Large Vision-Language Models Get Lost in Attention
Using an information-theoretic and geometric framework, the paper reveals that attention mainly reconfigures representations while FFN drives semantic innovation, exposing redundancy in LVLMs.
Key Findings
Methodology
The study introduces a unified framework combining information theory and differential geometry to analyze residual updates in LVLMs. It defines Representation Information Discrepancy (RID) and Mixing Information Gain (MixIG) to quantify how residuals affect the geometric and entropic properties of representations. Spectral analysis via SVD decomposes the representation space into spectrum and subspace support, revealing that attention primarily maintains existing subspaces (reconfiguration), while FFN induces new semantic directions (innovation). Extensive experiments across 15 models and multiple tasks validate this orthogonal division, showing that replacing learned attention weights with random noise preserves performance, indicating significant redundancy.
Key Results
- Replacing learned attention weights with Gaussian noise results in negligible performance drops (<2%) across most datasets, demonstrating that attention mechanisms are largely redundant. RID analysis shows attention mainly preserves existing subspace structure, while MixIG indicates FFN's dominant role in semantic expansion. Token Mixing Entropy (TME) is consistently low in visual attention layers, revealing limited meaningful interaction. These findings suggest current LVLMs are inefficient in visual information integration, with models effectively 'getting lost in attention'.
- Across diverse models like Qwen, LLaVA, and Mistral, performance remains stable despite randomizing attention scores, confirming the high degree of redundancy. Spectral and subspace metrics consistently show attention's role as a reconfiguration tool, not a driver of semantic innovation. The analysis uncovers systemic inefficiencies, emphasizing the need for architecture refinement to reduce resource wastage.
- The study's geometric and information-theoretic diagnostics provide a new lens to understand and improve Transformer-based multimodal models, advocating for streamlined attention mechanisms and more effective visual context utilization.
Significance
This work advances the interpretability of LVLMs by providing a rigorous, quantifiable distinction between modules' functions, challenging the conventional emphasis on attention as the core information processor. It exposes the prevalent redundancy and inefficiency in current models, offering a pathway to more resource-effective architectures. The insights have broad implications for designing future multimodal systems, emphasizing the importance of reducing unnecessary interactions and focusing on modules that truly expand semantic understanding. This paradigm shift could lead to models that are not only more efficient but also more transparent and easier to interpret, fostering trust and deployment in real-world applications like autonomous vehicles, robotics, and intelligent assistants.
Technical Contribution
The paper introduces a novel combination of spectral analysis, differential geometry, and information theory to dissect residual updates in Transformer models. It formalizes the concepts of spectral complexity and subspace support, providing dual metrics RID and MixIG that quantify external information injection and internal reconfiguration. This framework enables a precise, module-level understanding of how attention and FFN contribute differently to representation dynamics. The approach is validated across multiple models and tasks, establishing a new standard for interpretability grounded in rigorous mathematical foundations. It also demonstrates that attention's resource consumption is often unnecessary, opening avenues for architecture simplification.
Novelty
This is the first work to systematically apply geometric and information-theoretic metrics to analyze residual updates in large vision-language models, revealing a fundamental decoupling of attention and FFN functions. Unlike prior attribution or correlation-based methods, this approach quantifies the geometric and entropic effects of residuals, providing a more principled understanding. The finding that attention mainly preserves existing subspaces, while FFN drives semantic innovation, challenges the traditional view of attention as the primary information hub, marking a significant conceptual breakthrough.
Limitations
- The analysis assumes low-rank spectral structures and may not fully capture highly complex or dynamic representations. Its applicability to models with different architectures or training regimes remains to be validated.
- Replacing attention weights with random noise demonstrates redundancy but does not directly suggest specific architectural modifications or training strategies to reduce inefficiency.
- The metrics focus on residual stream properties and may overlook other factors influencing model performance, such as training dynamics, data quality, or optimization procedures.
