didates. We differentiate between imprecise and precise answer candidates to compare the differences in Fv and FT influenced by the textual precision. We observe that the Fy valley around layer 18 in LLaVA and 21 in Qwen is less pronounced, illustrated in Figures 2b and 3a. This finding suggests that both information filtering and injection are less intensive when MLLMs overly depend on imprecise answer texts while neglecting the visual representation. In the case of AOKVQA, both LLaVA and Qwen exhibit lower Fv prior to the first peak, specifically before layer 12 in LLaVA and 15 in Qwen, shown in Figures 2b and 3a. Moreover, the FT values before layer 12 in LLaVA and layer 15 in Qwen are higher than those observed in VQA, as shown in Figures 2d and 3b. This indicates an early task-related contextual information injection during the information pre-procsssigg stage influenced by the complexities of visual and inconsistent textual inputs.
4.2 (RQ2) How do disruptive textual instructions influence multimodal information?
We investigate the hallucinations and errors in MLLMs (Li et al., 2023a), which happen mostly when model focuses excessively on the current segment of generated content while ignoring input visual information (Wang et al., 2024a; Lee et al., 2024) and when model prior- itizing language patterns to produce fluent yet inaccurate content in visual-text tasks (Wang et al., 2023). Based on this, we construct several prompts to trigger these two kinds of errors in MLLMs, which is the "Excessive Text Focusing" and "Language Patterns Priority" in Figure 5. We refer to these prompts that trigger errors as "disruptive prompts". By comparing multimodal representation changes acToss layers using original POPE (Li al., 2023b) queries and these disruptive prompts with LLaVA, we aim to quantify their effects on visual information processing and textual guidance within the models.
Finding 6. Disruptive prompts impairs visual information processing. With disruptive prompts, the visual representation peaks of QA tasks in Fv become less significant, as shown Figure 2b, indicating a diminished visual information processing and reduced contextual information injection. The peak of Fy around layer 22 in Figure 2b diminishes in the Language Patterns Priority prompts pattern, suggesting less contextual information injection by MLLMs. Furthermore, when prompts that trigger excessive focus on text are added, there is almost no change in Fv, indicating a neglect of the visual representation.
Finding 7. Disruptive prompts impairs contextual information injection of MLLMs. Based on the trend of Fr in Figure 5b, we observe that the difference in Fr between layer 0 and the final layer is smaller when disruptive prompts are applied, indicating that less contextual information aligns with the final response are injected by the MLLMs. Additionally, we note a fluctuation in Fr before layer 12. This fluctuation of Fr prior to layer 12, combined with the reduced difference in Fy and stable Fy in Figure 5a across
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layers, indicates that MLLMs are overly focused on the disruptive textual instructions while neglecting the provided image and visual-to-text question.
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Figure 5: Impacts of disruptive prompts on LLaVA Fy and Fr across different layers.
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Figure 6: Impacts of visual prompt and textual instruction combination on Fy and Fr.
We investigate the influence of explicit visual prompting (e-g., bounding boxes and digit annotations on detectable objects) on multimodal representations within MLLMs (Zhang et al., 2023a; Yan al., 2025; Lin al., 2024; Wu al., 2024e). We compare four settings: original POPE queries without visual prompted images (baseline), original POPE queries with visual prompted images, textual queries correctly referencing visual prompts, and textual queries incorrectly referencing visual prompts, which are denoted as "No visual prompt", "Only visual prompt" (means having visual prompt without text reference), "Correct Text Reference" and "Wrong Text Reference" in Figure 6 respectively. We aim to quantify the distinct and combined impacts of visual and textual instructions on visual information processing across different layers in MLLMs.
Finding 8. The impact of textual instructions is more significant than that of visual prompts on multimodal information processing. Results in Figure shows that textual prompting alters the trends of Fy and Fr across different layers, whereas adding visual prompts does not. Additionally, the differences induced by changing textual prompting Fy and Fr are more significant than those results from changing visual prompting.

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Simply adding visual prompts without referencing them through textual instructions does not yield a noticeable impact on visual representation processing. This difference is even less significant in POPE compared to HAL-Eval. This phenomenon suggests that textual in- structions dominate visual information processing during inference, indicating that MLLMs are considerably more sensitive to language prompts than to visual prompts.
Finding 9. Textual references to visual prompts significantly enhance contextual informa- tion injection from MLLMs. As show in Figure 6a and 6c, before layer 22, regardless of whether we add visual prompts or refer to these visual prompts through textual instructions, Fy shows similar trends across layers. This indicates that the impact of visual prompting manifests in the later stages of MLLMs inference. Considering Fv after layer 22 where previously denote as information injection and compression, when visual prompts are referred by textual instructions, there is rapid increase in Fv. This indicates a more significant contextual information injection from MLLMs happens when visual prompts are referred by textual reference.
