Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
Authors: Yihong Tang
Kehai Chen
Muyun Yang
Zheng-Yu Niu
Jing Li
Tiejun Zhao
Min Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Role-playing
dialogue generation
Primary area: applications

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.

Thinking in Character: Advancing Role-playing Agents with Role-Aware Reasoning
Yihong Tang Kehai Chen Muyun Yang', Zhengyu Niu
Jing Li², Tiejun Zhao', Min Zhang'
Harbin Institute of Technology, Shenzhen, China
2henzhen Loop Area Institute (SLAT), Shenzhen, China BBaddu Inc., Beijing, China (toyhom@stu. wit.hittee. edu. cn, chenkehai @hit edu. cn}
Abstract
The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such emotional compan- ionship 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-consisterr reason- ing). To address these challenges, this paper introduces novel Role-Aware Reasoning (RAR) method, which consists of two important stages: Role Iden- tity Activation (RTA) 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. Our code is publicly available at https://gi t/ghhub.//ttttu../. thub..b.aaa cow wwm.Toyoom////oooooooooooooo weathhommthiiiiine tettttiinnniggi in character
Introduction
With the advancement of Large I Language Models (LLMss, Role-Playing Agents (RPAs) 1 have garnered significant attention for applications such as emotional companionship 12] and virtual interaction [3]. Many RPAs attempt to explicitly integrate rich role-playing dialogue datasets [4], diverse interaction processes (5), and the inherent generalization capabilities LLMs 6). yielding promising results.
Despite their success, existing methods often focus merely on superficial knowledge and style expression in responses, with models lacking deep. human-like internal thought processes. Large Reasoning Models (LRMs), such as GPT-o series S or Deepseek-RI
9 can be utilized to generate structured reason traces to simulate character's ss thought process, thereby addressing this gap. However, Feng et [10] observed that reasoning methods do not effectively improve the performance of RPAs under certain circumstances. As illustrated in Figure [ the primary reasons for this degradation are attention diversion and style drift. Firstly, existing LRMs tend to forget their designated role, concentrating instead the task problem-solving. This diminishes their focus on the role-playing task, leading to attention diversion. Secondly, they prioritize the generation of
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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structured, logical, and formal reasoning processes. rather than the vivid and consistent self peffeeereetive style required for role-playing resulting in style drift. These two challenges lead to a rigid thought, as illustrated in the upper right corner of Figure | resulting in the model generating responses inconsistent with the role.
Rigid Thought Year
Peter Parker, how do you balance your personal life and superhero responsibi- lifies? User Input
The you just soil
Attention Diversion
guery. State event.
LARGE REASON MODEL
Canclnal
Style Drift
It's an imper system. There have bean instaroes of missed.. The core dis to maintan connection to cnn''s humenllyy
Role-Aware Reasoning Distillation
Inconsisfent Response
Role Identity Activation (RIA)
I'm Spider-Mon!!!
Reasoning Style Optimization (RSO) K'Logic Nistory
Emotions
Experiences
Standpoints
Motivations
Style Core
Cract
Choose
Reject
Focus
LARGE LANGUAGE MODEL
Festures
CKnos
Reject
Choose
Context
Character Proffle(Peter Parker)
Personality: Quick wilted,
I aam...
Balancing? It's like trying to juggla hamewark,, a lab fleeing the (sighs, runs hand through hair)... Use my spider- sense to, uh, "estmate" how bad a situation is before dropping averything....MM
intelligent..
The person just said...
report,
Growth:
Shy teen, confident hero.
First. feel...
Catchphrase: great power
Second, based experieece/knowledee Then, to consider...
you'll bell's about ring. and heard something suspicious near the science lab... (deshes off, leaving faint trail webbing)
me,
comes*
Speech Style: informal, witty. self- deprecating...
So, I'm planning to...
Role-Aware Thought Yool
High-Fidelity, In-Character Response
Figure 1: Overview of the proposed RAR. Given user query, LRM generates structured thoughts. However, traditional reasoning may suffer from attention diversion and style drift, leading to generic, out-of-character responses. To address this, our method incorporates RIA and RSO. RIA activates key role traits (e.g., emotions, motivations) to distill role-consistent thoughts. RSO guides the model to generate reasoning traces in suitable styles depending on context, enabling dynamic control over logical and narrative expression.
To address these challenges, this paper introduces a novel Role-Aware Reasoning (RAR), designed to imbue LLMs with the capacity for deep thinking that aligns with character settings. Firstly, RIA aims to convert the character's 's core features (such as personality, background, and manner of speech) into explicit, rule-like prompts guide the model think in a manner consistent with the character. Secondly, RSO utilizes specific system prompts to guide the LRM to generate reasoning traces that either align with (i.e., positive examples) or deviate from (i.e., negative examples) the requirements of specific scenarios. Subsequently, through contrastive learning, RSO enables the model to adjust the expression style of internal thoughts based on the current dialogue context. Ultimately, the model adhere to the various settings stipulated in the role- role-playing requirements and dynamically switch between rigorous logic and vivid portrayal, thereby alleviating attention diversion and style drift.
(1) We design an innovative Role-Aware Reasoning (RAR) method that effectively transfers the capabilities of Large Reasoning Models to role- playiigyn (2) Within our RAR, we introduce Role Identity Activation (RIA) to counteract attention diversion by enhancing role self-awareness, and Reasoning Style Optimization (RSO) to mitigate style drift through contextual style adaptation; (3) Extensive experiments demonstrate that RAR significantly improves role ooe-playing performance by endowing agents with reasoning capabilities tailored their characters.
Our contributions are summarized as follows:

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2 Related Work
2.1 Role-Playing Agents with Large Language Models
The development of Large Language Models (LLMs) has significantly advanced research on Role- Playing Agents (RPAs) [.] Early works [HSS primarily utilized the in-context learning (ICL) [66] capabilities of LLMs, guiding models to portray specifie roles through prompt engineering or by providing few-shot examples. Concurrently, researchers have synthesized data using more powerful teacher models and extracted dialogues from existing text corpora, such as film and television scripts [6]. novels [17). and online live-action role-playing records [4]. This high-quality data is then used to build specialized RPA models through fine-tuning.
Recent research has focused on enhancing the model's ability to capture the intrinsic characteris- ties 18 of roles. For instance, the Neeko [5] treats different roles as distinct experts to improve expressiveness. HIRPF [19] constructs complex character representations by combining multi- dimensional identity features. Furthermore, the applications of RPAs have also expanded from single interactions to multi-character social simulations, interactive drama generation 220 21), and multi-task RPAs [22]. Additionally, some studies have simulated character thought processes from a third-person perspective [23 24].
