← 研踪 PaperTrailarXiv:2606.25632 · v2
Staying In Character: Role-Playing Agents with Perspective-Bounded Memory
首次提交 2026-06-24 17:37
版本更新 2026-11-10 02:20
雷达观测 2026-11-10 15:22
摘要
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. We evaluate ReverieMem and six reference methods on KBF-QA, a new benchmark of 4,386 questions across eight novels that assesses character knowledge boundaries. ReverieMem improves Knowledge Boundary Fidelity by 34.6 percentage points over the strongest reference method. On BookWorld's five-dimension pairwise narrative protocol, ReverieMem achieves a mean win rate of 78.86% against BookWorld across five generator LLMs and two generation modes, suggesting that perspective-bounded memory improves both boundary fidelity and character-grounded narrative generation.
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v2 · 2026-11-10 02:20 · 最新观测版本
Staying In Character: Role-Playing Agents with Perspective-Bounded Memory
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. We evaluate ReverieMem and six reference methods on KBF-QA, a new benchmark of 4,386 questions across eight novels that assesses character knowledge boundaries. ReverieMem improves Knowledge Boundary Fidelity by 34.6 percentage points over the strongest reference method. On BookWorld's five-dimension pairwise narrative protocol, ReverieMem achieves a mean win rate of 78.86% against BookWorld across five generator LLMs and two generation modes, suggesting that perspective-bounded memory improves both boundary fidelity and character-grounded narrative generation.
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