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VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
Multimodal AlignmentVision Language Model
Abstract:

Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing token reduction methods typically focus on isolated pipeline components and often neglect textual alignment, leading to performance degradation. In this paper, we propose VisionTrim, a unified framework for training-free MLLM acceleration, integrating two effective plug-and-play modules: 1) the Dominant Vision Token Selection (DVTS) module, which preserves essential visual tokens via global-local view, and 2) the Text-Guided Vision Complement (TGVC) module, which facilitates context-aware token merging guided by textual cues. Extensive experiments across diverse image and video multimodal benchmarks demonstrate the performance superiority of our VisionTrim, advancing practical MLLM deployment in real-world applications. Our full implementation will be publicly available.

Disclaimer
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

VisionTrim proposes a unified framework for training-free MLLM acceleration through two plug-and-play modules: Dominant Vision Token Selection (DVTS) for preserving essential tokens via global-local views, and Text-Guided Vision Complement (TGVC) for context-aware token merging guided by textual cues. The paper resides in the Training-Free Pruning Approaches leaf, which contains four papers including VisionTrim itself. This leaf sits within the broader Token Selection and Pruning Methods branch, indicating a moderately populated research direction focused on discarding redundant tokens without retraining. The taxonomy reveals this is an active but not overcrowded area, with parallel efforts in learning-based selection and adaptive pruning strategies.

The taxonomy structure shows VisionTrim's leaf neighbors include Learning-Based Selection (four papers employing trained modules for token importance) and Adaptive and Dynamic Pruning (three papers adjusting pruning ratios dynamically). Adjacent branches reveal complementary approaches: Token Merging and Aggregation Methods (seven papers across spatial, frequency, and conditional merging) and Video-Specific Compression Methods (four papers addressing temporal redundancy). VisionTrim's dual-module design bridges token selection and context-aware merging, positioning it at the intersection of pruning and conditional aggregation strategies. The taxonomy's scope notes clarify that training-free pruning excludes learned networks, while conditional merging emphasizes textual guidance—boundaries VisionTrim navigates by combining both philosophies.

Among thirty candidates examined, the VisionTrim unified framework shows two refutable candidates from ten examined, suggesting some prior work on training-free acceleration frameworks exists within the limited search scope. The DVTS module appears more novel, with zero refutable candidates among ten examined, indicating less direct overlap in global-local token selection heuristics. The TGVC module faces stronger prior work, with three refutable candidates from ten examined, suggesting text-guided token merging has received attention in conditional aggregation literature. These statistics reflect a targeted semantic search, not exhaustive coverage, meaning additional related work may exist beyond the thirty candidates analyzed.

Based on the limited search scope of thirty semantically similar papers, VisionTrim's core framework and TGVC module encounter moderate prior work overlap, while DVTS appears more distinctive. The taxonomy context reveals a field with multiple active research directions but no single dominant paradigm, suggesting room for methodological contributions that bridge pruning and merging strategies. The analysis covers top-K semantic matches and does not claim comprehensive field coverage, particularly for recent preprints or domain-specific applications outside the examined candidate set.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
5
Refutable Paper

Research Landscape Overview

Core task: vision token compression for multimodal large language models. The field addresses the computational bottleneck created by the large number of visual tokens that vision encoders produce when feeding images or videos into language models. The taxonomy reveals several complementary strategies: Token Selection and Pruning Methods discard redundant tokens based on attention scores or other heuristics, often without retraining; Token Merging and Aggregation Methods combine similar tokens to preserve information while reducing count; Semantic Abstraction and Representation Learning approaches learn compact latent representations; Video-Specific Compression Methods tackle the unique temporal redundancy in video; Architecture-Integrated Compression embeds efficiency directly into model design; Application-Specific and Task-Driven Compression tailors reduction to particular downstream tasks; Efficiency Analysis and Benchmarking systematically evaluates trade-offs; Multimodal Foundation Models and Architectures explores broader model designs; and Cross-Domain and Auxiliary Methods borrows techniques from related areas. Representative works like SparseVLM[6] and FastVLM[7] illustrate training-free pruning, while LLaVA-PruMerge[18] combines pruning with merging, and BLIP-3[22] exemplifies architecture-level integration. A central tension across these branches is the trade-off between compression ratio and task performance: aggressive pruning can yield dramatic speedups but risks losing fine-grained visual details critical for complex reasoning. Training-free approaches such as VisionTrim[0], SparseVLM[6], and Generic Token Compression[10] prioritize plug-and-play deployment without additional optimization, making them attractive for practitioners seeking immediate efficiency gains. In contrast, methods like Deco[1] and TokenCarve[2] invest in learned selection or merging strategies to better preserve semantic content. VisionTrim[0] sits squarely within the training-free pruning cluster, sharing the philosophy of SparseVLM[6] and Generic Token Compression[10] by avoiding retraining overhead, yet it distinguishes itself through its specific pruning heuristic and compatibility with diverse multimodal architectures. Nearby works like VScan[45] explore alternative scanning or selection patterns, highlighting ongoing exploration of which tokens matter most and when dynamic, query-aware compression outweighs static reduction.

Claimed Contributions

VisionTrim unified framework for training-free MLLM acceleration

The authors introduce VisionTrim, a comprehensive framework that accelerates multimodal large language models without requiring additional training. It optimizes the entire MLLM pipeline by reducing visual token redundancy through two integrated modules.

10 retrieved papers
Can Refute
Dominant Vision Token Selection (DVTS) module

A plug-and-play module that selects important visual tokens by considering both global semantic significance (via CLS token attention) and local spatial continuity (via the Local Token Affinity Measurement algorithm), ensuring retention of critical visual information.

10 retrieved papers
Text-Guided Vision Complement (TGVC) module

A plug-and-play module that leverages textual instructions to guide clustering and merging of discarded visual tokens, complementing the dominant tokens selected by DVTS and ensuring alignment between visual and textual representations.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

VisionTrim unified framework for training-free MLLM acceleration

The authors introduce VisionTrim, a comprehensive framework that accelerates multimodal large language models without requiring additional training. It optimizes the entire MLLM pipeline by reducing visual token redundancy through two integrated modules.

Contribution

Dominant Vision Token Selection (DVTS) module

A plug-and-play module that selects important visual tokens by considering both global semantic significance (via CLS token attention) and local spatial continuity (via the Local Token Affinity Measurement algorithm), ensuring retention of critical visual information.

Contribution

Text-Guided Vision Complement (TGVC) module

A plug-and-play module that leverages textual instructions to guide clustering and merging of discarded visual tokens, complementing the dominant tokens selected by DVTS and ensuring alignment between visual and textual representations.

Table of Contents