We describe the WSE Research system for the BEA 2026 Shared Task 2 on Rubric-based Short Answer Scoring for German. Our system combines rubric-conditioned prompting with TF-IDF exemplar retrieval, LoRA fine-tuning of open-source Qwen models, and prediction aggregation across complementary scorers. The central question is when prompt engineering, parameter-efficient adaptation, and aggregation each help for rubric-based grading. On the ALICE-LP-1.0 trial set, a fine-tuned Qwen2.5-32B reaches QWK 0.769, surpassing the strongest prompted commercial baseline (Gemini 3 Flash, 0.748). On the official test set, the system ranks second on three tracks and third on the remaining one. Overall, the results show that rubric-conditioned fine-tuning is a competitive and cost-effective alternative to commercial APIs for German short answer scoring, while aggregation helps on seen questions but larger single models generalize better to unseen rubrics.