不训练模型，直接解码内部语义响应，提升跨域裂缝分割精度。

Cross-domain infrastructure crack segmentation is challenged by variations in materials, imaging conditions, crack morphology, and background interference. Although text-promptable foundation models reduce the need for task-specific training, SAM3's native proposal interface may not fully expose the crack evidence already encoded by the frozen model. We identify and quantify an output-interface mismatch in which the internal language-conditioned response preserves ground-truth-aligned crack evidence that is absent from the native proposal output. Based on this finding, we propose \textbf{S}emantic-\textbf{E}dge \textbf{R}esponse \textbf{D}ecoding (\textbf{SERD}), a training-free method that replaces proposal-based readout with direct response decoding. SERD normalizes the dense response, calibrates it using a fixed Sobel prior, and applies a single threshold to produce the crack mask. SAM3 remains frozen without any target-domain annotations. Using one source domain solely for threshold selection, SERD achieves an average Crack IoU of 58.00\% on five unseen datasets, compared with 54.33\% for native SAM3. Across six rotated source-domain settings, it obtains a mean target-domain IoU of 60.23\% and a Boundary F1 of 67.18\%, exceeding SAM3 by 3.27 and 2.70 percentage points, respectively. The results show that task-aligned internal-response decoding provides a simple and transferable solution for cross-domain infrastructure crack segmentation. \textit{Code is available at: \href{https://github.com/xauat-liushipeng/SERD}{GitHub}.}