Persona Design and Applications

Neueste Paper

• Unlocking Latent Personalization in LLMs

  Wei Chen, Guanghui Zhu, Zhongliang Cai, Yihua Huang · 29. September 2026

  Large language models (LLMs) are increasingly expected to adapt to individual users, yet effective personalization remains challenging when only limited user-specific samples are available. In this work, we take an alternative perspective: pretrained LLMs may already possess latent capacity for pers…

• Latent Class Analysis of Digital Content Use Contexts in AI-Generated Synthetic Personas

  Eunjeong Song, Sehee Hong · 29. September 2026

  AI-generated synthetic personas are increasingly used for content planning and virtual-user simulation, yet the digital content use contexts embedded in their narratives remain underexamined. Using all 1,000,000 records of NVIDIA's Nemotron-Personas-Korea, this study coded mentions of five engagemen…

• His Name, Their Judgment: Expert Authority in Posthumous Persona AI

  Hanjing Shi, Dominic DiFranzo · 29. September 2026

  Persona AI can make deceased experts available for decisions they never encountered. Users may seek these personas precisely because they lack the knowledge needed to judge their advice. We thematically analyze 115 focal RedNote/Xiaohongshu posts and a nested comment sample concerning unofficial per…

• Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs

  Sang Bin Moon, Nicole Cho, Daniel Borrajo, Sumitra Ganesh, Abolfazl Hashemi · 28. September 2026

  Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singular and potentially suboptimal decision. We formulate persona diversification as a set-level conditioning problem and study two orthogonal design choi…

• Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content

  Ljubisa Bojic, Tijana Stanic, Joerg Matthes, Agariadne Dwinggo Samala, Bojana Dinic, Jue Wang · 28. September 2026

  Platform policies are increasingly tested on artificial users, making agent fidelity important. Yet convincing fake profiles could also manipulate perceived public opinion before elections. Validation has concentrated on agreement with human behaviour and has paid little attention to whether an agen…

• From Static Personal Values to Contextualized Personalization: Bayesian Personalized Value Alignment for LLMs

  Hanze Guo, Aixuan Song, Jing Yao, Xiangxu Zhang, Xiaoyuan Yi, Xing Xie, Xiao Zhou · 25. September 2026

  Personalized value alignment has become increasingly important as large language models (LLMs) are expected to accommodate diverse user preferences. However, existing methods typically align model outputs with a static value profile across prompts, overlooking that the salience of value dimensions v…

• PERSONAWEAVER: Controllable Diversity Beyond Conventional Archetypes in Procedural Character Generation

  Maan Qraitem, Kate Saenko, Bryan A. Plummer · 23. September 2026

  Procedural character generation aims to populate games, simulations, and other virtual worlds with diverse characters. Large language models (LLMs) offer a promising foundation for scaling this task. However, LLM-based procedural character generation remains at an early stage: existing methods eithe…

• Testing, not presuming, adequacy: calibrating generative social simulators against emergent network structure

  Tengfei Shao, Chao Li, Xu Wang, Masayuki Goto · 23. September 2026

  Validation of generative social simulators often stops at face validity: emergent network structure is compared descriptively, without quantified parameter uncertainty or an adequacy check. We present an adequacy-aware calibration protocol that couples amortized posterior estimation with a synthetic…

• Do Synthetic Personas Predict Real Audience Response? A Sim-to-Real Study Where a No-Persona Baseline Beats Persona-Based Copy Simulation

  Alexandre Cristov\~ao Maiorano · 23. September 2026

  Marketers increasingly use large language models (LLMs) as "synthetic personas" to predict how an audience will react to a piece of copy before it ships, encouraged by evidence that profile-conditioned LLMs mimic human samples. But is that prediction actually valid against real behaviour - and does …

• Measuring the Assistant's Harmlessness Preferences on the User Turn

  Jord Nguyen · 22. September 2026

  Post-training turns a general next-token predictor into a chat model with a persistent assistant persona. If that persona is a character the model plays only on its own turns, its preferences should govern what the assistant says, not what the model predicts other speakers will say. We test this bou…

• Pretrained Persona Mixture Models and Tandem Models for Human Simulation

  Minwoo Kang, T\'ea Wright, Seun Eisape, Ayush Raj, Suhong Moon, Joseph Suh, Alane Suhr, David M. Chan, John Canny · 22. September 2026

  We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas, is inaccurate and produces stereotyped predictions (lacking natural diversity). It has previously been shown that LLMs can be bound to personas using…

• Deep Persona: A Psychologically Grounded Architecture and Evaluation Framework for Role-Playing Agents and Simulations

  Rotem Dror, Zohar Elyoseph, Yuval Haber, Elad Refoua, Oshrat Ayalon, Adir Solomon · 22. September 2026

  Existing approaches to persona simulation with Large Language Models (LLMs) mostly rely on shallow character descriptions that fail to sustain coherent character behavior across extended interactions. We introduce Deep Persona, a psychologically grounded, three-layered architecture that organizes pe…

• The Situated Identity Test: Distinguishing Persistent Cognitive Identity from Persona Imitation

  Jun He, Deying Yu · 22. September 2026

  Large language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals a fundamental attribution problem: looking the part does not mean having lived the life. Two individuals can share identical public profiles--the s…