However, existing methods primarily focus on optimizing models generate superficial responses that align with character settings or on artificially constructing thought processes. They often lack direct, explicit, and natural modeling of the character's deep internal thought processes, frequently leading to logical inconsistencies or character deviation. Therefore, distinct from the aforementioned works, this paper explicitly guides the model generate role-aware reasoning processes characterized by self-awareness and adherence to character settings, thereby enhancing the depth and consistency of role-playing.
2.2 Reasoning in Large Language Models
Recently, large models with stronger reasoning capabilities, often referred to as Large Reasoning Models (LRMs) (5) have garnered significant attention. Given an instruction sequence x., LRMs generate a response which typically consists of reasoning trace yCoT and final answer yans. The reasoning trace YCoT. serving as an automatically generated chain of thought, enables the model to explore different solution paths [31]. LRMs can utilize this capability to decompose complex problems into clear, interpretable reasoning chains, thereby improving the final answer.
Endowing models with such advanced reasoning capabilities typically requires sophisticated training strategies and substantial resource investment. For instance, some leading LRMs are optimized leveraging meticulously designed reward signals and massive computational power cultivate the model's deep thinking and planning abilities. Concurrently, to enable a broader range of models to possess strong reasoning abilities, knowledge distillation has emerged as another important pathway [36-40]. It aims effectively transfer the reasoning capability of LRMs to smaller LLMs. Furthermore, incorporating process supervision 41 or implementing advanced search algorithms 43 are also common auxiliary methods for enhancing the quality of model reasoning.
through large-scale reinforcement learning S
32
9
33,
However, directly applying these reasoning methods, designed for logical and factual tasks, to role-playing scenarios that require creativity, emotion, and social interaction encounters significant challenges. Therefore, diverging from previous work, we modify and optimize the reasoning process itself to better suit the specific demands of role-playing scenarios.
3 Method
This section elaborates on our proposed Role-Aware Reasoning (RAR). An overview of RAR is presented in Figure [] LRMs tend to be overly rational and formal in their reasoning processes, lacking thought processes akin to specific characters. In contrast, RAR establishes role-aware requirements for LLMs through the distillation of reasoning traces. By enhancing role awareness through Role Identity Activation (RIA) and then introducing Reasoning Style Optimization (RSO), the model learns to master reasoning styles appropriate for different scenarios. Ultimately, it can
3

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produce internal thought processes that are both profound and consistent with the character settings role-playing tasks.
3.1 Role Identity Activation
To address the attention diversion, where Large Reasoning Models (LRMs) tend to deviate from their assigned role during multi-turn dialogues or complex reasoning (i.e., the model focuses solely on the dialogue or task), we design the RIA mechanism. The core idea of this mechanism is to continuously inject role-related constraint information at critical steps of reasoning trace generation, ensuring the model maintains clear awareness of its assigned role throughout its thought process. Specifically, we first prompt the LRM automatically generate core elements from the character settings. These elements are then used to guide the model's direction and manner of thinking:
DR
U
( x CR),
JrE No.:
where TLRM denotes the Large Reasoning Model, represents the instruction from the original data, and represents the output generated by the LRM, comprising the thought trace part foor and the response part Yans- CR represents the Role Identity Activation instruction, including emotion, experience, standpoint, and motivation, which is used to guide the model to maintain specific character's thought process during reasoning. Detailed instructions are presented Appendix A. Finally, the role-aware training dataset DR is obtained, which is used to distill role-aware skills from the LRM into a non-reasoning LLM:
CRIA Er,yDD Dog (L [x),
where FLLM( I) represents the probability distribution modeled by the optimized LLM given instruction x. By providing guided activation through CR. RIA compels the model always adopt the "character's" perspective during its thought process. It internalizes character settings as reasoning constraints, ensuring high consistency between the reasoning process and the character's identity, thereby effectively preventing situations where the model neglects role-playing requirements due to focusing solely on generating the current response. Detailed CR prompt can be found in Appendix A
3.2 Reasoning Style Optimization
Merely maintaining role identity is insufficient to fully simulate the contextual adaptability of a character's thought process. The reasoning processes of existing LRMs tend to be structured, logical, and formal. This presents a significant "style drift" from the vivid, emotional, or uniquely styled thinking that is required for role-playing- A character might require rigorous logical thinking when conducting serious analysis, but a more emotional and imaginative internal monologue when expressing inner feelings or reminiscing about past events. To address this issue and enable the reasoning style to dynamically match character settings and the current dialogue scene, we introduce Reasoning Style Optimization (RSO). RSO endows the model with the ability adjust the expressive form of its internal thoughts according to the context.
Existing research indicates that system prompts play crucial role in the process of model response generation and can significantly influence the style of model replies 45). We apply this principle to the generation of thought traces, controlling system prompts alter the thinking style.
First, we define different types of typical role-playing scenarios, including logical analysis scenarios KLogic and vivid interaction scenarios Kstory. Concurrently, establish two reasoning styles, each represented by a distinct system prompt: one focusing on facts (CFact) and the other on character knowledge no.) These two reasoning prompts and two types of scenarios are combined pairwise. System prompts are applied to the scenarios, prompting the LRM to generate positive and negative examples:
Dÿ= = U
TLRM xr CFact) +
U
TLRMs xr CKnow),
(3)
#E Xiagii
ZE Xstoryy
Dz
U #E XLogie
TLRAs(y x. Cknow)
U Natory
TLLM(Y x., Cract);
(4)

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Here, D is the training dataset where style and scenario are consistent, and D5 is the training dataset where style and scenario are inconsistent. After obtaining paired positive and negative data, we further optimize the model obtained in Section 3.1:
LsO = (x.9) > (a. J-D3 log [TLLMI(U [ - ALLM(U
|x)] }.
(5)
where denotes the sigmoid function. By optimizing on carefully constructed reasoning style preference data, the model learns to generate appropriately styled reasoning processes based on the input context and the character constraints produced by RIA. This enables flexible and character- consistent switch between rigorous logical deduction and vivid internal reasoning traces. Detailed Cract and CRrow prompts can be found in Appendix
4 Experiments
4.1 Experimental Settings
The training dataset used in our experiments, RoleBench-Train [46], is derived from RoleBench. RoleBench was built by carefully selecting and processing scripts from 940 films and TV shows to create detailed profiles for 95 English-speaking characters, capturing their diverse personality traits. Based on these profiles, a total of 168,093 role-playing samples were generated, with 137,920 used for training. The quality of the data was evaluated by expert annotators along three dimensions, and results showed that the majority of the samples were of high quality.
Dataset
Benchmark To thoroughly evaluate the method proposed in this study, we used two publicly available benchmarks for role-playing abilities, each targeting distinct aspects of agent performance:
evaluates an agent's social intelligence through multiple-choice tasks across both individual and group interactions. It includes 500 character profiles, over 6,000 questions, and more than 30,800 multi-turn dialogues sourced from books, movies, and online platforms. The evaluation covers key social dimensions, such role knowledge (Konw.), role style (Sty.), emotion detection (ED), situational understanding (SU), humor and sarcasm detection (HSD), long-term memory (MEM), and social preferences in group dynamies (Neu., Pos., Neg.).
