This paper presents the system developed by the YNU-HPCC team for SemEval-2026 Task 4: Narrative Story Similarity and Narrative Representation Learning. The task challenges computational systems to identify narrative similarity across three orthogonal dimensions: abstract theme, course of action, and outcomes. The primary scientific difficulty lies in distinguishing the underlying structural fabula from surface-level lexical overlaps, particularly when facing long-context narratives with subtle plot twists. To address this, our approach employs a hybrid architecture that strategically decouples retrieval and ranking tasks. For Track A, we introduce a dynamic routing mechanism where an instruction-tuned E5-Mistral-7B model handles clear cases, while ambiguous hard samples are routed to a Gemini-3-Flash reasoner. For Track B, we leverage the global semantic modeling capabilities of Gemini-Embedding-001 via a structure-preserving chunking strategy, enhanced by All-But-The-Top (ABTT) during inference. Extensive experiments on the official test set show that this divide-and-conquer strategy effectively balances local instruction following with global open-domain generalization. Our system performs competitively, ranking 5th in Track A and 2nd in Track B among all participating teams.