4.4 (RQ4) How do key words in instructions influence MLLM knowledge injection?
To investigate the influence of dif- ferent instructions on represen- tations in MLLMs (Yan et al., 2024; Kim et al., 2024b; Xia et al., 2024; Qin et al., 2024), we cate- gorized queries from the VQA- v2 dataset into distinct types based on their keyword prompts (e.g,, "Is," "What," " "Can," "Why," "Where"). By comparing vi- sual representation transforma- tions across these question cate- gories based on Fy and Fr in Figure 7, we evaluated how specific linguistic structures and query intents influence visual information processing within MLLMs.
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"Is" questions
"What" questions
"Can' questions
0.0
"Why" questions
"Where" questions
-0.2
10
15
20
25
30
Layer Index
Figure 7: Impacts of instruction types on LLaVA FT.
Finding 10: Our analysis shows that different types of textual instructions result in dis- tinct patterns of contextual information injection in MLLMs. The processing behavior of visual representations by MLLMs is influenced by keywords in instructions that deter- mine the type of instructions. We select several representative instruction types from those lists in VQA-v2 dataset and use LLMs to infer responses based on these different types of instructions. For "Why" and "Can" questions, MLLMs inject more contextual information aligned with the response, as indicated by Fr. Conversely, MLLMs inject less contextual information when answering "Where" questions compared to "Why" and "Can" questions, but more contextual information than when responding to "What" and "Is" questions.
5 Conclusion
In this work, we introduced information-theorett framework to systematically analyze and quantify the interactions between visual and textual modalities in MLLMs. Our metrics provided tractable and interpretable measures for visual information gain and textual contextual contributions across different model layers. Our empirical findings revealed four distinct stages of multimodal representation processing, highlighting the dynamic interplay between visual and textual instructions. We demonstrate that textual instructions dominate visual information processing, with disruptive prompts significantly impairing both visual encoding and contextual information injection. Furthermore, our analysis underscored the critical synergy between visual and textual instructions, showing that effective multimodal understanding relies heavily their alignment. These insights not only advance our understanding of MLLMs' internal mechanisms but also offer practical guidance for designing more robust and explainable multimodal systems.
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Published as a conference paper at COLM 2025
Zhiyu Zhu, Zhibo Jin, Jiayu Zhang, Nan Yang, Jiahao Huang, Jianlong Zhou, and Fang Chen. Narrowing information bottleneck theory for multimodal image-text representations interpretability, 2025. URL https://arxiv, wwwp://ggaabbbe/eee http//bbs2500.. 14889.
https://arxti..orr/bb/////
Yong Zhuang, Tong Yu, Junda Wu, Shiqu Wu, and Shuai Li. Spatial-temporal aligned multi-agent learning for visual dialog systems. In Proceedings of the 30th ACM International Conference on Multimedia, pp. 482-490, 2022.
A Implementation Details
A.1 Extracted Visual Information
We employ a three-layer Concept Encoder (CE) to encode features extracted from the /-th layer of LLMs, denoted as x into concept bottleneck information Cj. This CE is trained on hidden state information of LLaVA on the MSCOCO-2014 dataset, which represent q(C V). The concept bottleneck information Ć₁ in this context is represented as a 80-dimensional vector, corresponding to 80 object classes detectable in MSCÓCO-200 vector is normalized, with each dimension reflecting the importance score of the respective object. On each dataset YOLOv11 is firstly applied to generate the object distribution prior, denoted as r( r(C). implement margin on text, we use TF-IDF vectorize and classify the texts input of MLLMs into several clusters and margin the expectation of KL on each cluster.
using YOLOv11. The
A.2 Induced Contextual Information from MLLMs
We trained two MLPs for vector similarity evaluation, denoted as (..... When multiple hidden state representations are extracted from same image-text samples, the similarity between the them should be high. In our context, we assess information similarity in MLLMs at each decode layers with the last decode layer. There are two settings for Fr evaluation, task-aware similarity and baseline similarity, where task-aware similarity is evaluated with MLLMs given instruction texts and baseline similarity is evaluated with MLLMs only given system tokens and image tokens.
B Approximation of InfoNCE
To robustly track the similarity function .) in high-dimensional representations, we first apply low-rank projection that maps the original representations into joint embedding space. Specifically, for any representation X (i.e,, x()) x (()) x or X( we define projection function:
fproj (X) = x
where W E ddd,, is a learnable projection matrix with d; < d and b E R4 is bias term. The low-rank projection serves to distill the most salient features from the original representations and align them in a common, lower-dimensional space. The similarity function is then computed as the inner product in this joint space:
s(x,y) = (frro (x), (v)) = Spro (x) (p)o
which reduces computational complexity and enforces a structured joint distribution, ensur- ing the computed similarities accurately reflect the alignment between the representations.
