We present a synergistic dual-track approach for SemEval-2026 Task 4 on narrative similarity, covering Track A (triple-wise classification) and Track B (narrative representation) through failure-driven data enrichment. The shared task received 71 final submissions from 46 teams across its two tracks. For Track A, we explore three reasoning strategies: hybrid Cross-Encoder–LLM arbitration (66.5% dev), DSPy-based component-wise decomposition (68.0% dev), and a multi-stage pairwise reasoning pipeline with enforced moral agency hierarchies, where the final Gemini 2.5 Pro/Flash system achieves 77.39% on development and 69.25% on test data, ranking 17th among 46 participating teams in the official evaluation. For Track B, we propose BGE-M3 (LoRA), an instruction-guided dense representation model trained with Multiple Negatives Ranking Loss (MNRL); since Track B provides only unlabeled story instances, we specialize the embedding space using adversarial samples synthesized from Track A failure cases, achieving 68.75% in the official evaluation and ranking 6th among 26 participating teams. Our analysis shows that narrative similarity depends more on outcome alignment and moral trajectory than lexical overlap, highlighting the complementary roles of explicit reasoning and task-specific metric-space specialization.