CatchUp: Depth-Aware Re-Admission of Visual Tokens for Efficient Large Vision-Language Models
Large Vision-Language Models (LVLMs) repeatedly process hundreds of visual tokens across decoder layers, incurring substantial computation and memory costs. Existing inference-time visual token pruning methods often irreversibly remove low-ranked tokens, although the set of visual tokens receiving high query-conditioned attention can change across decoder depth. Recoverable routing preserves skipped tokens for later reconsideration, but re-admits them with stale representations after bypassing intermediate transformations. We further find that replacing stale re-admitted states with their current-depth oracle states from a full-token reference trajectory substantially improves grounding p...
| Authors | Eunju Park, Uiwon Hwang |
|---|---|
| Primary category | cs.CV |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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