This paper describes Team CV’s systems forSemEval-2026 Task 4: Narrative Story Sim-ilarity and Narrative Representation Learn-ing (Hatzel et al., 2026). For Track A (com-parative judgment), we explore five prompt-ing strategies—zero-shot, chain-of-thought,structured feature extraction, pairwise scor-ing, and few-shot—and QLoRA fine-tuningof smaller models. For Track B (narrativeembeddings), we benchmark twelve dedicatedtext embedding models of varying dimen-sionality (384–4096) spanning open-source(E5-Large-v2, BGE, GTE, Qwen3 Embed-ding) and closed-source (OpenAI, Gemini,Mistral) families, and fine-tune Qwen3 Em-bedding 4B on task-specific triples. Few-shot prompting with Qwen-2.5 7B (64.00%)outperforms all fine-tuned variants (best57.50%) on Track A; scaling to LLaMA-3.3-70B yields 75.00%. On Track B, Ope-nAI text-embedding-3-large (3072-d) achieves the best dev accuracy (67.00%),while fine-tuning Qwen3 Embedding 4B(2560-d) on synthetic triples slightly de-creases accuracy. Our final submission—LLaMA-3.3-70B (3-shot) for Track A andtext-embedding-3-large for Track B—achieves 70.75% and 64.50%, exceeding theGPT-4o-mini and STORY-EMB baselines respec-tively.

United Nations and UNESCO resolutions encode complex collective reasoning through highly structured preambles and operative clauses. Reconstructing this implicit argumentative structure is a challenging natural language processing task. This paper describes our submission to the UZH Shared Task at the ArgMining Workshop 2026. Adhering to the strict constraint of using open-weight models with at most 8B parameters, we propose a highly efficient, modular pipeline built entirely upon the Qwen-2.5-7B-Instruct architecture. To address Subtask 1, we decouple the problem, employing a 4-bit quantized LoRA adapter via the Unsloth framework for paragraph type classification and a type-informed chain-of-thought approach for thematic tagging and relation prediction.