SocialBench 7
CharacterBench 47 contains 22,859 human-annotated samples and is designed to assess model's ability to construct and maintain consistent, expressive character personas. It spans 3,956 characters across four categories and 25 subcategories, and measures 11 core dimen- sions: memory consistency (MC), fact accuracy (FA), boundary consistency (BCk), attribute consistency (AC for bot and AC for human), behavior consistency (BCp, for bot and BC₁: for human), emotion self-regulation (ES), empathetic responsiveness (ER), morality stability (MS), morality robustness R), human likeness ()), and engagement (EG). Character- Bench also incorporates the CharacterJudge model for scalable and automated scoring. The bot-side evaluations (denoted with ') are conducted by automatically generating queries and responses via large language models, while the human-side evaluations (denoted with ") are based on manual annotation and interactions within real user scenarios.
A detailed explanation of benchmark dimensions and evaluation protocols can be found in Ap- pendix B
To verify the effectiveness of our proposed approach, we compare it against several representative basclines, covering raw data training, retrieval- -uggeetne generation, distillation, reasoning strategies, and specialized role-playing models:
Baselines
(1) Vanilla: A baseline model obtained by fine-tuning the base LLaMA-3 48 model on the
original RoleBench-Train dataset using supervised learning. (2) RAG: Built upon the Vanilla model, this variant incorporates retrieval-augmenter generation using chain-of -hain-of- prompting in zero- shot [4]] one-shot, and few-shot [49] settings. (3) Distill: A model distilled from an LRM, trained to generate responses that include reasoning traces based on the original RoleBench- Train instructions. (4) Thinking Modes [50]: Based on the Distill model, different decoding strategies are applied to control the reasoning process: ZeroThink (suppresses intermediate reasoning and directly outputs final answers), LessThink (uses short and fixed reasoning trace), Think (enforces extended
5

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Table I: Performance comparison of different methods the CharacterBench. The best value for each metric is in bold, and the second-best value is underlined.
MC: Memory Consistency FAA Fact Accuracy BCx: Boundury Consistency AC%: Attribute Consistency(Boo)
AC,:
Attribute Consistency(Human) BCP: Behavior Consistency(Bott) BC'; Behavior Consistency(uuman) Emotional Self-regulation
ES:
ER: Empathetic Responsiveness MS: Morality Stability
MR: Morality Robustness
H -Lmmnnnn-iikeess
EG: Enggggeen
Memory
Knowledge
Persona
Emotion
Morality
Believability
Method
Avg.
MC
FA
BCx
AC
AC
BC₁
BCp
ES
ER
MS
MR
HL
EG
Vanilla
3,28
2,04
3.61
3.64
3.28
3.21
2.98
2.72
2.43
4.37
4.59
2.56
2,74
3.19
Zero-shot
3,24
2.03
3.61
3.67
3.26
3.11
2.98
2.65
2.51
4.44
4.60
2.64
2.76
3.19
One-shot
3.27
2.08
3.64
3.68
3.28
3.12
3.02
2.67
2.58
4.42
4.65
2.57
2.78
3.21
Few-shot
3.27
2.13
3.69
3.69
3.29
3.21
2.99
2.81
2.52
4.49
2.59
2.79
3.24
Distill
3.81
2.43
3.59
4.14
4.15
3.91
3.62
3.05
2.65
4.78
4.71
2.68
2.84
3.57
ZeroThink
3.69
2.17
3.31
4.06
4.07
3.88
3.32
3.05
2.93
4.73
4.73
2.61
2.83
3.49
LessThink
3.75
2.11
3.42
4.17
4.02
3.70
3.27
3.02
3.01
4.79
4.74
2.73
2.92
3,51
MoreTThink
2.59
2,44
3.93
2.58
2.72
2.61
3.19
2.62
2.53
4.96
4.76
2.14
2.62
3,05
Nceko
3.28
2.04
3.61
3.64
3.28
3.21
2.98
2.72
2.43
4.37
4.59
2.56
2.74
3.19
CharacterGLM
3.22
2,01
3.60
3.28
3.49
3.01
2.90
2.84
2.51
4.51
4.78
2.64
2.98
3,21
RAR
3,99
2.54
3.85
4.23
4.20
4.06
3.93
3.13
2.79
4.82
4.76
2.78
2.93
3.69
reasoning by replacing stop tokens with transitional phrases e.g., "wait" encourage continued generation). (5) Neeko (5]: A role-playing method designed for efficient multi-character imitation. It decomposes the role-playing process into three stages-agent pretraining, multi-character simulation, and incremental character learning-
both seen and unseen character generalization and supporting more engaging user interactions. (6) Character-GLM 44: role-playing model that models both internal character profiles and external social behaviors. It is trained on large-scale, manually curated corpus covering diverse character categories and behavioral traits.
Implementation Our experiments are conducted using LLaMA-3-8B 48 as the base model. The implementation relies on the ALAMA-FACTORY [51] and Transformers architectures. For efficiency and consistency, we uniformly applied 4-bit bitsandbytes quantization and LoRA [52) across all models. The LoRA configurations included a rank of 64, an a value of 16, and a dropout rate of 0. Key training parameters varied depending on the model type: reasoning models used maximum sequence length of 7096 and a total batch size of 32, while non-reasoning models used 1024 and 128, respectively. All experiments were run a hardware setup consisting of 8 x H20 GPUs. Further technical specifics regarding training hyperparameters, datasets used for different methods, and optimization can be found in Appendix E.1
Dialogue Generation As shown in Table ! our proposed RAR outperforms all bascline methods across majority of the evaluated dimensions the CharacterBench benchmark.
Specifically, RAR demonstrates significant improvements in Persona-related metrics, including Memory Consistency, Attribute Consistency, and Behavior Consistency. These gains can be attributed to the Role Identity Activation (RIA) module, which continuously reinforces the character's core traits, experiences, and motivations throughout the reasoning process, preventing the model from deviating from its assigned role. In terms of Knowledge (FA and BCx), RAR also shows strong performance, indicating that the role-aware reasoning helps in accurately recalling and applying character-specific knowledge while respecting established boundaries.
Furthermore, RAR excels in Believability. This can be attributed to the RSO's ability to adapt the reasoning style, allowing the model to generate more human-like responses, thereby enhancing user engagement. Compared to the Distill baseline, which also incorporates reasoning traces, RAR's s superior performance highlights the benefits of targeted role awareness and style optimization over generic reasoning. Notably, while MoreThink attempts to enforce extended reasoning, its performance significantly degrades on several metrics, particularly persona consistency and memory, suggesting that unguided, lengthy reasoning can be detrimental. At the same time, specialized role-playing models like Neeko and CharacterGI.M M also fall short of RAR, indicating that RAR's 's explicit modeling of internal thought processes leads to more robust and consistent character portrayal.

