Human-like virtual characters are crucial for games, storytelling, and virtual reality, yet current methods rely heavily on annotated data or handcrafted persona prompts, making it difficult to scale up and generate realistic, contextually coherent personas. We create the first QA dataset for BaZi-based persona reasoning, where real human experiences categorized into wealth, health, kinship, career, and relationships are represented as life-event questions and answers. Furthermore, we propose the first BaZi-LLM system that integrates symbolic reasoning with large language models to generate temporally dynamic and fine-grained virtual personas. Compared with mainstream LLMs such as DeepSeek-v3 and GPT-5-mini, our method achieves a 30.3%-62.6% accuracy improvement. In addition, when incorrect BaZi information is used, our model's accuracy drops by 20%-45%, showing the potential of culturally grounded symbolic-LLM integration for realistic character simulation.
Personality Understanding of Fictional Characters during Book Reading
Princeton University（普林斯顿大学）；Syracuse University（雪城大学）；Pattern Recognition Center, WeChat AI（微信人工智能模式识别中心）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）；reciTAL（reciTAL公司）
In the context of chit-chat dialogues it has been shown that endowing systems with a persona profile is important to produce more coherent and meaningful conversations. Still, the representation of such personas has thus far been limited to a fact-based representation (e.g. "I have two cats."). We argue that these representations remain superficial w.r.t. the complexity of human personality. In this work, we propose to make a step forward and investigate stance-based persona, trying to grasp more profound characteristics, such as opinions, values, and beliefs to drive language generation. To t…
University of Waterloo（滑铁卢大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）
For conversational AI and virtual assistants to communicate with humans in a realistic way, they must exhibit human characteristics such as expression of emotion and personality. Current attempts toward constructing human-like dialogue agents have presented significant difficulties. We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the personalities of fictional characters. Tropes are characteristics of fictional personalities that are observed recurrently and determined by viewers' impressions. By combining detailed…
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
Fudan University（复旦大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；Renmin University of China（中国人民大学）；Beijing University of Technology（北京工业大学）；Boston University（波士顿大学）；RhineAI（莱茵AI公司）；SenseTime（商汤科技）
The ability to infer persona from dialogue can have applications in areas ranging from computational narrative analysis to personalized dialogue generation. We introduce neural models to learn persona embeddings in a supervised character trope classification task. The models encode dialogue snippets from IMDB into representations that can capture the various categories of film characters. The best-performing models use a multi-level attention mechanism over a set of utterances. We also utilize prior knowledge in the form of textual descriptions of the different tropes. We apply the learned emb…
Persona-Pruner: Sculpting Lightweight Models for Role-Playing
Department of Artificial Intelligence, Korea University, Seoul, South Korea（韩国大学人工智能系）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Baidu Research（百度研究院）；Georgia Institute of Technology（佐治亚理工学院）；The City University of New York（纽约城市大学）
Understanding narrative content has become an increasingly popular topic. Nonetheless, research on identifying common types of narrative characters, or personae, is impeded by the lack of automatic and broad-coverage evaluation methods. We argue that computationally modeling actors provides benefits, including novel evaluation mechanisms for personae. Specifically, we propose two actor-modeling tasks, cast prediction and versatility ranking, which can capture complementary aspects of the relation between actors and the characters they portray. For an actor model, we present a technique for emb…
Princeton University（普林斯顿大学）；New Jersey Institute of Technology（新泽西理工学院）；Syracuse University（雪城大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Pattern Recognition Center, WeChat AI（微信人工智能模式识别中心）
We propose a new task for assessing machines' skills of understanding fictional characters in narrative stories. The task, TVShowGuess, builds on the scripts of TV series and takes the form of guessing the anonymous main characters based on the backgrounds of the scenes and the dialogues. Our human study supports that this form of task covers comprehension of multiple types of character persona, including understanding characters' personalities, facts and memories of personal experience, which are well aligned with the psychological and literary theories about the theory of mind (ToM) of human…
