[2603.19313] Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs

Computer Science > Computation and Language arXiv:2603.19313 (cs) [Submitted on 14 Mar 2026] Title:Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs Authors:Kai Wang, Haoyang You, Yang Zhang, Zhongjie Wang View a PDF of the paper titled Memory-Driven Role-Playing: Evaluation and Enhancement of Persona Knowledge Utilization in LLMs, by Kai Wang and 3 other authors View PDF HTML (experimental) Abstract:A core challenge for faithful LLM role-playing is sustaining consistent characterization throughout long, open-ended dialogues, as models frequently fail to recall and accurately apply their designated persona knowledge without explicit cues. To tackle this, we propose the Memory-Driven Role-Playing paradigm. Inspired by Stanislavski's "emotional memory" acting theory, this paradigm frames persona knowledge as the LLM's internal memory store, requiring retrieval and application based solely on dialogue context, thereby providing a rigorous test of depth and autonomous use of knowledge. Centered on this paradigm, we contribute: (1) MREval, a fine-grained evaluation framework assessing four memory-driven abilities - Anchoring, Recalling, Bounding, and Enacting; (2) MRPrompt, a prompting architecture that guides structured memory retrieval and response generation; and (3) MRBench, a bilingual (Chinese/English) benchmark for fine-grained diagnosis. The novel paradigm provides a comprehensive diagnostic for four-staged role-playing abil...