Extracting argument structures from legal-political discourse reveals how policies and actions are proposed, debated, and formalized, but remains challenging due to the complexity of long-form, structured text. This work proposes a modular, retrieval-augmented system for traceable and structured argument mining in long, bilingual United Nations resolutions. This paper describes our system submission to the UZH Shared Task 2026, focusing on practical design choices for argument structure modeling under task and model constraints. Our system employs a parameter-efficient (at most 8B) open-source model, Qwen3:8B in thinking mode, to perform paragraph classification, multi-label tag assignment, and multi-label relation prediction through a modular, retrieval-augmented pipeline.