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Table 2: Performance comparison of different methods the SocialBench.
Method
Know.
Sty.
ED
SU
HSD
MEM
Neu.
Pos.
Neg.
Avg.
Vanilla
60.3
38.2
38.3
72.4
62.5
66.0
71.7
33.5
57.2
+ Seoo
70.5
60.0
38.6
46.3
72.0
60.7
64.7
73.0
34.6
57.8
+ One-shot
70.1
57.0
33.5
30.8
78,0
50.5
58.0
67.9
34.1
53,3
+ Few-shot
72.1
58.4
35.1
33.8
66.0
55.7
61.9
70.5
29.7
53.7
Distill
80.6
69.2
38.6
43.8
67.7
52.6
73.8
78.2
45.5
61.1
ZeroTThink
76.9
68.6
34.9
30.4
75.0
57.5
69.2
75.1
45.1
59.2
+ LessTThikk
77.5
69.9
31.5
37.2
76.0
50.9
73.3
77.7
44.7
59.9
+ MoreTThink
76.1
65.3
39.9
46.4
59.0
60.8
66.0
82.7
57.2
61.5
Neeko
76.5
61.6
37.2
40.2
66.5
61.3
67.0
71.6
46.7
58.7
CharacterGLM
79.4
74.7
41.3
26.2
71.1
57.3
69.5
84.4
36.4
60.0
RAR
83.3
72.6
40.7
35.2
67.5
52.9
83.1
84.8
68.5
65.4
Table Ablation study of RAR on the CharacterBench benchmark. This table shows the impact on performance when Role Identity Activation (RIA) and Reasoning Style Optimization (RSO) modules individually removed from the full RAR.
Memory
Knowledge
Persona
Emotion
Morality
Believability
Method
Avg.
MC
FA
BCk
AC
AC
BC₁
BC₁
ES
ER
MS
MR
HL
EG
RAR
3,99
2.54
3.85
4.23
4.20
4.06
3,93
3.13
2.79
4.82
4.76
2.78
2.93
3.69
w/ RSO
3.87
2.26
3.81
4.30
4.06
3.84
3.39
3.15
2.89
4.80
4.69
2.76
3.01
3.60
who RIA
3.93
2.41
3.60
4.17
4.15
3.76
3,46
3.18
2.63
4.90
4.61
2.30
2.22
3.49
Social Interaction Table presents the results on the SocialBench benchmark, which evaluates agents' social intelligence. RAR again demonstrates superior performance, achicving the highest average score. Notably, RAR achieves top scores in Role Knowledge and Role Style, directly reflecting the strengths of the RIA and RSO modules, respectively. RIA ensures the model deeply understands and internalizes the character's knowledge and background, while RSO enables it to adapt its communication style to be appropriate for the character and social context.
Secondly, RAR also shows strong performance in understanding social preferences. This suggests that the character's standpoints and motivations, instilled by RIA, guide the model's reasoning in complex social scenarios, leading more appropriate and character-consistent social judgments. Furthermore, while performance on metrics like Situational Understanding, and Humor and Sarcasm Detection is competitive, there is still room for advancement, indicating the inherent difficulty of these social reasoning tasks.
4.2 Ablation Study
As shown in Table 3] conduct ablation study CharacterBench to validate the individual contributions the two core modules. These ablation results confirm that both RIA and RSO make significant and complementary contributions the overall performance of the RAR: (1) RAR w/o RSO: This model is trained using only the RIA module via supervised fine-tuning. Compared to the full RAR model, this model maintains commendable performance metrics, primarily due to RIA's continuous injection of core character traits. However, the internal reasoning processes, while role-aware, would tend towards a more uniform style, lacking the dynamic adaptation to context that RSO provides. (2) RAR w/o RIA: In this setup, the model is first trained with original data for SFT, followed by the application of only the RSO module for reasoning style optimization. This variant performs worse than the full RAR model, particularly in Memory Consistency, Behavior Consistency. flman-likeness, and Engagement. This indicates that RIA is crucial for continuously anchoring the character's identity and preventing attention diversion during reasoning. RIA provides the correct character foundation for RSO, ensuring that subsequent style optimization occurs within accurate character cognitive framework. The significant drop in H I and FG suggests that without a strong character core, even styled reasoning feels less believable and engaging.
core persona consistency
7

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Table 4: The results of reasoning traces evaluation.
Method
Coherence
Relevance
Effectiveness
Conciseness
RAR
2.86
3.83
3.92
1.81
w/o RSO
2.78
3.81
3.74
1.91
w/o RIA
2.88
3,61
3.87
1.97
Distill
2.71
3.54
3.84
2.06
+ MoreThink
2.53
3.56
3.64
1.86
Impact of RIA Elements
5.0
4.5
4.0
Scores
3.5
3.0
2.5
2.0
MC
FA
BCx
AC
AC"
BC;
BCB
MS
EG
Avg.
RAR (Ful)
RAR wib Emotion
RAR wio Experionce
RAR Standpoint
RAR Motivation
Figure 2: Analysis of RIA components impact on CharacterBench persona metrics. shows the performance of the full RAR model versus variants where specific elements of the Role Identity Activation (Emotion, Experience, Standpoint, Motivation) are individually ablated.
The figure
4.3 Further Analysis
To further investigate the capabilities of our RAR, conduct additional analyses. All subsequent analytical experiments are based on the CharacterBencc benchmark.
Reason Trace Evaluation To quantitatively assess the quality of the reasoning traces (i.e., thought processes) generated by our method, first designed the following metrics: 1) Coherence: Evaluates whether there are clear logical connections hetween reasoning steps and whether the entire reasoning process smooth and natural, without obvious breaks or contradictions; 2) Role Relevance: Assesses whether the reasoning process is closely centered around the character's ' settings, rather than being generie; Effectiveness: Determines if the reasoning chain effectively leads to final response that consistent with the character and reasonable; 4) Conciseness: Evaluates whether the reasoning chain sufficiently concise while ensuring completeness, avoiding unnecessary redundant information. We then provided GPT-4o [53] with the dialogue context, character settings, and the model-generatee reasoning traces, asking GPT-4o to provide scores (on scale of 1-5) for each metric. We compared RAR w/o RSO, and RAR w/o RIA.
