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To Sink or Not to Sink: Visual Information Pathways in Large Vision-Language Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.4Download Report PDF
Vision TransformerVisual Attention SinkAttention SinkMultimodal LLMLarge Vision Langauge Model
Abstract:

Large Vision Language Models (LVLMs) have recently emerged as powerful architectures capable of understanding and reasoning over both visual and textual information. These models typically rely on two key components: a Vision Transformer (ViT) and a Large Language Model (LLM). ViT encodes visual content into a sequence of image tokens and serves as the perceptual front-end -- the eyes of the model. In contrast, the LLM interprets these tokens to perform high-level reasoning, generates responses, and functions as the cognitive core -- the brain of the model. However, it remains unclear which visual tokens contribute most significantly to understanding and reasoning, and how effectively these signals are propagated from ViT to the LLM. While most existing works have focused on identifying attention sinks, low-semantic tokens receiving disproportionately high attention, within the LLM, we shift the focus to the vision encoder by identifying a class of high-norm visual tokens from ViT, referred to as ViT attention sinks -- a problem that has been rarely studied but is indeed very important for LVLMs. Our findings show that these ViT sinks encapsulate high-level semantic concepts from images, allowing the LLM to perform more effective understanding and reasoning. Despite their importance, these sink tokens are often overlooked in existing LVLM architectures. To explore their contribution, we present both qualitative and quantitative analyses of the information embedded in these sink tokens. We also propose both training-free and training-based approaches to better leverage how this information is interpreted by the LLM, and to what extent. By explicitly utilizing these tokens, we demonstrate substantial improvements across a range of LVLMs and visual reasoning tasks, including but not limited to mathematical problem solving, logical inference, and geometric understanding, highlighting the untapped potential of ViT attention sinks in enhancing visual reasoning.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper identifies and analyzes ViT attention sinks in large vision-language models, proposing a training-free repositioning strategy and a DIYSink framework with dual MLP projection and dynamic token selection. It resides in the 'Visual Token Attention Sink Discovery' leaf, which contains only two papers including this work and one sibling (Visual Attention Sink). This represents a relatively sparse research direction within a 16-paper taxonomy, suggesting the specific focus on ViT-level attention sinks is an emerging area with limited prior exploration.

The taxonomy reveals that most related work concentrates on downstream interventions rather than ViT-level analysis. Neighboring leaves address cross-modal attention dynamics across layers, hallucination mitigation through attention reallocation, and token compression for efficiency. The paper's focus on visual encoder sinks distinguishes it from these directions: while sibling work examines sink phenomena, the broader field emphasizes LLM-side attention patterns or efficiency gains. The taxonomy's scope notes explicitly separate sink characterization from mitigation methods, positioning this work as foundational analysis rather than application-oriented intervention.

Among 17 candidates examined across three contributions, none were found to clearly refute the work. The ViT sink identification contribution examined 2 candidates with no refutations, the training-free repositioning strategy examined 10 candidates with no refutations, and the DIYSink framework examined 5 candidates with no refutations. This suggests that within the limited search scope, the specific combination of ViT-level sink analysis, training-free repositioning, and the dual-MLP framework appears relatively unexplored. However, the small candidate pool means the analysis captures top semantic matches rather than exhaustive field coverage.

Based on the limited literature search, the work appears to occupy a distinct position by shifting attention sink analysis from LLM layers to the vision encoder. The sparse taxonomy leaf and absence of refuting candidates among 17 examined papers suggest novelty, though the small search scope limits definitive conclusions. The framework's combination of sink identification, repositioning, and dynamic selection represents a multi-faceted approach not clearly anticipated by the examined prior work.

Taxonomy

Core-task Taxonomy Papers
16
3
Claimed Contributions
17
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: visual information pathways and attention sink tokens in large vision-language models. The field structure reflects a growing recognition that attention mechanisms in vision-language models exhibit distinctive patterns—particularly the emergence of "attention sink" tokens that accumulate disproportionate attention weights. The taxonomy organizes research into several main branches: one branch focuses on characterizing and analyzing these attention sinks, uncovering where and why certain visual tokens become focal points; another addresses hallucination mitigation by leveraging attention mechanisms to ground model outputs more reliably; a third explores token compression and pruning strategies to improve efficiency; a fourth targets efficient long-context visual processing; and a fifth examines attention-based segmentation and localization. Works such as Visual Attention Sink[1] and Visual Tokens Withdrawal[2] exemplify early efforts to identify and understand these phenomena, while methods like VASparse[5] and VisiPruner[6] demonstrate how such insights can be harnessed for computational savings. Particularly active lines of work reveal trade-offs between interpretability, efficiency, and model robustness. Some studies investigate shallow versus deep layer attention dynamics (e.g., Shallow Layers Attention[3]), exploring whether attention sinks emerge early or late in the network and what this implies for information flow. Others, such as VASparse Sparsification[8] and StreamingVLM[10], push toward real-time or streaming scenarios where aggressive token pruning is essential. The original paper, Visual Information Pathways[0], sits within the attention sink characterization branch alongside Visual Attention Sink[1], but it appears to take a broader view of how visual information propagates through the model rather than focusing solely on sink token identification. Compared to Visual Attention Sink[1], which zeroes in on the sink phenomenon itself, Visual Information Pathways[0] likely examines the entire pathway structure, offering a more holistic perspective on how attention patterns shape multimodal understanding.

Claimed Contributions

Identification and analysis of ViT attention sinks in LVLMs

The authors identify and analyze ViT attention sinks, which are high-norm visual tokens from the Vision Transformer that encapsulate high-level semantic concepts. They show these sinks propagate into the LLM alongside LLM-emerged sinks, and demonstrate that ViT sinks capture coarse-grained contextual information beneficial for certain reasoning tasks.

2 retrieved papers
Training-free sink-to-the-front repositioning strategy

The authors propose a simple inference-time strategy that repositions ViT sink tokens to the beginning of the visual token sequence. This training-free method requires no additional training and can be applied post hoc to any existing LVLM, showing improvements particularly for tasks requiring high-level understanding.

10 retrieved papers
DIYSink framework with dual MLP projection and dynamic token selection

The authors introduce DIYSink, a training-based framework featuring dual MLP projectors that separately process sink and non-sink tokens, combined with dynamic token selection mechanisms (CoT routing or learned reweighting) to adaptively choose which tokens to use based on task demands and image complexity.

5 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Identification and analysis of ViT attention sinks in LVLMs

The authors identify and analyze ViT attention sinks, which are high-norm visual tokens from the Vision Transformer that encapsulate high-level semantic concepts. They show these sinks propagate into the LLM alongside LLM-emerged sinks, and demonstrate that ViT sinks capture coarse-grained contextual information beneficial for certain reasoning tasks.

Contribution

Training-free sink-to-the-front repositioning strategy

The authors propose a simple inference-time strategy that repositions ViT sink tokens to the beginning of the visual token sequence. This training-free method requires no additional training and can be applied post hoc to any existing LVLM, showing improvements particularly for tasks requiring high-level understanding.

Contribution

DIYSink framework with dual MLP projection and dynamic token selection

The authors introduce DIYSink, a training-based framework featuring dual MLP projectors that separately process sink and non-sink tokens, combined with dynamic token selection mechanisms (CoT routing or learned reweighting) to adaptively choose which tokens to use based on task demands and image complexity.

Table of Contents