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Reviews:
[{"review_id": "CQHSO74irC", "reviewer": "Reviewer_WuoZ", "summary": "This paper proposes a new IB-based framework for analyzing multimodal LLMs. The key idea is to leverage the concept bottleneck [1] to quantify the layer-wise visual information proportion and obtain the concept vector, which can be utilized for MLLM analysis. Experiments have been conducted to answer various interesting questions, and some useful insights have been obtained.\n\n[1] Koh, Pang Wei, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. \"Concept bottleneck models.\" In International conference on machine learning, pp. 5338-5348. PMLR, 2020.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The idea of utilizing the IB-based theory and concept bottleneck to analyze MLLMs is novel to me, and the theory looks sound.\n- This paper studies several important research questions, which is a good contribution to the community.", "reasons_to_reject": "- The method is built upon previous work [1], which impacts the technical contributions to some degree.\n- Some research questions may be too vague and difficult to answer. Though the paper uses the proposed framework to reach some conclusions, it may still be hard to answer the proposed questions. A suggestion would be modifying the questions into some smaller questions, instead of in the current form.\n- The used model is LLaVA1.5, which is old and may not be powerful enough. The authors should consider using more advanced MLLMs such as Qwen-VL. The evaluation is conducted on benchmarks that may be too simple. Please also consider more advanced benchmarks such as MM-Vet 2.", "questions_to_authors": "N/A", "extra_scores": {}}, {"review_id": "VcHlVQeUtS", "reviewer": "Reviewer_JoXW", "summary": "This paper proposes an information‑theoretic framework for tracing multimodal interactions in vision‑language models. By estimating layer‑wise mutual‑information metrics, the authors reveal how visual features are progressively filtered, blended, and ultimately compressed as textual context flows through the layers of LLaVA‑1.5.\n\nOverall, the motivation is clear and the framework could become a useful diagnostic tool for multimodal LLMs, pinpointing where visual and textual signals converge. Nonetheless, the study has two notable weaknesses: (a) crucial mathematical notation in Section 3.1 is ambiguous, and (b) the main claims rely entirely on the behaviour of the two proposed metrics without additional supporting experiments. Further empirical validation—such as interventions on hidden states—would be needed to substantiate the conclusions.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Introduces a novel information‑theoretic diagnostic toolkit (F_V^{(l)}, F_T^{(l)}) enabling layer‑wise traceability in multimodal LLMs, a capability not provided by prior work.\n- The method is lightweight (post‑hoc, no retraining), making it a practical diagnostic for both researchers and practitioners.", "reasons_to_reject": "- Key mathematical notation in §3.1 is ambiguous (e.g., inconsistent use of T, N, X_0^{(l)}, X_T^{(l)}), making it difficult to reproduce results or even compute the proposed metrics correctly.\n    - $T$ is introduced as raw text, yet in Eq. (1) it denotes a sequence of tokens. This usage is inconsistent with $I$, which represents a raw image fed to the VLM through the function $f$.\n    - $N$ is never defined (I presume it is the number of layers in $g_\\pi$).\n    - $X_{0}^{(l)}$ is labeled “encoded visual representation,” but the 0-th token is merely the first image token—it does not capture the full visual representation. The authors may have meant $X_{|f(V)|}^{(l)}$ (the last image token).\n    - $X_{T}^{(l)}$ is called the “multimodal representation,” yet $T$ is not an index but the text itself. Even if one sets $T = |T|$, the notation still seems off; perhaps $X_{|f(V)|+|T|}^{(l)}$ (the last input token) was intended.\n    - Because these symbols form the foundation of the method—and are reused in the proposed information-theoretic measurements—the notation must be clarified. In my review, I assumed $X_{0}^{(l)}$ refers to the last image token and $X_{T}^{(l)}$ to the last input token.\n- Core empirical claims rely solely on the shapes of two mutual‑information curves; without causal interventions or ablation studies, the four‑stage interpretation remains speculative.\n    - For example, the authors identify four stages of multimodal encoding solely from the shapes of the proposed metric curves. However, it is difficult—at least for me—to draw firm conclusions from these plots alone. They indicate when information from image tokens is injected into text tokens, but not how this integration occurs. We can certainly hypothesize, but for such hypotheses to be scientifically convincing we need more rigorous experiments—for instance, interventions on the model’s hidden states—to corroborate the claims.\n- Evaluation is limited to a single model (LLaVA‑1.5‑7B) and a concept bottleneck tied to 80 MS‑COCO classes, leaving generality to other architectures and tasks untested.\n- The experiments lack comparisons to simpler diagnostic tools (e.g., similarity of hidden states between image tokens and text tokens [1]), so the incremental benefit of the proposed toolkit is unclear.