Title: Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Role-playing

dialogue generation
Abstract: The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interaction. However, recent RPAs are often built on explicit dialogue data, lacking deep, human-like internal thought processes, resulting in superficial knowledge and style expression. While Large Reasoning Models (LRMs) can be employed to simulate character thought, their direct application is hindered by attention diversion (i.e., RPAs forget their role) and style drift (i.e., overly formal and rigid reasoning rather than character-consistent reasoning). To address these challenges, this paper introduces a novel Role-Aware Reasoning (RAR) method, which consists of two important stages: Role Identity Activation (RIA) and Reasoning Style Optimization (RSO). RIA explicitly guides the model with character profiles during reasoning to counteract attention diversion, and then RSO aligns reasoning style with the character and scene via LRM distillation to mitigate style drift. Extensive experiments demonstrate that the proposed RAR significantly enhances the performance of RPAs by effectively addressing attention diversion and style drift.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a Role-Aware Reasoning (RAR) method to enhance the role-play capability of LLMs. RAR comprises two parts: Role Identity Activation (RIA) and Reasoning Style Optimization (RSO). In RIA, a large reasoning model is required to generate responses based on the instruction and some key points for better role-playing, and then the model is trained on this data. In RSO, LRM first generates responses based on two scenarios: logical analysis and vivid interaction, and two system prompts: focusing on facts or character knowledge. After training, RAR surpasses baselines on SocialBench and CharacterBench, relieving the attention diversion and style drift problems.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. This paper has a clear structure and is well-written. \n2. The paper cleverly uses the response preferences of role-play agents in different scenarios to construct positive and negative sample pairs for comparative learning, which effectively improves the quality of role-play.\n3. The evaluation benchmarks and metrics in this paper are comprehensive, including memory consistency, fact accuracy, etc.\n\n**Weaknesses**\n1. There are doubts about the generability of RAR. The components of RIA, the scenarios and preferences in RSO are all based on human heuristics. Are they general? For example, if the figure being evaluated needs to perform logical analysis based on what he knows, will the negative samples in RSO no longer be useful?\n2. Data quality is not guaranteed. The authors first argue that LRM has problems with attention diversion and style drift, and then use LRM to generate training data. How to ensure that the data generated by LRM fully complies with the instructions? Additionally, there is no relevant evaluation of the quality of the RAR training data.\n3. The article lacks formulation for attention diversion and style drift. For attention diversion, why do you add some constraints to the prompt and train the model with the distilled data? Is there a comparison with the method of simply rewriting the prompt to prove the necessity of training and distilling? For style drift, the author just manually delineates two styles for training, and actually, the styles are difficult to completely classify. Is the comparative learning of the two styles general enough? In addition, if not doing distillation (for example, using the model's own responses in RSO), can RAR surpass other methods?\n\nQUESTIONS:\n1. What is the LRM model used to create data? Do Distill and RAR use the same LRM?\n2. What is the number of training samples of the RAR and Distill methods? Does RSO use all the data to construct the comparison pairs?\n3. What do the horizontal and vertical axes of Figure 3 represent? Are there any quantitative metrics that can show that there is a difference between the two distributions? It seems that the overlap of (c) is also very high. Does the difference between the two distributions in Figure 3 mean that LLM performs well in both scenarios?\n4. If the content and number of training data are different, is it fair to compare RAR with Neeko and CharacterGLM?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel method, Role-Aware Reasoning (RAR), to improve the performance of Role-Playing Agents (RPAs). The authors identify two key failure modes in existing systems: \"attention diversion\" (the model forgets its role) and \"style drift\" (the model's reasoning is overly formal). The proposed RAR method consists of two main stages: Role Identity Activation (RIA), which uses character profiles to maintain focus, and Reasoning Style Optimization (RSO), which uses distillation to align the model's reasoning style with the character and context. The primary contributions are the RAR method itself, the specific techniques of RIA and RSO to counteract attention diversion and style drift, and extensive experiments demonstrating that this approach significantly improves role-playing performance on established benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n#### **Strengths**\n\n1.  **Clear Problem Formulation and Novel Solution:** The paper clearly identifies a significant gap in existing research—the lack of deep, character-consistent thought processes in RPAs—and proposes a novel and intuitive solution (RAR) to address it. The conceptual separation of role identity (RIA) and reasoning style (RSO) is a strong architectural choice.\n\n2.  **Robust Experimental Design and Baselines:** The authors conduct a thorough evaluation using a strong and well-selected set of baseline models. The baselines cover foundational methods (`Vanilla`), alternative approaches (`RAG`), state-of-the-art competitors (`Neeko`, `Character-GLM`), and crucial ablations (`Distill`, `Thinking Modes`) that effectively isolate the impact of the paper's core contributions. This rigorous comparison strongly supports the claims of `RAR`'s effectiveness.\n\n3.  **Strong Performance on Established Benchmarks:** The paper's primary claims are supported by strong empirical results on independent, public benchmarks (`CharacterBench` and `SocialBench`). As these benchmarks rely on more objective measures (e.g., multiple-choice questions in `SocialBench`) or their own validated judge models (`CharacterBench`), the results presented in Tables 1 and 2 are methodologically sound and demonstrate a significant performance improvement.\n\n---\n\n#### **Weaknesses**\n\n1.  **Methodological Flaw in Reasoning Trace Evaluation:** The \"Reason Trace Evaluation\" (Section 4.4, Table 4) is methodologically weak. The authors use a GPT-4 auto-rater but do not provide any validation for it. There is no reported correlation with human judgments, which is the standard for such an evaluation, nor is there mention of manual inspection of the auto-rater's output. This lack of rigor makes the claims about the qualitative superiority of `RAR`'s reasoning traces (e.g., coherence, relevance) unsupported and unreliable.\n\n2.  **Inconsistent Analysis of Ablation Study Results:** The textual analysis of the ablation study (Section 4.3) oversimplifies and misrepresents the data in Table 3. The paper claims RIA is the key driver for all persona consistency, but the data shows a more nuanced picture where the RSO-only model (`RAR w/o RIA`) actually outperforms the RIA-only model (`RAR w/o RSO`) on human-side consistency metrics. This inconsistency between the text and data points to a flaw in the authors' analysis of their own results.\n\n3.  **Presentation and Reporting Issues:**\n    * The captions for Table 1 and Table 2 have been swapped, which is a significant typographical error that could confuse readers.\n    * The paper does not explain how the metrics for the evaluation benchmarks (`CharacterBench`, `SocialBench`) are calculated, instead relying on readers to consult the original papers. While common, this makes the paper less self-contained.\n\n4.  **Missed Context in Related Work:** While the related work section is adequate, it could have been more comprehensive. It misses some recent and conceptually similar work in areas like cognitive architectures (e.g., `MIRROR`) and advanced persona control (e.g., `PCL`, `Activation Engineering`). Acknowledging these would have better positioned the work at the cutting edge of the field.\n\nQUESTIONS:\nThank you for your work on this interesting and important problem. Your proposed RAR method shows compelling results on the primary benchmarks. I have a few key questions and suggestions that, if addressed, could clarify some of the paper's weaknesses and potentially improve my evaluation of its overall quality and clarity.\n\n**1. On the Validation of the \"Reason Trace Evaluation\"**\n\n* The evaluation of the reasoning traces in Section 4.4 relies entirely on a prompted GPT-4 auto-rater. While the prompts are transparently provided, there is no validation of this evaluation method itself. As LLM-based judgments can be unreliable without calibration, this undermines the confidence in the results presented in Table 4.\n* **Question:** Did you perform any correlation studies between the GPT-4 scores and human judgments, even on a small subset of the data? If so, providing these correlation scores (e.g., Pearson or Spearman) would significantly strengthen this section.\n* **How this could change my score:** Providing strong correlation data (e.g., r > 0.7) with human annotators would substantially increase my confidence in this part of your analysis, likely raising the **Quality** score. If no such data is available, could you elaborate on why this validation was omitted and how you ensured the reliability of the auto-rater's judgments?