CatchUp: Depth-Aware Re-Admission of Visual Tokens for Efficient Large Vision-Language Models

Eunju Park, Uiwon Hwang

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 performance. We therefore propose CatchUp, a training-free framework for depth-aware visual-token re-admission. CatchUp repeatedly re-evaluates query-conditioned visual relevance, processes only selected visual tokens through each decoder interval, and preserves deferred tokens for later re-admission. Upon re-admission, CatchUp approximates missed representation evolution using reference-to-current transition signals from semantically and spatially related visual tokens. Across three…