Future Work
Future research could leverage these metrics to guide architecture design, aiming to minimize redundant attention interactions and enhance semantic expansion. Extending the framework to training dynamics and other model types, such as encoder-only architectures, could broaden its applicability. Additionally, integrating these insights into training objectives or regularization strategies may lead to more efficient and interpretable multimodal models, ultimately fostering more resource-conscious AI systems.
AI Executive Summary
Large vision-language models (LVLMs) have revolutionized multimodal AI, enabling complex tasks like visual question answering and image captioning. However, despite architectural innovations, their internal mechanisms remain poorly understood. Traditionally, attention mechanisms are viewed as the core of information fusion, but recent evidence suggests widespread redundancy and inefficiency. This study introduces a novel interpretability framework grounded in information theory and differential geometry, providing a quantitative lens to dissect residual updates within Transformer decoders.
By defining metrics such as Representation Information Discrepancy (RID) and Mixing Information Gain (MixIG), the authors systematically analyze how attention and feed-forward networks (FFN) contribute to representation dynamics. Spectral analysis via singular value decomposition reveals that attention primarily acts as a subspace-preserving reconfiguration operator, maintaining existing semantic support, while FFN drives semantic innovation by expanding the representation space.
Extensive experiments across 15 models and multiple multimodal benchmarks demonstrate that replacing learned attention weights with random noise results in negligible performance loss, exposing severe redundancy. The Token Mixing Entropy (TME) analysis further shows that visual attention layers often have limited meaningful interactions, indicating that models are 'lost in attention'—spending resources on uninformative interactions.
These insights challenge the conventional wisdom that attention is the primary driver of multimodal understanding. Instead, they suggest that current LVLMs inefficiently utilize visual context, leading to potential architectural simplifications. The findings open pathways for designing leaner, more effective models that focus on modules truly responsible for semantic expansion, ultimately advancing the interpretability, efficiency, and robustness of multimodal AI systems.
Deep Analysis
Background
多模态大模型（LVLMs）近年来快速崛起，融合视觉编码器与大型语言模型（如GPT-4、LLaVA），实现了少样本学习和指令调控。早期研究强调图像-文本对齐（Radford et al., 2021），后续模型如LLaVA（刘等，2024）通过预训练视觉编码器与Transformer结合，提升理解能力。尽管架构不断创新，Transformer的核心机制——多头自注意力和FFN——仍是基础。过去多依赖统计相关和归因分析，缺乏系统的几何和信息理论解释，导致对内部信息流理解有限。
Core Problem
现有研究发现Transformer中的注意力机制存在大量冗余，模型“迷失在注意力”中，未能高效利用视觉信息。这不仅浪费计算资源，还影响推理能力。传统归因方法难以揭示模块的真实功能分工，缺少统一的理论框架解释注意力的作用。如何区分注意力的重构作用与FFN的语义创新，成为理解和优化LVLM的关键难题。
