We present NarSiL (Narrative Similarity Learners), our system for SemEval-2026 Task 4 Track A on Narrative Story Similarity. NarSiL employs a two-stage architecture: a Mixture-of-Experts (MoE) initial classifier that also leverages supermajority voting across three large language models (Gemma-3-12B, GPT-3.5-turbo-instruct, and Gemini-2.5-Flash) over multiple runs, followed by a structured three-pathway fallback for ambiguous cases. The three pathways correspond directly to the task’s three core similarity components, abstract theme, narrative outcome, and course of action. Each path yields a similarity score corresponding to its respective component, and the scores are then combined through a weighted aggregation step. NarSiL achieves 64.25% accuracy on the official test set. An improved score of 70.25% is obtained by considering only the supermajority voting of GPT, followed by the previously described fallback.

The highly formal and negotiated language of United Nations (UN) resolutions presents unique challenges for argument mining. This paper describes our system submitted to the ArgMining 2026 Shared Task: Reconstructing the Reasoning in United Nations Resolutions. Adhering to the strict constraint of utilising open-weight models with at most 8 billion parameters, we propose a hybrid, compute-efficient architecture powered by Qwen3-8B. For the preambular-operative classification, we implement a set of deterministic rules related to the specificity of UN documents, supplemented by an LLM-based multi-label classifier for thematic dimensions and a directed-graph extraction approach for argumentative relation prediction.