mlsanityData to 14 Oct 2026

Why and When Visual Token Pruning Fails? A Study on Relevant Visual Information Shift in MLLMs Decoding

arXiv 2604.12358 ↗First posted 14 Apr 2026cs.CVcited by 7

On the map: Multimodal LLMs / vision-language models → Visual token pruning and efficiency

also in 1 more
Abstract (arXiv)

Recently, visual token pruning has been studied to handle the vast number of visual tokens in Multimodal Large Language Models. However, we observe that while existing pruning methods perform reliably on simple visual understanding, they struggle to effectively generalize to complex visual reasoning tasks, a critical gap underexplored in previous studies. Through a systematic analysis, we identify Relevant Visual Information Shift (RVIS) during decoding as the primary failure driver. To address this, we propose Decoding-stage Shift-aware Token Pruning (DSTP), a training-free add-on framework that enables existing pruning methods to align visual tokens with shifting reasoning requirements during the decoding stage. Extensive experiments demonstrate that DSTP significantly mitigates performance degradation of pruning methods in complex reasoning tasks, while consistently yielding performance gains even across visual understanding benchmarks. Furthermore, DSTP demonstrates effectiveness across diverse state-of-the-art architectures, highlighting its generalizability and efficiency with minimal computational overhead.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in Visual token pruning and efficiency, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 46 of its 55 references that are in the map. First posted on arXiv 14 Apr 2026.

Builds on

46 of 55 references.

Closest ancestors · 14

Same direction, newest first

  1. 2026VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration30 Jan
  2. 2025Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models4 Nov
  3. 2025SparseVILA: Decoupling Visual Sparsity for Efficient VLM Inference20 Oct
  4. 2025VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Multimodal LLMs18 Oct
  5. 2025LLMC+: Benchmarking Vision-Language Model Compression with a Plug-and-play Toolkit13 Aug
  6. 2025AirCache: Activating Inter-modal Relevancy KV Cache Compression for Efficient Large Vision-Language Model Inference31 Mar
  7. 2025DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models4 Mar
  8. 2024VisionZip: Longer is Better but Not Necessary in Vision Language Models5 Dec
  9. 2024VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models2 Dec
  10. 2024PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction22 Oct
  11. 2024SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference6 Oct
  12. 2024Efficient Large Multi-modal Models via Visual Context Compression28 Jun
2 more
  1. 2024LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models22 Mar
  2. 2024An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models11 Mar

Other references · 32

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  3. 2023Sigmoid Loss for Language Image Pre-Trainingcited by 3,109
  4. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224
  5. 2022Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answeringcited by 1,919
  6. 2019Towards VQA Models That Can Readcited by 1,718
  7. 2022Fast Inference from Transformers via Speculative Decodingcited by 1,653
  8. 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Headscited by 843
  9. 2024MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?cited by 586
  10. 2024EAGLE: Speculative Sampling Requires Rethinking Feature Uncertaintycited by 578
  11. 2024Measuring Multimodal Mathematical Reasoning with MATH-Vision Datasetcited by 526
  12. 2024MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmarkcited by 512
20 more
  1. 2025GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learningcited by 466
  2. 2022DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scalecited by 407
  3. 2024We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?cited by 286
  4. 2024LogicVista: Multimodal LLM Logical Reasoning Benchmark in Visual Contextscited by 245
  5. 2024LLM Inference Unveiled: Survey and Roofline Model Insightscited by 152
  6. 2024DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Modelscited by 146
  7. 2024Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lenscited by 143
  8. 2024LaCo: Large Language Model Pruning via Layer Collapsecited by 126
  9. 2023OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Modelscited by 114
  10. 2024DyCoke: Dynamic Compression of Tokens for Fast Video Large Language Modelscited by 96
  11. 2024LLaVA-KD: A Framework of Distilling Multimodal Large Language Modelscited by 40
  12. 2024Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Mergingcited by 19
  13. 2025ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shapingcited by 17
  14. 2025CompoDistill: Attention Distillation for Compositional Reasoning in Multimodal LLMscited by 14
  15. 2025GenRecal: Generation after Recalibration from Large to Small Vision-Language Modelscited by 10
  16. 2025Unified Reinforcement and Imitation Learning for Vision-Language Modelscited by 8
  17. 2025Masking Teacher and Reinforcing Student for Distilling Vision-Language Modelscited by 6
  18. 2025SelfJudge: Faster Speculative Decoding via Self-Supervised Judge Verificationcited by 4
  19. 2025Sparsity Forcing: Reinforcing Token Sparsity of MLLMscited by 3
  20. 2025VCM: Vision Concept Modeling Based on Implicit Contrastive Learning with Vision-Language Instruction Fine-Tuningcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 7.

7 papers.

  1. 2026Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Taskscited by 6
  2. 2026Agent Explorative Policy Optimization for Multimodal Agentic Reasoningcited by 4
  3. 2026Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inferencecited by 1
  4. 2026Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agentscited by 1
  5. 2026Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMscited by 0
  6. 2026Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMscited by 0
  7. 2026EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discoverycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026When Text Matters: Design Principles for Visual Token Pruning in Vision-Language Model
  2. 2025ToDRE: Effective Visual Token Pruning via Token Diversity and Task Relevance
  3. 2026Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More
  4. 2026SwiftVLM: Efficient Vision-Language Model Inference via Cross-Layer Token Bypass
  5. 2026OTPrune: Distribution-Aligned Visual Token Pruning via Optimal Transport
  6. 2025VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models
  7. 2026Structured Redundancy Modeling for Efficient Visual Token Pruning in High-Resolution MLLMs
  8. 2026SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models