From Redundancy to Relevance: Information Flow in LVLM s Across Reasoning Tasks (2025)

Abstract: Large Vision Language Models (LVLMs) achieve great performance on visual-language reasoning tasks, however, the black-box nature of LVLMs hinders in-depth research on the reasoning mechanism. As all images need to be converted into image tokens to fit the input format of large language models (LLMs) along with natural language prompts, sequential visual representation is essential to the performance of LVLMs, and the information flow analysis approach can be an effective tool for determining interactions between these representations. In this paper, we propose integrating attention analysis with LLaVA-CAM, concretely, attention scores highlight relevant regions during forward propagation, while LLaVA-CAM captures gradient changes through backward propagation, revealing key image features. By exploring the information flow from the perspective of visual representation contribution, we observe that it tends to converge in shallow layers but diversify in deeper layers. To validate our analysis, we conduct comprehensive experiments with truncation strategies across various LVLMs for visual question answering and image captioning tasks, and experimental results not only verify our hypothesis but also reveal a consistent pattern of information flow convergence in the corresponding layers, and the information flow cliff layer will be different due to different contexts.

## introduction multimodal models are more prevalent due to the capability of understanding vision-language rather † work done during an internship at alibaba cloud computing * these authors contributed equally to this work corresponding author ‡ project lead than merely textual information. most large visionlanguage models (lvlms) consist of various visual encoders and large language models (llms). when images are fed to llms together with language prompts, they are transformed into hundreds or thousands of tokens @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . such a transformation leads lvlms to rely on sequential visual representation. albeit popular lvlms exhibit impressive generation capabilities, the blackbox design of transformers decoder-stacked llms hinders the interpretability of visual-language models. in this paper, we intend to explore the inner mechanisms during lvlms reasoning. prior works have commenced preliminary exploration into the caption mechanisms of llms @xcite @xcite @xcite . opera @xcite traces the potential causes of hallucinations in lvlms via attention maps, suggesting that during the inference phase, the model may produce hallucinations by sequentially summarizing previous tokens when processing special tokens such as '-', '?', and other special symbols. opera mitigates hallucinations by imposing penalty constraints on attention scores, marking the first work to visualize multimodal hallucinations. in addition, fastv @xcite identifies that the computation of attention for visual tokens in the deep layers (near the output) of the lvlms is extremely inefficient. although previous studies have made significant strides in enhancing lvlms perception by leveraging attention-based approaches, they focus less on the dynamic interactions between images and texts. to this end, we aim to deepen the understanding of how images and texts influence each other within reasoning tasks. we define 'information flow' as the degree of influence of image, user, and system tokens on answer tokens. understanding the information flow between image and text tokens is crucial for dissecting multimodal reasoning. in this paper, as shown in figure 1 , we discover an important phenomenon in a view of information flow: lvlms tend to depend on the prompt, image tokens do not impact the answers in the middle or deep layers. we refer to this first sparse layer as the information flow cliff layer. as shown in figure 2 , we first visualize the attention scores and maps for system, image, and prompt tokens in relation to the answer. we observe that after the third layer, image tokens account for less than 2% of the total compared to system and prompt tokens, with their weights ranging from 0 to 0.2. this raises two questions: q1: is there any information flow from the third layer to the deeper layers? q2: does the attention score give a complete reflection of the information flow of the image token? though attention scores highlight relevant regions during forward propagation, they don't fully explain the role of image tokens as they lack gradients and can't reveal the model's decision-making process. to address this, we introduce llava-cam, which captures gradient changes through backward propagation to show how image features contribute to the answer. with llava-cam, we consistently observe that image token information flow is concentrated in the shallow layers due to differences in the prompt, while deeper image tokens contribute little to the answer. we refer to the deeper layer where the image ceases to contribute to the answer as the 'image information flow (i-f) cliff layer'. given the information flow convergence in shallow layers, we find that the llava-cam results vary in distinct tasks. for example, in complex reasoning datasets like scienceqa @xcite , the model focuses on relevant regions in shallow rather than deeper layers. in contrast, the model emphasizes relevant regions from shallow to deep layers for general reasoning tasks such as textvqa @xcite and pope @xcite . to validate this hypothesis, we conduct multiple truncation experiments. empirical results show that when applying fully truncated image tokens based on attention scores, certain layers can maintain the model's inference accuracy and even enhance performance in some cases. our multiperspective analysis elucidates the model's inner mechanism and reveals the complex interactions during inference. notably, we verify in the models llava @xcite , intern-vl @xcite , qwen-vl @xcite , shirak @xcite , instructblip2 @xcite and llava1.6 @xcite ) that the information flow converges in the shallow or middle layers, and becomes sparse in the deeper layers, and that there are information-flow cliff layers in each of the models, this means that image tokens are highly redundant after the cliff layer. our methods offer a new perspective for interpretability in lvlms. in summary, our main contributions are as follows: ## conclusion in this paper, we employ llava-cam and attention score to visualize the information flow of reasoning processes layer-by-layer within lvlms. our analysis of several models shows the same pattern that information flow converges in the shallow layers and diverges in the deep layers. comprehensive experiments using truncation strategies across various large-vision language models for vqa and image captioning tasks further validate our findings, confirming a consistent pattern of information flow convergence in the relevant layers across different models. these insights contribute to a deeper understanding of lvlms and their intrinsic functioning, particularly in the context of complex reasoning tasks. to pass through, simplifying the inference process by focusing on these key tokens. we applied this attention-score truncation strategy with different values of k: 0. when the @xmath0, there is no image token passed to the llm, while the llm can surprisingly achieve an accuracy of 47.5 %, which suggests that in some scenarios the llm relies only on the text to answer the questions on their own through prior knowledge, such as 'what is the capital of nebraska?' in figure 9 . therefore, llm does not attend to graphical information when llm's knowledge can directly answer a question.