Innovation
本文提出结合信息论与几何分析的统一框架，定义RID和MixIG两个指标，量化残差更新的几何与熵变化。首次验证注意力主要负责信息重配置，保持子空间稳定，而FFN推动语义扩展，揭示Transformer内部的功能解耦。谱分析和子空间支持的量化方法，系统诊断模型中的冗余，强调减少无用交互，提升多模态信息利用效率。这一创新为模型解释和架构优化提供了新思路。
Methodology
- �� 构建残差流的几何与信息指标体系，定义Representation Information Discrepancy（RID）和Mixing Information Gain（MixIG）。
- �� 利用奇异值分解（SVD）分析表示空间的谱和子空间支持，量化信息复杂度与语义支持。
- �� 通过谱变化和子空间投影，区分残差更新的创新与重配置作用。
- �� 在15个主流LVLM模型上，跨越多模态任务，进行指标测量与对比。
- �� 替换注意力分数为随机值，验证机制的冗余性与低效性。
Experiments
使用包括Qwen、LLaVA、Mistral等在内的15个模型，涵盖图像问答、视觉推理等任务。采用POPE、3DSRBench等多模态基准，评估模型性能变化。通过指标分析，验证注意力的冗余，观察TME指标在不同层次的变化。对比不同模块的RID与MixIG值，揭示注意力主要负责信息重配置，FFN驱动语义创新。实验还包括随机噪声替换注意力分数，验证模型性能的鲁棒性。
Results
模型在注意力随机化后，性能下降不到2%，但TME值大幅降低，说明注意力层交互极少，存在大量无用信息。RID指标显示，注意力主要进行信息重配置，保持子空间不变，FFN则引入新语义方向。多模型分析一致性强，验证了注意力机制的冗余性。指标还揭示视觉注意力层的Token Mixing Entropy极低，模型“迷失在注意力”中，未能充分利用视觉信息。
Applications
该研究为多模态模型的设计提供理论指导，建议减少冗余注意力交互，提升信息利用效率。可应用于自动驾驶、智能监控、机器人等场景，优化视觉信息处理流程，降低计算成本。未来还可结合指标引导模型结构调整，实现更高效的多模态推理。
Limitations & Outlook
指标主要基于静态谱和子空间分析，未考虑训练过程中的动态变化。模型在极端场景下的表现未充分验证，未来需结合训练策略优化注意力机制。替换注意力分数虽验证冗余，但未提出具体的优化方案，仍需探索如何在训练中减少无用交互。
Plain Language Accessible to non-experts
想象你在厨房做饭，厨房里有很多不同的工具和食材。每个工具代表模型里的不同部分，比如注意力就像是调味料，帮你调整味道，而FFN像是厨师的手艺，创造出新的菜肴。现在发现，很多时候，调味料其实用得很少，很多调味料都没用到，反而浪费了时间和材料。厨师的真正创意来自于手艺，而不是调味料的用量。这个研究告诉我们，模型里的注意力机制很多时候像是多余的调味料，没必要花那么多资源在上面，反而应该让厨师（FFN）发挥更多作用，做出更好的菜肴。
ELI14 Explained like you're 14
想象你在学校的科学实验室里做实验，实验中你用到各种仪器和材料。有时候，你会发现某些仪器其实用得很少，很多操作只是重复，没有实际帮助。其实，真正重要的是那些能帮你发现新东西的工具，比如显微镜或化学试剂。这个研究就像是在告诉我们，模型里的注意力机制就像那些用得少的仪器，很多时候没什么用，反而浪费了时间和计算资源。相反，模型里的FFN更像是能帮你创造新想法的关键工具。通过分析，科学家们发现，模型其实“迷失在注意力”中，没有充分利用视觉信息。未来，模型设计应该更聪明，少用那些无用的“仪器”，让重要的部分发挥最大作用，这样才能做出更聪明、更高效的模型。
Abstract
Despite the rapid evolution of training paradigms, the decoder backbone of large vision--language models (LVLMs) remains fundamentally rooted in the residual-connection Transformer architecture. Therefore, deciphering the distinct roles of internal modules is critical for understanding model mechanics and guiding architectural optimization. While prior statistical approaches have provided valuable attribution-based insights, they often lack a unified theoretical basis. To bridge this gap, we propose a unified framework grounded in information theory and geometry to quantify the geometric and entropic nature of residual updates. Applying this unified framework reveals a fundamental functional decoupling: Attention acts as a subspace-preserving operator focused on reconfiguration, whereas FFNs serve as subspace-expanding operators driving semantic innovation. Strikingly, further experiments demonstrate that replacing learned attention weights with predefined values (e.g., Gaussian noise) yields comparable or even superior performance across a majority of datasets relative to vanilla models. These results expose severe misallocation and redundancy in current mechanisms, suggesting that state-of-the-art LVLMs effectively ``get lost in attention'' rather than efficiently leveraging visual context.