Challenge: Existing research has focused on role-playing agents’ ability to portray specified characters, but their ability to advance the plot requires substantial improvements to deliver more engaging interaction.
Approach: They propose a role-playing framework to evaluate and enhance the plot-progression capabilities of role-players.
Outcome: The proposed framework improves RPAs’ ability to time plot developments and yields a significant increase in conversation turns and sustained higher arousal levels.

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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.
Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents (2025.emnlp-main)

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Challenge: Existing approaches to RPAs focus on static role profiles, overlooking dynamic perceptual abilities inherent to humans.
Approach: They propose a framework that combines adaptive temporal sampling with dynamic and static role profiles.
Outcome: The proposed framework combines adaptive temporal sampling with dynamic and static role profiles.
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.
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.
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.
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.
Towards a Progression-Aware Autonomous Dialogue Agent (2022.naacl-main)

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Challenge: Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios.
Approach: They propose a framework in which dialogue agents can evaluate the progression of a conversation toward or away from desired outcomes and use this signal to inform planning for subsequent responses.
Outcome: The proposed framework evaluates the progression of a conversation toward or away from desired outcomes and uses this signal to inform planning for subsequent responses.
RPTCS: A Reinforced Persona-aware Topic-guiding Conversational System (2023.eacl-main)

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Challenge: Existing systems that control concept transitions in a conversation lack a persona-aware topic transition dataset.
Approach: They propose a persona-aware topic-guiding conversational system that leads the conversation to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation.
Outcome: The proposed system produces fluent responses with no useful information and is based on a conversational dataset with a human-in-loop only quality checks.
Interactive Plot Manipulation using Natural Language (2021.naacl-demos)

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Challenge: a new interactive plotting agent is available for programming with natural language . the interactive aspect allows users to manipulate plots using natural language instructions.
Approach: They propose an interactive natural language interface for plotting that maps language to plot updates.
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PersonaArena: Dynamic Simulation for Evaluating and Enhancing Persona-Level Role-Playing in Large Language Models (2026.findings-acl)

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Challenge: Existing research focuses on character-level settings and static evaluation formats fail to capture the complexity of everyday social interactions.
Approach: They propose a dynamic simulation framework for evaluating and improving persona-level role-playing in large language models (LLMs).
Outcome: The proposed framework leverages user-generated social content to construct a nuanced persona bank and elicits multi-turn, context-rich interactions within simulated social environments.

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