Challenge: Existing methods focus on knowledge and linguistic patterns of characters.
Approach: They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits.
Outcome: The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits.

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EmoCharacter: Evaluating the Emotional Fidelity of Role-Playing Agents in Dialogues (2025.naacl-long)

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Challenge: EmoCharacter evaluates emotional fidelity of role-playing agents in dialogues . current evaluations focus on personality fidelity, tone imitation, and knowledge consistency .
Approach: They propose a benchmark to assess emotional fidelity of role-playing agents in dialogues using large language models.
Outcome: The proposed benchmark measures emotional fidelity of role-playing agents and the characters they portray.
Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data (2024.findings-emnlp)

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Challenge: Existing role-playing models focus on character knowledge and tones, but lack personality-indicative data to capture characters' minds.
Approach: They propose to enhance role-playing agents (RPAs) via personality-indicative data by asking psychological scales to capture broad aspects of personality traits in individuals.
Outcome: The proposed model exhibits advanced role-playing capabilities for both general and personality-related evaluations.
Towards a Design Guideline for RPA Evaluation: A Survey of Large Language Model-Based Role-Playing Agents (2025.findings-acl)

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Challenge: Role-Playing Agents (RPAs) are increasingly popular due to diverse task requirements and agent designs.
Approach: They propose an evidence-based evaluation design guideline for LLM-based RPAs based on agent attributes, task attributes, and evaluation metrics.
Outcome: The proposed evaluation design guideline is based on a systematic review of 1,676 papers published between Jan. 2021 and Dec. 2024.
Investigating the Personality Consistency in Quantized Role-Playing Dialogue Agents (2024.emnlp-industry)

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Challenge: Using the Big Five personality traits model, we evaluate how stable assigned personalities are for Quantized Role-Playing Dialog Agents (QRPDA) during multi-turn interactions.
Approach: They propose a non-parametric method to evaluate the stability of assigned personalities in quantized large language models (LLMs) for role-playing scenarios.
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CharacterCraft: Bridging the Literature-Reality Dialogue Gap for Practical Role-Playing Agents (2025.findings-emnlp)

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Challenge: Existing dialogue datasets have a bias between query distributions and real-world user language usage.
Approach: They propose a framework for Chinese role-playing and a robust evaluation method . they propose specialized Chinese dialogue extraction model and specialized memory retrieval module .
Outcome: The proposed framework extracts character dialogue from novels and ensures high data quality.
PersonaGym: Evaluating Persona Agents and LLMs (2025.findings-emnlp)

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Challenge: Persona agents are LLM agents conditioned to act according to an assigned persona . evaluating how faithfully these agents adhere to their personas remains a challenge .
Approach: a new study evaluates persona agents' ability to act according to an assigned persona . a persona agent's person score is a human-aligned automatic metric that can be used to evaluate a model .
Outcome: a new evaluation framework and a human-aligned automatic metric show that persona agents can perform better.
Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works (2024.emnlp-main)

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Challenge: Recent advances in large language models (LLMs) have catalyzed numerous AI applications, among which role-playing agents (RPAs) are particularly popular.
Approach: They propose to evaluate LLMs' character understanding capability via the character profiling task, i.e., summarizing character profiles from corresponding materials, a widely adopted yet understudied practice for RPA development.
Outcome: The proposed model outperforms existing models and literature summarization methods and proves its ability to understand fictional characters in downstream tasks.
ThinkPersona: Thinking with Persona Graphs for Faithful Individualized Role-Playing (2026.acl-long)

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Challenge: Large Language Models are increasingly utilized as role-playing agents to simulate personas in interactive settings.
Approach: They propose a role-playing agent trained to explicitly ground responses in individual identity.
Outcome: The proposed agent can generate persona-consistent responses in long-context dialogues while maintaining general instruction-following capabilities.
Modeling, Evaluating, and Embodying Personality in LLMs: A Survey (2025.findings-emnlp)

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Challenge: This survey provides a comprehensive overview of the LLM-driven personality scenario.
Approach: This survey provides a comprehensive overview of the LLM-driven personality scenario.
Outcome: The proposed taxonomy analyzes the limitations of existing methods and identifies key research gaps.
CharacterGPT: A Persona Reconstruction Framework for Role-Playing Agents (2025.naacl-industry)

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Challenge: Maintaining consistent character personas remains a significant challenge due to variability in information extraction.
Approach: They propose a framework to dynamically reconstruct character personas through Character Persona Training.
Outcome: The proposed framework is evaluated through Big Five personality evaluations and creative tasks, in which characters generate original narratives.

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