Recent large language model role-playing systems often fail in long-narrative contexts due to factual overreach and stylistic monotony. Factual overreach occurs when characters access information outside their narrative perspective, while stylistic monotony flattens character voices through static profile descriptions. To address these issues, the authors propose REVERIEMEM, a three-layer memory architecture designed for book-based character agents. This system utilizes an episodic layer for first-person scene memories, a semantic layer for visibility-tagged facts, and a personality layer for situation-dependent behavioral patterns. The researchers also introduce KBF-QA, a benchmark consisting of 4,386 questions across eight novels to test knowledge boundaries. Experimental results show that REVERIEMEM improves Knowledge Boundary Fidelity by 34.6 percentage points compared to prior methods. Additionally, the model achieves approximately a 79% win rate on BOOKWORLD's five-dimension pairwise narrative protocol. These findings suggest that perspective-bounded memory effectively enhances both factual accuracy and character-grounded narrative generation.