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  3. Rényi Entropy: A New Token Pruning Metric for Vision Transfo
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Rényi Entropy: A New Token Pruning Metric for Vision Transformers

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Evidence Receipt

Freshness: 2026-04-02T02:30:40.136932+00:00

Claims: 0

References: 0

Proof: pending

Distribution: unknown

Source paper: Rényi Entropy: A New Token Pruning Metric for Vision Transformers

PDF: https://arxiv.org/pdf/2603.27900v1

First buyer signal: unknown

Distribution channel: unknown

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Dimensions overall score 7.0

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Builds On This
Unified Spatio-Temporal Token Scoring for Efficient Video VLMs
Score 3.0down
Prior Work
IWP: Token Pruning as Implicit Weight Pruning in Large Vision Language Models
Score 7.0stable
Prior Work
Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models
Score 7.0stable
Prior Work
ReDiPrune: Relevance-Diversity Pre-Projection Token Pruning for Efficient Multimodal LLMs
Score 7.0stable
Prior Work
Video Patch Pruning: Efficient Video Instance Segmentation via Early Token Reduction
Score 7.0stable
Higher Viability
Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity
Score 8.0up
Higher Viability
The Model Knows Which Tokens Matter: Automatic Token Selection via Noise Gating
Score 8.0up
Higher Viability
PixelPrune: Pixel-Level Adaptive Visual Token Reduction via Predictive Coding
Score 8.0up

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