By exploiting the spatial selectivity of wireless channels, physical layer security (PLS) protects confidential wireless transmissions. Flexible reflectors (FRs), which are passive metallic plates mounted on movable platforms, offer a low-cost and low-complexity solution for reshaping the propagation environment. However, their potential for secrecy-oriented signal propagation shaping has not been explored. In this paper, we study an FR-aided secure communication over non-line-of-sight links and jointly optimize the positions and rotations of FRs to maximize the secrecy rate under geometric reflection and mutual-blocking constraints. For the single-FR case, we propose a Fibonacci segmentation search (FSS) method, which partitions the feasible region into multiple unimodal intervals and obtains the optimal solution with Fibonacci search over the unimodal intervals. To reduce the computational overhead, we also propose a low-complexity cross-entropy search (CES) method, which employs Gaussian mixture model-based adaptive importance sampling to localize promising search intervals before deterministic refinement. After deriving the blocked placement interval induced by the deployed FRs and integrating it into a sequential deployment scheme, we extend the FSS and CES methods to the multi-FR case. Simulation results show that our proposed FSS method achieves significant secrecy rate performance gains over various benchmark schemes, and our proposed CES method achieves a favorable runtime-performance tradeoff. These results indicate that FRs provide a low-cost and reconfigurable mechanism for PLS.
Visual foundation models learn rich pathological morphology but lack explicit concept-level supervision. Vision--language pretraining can provide this supervision but requires costly image--text pairs. Here, we introduce GloSAGE, a semantic self-distillation framework that uses lesion profiles predicted by a pretrained visual teacher to construct structured pathology descriptions. Reliability-guided selection identifies image--text anchors, while Knowledge-Structured Pathology Text Synthesis and Evidence-Guided Negative-pair Construction account for partially observed lesions. Image--text alignment trains projection layers while retaining the frozen visual encoder; an intermediate Semantic Transfer Layer supplies features for downstream tasks. Developed on GloMillion and evaluated across seven glomerulus- and case-level cohorts, GloSAGE supported zero-shot lesion recognition, image--text retrieval, hard-negative discrimination and concept-intervention analyses. It outperformed its source visual model in most lesion-recognition tasks and improved lesion grading, while supporting competitive case-level disease classification, injury grading and baseline renal function estimation. Ablations supported the combined contributions of knowledge-structured text and evidence-guided negatives, and the intermediate transfer layer improved mean lesion-recognition ROC-AUC relative to the complete projection output. A frozen language model conditioned on GloSAGE features generated glomerular descriptions. These findings suggest that teacher-derived lesion profiles can provide annotation-efficient semantic supervision for extending a glomerular visual foundation model.