\n\n**2. On the Inconsistency in the Ablation Study Analysis**\n\n* There appears to be a direct contradiction between the textual analysis of the ablation study (Section 4.3) and the data in Table 3. The text suggests that the `RIA` module is the primary driver of all persona consistency metrics. However, the data shows that the `RSO`-only model (`RAR w/o RIA`) actually performs better on human-side consistency metrics ($AC^h$ and $BC_P^h$).\n* **Question:** Could you please clarify this discrepancy? Is the textual summary an oversimplification, or is there a more nuanced interpretation of why the RSO module appears to boost human-side persona consistency more than the RIA module does?\n* **How this could change my score:** A convincing explanation of this phenomenon would demonstrate a deeper understanding of the interactions between your model's components. A revised, more accurate analysis of these results in the text would improve the paper's **Clarity** and **Quality** scores.\n\n**3. On the Swapped Table Captions and Reporting Clarity**\n\n* As noted in the weaknesses, the captions for Table 1 and Table 2 appear to be swapped, with Table 1 showing results for `CharacterBench` while being labeled `SocialBench`.\n* **Question:** Could you please confirm if this is a typographical error? Additionally, to improve the paper's self-contained clarity, would you be willing to add a brief subsection in the appendix that summarizes the calculation methods for the key metrics from `CharacterBench` and `SocialBench`, even if just at a high level?\n* **How this could change my score:** Correcting the table captions is a necessary fix for the final version. Adding a brief explanation of the metric calculations would not change my overall recommendation but would significantly improve the **Clarity** score of the paper, making it more accessible to readers not deeply familiar with those specific benchmarks.\n\nLIMITATIONS:\nThe authors have included a dedicated \"Limitations\" section (Appendix C) and an \"Ethical Statements\" section (Appendix D) that address these points.\n\nHowever, a more thorough discussion would improve the paper. My suggestions are:\n\n* **Refining Technical Limitations:** The current limitations section is somewhat generic (e.g., performance depends on the teacher LRM, evaluation is an ongoing research area). A more insightful discussion would connect the limitations directly to the proposed `RAR` architecture. For example, acknowledging that the binary distinction between \"logical\" and \"vivid\" scenarios for RSO training is a simplification and discussing how the system might handle mixed-mode conversations would be a valuable addition.\n\n* **Expanding on Potential Negative Societal Impact:** The \"Ethical Statements\" section correctly notes that the work relies on public datasets and that base models incorporate safety measures. However, a more proactive discussion would be beneficial. The authors could elaborate on the specific risks posed by highly consistent and believable role-playing agents, such as their potential for use in creating sophisticated disinformation, enabling parasocial relationships that are emotionally manipulative, or facilitating fraud through impersonation. Discussing potential mitigation strategies beyond relying on existing safety protocols would also strengthen this section.\n\n* **Suggestion for Future Work (Dynamic Personas):** The paper focuses on maintaining static character personas. A key missing point in the limitations/future work discussion is the challenge of **dynamic character development**. The authors could acknowledge that real human interaction leads to character evolution and that endowing their agents with the ability to change over time based on long-term memory is a significant and important next step for the field.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n## Paper Summary\n\nThe paper proposes Role-Aware Reasoning (RAR) to improve Role-Playing Agents (RPAs) by explicitly modeling internal character-consistent thinking. RAR consists of two key modules: Role Identity Activation (RIA), which injects character traits (e.g., emotion, motivation) to maintain role awareness, and Reasoning Style Optimization (RSO), which guides the model to generate context-appropriate reasoning styles. Experiments on CharacterBench and SocialBench show that RAR outperforms prior methods by reducing attention diversion and style drift, resulting in more consistent and believable in-character responses.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n\n1. The paper highlights two issues that show up a lot in role-playing agents: they either lose track of their character (attention diversion) or sound too formal and generic (style drift). These problems are pretty common in practice, but not many papers tackle them directly, so I appreciate that the authors make them central to their motivation.\n\n2. The method splits reasoning into two parts—role grounding and style control—which makes the idea easier to follow. RIA keeps the model thinking in-character, and RSO helps adjust the reasoning style based on context. I think this separation is helpful, especially for understanding what each part of the method is doing.