• Recognition, Simulation, and Refusal: A Contamination-Aware Study of Classic Psychological Effects in LLM Agents

  Joy Bose · 22. September 2026

  An LLM producing the response pattern associated with a human psychological effect is not the same claim as the LLM possessing that bias. We present PsyAgentBench, a benchmark that re-runs classic psychology experiments on LLM agents under a factorial design built to separate these: each paradigm is…

• Evaluating Personal Information Output from Conversational Interactions in Generative AI Systems

  Yosuke Seki, Hirotaka Tahara · 22. September 2026

  This exploratory pilot study evaluates the scope and perceived accuracy of personal information output from ongoing conversational interactions in generative AI systems using GPT-5.2 Instant and GPT-5.2 Thinking, categorized into three output types: Fact, Inference, and Confidence. Based on the eval…

• Do Personality-Tuned LLMs Make Better Social Agents?

  Tim Krabbe, Xiaodan Shi · 21. September 2026

  LLMs are increasingly used in social simulations for socially interactive agents and robots, offering more flexibility than rule-based systems. However, even though they mimic human behaviour very well, there is a persistent alienness to them. This work investigates whether personality-aware fine-tu…

• From Memory to Behavior: A Behavior-Aware Role-Playing Framework for Social Media Influencers

  Ji-Lun Peng, Yi-Zhen Zhang, Chun-Nan Chou, Yun-Nung Chen · 21. September 2026

  Large language models have shown strong potential as role-playing agents for real individuals, yet faithful impersonating remains challenging. Existing in-context learning-based methods fail to capture how individuals react under different situations. In addition, LLM-based evaluation is difficult f…

• Tailored to you: longitudinal effects of personalising language models

  Canfer Akbulut, Justine Breuch, Arianna Manzini, Lujain Ibrahim, Matija Franklin, Roma Patel, Iason Gabriel, Kristian Lum, Laura Weidinger · 18. September 2026

  Interest in developing personalised language models is rapidly growing. While personalisation is often viewed as a mechanism to better serve diverse user needs, the effects of sustained interactions with personalised models on people's perception of and behaviour toward AI remain poorly understood. …

• RoleBreak: Benchmarking Long-Horizon Role-Playing Robustness in Spoken Dialogue

  Yuqi Wang, Fengyuan Liu, Haochen Luo, Zhiqi Yu, Qi Liu · 16. September 2026

  Speech-to-speech dialogue models increasingly support persona control, yet existing spoken role-playing benchmarks remain largely character-centric and short-horizon. This leaves open whether spoken dialogue models can sustain diverse roles over extended interactions, especially beyond predefined fi…

• Creating an Atomic User Model for Personality-Aware Large Language Model Interaction

  B. Sankar, Deepthika S, Pawni Yadav, Amogh A S · 14. September 2026

  Assistants built on large language models are expected to write as their user would, and the dominant approach is single-channel: preferences summarised from conversation history and reinserted into context. This inverts the order of inference. Preferences are the task-dependent surface of a compara…

• Enabling and Understanding Personalization in AI-Generated Advertising Imagery

  Victor Kolominsky-Rabas, Leopold M\"uller, Claudius Budcke, Claas Christian Germelmann, Niklas K\"uhl · 14. September 2026

  Personalized marketing traditionally matches static products to customers, while dynamic creative optimization focuses mainly on AI-driven text personalization or basic product image modifications. We address this gap by developing and implementing an AI-based framework that generates personalized a…

• I Am AdMan: A Pipeline for Automatic Generation of Personalized Advertising Imagery

  Victor Kolominsky-Rabas, Leopold M\"uller, Claudius Budcke, Niklas K\"uhl · 14. September 2026

  Personalized marketing can increase customer engagement, satisfaction, and conversion. While existing personalization approaches have become effective at matching the right product to the right customer, the visual representation of advertisements remains generic and only weakly tailored to the indi…

• Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue

  Youyuan Zhang, Siyuan Li, Fangming Liu, Jing Li · 14. September 2026

  Persona drift remains a central challenge for personalized language models, as user profiles evolve over long interactions rather than remain permanently fixed. Models must therefore revise persistent persona states when preferences genuinely change, while avoiding updates driven by transient, ambig…

• Story Imprinting: AI Assistants Absorb Traits from Human Characters They Resemble

  Jorio Cocola, Lev McKinney, Harry Mayne, Jan Betley, Owain Evans · 11. September 2026

  Language models are trained to implement a helpful AI Assistant character (e.g., Claude). We explore how finetuning on synthetic stories affects this character. Does it change the Assistant's behavior in multi-turn conversations with users, a format quite different from the stories? And does the Ass…

• K/V-Cache Interventions Dissociate Representation Alignment from Persona Expression in Decoder-Only Language Models

  Yu Sun, Mengyin Lu, Cong Feng, Guangming Lu, Huimin Han · 11. September 2026

  We study K/V-cache interventions -- transplanting a target-conditioned K/V trajectory into a source-persona generation -- as a structured surface for persona control in decoder-only language models. Across 13 intervention configurations applied to Llama-3.1-8B for a fixed source-to-target persona pa…