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.

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InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)

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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.
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.
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.
RAIDEN Benchmark: Evaluating Role-playing Conversational Agents with Measurement-Driven Custom Dialogues (2025.coling-main)

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Challenge: Existing benchmarks for RPCA evaluation are lacking for dialogues . authors introduce specialized judging LLM for automatic RPca evaluation . compelling role-playing agent is expected to lead to more in-depth conversations .
Approach: They propose a benchmark to assess the effectiveness of RPCA interactions using dialogues . they introduce a specialized judging LLM tailored for automatic RPca evaluation .
Outcome: The proposed benchmark focuses on assessing particular dimensions at different stages of a conversation, facilitated through interactions conducted by annotators.
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)

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Challenge: Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance.
Approach: They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 .
Outcome: The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs.
CharacterBox: Evaluating the Role-Playing Capabilities of LLMs in Text-Based Virtual Worlds (2025.naacl-long)

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Challenge: Evaluating role-playing capabilities in large language models is challenging due to complex dynamics involved in role-playering.
Approach: They propose a simulation sandbox that generates situational fine-grained character behavior trajectories to enhance LLM performance.
Outcome: The proposed model generates situational fine-grained character behavior trajectories to enhance performance.
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.
RoleCDE: Benchmarking and Mitigating Role–Alignment Trade-offs in Role-Playing Agents (2026.findings-acl)

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Challenge: Existing benchmarks for role-playing agents only evaluate surface-level fidelity and provide limited insight into decision making under role–alignment value conflicts.
Approach: They propose a benchmark to evaluate RPAs under role–alignment value conflicts . they use 8k diverse role profiles and 240k dilemma instances to evaluate role-aware decision making .
Outcome: The proposed benchmark covers 8k diverse role profiles and scenarios and nearly 240k dilemma instances across three difficulty levels and eight role categories.
ChatAnime: Towards User-Centered Emotional Support in LLM-based Virtual Character Chat (2026.acl-long)

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Challenge: Existing research focuses on character consistency in fictional or game-based scenarios . ESRP framework is designed to align role-playing with real-world user scenarios based on emotional needs.
Approach: They propose a framework to align role-playing with real-world user scenarios and emotional needs.
Outcome: The proposed framework aligns role-playing with real-world user scenarios and emotional needs.
CharacterEval: A Chinese Benchmark for Role-Playing Conversational Agent Evaluation (2024.acl-long)

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Challenge: CharacterEval is a benchmark for comprehensive RPCA assessment in Chinese . authors show that Chinese LLMs exhibit more promising capabilities than GPT-4 in role-playing conversation.
Approach: They propose a Chinese benchmark for comprehensive RPCA assessment . they use a dataset of Chinese role-playing dialogues and character profiles .
Outcome: The proposed benchmark demonstrates that Chinese LLMs exhibit more promising capabilities than GPT-4 in Chinese role-playing conversation.

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