Calibrate Before Reason: Robust Visual Token Reduction against Semantic Drift in VLMs

Large Vision-Language Models (VLMs) suffer from prohibitive inference overhead due to long sequences of visual tokens. However, existing visual token reduction methods mainly improve efficiency by pruning or compressing redundant tokens without examining whether the resulting representation remains semantically consistent with the original representation. Mapping the original $N$-token visual sequence to $K$ tokens may discard, dilute, or misassign critical visual cues, triggering severe **semantic drift** that deviates the VLM’s understanding. In this paper, we first introduce the principle of **"Calibrate Before Reason"** to visual token reduction and propose **CaRe**, a training-free robust framework that calibrates compact visual representations before reasoning to preserve semantic fidelity in VLMs. CaRe consists of two mutually complementary modules: 1) Perturbation-Robust Calibration Anchoring, which identifies calibration anchors with stable model-side influence under multi-directional perturbations; 2) Confidence-Gated Token Calibration, which extracts reliable calibration signals from unselected tokens and injects them into anchors. Extensive evaluations across diverse VLM architectures and benchmarks verify that CaRe outperforms state-of-the-art token reduction baselines. While pruning **94.4%** of visual tokens, our method retains **96.4%** of the original full-token performance, delivering up to $2.30\times$ faster end-to-end inference speed relative to unpruned vanilla models.