Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models
Token compression has become a key technique for reducing the inference cost of large foundation models, with approaches such as token pruning and KV-cache reuse widely adopted in vision-language models and recently explored for embodied agents. In embodied agents, tokens not only support perception and semantic understanding but also directly affect latency-sensitive closed-loop robot action prediction. Existing schemes typically guide compression using redundancy or importance cues, such as visual similarity, attention scores, and saliency. However, these cues only indirectly measure the key
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- FuzzySimilar title/name (fuzzy) · 59%VioletVision-3B →
“Fuzzy title match (0.73): “Just Noticeable Difference Modeling for Token Compression in” ≈ “VioletVision-3B””
- FuzzySimilar title/name (fuzzy) · 84%liguodongiot/llm-action →
“Fuzzy title match (0.92): “Just Noticeable Difference Modeling for Token Compression in” ≈ “liguodongiot/llm-action””
- FuzzySimilar title/name (fuzzy) · 84%pytorch/vision →
“Fuzzy title match (0.92): “Just Noticeable Difference Modeling for Token Compression in” ≈ “pytorch/vision””
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%Zeyi-Lin/HivisionIDPhotos →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Zhuoyuan Li →
“Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models”
- LinkedLinked via arxiv author · 85%Chenrui Zhao →
“Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models”