We present a winning three-stage system for SemEval 2026 Task 12: Abductive Event Reasoning that combines graph-based retrieval, LLM-driven abductive reasoning with prompt design informed by reflective prompt evolution, and post-hoc consistency enforcement; our system ranks first on the evaluation-phase leaderboard with an accuracy score of 0.95. Cross-model error analysis across 14 models (7 families) reveals three shared inductive biases: causal chain incompleteness, proximate cause preference, and salience bias, whose cross-family convergence (51% cause-count reduction) indicates systematic rather than model-specific failure modes in multi-label causal reasoning.