We describe our system for the UZH ArgMining 2026 Shared Task on reconstructing argumentative structure in UN/UNESCO resolutions. The task requires (1) classifying paragraph types and assigning thematic tags from a 141-label taxonomy, and (2) predicting directed argumentative relations between paragraphs. Our pipeline combines a quantised Qwen2.5-7B-Instruct model with retrieval-augmented generation (RAG) backed by FAISS-indexed dense embeddings for few-shot prompting and tag candidate pre-filtering. For relation prediction, we apply a sliding-window cosine pre-filter that reduces the quadratic pair space to near-linear cost. A parallelisable, fault-tolerant pipeline with atomic checkpointing enabled complete processing of 2,959 paragraphs across three concurrent Kaggle T4 sessions despite 12-hour GPU limits. Our system achieved 2nd place overall on the shared task leaderboard.