Staying In Character: Perspective-Bounded Memory For Book-Based Role-Playing Agents
For developers of LLM-based character agents, this work provides a solution to maintain character consistency in long narratives, though it is incremental as it combines known memory concepts.
REVERIEMEM, a three-layer memory architecture, improves Knowledge Boundary Fidelity by 34.6 percentage points over prior methods and achieves ~79% win rate in narrative generation, addressing factual overreach and stylistic monotony in book-based role-playing agents.
Recent LLM role-playing systems build character agents from novels by extracting characters, scenes, and relations. Yet long-narrative role-playing suffers from two failures: Factual Overreach, where shared retrieval or parametric memory lets a character use facts outside its perspective, and Stylistic Monotony, where profile descriptions flatten a character into a fixed voice. To address these failures, we propose REVERIEMEM, a three-layer memory architecture for book-based character agents. The episodic layer stores first-person scene memories; the semantic layer stores visibility-tagged facts; and the personality layer stores situation-dependent speech and behaviour patterns. For evaluation, we construct KBF-QA, a 4,386-question benchmark over eight novels for testing knowledge boundaries. REVERIEMEM improves Knowledge Boundary Fidelity by 34.6 percentage points over the strongest prior method. On BOOKWORLD's five-dimension pairwise narrative protocol, REVERIEMEM achieves a ~ 79% win rate, suggesting that perspective-bounded memory improves both boundary fidelity and character-grounded narrative generation.