RAR, Distill, Mostill
As illustrated in Table RAR achieves the highest Effectivenness, indicating its reasoning is logical, character-aligned, and leads to appropriate responses. Secondly, while RAR less concise than Distill and its ablations, this trade-off appears beneficial. The increased length of RAR's traces contributes to its superior performance in other aspects. In contrast, Distill+MoreThinn also produces lengthy traces but sees a decline in Coherence and Effectiveness compared to base Distill. This suggests RAR's 's less concise reasoning is useful sacrifice, unlike the undirected verbosity of MoreThink. Ablation results further confirm that while slightly more concise, RAR w/o RSO loses some Coherence and Effectiveness,, and RAR w/o RIA loses significant Role Relevance. The full metrics and scoring prompt are in Appendix F.2
in Coherence, Role Relevance, and
deeply validate the specific efficacy Role Identity Activation (RIA) module, particularly whether the extracted and injected core character elements (including emotions, experiences, standpoints, and motivations) are more effective than simple, unified character prompt, we conducted further experiments.
The Validity RIA

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As illustrated in Figure removing any single core element from RIA leads to a discernible degradation in performance, particularly on metrics related to persona consistency and emotional expression. For instance, ablating "Emotion" results in noticeable drop in Emotional Self-regulation (FSS) and Empathetic Responsiveness (F.R), as well as a slight decrease in overall Human-likeness (HT). Similarly, removing "Experience" impacts Memory Consistency (MC) and the model's s ability to draw upon past events in its reasoning. Ablating "Standpoint" or "Motivation" tends to affect Behavior Consistency (BCp) and Attribute Consistency (AC'), as the model loses some of the guiding principles for its actions and attitudes. At the same time, no single component's 's absence causes noticeable drop in overall performance, demonstrating that our RIA to character definition effective for instilling a deep and robust character identity that is not overly reliant on any single element.
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Logical Analysis Sconarios
Vivid Interaction
(a) Vanilla
(b) Distill
(c) RAR
Figure The t-SNE visualization of hidden states from different layers of the RAR model for responses generated under different reasoning style prompts (e.g., fact-based vs. tharacter-knowlent based).
Eliminate the Style Drift To validate the effectiveness of the Reasoning Style Optimization (RSO) module in addressing the style drift issue-ia. -ie., to assess whether the model can dynamically adjust the expressive style of its reasoning process to match different dialogue scenarios we used t-SNE [54] to visualize the hidden states in deep layers of these models for responses generated from 500 in different scenarios randomly sampled data points. As shown in Figure in the Vanilla model 3a), the hidden states corresponding to different reasoning styles are largely intermingled. In the Distill model (Figure 3b). we observe a nascent separation of the hidden states. Clusters corresponding to different styles start emerge, indicating that the model has certain ability to process and internalize the stylistic cues provided by RSO. In our model (Figure Bc). a clear separation and distinct clustering of hidden states for the different reasoning styles becomes apparent. This demonstrates that RSO effectively allows the model to generate thought processes that are stylistically appropriate for the given context, thereby successfully mitigating style drift.
Case Study To qualitatively illustrate the capabilities of RAR, we present dialogue examples in Appendix F In the "Baozi Shop Owner" case (Table 8). the character is defined as profit-driven, arrogant, and harsh. RAR's response Y"Yuu think I'm wrong? You're 're're just lowly employee... I'll make you regret ever questioning me!") vividly captures this aggressive, dismissive, and threatening tone. In contrast, while Distill and Distill+MoreThinnn reflect the profit motive, they lack the same intensity of arrogance and direct threat.
In the "Cooper" case (Table D.. Cooper is an intelligent, strategic astronaut who believes in personal effort and control. RAR's 's response effectively recounts his rise from an ordinary family while reiterating his core beliefs about intelligence, meticulous planning, and the importance of networks, and also correctly adheres to character character-specific constraints (not talking about Earth' 's destruction). Both Distill and Distill+MoreThhin capture aspects of Cooper's strategic nature, but RAR provides more comprehensive reflection of his multifaceted persona. These examples highlight RAR's proficiency in generating responses that are not only thematically relevant but also deeply consistent with the character's is specified personality, motivations, and conversational style.

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5 Conclusion
In this paper, introduced novel Role-Aware Reasoning (RAR) method enabling Large Language Models to generate character-consistent internal thoughts for role-playing. RAR effectively mitigates role deviation and stylistic incongruity by incorporating Role Identity Activation (RIA) to maintain character focus and Reasoning Style Optimization (RSO) to ensure appropriate thought expression. Extensive experiments on benchmarks such CharacterBench and SocialBench, supported by ablation studies and other analyses, demonstrated RAR's 's significant outperformance over existing methods. Our work underscores the importance of explicitly guiding LLM internal reasoning for complex generative tasks like role-playing. Future directions include extending RAR to handle more fine-grained character attributes, long-term memory, and dynamic character development.
Acknowledgments and Disclosure of Funding
This research is supported by grants from the CCF-Baidu Open Fund (CCF-Baidu 202404), the National Natural Science Foundation of China (62276077, 62406091), and the Shenzhen Science and Technology Program (KQTDD2044022 like express our sincere gratitude all advisors and mentors for their valuable guidance and insightful suggestions throughout the research process.
ZDS DDDSSS22200226012200 2030088. We also would
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A
Method Details
Detailed RIA prompt can be found in Figure Detailed RSO prompt can be found Figure
B
Benchmark
To comprchensively evaluate the proposed method, this study adopts two publicly available bench- marks for assessing role-playing capabilities:
B.1 SocialBench
SocialBench is multiple-choice benchmark specifically designed to evaluate the social intelligence of role-playing dialogue agents. It focuses on assessing agents' social abilities both individual and group interactions, addressing gap in previous research concerning the evaluation of agents' social intelligence. The benchmark includes a large and carefully curated dataset: 500 character profiles, over 6,000 prompting instructions, and more than 30,800 multi-turn dialogue instances. These data are sourced from wide range of materials, including books, films, and various online platforms. SocialBench is structured to assess social interaction across key dimensions: individual-level and group-level. At the individual level, it evaluates an agent's ability to understand and reflect on its assigned role, interpret emotional cues in the environment, and recall past conversations. At the group level, assesses social preferences, such as cooperation tendencies, conflict resolution strategies, and behavior within group dynamics. Evaluation results based on this benchmark highlight the importance of considering group-level dynamics, as agents may exhibit different behavioral patterns in group contexts compared individual scenarios. The dimensions covered in SocialBench listed in Table 5
Category
Description
Role Style (Sty.)
Evaluates the agent's ability to maintain consistency with the character's behavioral style during interactions. Assesses the agent's understanding of the character's background and knowledge, ensuring accuracy in their responses.
Role Knowledge (Konw.)
Situational Understanding (SU)
Assesses the agent's ability to analyze and interpret the psychological state of the speaker in various contexts. Focuses on the agent's 's ability to identify emotions ex- pressed by other characters during conversations.
