SC-VTP: State-Consistent Visual Token Pruning via Deletion-Consistent Training

Visual-token pruning requires selector training to reflect deployed computation. In hybrid decoders, attention masks leave discarded tokens active in convolutional history and recurrent memory. State-Consistent Visual Token Pruning (SC-VTP) coordinates key access, history advancement and memory decay/write with one differentiable retention gate. A compositional contract specifies when binary- gated execution recovers native deletion. With a frozen backbone and a 0.52M-parameter selector trained on 26,143 DocVQA questions, three paired seeds improve official-validation ANLS over attention-only training by 5.74 points on average. A separately trained Qwen3.8-27B pair improves ANLS by 10.38 points. Crossed soft/hard evaluation and trained component ablations connect execution fidelity to selector utility. The original seed reaches 93.67 ANLS versus 93.05 for native-deletion policy gradients and transfers to 84.18% TextVQA accuracy. Frozen scorers retain their attention-only advantage at 25% and 75% retention. On 32 hardware-test sources, full-input/SC-VTP geometric latency ratios are 1.16 and 1.04 for one-token and 32-token generation, including encoding and selection. SC-VTP connects deletion-consistent operators to measured pruning performance in hybrid decoders.