\n\n3. The experiments are solid and cover both character and social benchmarks, with some nice ablations and analysis. I found the reasoning trace evaluations and the visualizations useful—they actually help explain how the model improves, not just that it gets better scores.\n\n## Weakness\n\n1. Not very different from Theory of Mind work: The Role Identity Activation (RIA) module adds elements like emotion, motivation, standpoint, and experience into the reasoning process, but to me, this feels quite similar to what’s already being explored in theory of mind area (I know they are different things). It's not entirely clear how this approach is conceptually new or different.\n\n2. Still limited by teacher model quality: Since the method relies on distillation from a large reasoning model, it inherits both the strengths and weaknesses of the teacher. I get that distillation is widely used, but it does mean the performance ceiling is largely determined by how good the teacher model is—which feels like a fundamental limitation.\n\n3. Character modeling could be more detailed: The character setup includes a few fixed traits, but overall it still feels a bit shallow. For more complex or dynamic characters, this level of modeling probably isn’t rich enough—there’s no handling of things like shifting identities, layered personalities, or more subtle behavioral traits.\n\nQUESTIONS:\nSee the weakness part.\n\nLIMITATIONS:\nThe authors have discussed the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a Role - Aware Reasoning (RAR) method aiming to address the issues of attention diversion and style drift in Role - Playing Agents (RPAs) when using Large Reasoning Models (LRMs). Traditional RPAs lack deep, human - like internal thought processes, and direct application of LRMs leads to problems such as the model forgetting its role or generating overly formal reasoning.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. Clearly targets the two core challenges (attention diversion and style drift) in the application of LRMs in RPAs, and proposes a structured solution with practical application value.\n2. The design of RIA and RSO combines character feature activation and scene - based style optimization, achieving end - to - end character - consistent reasoning through distillation and contrastive learning. The method is novel and logically self - consistent.\n3. Evaluates using multiple authoritative benchmarks (CharacterBench, SocialBench), compares with multiple baseline methods, and validates the effectiveness of the modules through ablation experiments and case studies, making the results highly reliable.\n\nWeaknesses:\n1. RAR is implemented through LRM distillation, but the computational cost of the reasoning process (such as the efficiency of long - sequence generation) is not discussed. It may face performance challenges in practical deployment.\n2.  RIA relies on automatically extracted core character elements (emotions, motivations, etc.), which may not be able to depict complex characters (such as multi - faceted personalities) in sufficient detail and may require manual intervention for optimization.\n3. The predefined scenario types (logical analysis, vivid interaction) in RSO are relatively basic, and there is a lack of verification of adaptability to more complex situations (such as cross - cultural social interactions, emotional conflicts).\n\nQUESTIONS:\n1. Relationship between reasoning efficiency and model scale:\nThe paper uses LLaMA - 3 - 8B as the base model. If it is extended to larger - scale models (e.g., 70B parameters), will the reasoning efficiency of RAR significantly decline? Are there any optimization strategies for model compression or parallel reasoning? It is recommended to supplement performance comparison experiments under different model scales or explain the adaptability of the method in efficient reasoning scenarios.\n2. Ability to support dynamic character development:\nDoes the existing RAR support the dynamic growth of characters in multi - turn dialogues (such as a character evolving from a \"shy teenager\" to a \"confident hero\")? For example, if a character's personality changes with experience, how does RIA update the character features? It is suggested to explain the support mechanism of the method for dynamic character modeling or supplement relevant experiments in future work.\n3. Feasibility of cross - language character adaptation:\nThe experimental data are mainly based on English - speaking characters (such as 95 English - speaking characters in RoleBench). When applied to Chinese or other language characters, does the prompt engineering and style optimization of RAR need to be adjusted? Are there any preliminary verification results in cross - language scenarios? It is recommended to supplement the analysis or experiments of cross - language adaptation to enhance the universality of the method.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}