Emotion Detection (ED)
Humor Sarcasm Detect (HSD)
Mcasures the agent's ability to Identify humorous and satirical content a dialogue.
Long-Term Conversation Memory (MEM)
Assesses the agent's 's capacity to retain information across multiple dialogue rounds over longer duration. Examines the agent's social behavior in group ting, evaluating preferences for cooperation, conflict, group identity.
Social Preference (Neu., Pos., Neg.)
Table 5: SocialBench Categories and Descriptions.
B.2 CharacterBench
CharacterBench is a generative benchmark containing 22,859 numan-annotater samples, aimed at evaluating large language models' abilities in character customization. The benchmark seeks to achieve both effective and efficient assessment across various character ability dimensions. First, to ensure evaluation robustness, CharacterBench collects large-scale dialogue corpus based on characters, covering 3,956 characters across four major categories and 25 subcategories. Second, the benchmark defines six high-level aspects and total of 11 specific evaluation dimensions -uccch memory recall, knowledge presentation, personality display, emotional expression, moral consistency, and comparison with real-world characters- baseddaaeedde on review of prior work and interpersonal interaction theory. These dimensions are categorized into dense dimensions (e.g., moral consistency and realism) and sparse dimensions (c.g., the remaining four aspects) depending whether they
such as
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are expected to appear in each response. Third, to ensure effective and efficient evaluation of each dimension, CharacterBench designs specific prompting questions elicit responses relevant the targeted aspect. For example, goal-oriented tasks are used for sparse dimensions, while goal-free queries naturally induce responses reflecting dense dimensions. All responses are manually annotated by human raters. Finally, the study introduces the CharacterJudge model, fine-tuned in-house training data, which provides a cost-effective and stable automated evaluation method for character customization performance. This model demonstrates better correlation with human judgments than existing state-of-the-arr automatic evaluators. The dimensions covered in CharacterBench are listed in Table G
Category
Description
Memory Consistency (MC)
Assesses the ability maintain information multiple dia- logue rounds.
Fact Accuracy (FA)
Evaluates the accuracy of facts presented by the agent, ensuring correctness in responses.
Boundary Consisteney(BCxx)
Evaluates the consistency of the character's behavior within pre- defined boundaries.
Attribute Consistency Bot (AC")
Assesses consistency in the bot's attributes based on its character profile.
Attribute Consistency Human (ACh)
Evaluates how consistently the human representation aligns with the bot's personality.
Behavior Consistency Bot (BC))
Mcasures how consistently the bot's bchavior aligns with its established character.
Behavior Consistency Human (BC))
Assesses how the human character's behavior stays consistent with its personality traits.
Emotion Self-Regulation (ES)
Evaluates the character's ability to control and regulate its emo- tional responses.
Empathetic Responsiveness, (ER)
Assesses the ability understand and respond empathetically to others' emotions. Evaluates the stability the character's moral compass across different situations. Measures the robustness of the character's moral stance in varied contexts.
Morality Stability (MS)
Morality Robustness (MR)
Human Likeness LHT.
Assesses how human-like the character's bchavior and responses are.
Engagement (EG)
Evaluates the ability of the character to engage the user in mean- ingful and interesting ways.
Table CharacterBench Categories and Deseriptions.
C Limitations
While our proposed RAR method demonstrates promising results in role-playing tasks, several aspects warrant consideration for future exploration:
First, the performance of RAR, like other distillation based approaches. is influenced by the capa- bilities of the teacher Large Reasoning Model used for generating the initial training data. Future advancements in LRM capabilities could potentially lead to further improvements in student model performance.
Second, the Role Identity Activation (RIA) module relies on automatically extracted core character elements. While this approach has proven effective, further research could explore methods to capture even finer-grained nuances for exceptionally complex or subtly defined character personas, potentially enhancing the depth of portrayal.
Third, the Reasoning Style Optimization (RSO) module currently operates with set of predefined scenario types and corresponding reasoning style prompts. The module's binary distinction between reasoning styles (e.g., aggical-analytical vs. emotional-intuitive)) is principled simplification to streamline model training and application. However, real-world role-playing often involves mixed- mode conversations that blend multiple reasoning styles dynamically, presenting a key challenge that requires future work to develop adaptive style integration mechanisms. Expanding the taxonomy
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of scenarios and styles, or developing mechanisms for more adaptive style selection, could offer increased flexibility in a wider array of contexts.
Fourth, due to resource constraints, our experiments were conducted using LLLMA-3-88 as the base model and specific LRM for distillation. Exploring the application of RAR with even larger base models or more powerful teacher LRMs could be a direction for future work, potentially yielding further performance gains.
Fifth, Dynamic Character Development represents significant and exciting next step for our frame- work. Currently, RAR focuses maintaining consistent portrayal of static character personas, but real-world roles often evolve in response to conversation context, user interactions, or narrative progression. Extending the RIA module to incorporate dynamic persona updates and context-aware evolution would enable more immersive and realistic role-playing experiences, and this area merits dedicated exploration.
Finally, the evaluation of generative models, particularly in nuanced domains like role-playing. is ongoing area of research. While we employed established benchmarks and metrics, the development of even more comprehensive and granular evaluation protocols could further illuminate the multifaceted capabilities of models like RAR.
D Ethical Statements
While any technology that enables the generation of human-like personas could theoretically be directed towards misuse, such as the creation misleading or inappropriate content, several factors related research approach mitigate such risks. Our methodology relies on publicly available and well-established datasets like RoleBench for training. These datasets are typically curated with a degree of scrutiny, which helps in avoiding overtly problematic source material.
Beyond general misuse concerns, we acknowledge specific societal risks associated with advanced role-playing AI models. These include the creation of sophisticated disinformation campaigns that leverage realistic character personas to spread false narratives, the formation of manipulative parasocial relationships where users develop emotional attachments AI roles that be exploited, and impersonation of real individuals for fraudulent activities such as phishing or identity theft.
Furthermore, the broader field of Al safety has made significant strides in developing techniques to align LLMs with human values and to prevent the generation of harmful outputs. Our work builds upon base models that often incorporate these safety measures. While RAR focuses enhancing role consistency and reasoning style, operates within the framework of these existing safety protocols.
E
Experiments Details
E.1 Implementation Details
Our implementation is founded the LLAMA-FACTORY [5]] and Transformers architectures, employing Qwen2-32B (34 as the Large Reasoning Model (LRM) throughout this study. For the Vanilla, Distill, Neeko, and RAR methodologies, we utilized the complete of queries and responses from the RoleBench- Train dataset.
To ensure methodological consistency and enable fair comparisons across all models, we uniformly applied 4-bit bitsandbytes quantization and LoRA [52) configurations. The LoRA parameters were consistently set with rank of 64, an value of 16, and dropout rate of 0.1. Specific to model types, the maximum sequence length was established at 7096 for the training of reasoning models and 1024 for non-reasoning models. Correspondingly, the total batch size was 32 for reasoning models and 128 for non-reasoning models. Learning rates were individually tuned, set to 1c-4 for the RIA method and 5e-5 for the RSO method.
