Cloud-edge collaboration provides a practical paradigm for deploying MLLMs on resource-constrained devices, where compact edge-side models are improved with supervision from stronger cloud-side models. However, existing cloud-edge distillation methods mainly rely on pseudo labels or static feature alignment, overlooking how the teacher progressively transforms multimodal inputs into answers. In this paper, we propose A²-MED, Adaptive Anchor-Guided Multimodal Evolution Distillation for cloud-edge MLLMs. Our key idea is to organize the teacher’s layer-wise multimodal evolution into functional stages through adaptive teacher-side anchoring, and transfer the resulting stage-wise evolution as process-level supervision. Specifically, we adaptively identify functional anchors from the teacher according to layer-wise indicators of cross-modal binding, semantic integration, and answer prediction, and then project these anchors to the student to establish corresponding functional stages. Under these teacher-defined stages, we align teacher-student anchor states and distill inter-stage multimodal evolution relations, enabling the student to learn how the teacher progressively transforms multimodal inputs into answers. Experiments on A-OKVQA and VizWiz show that our method consistently improves over representative cloud-edge and distillation baselines, with further analyses validating the effectiveness of adaptive anchors and evolution distillation.