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Reading Images Like Texts: Sequential Image Understanding in Vision-Language Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
multimodal language modelsinterpretabilityspatial reasoning
Abstract:

Vision-Language Models (VLMs) have demonstrated remarkable performance across a variety of real-world tasks. However, existing VLMs typically process visual information by serializing images, a method that diverges significantly from the parallel nature of human vision. Moreover, their opaque internal mechanisms hinder both deeper understanding and architectural innovation. Inspired by the dual-stream hypothesis of human vision, which distinguishes the "what" and "where" pathways, we deconstruct the visual processing in VLMs into object recognition and spatial perception for separate study. For object recognition, we convert images into text token maps and find that the model's perception of image content unfolds as a two-stage process from shallow to deep layers, beginning with attribute recognition and culminating in semantic disambiguation. For spatial perception, we theoretically derive and empirically verify the geometric structure underlying the positional representation in VLMs. Based on these findings, we introduce an instruction-agnostic token compression algorithm based on a plug-and-play visual decoder to improve decoding efficiency, and a RoPE scaling technique to enhance spatial reasoning. Through rigorous experiments, our work validates these analyses, offering a deeper understanding of VLM internals and providing clear principles for designing more capable future architectures.

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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.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a dual-stream analysis of visual processing in VLMs, decomposing it into object recognition (two-stage attribute-to-semantic progression) and spatial perception (geometric structure of positional representations). It resides in the 'Long Video and Image Sequence Modeling' leaf alongside three sibling papers (Longvlm, mPLUG-Owl3, Imagechain). This leaf is part of the broader 'Temporal and Sequential Visual Understanding' branch, which contains four sub-categories and approximately twelve papers. The research direction is moderately populated, indicating active interest in efficient long-context visual modeling without being overcrowded.

The taxonomy reveals neighboring work in 'Temporal Concept and Causal Understanding' (TimeCausality, three papers) and 'Unified Image-Video Representation Learning' (three papers), both emphasizing temporal dependencies. The paper diverges by focusing on internal mechanisms—how VLMs process serialized images—rather than end-to-end temporal reasoning or unified pretraining. Its dual-stream framing connects conceptually to 'Spatial Perception and Region-Level Understanding' (nine papers across four leaves), yet it remains anchored in sequential processing rather than static spatial grounding. This positioning suggests a bridge between mechanistic analysis and temporal modeling.

Among twenty-two candidates examined, no contribution was clearly refuted. Contribution A (two-stage visual processing) examined two candidates with zero refutations; Contribution B (2D RoPE spatial analysis) and Contribution C (token compression and RoPE scaling) each examined ten candidates, also with zero refutations. The limited search scope—top-K semantic retrieval plus citation expansion—means the analysis captures nearby work but does not exhaustively cover all prior mechanistic studies or compression techniques. The absence of refutations within this sample suggests the dual-stream decomposition and geometric RoPE analysis may offer fresh perspectives, though broader literature could reveal overlapping insights.

Based on the examined candidates, the work appears to introduce novel analytical lenses—dual-stream decomposition and geometric positional structure—within a moderately active research area. The instruction-agnostic compression and RoPE scaling methods show no direct prior overlap in the sample, though the limited scope (twenty-two papers) leaves open the possibility of related techniques in unexamined literature. The taxonomy context indicates the paper occupies a distinct niche between mechanistic interpretation and sequential efficiency, complementing rather than duplicating sibling works focused on hierarchical attention or explicit chaining.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Sequential image understanding in vision-language models. The field has evolved into a rich landscape organized around several complementary directions. Sequential Visual Reasoning and Chain-of-Thought methods (e.g., LLaVA-CoT[27], Vision-r1[6]) emphasize step-by-step inference over visual inputs, while Temporal and Sequential Visual Understanding focuses on modeling long videos and image sequences, as seen in works like Longvlm[1] and mPLUG-Owl3[5]. Spatial Perception and Region-Level Understanding addresses fine-grained localization (RegionGPT[3]), and Vision-Language-Action Models and Embodied AI (CoT-VLA[4], FlowVLA[11]) bridge perception with robotic control. Visual Prompting and In-Context Learning explores few-shot adaptation mechanisms (Visual In-Context Learning[2]), Domain-Specific Applications target specialized areas such as medical imaging (CoCa-CXR[37]) and remote sensing (Remote Sensing VLM Survey[31]), Training Methodologies and Architectural Innovations investigate optimization strategies (Reason-RFT[18], SFT or RL[26]), and Evaluation Benchmarks and Analysis provide systematic assessment frameworks (VLRMBench[30], MMBench-Video[48]). Within Temporal and Sequential Visual Understanding, a particularly active line of work tackles the challenge of processing extended visual sequences without overwhelming computational costs. Some approaches introduce hierarchical or memory-augmented architectures to compress long contexts (Longvlm[1], mPLUG-Owl3[5]), while others explore causal reasoning over temporal dependencies (TimeCausality[44]) or chain-based representations (Imagechain[9]). Sequential Image Understanding[0] sits naturally within this cluster, sharing the emphasis on handling multiple frames or images in sequence. Compared to mPLUG-Owl3[5], which integrates hyper-attention mechanisms for efficiency, and Imagechain[9], which focuses on explicit chaining of visual tokens, Sequential Image Understanding[0] appears to prioritize coherent cross-frame reasoning. These neighboring works collectively highlight ongoing trade-offs between model scalability, temporal granularity, and the ability to capture long-range dependencies across visual sequences.

Claimed Contributions

Two-stage visual processing analysis in VLMs

The authors analyze how VLMs process visual information layer by layer, revealing a two-stage pattern: attribute recognition in shallow-to-middle layers (detecting local features like color and texture) followed by semantic disambiguation in middle-to-deep layers (integrating features into specific object categories). This process resembles Gestalt cognition principles.

2 retrieved papers
Theoretical and empirical analysis of spatial perception via 2D RoPE

The authors provide theoretical derivations showing how 2D Rotary Position Embeddings encode spatial relationships through conjugate symmetric terms and orthogonal subspaces. They empirically verify that direction vectors exhibit collinearity for opposite directions (left/right) and orthogonality for perpendicular directions (left vs. front/behind).

10 retrieved papers
Instruction-agnostic token compression and RoPE scaling methods

The authors propose two practical methods: (1) a token compression algorithm using run-length encoding on text token maps with a distilled visual decoder for faster inference, and (2) RoPE scaling that adaptively amplifies positional information in low-frequency regions to improve spatial reasoning while preserving general capabilities.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Two-stage visual processing analysis in VLMs

The authors analyze how VLMs process visual information layer by layer, revealing a two-stage pattern: attribute recognition in shallow-to-middle layers (detecting local features like color and texture) followed by semantic disambiguation in middle-to-deep layers (integrating features into specific object categories). This process resembles Gestalt cognition principles.

Contribution

Theoretical and empirical analysis of spatial perception via 2D RoPE

The authors provide theoretical derivations showing how 2D Rotary Position Embeddings encode spatial relationships through conjugate symmetric terms and orthogonal subspaces. They empirically verify that direction vectors exhibit collinearity for opposite directions (left/right) and orthogonality for perpendicular directions (left vs. front/behind).

Contribution

Instruction-agnostic token compression and RoPE scaling methods

The authors propose two practical methods: (1) a token compression algorithm using run-length encoding on text token maps with a distilled visual decoder for faster inference, and (2) RoPE scaling that adaptively amplifies positional information in low-frequency regions to improve spatial reasoning while preserving general capabilities.

Table of Contents

Reading Images Like Texts: Sequential Image Understanding in Vision-Language Models | Novelty Validation