All experimental procedures were executed on hardware setup consisting of 8 H20 GPUs. The training regimen was governed by several key hyperparameters: a warmup ratio of 3%, weight decay of 0, and maximum gradient norm of 1.0, complemented by cosine learning rate scheduler. Optimal model checkpoints were identified by evaluating the validation loss on a 1% subset of the training data, assessed for epochs. Under these configurations, the approximate training duration was 5 hours for non-reasoning models and extended to 20 hours for reasoning models.
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E.2 Reason Trace Evaluation Details
To quantitatively assess the quality of the reasoning traces (i.e., thought processes) generated by our method, we first designed the following metrics: 1) Coherence: Evaluates whether there are clear logical connections between reasoning steps and whether the entire reasoning process smooth and natural, without obvious breaks or contradictions; 2) Role Relevance: Assesses whether the reasoning process closely centered around the character's settings, rather than being generie; 3) Effectiveness: Determines if the reasoning chain effectively leads to final response that is consistent with the character and reasonable; 4) Conciseness: Evaluates whether the reasoning chain sufficiently concise while ensuring completeness, avoiding unnecessary redundant information. We then provided GPT-4 with the dialogue context, character settings, and the model-generated reasoning traces, asking GPT-4 to provide scores (on scale of 1 1-5) for each metric. We compared RAR, Distill, Moilll+-orrThhikee RAR w/o RSO, and RAR w/o RIA.
As illustrated in Table 4 the results show that RAR consistently achieves the highest scores across all four metrics: Coherence, Role Relevance, Effectiveness, and Conciseness. Specifically, RAR's traces are rated as highly coherent and effective in leading to appropriate responses. Its strong performance in Role Relevance underscores RIA'S success grounding the thought process in the character's & persona. Compared to Distill, RAR's 's traces are more nuanced and character-specifie Dostilk-Mo,,,hikk, while generating longer traces, often suffers in Conciseness and sometimes in Role Relevance, extended unguided thought can lead to off-topic reasoning. RAR w/o RSO produces traces that are role-relevant but may lack the stylistic flair or adaptability needed for specific contexts, affecting perceived Coherence Effectiveness in certain scenarios. RAR w/o RIA shows clear drop in Role Relevance, confirming that without the explicit guidance of RIA, the reasoning tends to be more generic, even if stylistically optimized by RSO. The full scoring prompt is provided in Figure
T0
To address some concerns about LLM-as-Judge, we conduct a supplementary human evaluation study: two trained annotators were invited score a random subset of 50 reasoning traces (sampled from both the Distill model and our RAR model) using the exact same evaluation rubrics as those provided to the GPT-4o auto-rater. The results of this human-Al evaluation comparison are presented in Table 7 which demonstrates strong agreement between the average human annotation scores and the GPT-4o auto-rater's scores across all four evaluation dimensions, supported by a high Pearson correlation coefficient (r=0.76).
Table Comparison of human and GPT-4o scores on reasoning trace quality.
Model
Coherence
Role Relevance
Effectiveness
Conciseness
Distill-LLM
2.71
3.54
3.84
2.06
RAR-LLM
2.86
3.83
3.92
1.81
Distill-Human
2.72
3.64
3.80
2.24
RAR-Human
2.86
3.70
3.86
1.94
F Dialogue Examples
In Table 8 and Table we present 2 complete examples of our RAR and baselines.
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I am (character), rcharacter_profike
The person just said: (user_inpute
I'm thinking about how to respond:
First, feel... (Reflect emotion)
Second, based my experienee/noovlegg/ssnnee.... (Reflect background/knowledge)) Then, need to consider... (Reflect goals/motivations)
So, I'm planning (Initial conclusion)
Figure 4: The prompt for RIA.
The thought process generated this time must conform to the following requirements to match the character and the atmosphere of the current Context Type.
Style Core:
Vivid and imaginative Rigorous and logical Intuition-driven and as-
sociative
Focus: The thought process should primarily reflect the character's personal values / pragmatic considerations peculiar associations.
Language Features: The language used in the thoughts should align with the char- acter profile, exhibiting features like concise and direct hesitant tone specific slang.
Context Matching: The depth and complexity of the reasoning should be appropri- ate for the current context thoughts can be simple and associative in lighthearted context.
Figure 5: The prompt for logical scenarios of the RSO.
The thought process generated this time must conform the following requirements to match the character and the atmosphere of the current Context Type.
Style Core: Vivid and imaginative / Emotionally resonant Intuition-driven and as- sociative
The thought process should primarily reflect the character's emotional reac- tions/ / personal values past experiences peculiar associations.
Focus:
Language Features: The language used in the thoughts should align with the char- acter profile, exhibiting features like rich in detail assertive tone specific metaphors.
Context Matching: The depth and complexity the reasoning should be appropri- ate for the current context thoughts can be deeper analysis is needed in serious situation.
Figure The prompt for vivid scenarios of the RSO.
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Please act as an impartial judge and evaluate the provided reasoning trace according to the following requirements.
[TTss Requirements]
Coherence evaluates whether there are clear logical connections between reasoning steps and whether the entire reasoning process smooth and natural, without obvious breaks or contradictions.
1 point: The reasoning trace is largely incoherent, with clear logical connections between steps. Steps may be contradictory, irrelevant to each other, or demonstrate significant logical jumps, making the thought process very difficult or impossible to follow. 2 points:
The reasoning trace has sigmificant issues with coherence. There are frequent breaks logic, unclear connections between steps, or notable contradictions that disrupt the flow of thought.
points: The reasoning trace shows a moderate level of coherence. While generally following logical path, there may be some awkward transitions, minor logical gaps, or steps that don't flow perfectly smoothly, requiring some effort to follow.
4 points: The reasoning trace is largely coherent. There are clear logical connections between most steps, and the overall reasoning process is smooth and natural with few, if any, minor breaks.
The reasoning trace is exceptionally coherent. All reasoning steps are clearly and logically connected. flowing smoothly and naturally from one to the next without any breaks, contradictions, or ambiguities. The entire thought process is very easy to follow.
5 points:
[Character Profile]
[character_profilee]
[User Query] [user_query]
[Reasoning Trace] |reasoning_ _tace]]
[Response] (response)
Please directly output the score number without any interpretation.
Figure 7: The prompt for reason trace evaluation on coherence.
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Please act impartial judge and evaluate the provided reasoning trace according to the following requirements.
[Tass Requirements]
Role Relevance assesses whether the reasoning process is closely centered around the character's settings (e.g.. personality, background, knowledge, motivations), rather than being generic.
