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IVC-Prune: Revealing the Implicit Visual Coordinates in LVLMs for Vision Token Pruning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Large Vision Lanuage ModelToken Pruning
Abstract:

Large Vision-Language Models (LVLMs) achieve impressive performance across multiple tasks. A significant challenge, however, is their prohibitive inference cost when processing high-resolution visual inputs. While visual token pruning has emerged as a promising solution, existing methods that primarily focus on semantic relevance often discard tokens that are crucial for spatial reasoning. We address this gap through a novel insight into how LVLMs process spatial reasoning. Specifically, we reveal that LVLMs implicitly establish visual coordinate systems through Rotary Position Embeddings (RoPE), where specific token positions serve as implicit visual coordinates (IVC tokens) that are essential for spatial reasoning. Based on this insight, we propose IVC-Prune, a training-free, prompt-aware pruning strategy that retains both IVC tokens and semantically relevant foreground tokens. IVC tokens are identified by theoretically analyzing the mathematical properties of RoPE, targeting positions at which its rotation matrices approximate identity matrix or the 90∘90^\circ rotation matrix. Foreground tokens are identified through a robust two-stage process: semantic seed discovery followed by contextual refinement via value-vector similarity. Extensive evaluations across four representative LVLMs and twenty diverse benchmarks show that IVC-Prune reduces visual tokens by approximately 50% while maintaining ≥\geq 99% of the original performance and even achieving improvements on several benchmarks.

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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 introduces IVC-Prune, a training-free pruning strategy that identifies implicit visual coordinate (IVC) tokens through mathematical analysis of Rotary Position Embeddings (RoPE). It resides in the 'Implicit Visual Coordinate Systems' leaf of the taxonomy, which contains only two papers total. This leaf sits under 'Token Pruning Methods Based on Spatial Awareness,' a relatively sparse branch compared to the more crowded semantic and attention-based categories. The work addresses a specific gap: preserving spatial reasoning during token reduction by retaining positionally critical tokens alongside semantically relevant ones.

The taxonomy reveals that most token pruning research concentrates on semantic and attention mechanisms, with multiple leaves dedicated to attention-driven selection and critique-based enhancements. The spatial awareness branch, where this paper sits, is notably less populated. Neighboring directions include 'Spatial Coverage and Distribution Optimization' (focusing on geometric distribution) and 'Attention-Driven Token Selection' (emphasizing cross-modal alignment). IVC-Prune diverges from these by grounding pruning decisions in the mathematical properties of position embeddings rather than learned attention patterns or explicit spatial features like depth maps.

Among thirteen candidates examined across three contributions, none were found to clearly refute the proposed approach. The core RoPE analysis contribution examined three candidates with zero refutations, while the IVC-Prune strategy itself examined ten candidates with no overlapping prior work identified. The two-stage foreground identification process had no candidates examined. This limited search scope—thirteen papers from semantic search and citation expansion—suggests the analysis captures nearby work but cannot claim exhaustive coverage. The absence of refutations within this scope indicates the specific combination of RoPE-based coordinate identification and prompt-aware pruning appears distinctive among examined candidates.

Given the sparse population of the spatial awareness branch and the limited search scope, the work appears to occupy a relatively underexplored niche within VLM token pruning. The analysis covers top semantic matches and immediate citations but does not extend to exhaustive field-wide comparison. The novelty assessment reflects what is visible within these thirteen examined papers, acknowledging that broader literature may contain related ideas not surfaced by this particular search strategy.

Taxonomy

Core-task Taxonomy Papers
24
3
Claimed Contributions
13
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Vision token pruning in large vision-language models while preserving spatial reasoning. The field has organized itself around several complementary strategies for reducing computational overhead in VLMs without sacrificing performance on spatially demanding tasks. Token Pruning Methods Based on Spatial Awareness emphasize geometric and positional information, ensuring that pruning decisions respect the spatial layout of visual content. Token Pruning Methods Based on Semantic and Attention Mechanisms leverage learned importance scores and cross-modal attention patterns to identify redundant tokens. Hybrid and Multi-Pathway Token Compression Frameworks combine multiple reduction strategies, while Token Fusion and Aggregation Strategies merge similar tokens rather than discarding them outright. Specialized Token Reduction for Specific Modalities and Tasks tailors compression to particular domains such as video or 3D reasoning, and Benchmarking and Evaluation Frameworks for VLM Compression provide standardized metrics to assess trade-offs between efficiency and accuracy across diverse benchmarks. Recent work has explored how to maintain fine-grained spatial understanding during aggressive token reduction. Some studies focus on attention-driven pruning that adapts dynamically to query complexity, as seen in Atp-llava[8] and SiLVR[10], while others investigate semantic clustering and hierarchical compression strategies like those in Rethinking Visual Token[3] and Hierarchical Token Compression[20]. IVC-Prune[0] sits within the spatial-awareness branch, specifically under implicit visual coordinate systems, where it shares conceptual ground with ToSA[4]. Both approaches embed positional or coordinate information to guide pruning, but IVC-Prune[0] emphasizes implicit encoding of spatial relationships rather than explicit grid-based representations. This contrasts with methods like B-vllm[2] or Cogvla[9], which rely more heavily on semantic attention patterns. The central challenge across these lines of work remains balancing compression ratios with the preservation of spatial reasoning capabilities, particularly for tasks requiring precise localization or relational understanding.

Claimed Contributions

Revealing implicit visual coordinates in LVLMs through RoPE analysis

The authors theoretically analyze how LVLMs use Rotary Position Embeddings to establish implicit visual coordinate systems. They identify special token positions (IVC tokens) whose RoPE rotation matrices approximate identity or 90-degree rotation, serving as spatial references for absolute position encoding.

3 retrieved papers
IVC-Prune: training-free, prompt-aware pruning strategy

The authors introduce IVC-Prune, a novel visual token pruning method that preserves both spatially critical IVC tokens (identified through mathematical properties of RoPE) and semantically relevant foreground tokens (discovered through a two-stage process of semantic seed discovery and contextual refinement).

10 retrieved papers
Two-stage foreground token identification process

The authors develop a two-stage method for identifying semantically relevant foreground tokens. The process begins with semantic seed discovery and is followed by contextual refinement using value-vector similarity to ensure robust token selection.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Revealing implicit visual coordinates in LVLMs through RoPE analysis

The authors theoretically analyze how LVLMs use Rotary Position Embeddings to establish implicit visual coordinate systems. They identify special token positions (IVC tokens) whose RoPE rotation matrices approximate identity or 90-degree rotation, serving as spatial references for absolute position encoding.

Contribution

IVC-Prune: training-free, prompt-aware pruning strategy

The authors introduce IVC-Prune, a novel visual token pruning method that preserves both spatially critical IVC tokens (identified through mathematical properties of RoPE) and semantically relevant foreground tokens (discovered through a two-stage process of semantic seed discovery and contextual refinement).

Contribution

Two-stage foreground token identification process

The authors develop a two-stage method for identifying semantically relevant foreground tokens. The process begins with semantic seed discovery and is followed by contextual refinement using value-vector similarity to ensure robust token selection.

Table of Contents