C haracter GPT : A Persona Reconstruction Framework for Role-Playing Agents (2025)

Abstract: The recent introduction of the Assistants API highlights its potential for large language models (LLMs) in role-playing agents (RPA). However, maintaining consistent character personas remains a significant challenge due to variability in information extraction, which frequently omits critical elements such as backstory or interpersonal relationships. To address this limitation, we introduce CharacterGPT, a framework designed to dynamically reconstruct character personas through Character Persona Training (CPT). This approach incrementally updates personas by extracting traits from chapter-wise novel summaries, reflecting the progression of the narrative. Our framework is evaluated through Big Five personality evaluations and creative tasks, in which characters generate original narratives, demonstrating the efficacy of CharacterGPT in preserving persona consistency. The code and results are available at https://github.com/Jeiyoon/charactergpt

## introduction the rapid advancements in large language models (llms) have positioned them as the core module of various ai systems @xcite @xcite @xcite , enabling a wide range of applications. building on this progress, the recent introduction of the assistants api (openai, 2023b), a tool designed for document-based information retrieval, demonstrates the potential of llm in multiple domains, especially in role-playing agents (rpa) @xcite @xcite @xcite @xcite . however, rpas that rely solely on documents as input often face problems of inconsistent information extraction, where key personality traits or background knowledge are omitted, leading to degraded persona coherence @xcite . for example, as illustrated in figure 1 , when the assistants api is provided with an unstructured wiki document about the novel god's blessing on this wonderful world!, it often fails to provide accurate responses, while structured character traits produce more reliable, role-specific answers. in this paper, we propose a novel framework called charactergpt, which addresses this challenge through a structured persona reconstruction process. drawing inspiration from cognitive memory models, we introduce character persona training (cpt), a method that incrementally updates character personas by extracting traits from chapterwise summaries of novels. this approach mirrors how human memory consolidates information into schemas over time @xcite , enabling more consistent and contextually appropriate responses from rpas. cpt operates by identifying eight essential traits-personality, physical description, motivations, backstory, emotions, relationships, growth and change, and conflict-based on character analysis literature @xcite . for each chapter of a novel, these traits are extracted from summaries and appended to a character's evolving persona, forming a document that reflects the character's development in chronological order. note that the extracted traits are updated separately to ensure they remain distinct and are not coalesced. this reconstructed persona document is then used as input to the assistants api, allowing it to generate more contextually accurate and coherent responses based on the character's evolving identity. this framework minimizes information loss and computational cost associated with traditional document-based retrieval methods, as it systematically organizes and updates persona traits over time. moreover, by generating personas at different narrative points, charactergpt enables users to interact with characters at specific moments within the novel (e.g., a user can experience a hero's thoughts just before confronting the devil king!). we evaluate the effectiveness of charactergpt through human assessments, examining how well our method captures role-specific knowledge. each character undergoes the big five inventory (bfi) personality test @xcite to evaluate personality consistency, and characters are tasked with generating short narratives to assess creative capabilities. 7 crowd-workers evaluate these narratives across six metrics using a 5-point likert scale. our results demonstrate that charac-tergpt significantly improves persona consistency, controllability, and role-specific knowledge compared to standard document-based systems. ## proposed method the goal of charactergpt is to build a personabased assistant, denoted as f , which takes as input a persona document d and an inference prompt p f , and generates a character response r. let @xmath0, s 2 , . . . , s n } represent a persona document with n sentences. a naive approach using the assistants api would treat the entire sampled document as input. however, as illustrated in figure 1 , this method often fails to capture essential character traits, leading to inconsistent and unnatural responses. to address this, we reorganize the persona document into a refined version d r and define the assistant's output as: ## conclusion we introduce charactergpt, a persona-based assistant designed to enhance persona consistency by utilizing structured character traits as input. the proposed framework consists of two primary phases: initialization and training. in the initialization phase, we treat the character as if the narrative has not yet advanced, thus excluding any content related to story progression. during the training phase, the character persona is incrementally refined at each epoch by extracting relevant traits from chapter summaries, emulating the natural development of a character throughout a novel. our approach has been rigorously evaluated through human assessments and case studies, demonstrating its effectiveness in preserving persona coherence and retaining character-specific knowledge. future directions include extending this framework to enable deeper reasoning and decision-making capabilities, supported by more comprehensive personality models.