VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning

• URL: http://arxiv.org/abs/2605.31457v1
• Date: Fri, 29 May 2026 15:51:12 GMT
• Title: VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning
• Abstract summary: We propose VisionPulse, a step-wise visual token pruning framework during reasoning.<n>By enforcing visual sparsity during reasoning, VisionPulse filters redundant visual context while preserving relevant visual evidence, shortening reasoning traces naturally.
• Score: 15.03674139835036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advancement of large multimodal models (LMMs), inference-time overhead has become a key bottleneck for real-world deployment. Existing methods typically prune visual tokens at prefill, assuming the required visual evidence remains static during reasoning. However, we empirically show that visual evidence is strongly step-dependent: only a sparse subset of visual tokens is critical at each decoding step, and the critical set evolves across reasoning. Furthermore, we identify a coupled bottleneck where redundant visual context can steer the model toward query-irrelevant regions, lengthening the reasoning trace. Guided by these insights, we propose VisionPulse, a step-wise visual token pruning framework during reasoning. VisionPulse computes a lightweight visual attention mass to estimate the step-wise retention budget by exploiting its strong positive correlation with LMMs' effective visual token usage and retain only the most critical tokens under this budget. By enforcing visual sparsity during reasoning, VisionPulse filters redundant visual context while preserving relevant visual evidence, shortening reasoning traces naturally. Extensive experiments show that VisionPulse only retains 5% of visual tokens per step with reasoning traces shortened by 11.2%, while keeping accuracy almost unchanged.

Related papers

• Remember-R1: Mitigating Long-Context Visual Forgetting through Reinforcement Learning [50.56518447675037]
  Multimodal large language models (MLLMs) increasingly rely on long chain-of-thought reasoning for complex tasks.<n>As reasoning sequences lengthen, models may gradually rely less on visual evidence and more on accumulated textual context, leading to visual forgetting.<n>We propose Remember-R1, a reinforcement learning framework that mitigates long-context visual forgetting by applying process-level supervision directly on the original reasoning trajectory.
  arXiv  Detail & Related papers  (2026-08-02T15:31:31Z)
• Look Less, Reason More: Block-wise Attention Skipping for Efficient Multimodal LLMs [89.7106332677868]
  We present Visual-Skip, a training-free inference paradigm that decouples spatial interaction from semantic evolution.<n>V-Skip effectively bypasses redundant vision attention to achieve block-wise sparsity, maintaining a 94.16% to 100.31% performance retention across diverse MLLMs.
  arXiv  Detail & Related papers  (2026-06-07T08:32:13Z)
• OccamToken: Efficient VLM Inference with Training-Free and Budget-Adaptive Token Pruning [24.164883144694656]
  OccamToken is a training-free framework that replaces absolute token ranking with register-anchored relative evidence testing.<n>We show that OccamToken consistently improves the accuracy-efficiency trade-off without additional training.
  arXiv  Detail & Related papers  (2026-05-28T09:20:47Z)
• Self-Consistent Latent Reasoning: Long Latent Sequence Reasoning for Vision-Language Model [56.21523258053447]
  SCOLAR (Self-COnsistent LAtent Reasoning) introduces a lightweight detransformer that generates auxiliary visual tokens in a single shot.<n>SCOLAR extends acceptable latent CoT length by over $30times$, achieves state-of-the-art among open-source models on real-world reasoning benchmarks.
  arXiv  Detail & Related papers  (2026-05-12T14:13:08Z)
• LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models [8.039490357019801]
  Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in visual understanding and reasoning.<n>They also impose significant computational burdens due to long visual sequence inputs.<n>Recent works address this issue by pruning unimportant visual tokens, achieving substantial computational reduction.
  arXiv  Detail & Related papers  (2026-04-27T01:56:59Z)
• Latent Implicit Visual Reasoning [59.39913238320798]
  We propose a task-agnostic mechanism that trains LMMs to discover and use visual reasoning tokens without explicit supervision.<n>Our approach outperforms direct fine-tuning and achieves state-of-the-art results on a diverse range of vision-centric tasks.
  arXiv  Detail & Related papers  (2025-12-24T14:59:49Z)
• ZSPAPrune: Zero-Shot Prompt-Aware Token Pruning for Vision-Language Models [7.7352936204066]
  We propose a novel, zero-shot method to model visual token pruning as a balance between task relevance and information diversity.<n>Our method achieves performance that matches or surpasses the state-of-the-art with only minimal accuracy loss.<n>These gains are accompanied by significant reductions in GPU memory footprint and inference latency.
  arXiv  Detail & Related papers  (2025-10-20T06:18:47Z)
• VFlowOpt: A Token Pruning Framework for LMMs with Visual Information Flow-Guided Optimization [70.98122339799218]
  Large Multimodal Models (LMMs) excel in visual-language tasks by leveraging numerous visual tokens for fine-grained visual information.<n>Previous research aimed at reducing visual tokens during inference typically leverages importance maps derived from attention scores among vision-only tokens or vision-language tokens to prune tokens across one or multiple pruning stages.<n>We propose VFlowOpt, a token pruning framework that introduces an importance map derivation process and a progressive pruning module with a recycling mechanism.<n> Experiments demonstrate that VFlowOpt can prune 90% of visual tokens while maintaining comparable performance, leading to an 89% reduction in KV-Cache memory and 3.8
  arXiv  Detail & Related papers  (2025-08-07T09:47:21Z)
• TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model [56.43860351559185]
  We introduce textbfTopV, a compatible textbfTOken textbfPruning with inference Time Optimization for fast and low-memory textbfVLM.<n>Our framework incorporates a visual-aware cost function to measure the importance of each source visual token, enabling effective pruning of low-importance tokens.
  arXiv  Detail & Related papers  (2025-03-24T01:47:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.