\n\n[1] https://arxiv.org/abs/2411.00646", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WKlVRe2W7d", "reviewer": "Reviewer_j1jo", "summary": "The paper introduces an information-theoretic framework to understand and quantify how textual instructions shape multimodal representations in Multimodal Large Language Models (MLLMs). By leveraging principles from the Information Bottleneck and Concept Bottleneck, the authors proposed a framework that maps complex multimodal representations into interpretable latent spaces. An InfoNCE-based contrastive mechanism is then used to explicitly separate and quantify the contribution of textual input from raw visual information. Empirical studies are conducted to demonstrate the dynamics of multimodal processing, including how visual information undergoes multi-stage transformations when influenced by various types of textual instructions, and how disruptions in text input can lead to degradation or hallucinations in visual representation quality. Overall, the paper is theoretical strong and has several empirical results backed inferences that would be useful to the multimodal community.", "questions": "", "limitations": "", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Strong theoretical and novel framework to combine the Information bottleneck principle with a concept bottleneck approach, offering a systematic, quantitative way to disentangle and study the role of text in shaping visual representations.", "reasons_to_reject": "1. It remains unclear whether the proposed framework can be easily generalized across different architectures and tasks within multimodal systems. Ideally, it would be nice to see this study across two broad categories of MLLMs, i.e. unified embeddings ones like LLAVA and cross modality attention ones like NVLM from NVIDIA. \n\n2. It would be interesting to see if inference/findings are consistent across different vision embedding models like CLIP, ImageBind, other unified embedding models. \n\n3. Need some discussion on how the findings can help us to design better multimodal architecture or training mechanism in future.", "questions_to_authors": "Findings 7 and 8 are not clear. can you elaborate how the results are supporting these claims.", "extra_scores": {}}, {"review_id": "UJJTRGnIn5", "reviewer": "Reviewer_fJZx", "summary": "The paper introduced a information theory based method for analyze interactions between visual tokens and text tokens in multimodal LLMs.  The framework trained a Concept encoder to extract concept bottleneck information. \nThe author did ablation studies on a few benchmarks with carefully designed visual and textural prompting to study the interaction relationships.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This is the first method to measure visual and text tokens relationship quantitatively with the help of information theory.\n2. The author did studies on a few benchmarks with different experiment setting. Quantitative results align with human intuition for MLLM. \n3. The information theory based analysis provide new direction and inspiration for MLLM interpretability research", "reasons_to_reject": "1. It would be great for audience to understand if the author can provide more detailed proof of Lemma 3.3 and 3.4\n2. In Experiments part, the author only test with LLaVA 1.5, it would be more convincing to include other MLLMs, such as Qwen-2.5-VL, LLaMA3-V, etc.\n3. The 4 stage observed in LLaVA may not exist in other MLLMs\n4. In Finding 3, caption can also be regarded as one type of QA, please clarify the experiment setting here. \n5. In Finding 4 and 5, conclusions are drawn from comparing QA task with caption task, however, caption task benchmark is COCO-Cap, the concept encoder is also trained with MS-COCO, there may exists potential distribution shift, more detailed analysis or change caption evaluation with other benchmarks may be helpful.\n6. In Line 288, the author mentioned \"a stable F_v in Figure 1b\", please clarify while line refer to?\n7. In Line 295, the experiment setting is \"original POPE queries with visual prompted images\", however in later sentence \"Only visual prompt\", which is confusing.\n8. In Finding 9, textual reference to image can enhance visual information injection substantially, however, this might related to LLaVA instruction following training data is formated. Need more investigation.", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes a new IB-based framework for analyzing multimodal LLMs. After rebuttal, it received scores of 6667. Overall, the authors provided a strong rebuttal, and all reviewers were positive about the paper. They noted that the use of IB theory and concept bottlenecks to analyze MLLMs is novel, and the proposed framework has the potential to become a valuable diagnostic tool for multimodal LLMs.

However, reviewers also raised some concerns. They agreed that analyzing only LLaVA-1.5 was insufficient. In response, the authors added results on Qwen2.5-VL during the rebuttal. Additionally, reviewers pointed out that (a) key mathematical notations in Section 3.1 were ambiguous, and (b) comparisons to simpler diagnostic tools were lacking. The authors addressed these by clarifying the notations and providing additional comparisons.

Overall, the AC recommends accepting the paper.
Decision: Accept