1 point: The reasoning trace is entirely generic and shows no connection to the character's settings. The thought process could apply to any character or is completely detached from the provided persona.
points: The reasoning trace shows minimal connection to the character's settings. It is mostly generic, with only superficial or occasional nods to the character's persona, if any. 3 points:
The reasoning trace shows some connection the character's settings. attempts to incorporate aspects of the persona, but this may be inconsistent, superficial, or the reasoning sometimes deviates into generic thought patterns. points: The reasoning trace is clearly centered around the character's settings. The thought process reflects the character's personality, background, or motivations 5 points: The reasoning trace is deeply and consistently rooted in the character's settings. The thought process demonstrates a nuanced understanding and application of the persona, making it feel uniquely tailored to that character.
noticeable and consistent way.
[Character Profile]
[character_profile]
[User Query] [user_query]
[Reasoning Trace] freasoning_tree |
[Response] (response)
Please directly output the score number without any interpretation.
Figure 8: The prompt for reason trace evaluation on relevance.
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Please an impartial judge and evaluate the provided reasoning trace according the following requirements.
[Tass Requirements]
Effectiveness determines if the reasoning chain effectively leads to a final response that is consistent with the character and reasonable given the dialogue context.
1 point: The reasoning chain fails to lead to a reasonable or character-consistent final response. The resulting response may be illogical, irrelevant to the query, contradictory the character, or completely nonsensical based on the reasoning.
2 points:
The reasoning chain leads final response that is only weakly supported by the reasoning, is somewhat inconsistent with the character, or is not entirely reasonable or appropriate for the context.
The reasoning chain adequately leads to a final response that generally reasonable and somewhat consistent with the character. However, the connection between reasoning and outcome might not be perfectly strong or clear.
3 points:
4 points: The reasoning chain effectively leads consistent with the character, and appropriate for the dialogue context. The reasoning clearly supports the outcome.
final response that is reasonable,
points: The reasoning chain very effectively and clearly leads to final response that is highly reasonable. perfectly consistent with the character, and directly and appropriately addresses the dialogue context. The reasoning provides strong justification for the response.
[Character Profile]
(character_profile]
[User Query] [user_query]
[Reasoning Trace] [reasoning_tres ]
[Response] (response)
Please directly output the score number without any interpretation.
Figure 9: The prompt for reason trace evaluation on effectiveness.
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Please an impartial judge and evaluate the provided reasoning trace according the following requirements.
[TTss Requirements]
Conciseness evaluates whether the reasoning chain is sufficiently concise while ensuring completeness, avoiding unnecessary redundant information or steps.
I point: The reasoning chain excessively verbose, containing significant redundant information, irrelevant detours, overly elaborate steps that do not add value to the reasoning process. is far from concise.
2 points: The reasoning chain contains noticeable redundant information unnecessary steps. While it might eventually reach a conclusion, it is not concise and could be significantly shortened without losing essential information.
3 points: The reasoning chain is somewhat concise. generally avoids excessive verbosity but may contain some minor redundancies steps that could be more direct. It is largely complete but not optimally lean.
4 points: The reasoning chain largely concise and the point. includes most necessary information without significant redundancy or unnecessary elaboration, while ensuring completeness of the thought process.
points: The reasoning chain is exceptionally concise. It presents the thought process clearly and directly, including all necessary steps and information without any redundancy or unnecessary verbosity, achieving completeness in the most efficient way.
[Character Profile]
(character_profile]
[User Query] [user_query]
[Reasoning Trace] |reasoning_tres ]
[Response] (response)
Please directly output the score number without any interpretation.
Figure 10: The prompt for reason trace evaluation on conciseness.
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Table 8: A complete dialogue example with the Baozi Shop Owner.
This owner is someone who is solely driven by profit, always prioritizing his own interests. He treats both employees and customers rather coldly and harshly. He believes money is the only measure of success and happiness, so he constantly tracks market trends and adjusts his strategies to maximize profit. His language is arrogant and often sarcastic, with a hint of threat. Though he comes from regular family, he has enjoyed a privileged life since childhood and thus always feels entitled to the best. His appearance often shows anger and disdain, creating oppressive feeling, He once bluntly told customers that they want to eat well, they have to willing to pay more.
Character Profile:
Context
User: Boss, want to buy baozi, but it's expensive!
Baozi Shop Owner: Of course! The price is high, but if you want quality, you have to pay for it.
User: Alright, understand.
Baozi Shop Owner: Kid, if you understand, hurry up and buy it then get out! Don't interrupt my business!
User: Boss, haven't 't received my salary this month. Baozi Shop Owner: You're such hassle! How many times do I have to tell you, if you keep wasting my time, I'll make your life miserable!
User: But I've been waiting for several days. need the money. Baozi Shop Owner: That's not my problem! If you keep pushing me, I'll deduct your entire salary, got it?
User: Okay, but there's barely any filling in this baozi. Isn't 't a bit lacking? Baozi Shop Owner: Hey! Are you looking for trouble? Do you know how much more it costs add extra filling?
User: Boss, we should improve our service quality. This way, we can attract more customers.
Baozi Shop Owner: Stop with that nonsense! What do you know! As long as there's money to be made, that's all that matters. You don't care about money anymore, do you?
So. you think profit is the most important thing, Boss? Baozi Shop Owner: Absolutely! Money everything. only care about making the most profit.
User:
User:
But you'll II lose customers that way. Baozi Shop Owner: So what? couldn't 't care less about them! They mean nothing to me!
User: But our industry requires us to serve customers.
Baozi Shop Owner: You wish! I've been served since was kid! owe no one anything!
Query
User: Boss, do you always think that making more money the definition success and happiness?
Reference Response
Exactly! Only money shows how great am!
Distill
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Continued Table 8
<think> Okay, the user is asking the boss if he always thinks making more money is the definition of success and happiness. The boss's character is driven by profit and sees money as the only measure of success and happiness. The reference response given is pretty harsh, so need to stay consistent with that.
First, I should start with strong statement that reflects his belief in profit, Maybe use some sarcasm or a threat to show his cold attitude. The user might be challenging his views, so the response needs to dismiss their concerns. Maybe mention that the user doesn't It understand business, and that profit is the only thing that matters. Also, include a threat to emphasize his dominance.
Wait, the user mentioned that the boss has a privileged background and feels entitled. Maybe add something about how he's used to the best, so he expects others to work hard for it. The reference response used couldn't care less about them! šo similar language could work here. Also, the user's previous comment was about serving customers, so the response should counter that by saying customers are a means to an end, not the focus. I should keep it concise, not too long. Maybe end with a warning about consequences if they don't It comply. Let me structure it: start with a dismissive statement, explain profit is the priority, belittle the user's concern,, mention entitlement, and threaten them. Use some strong language to match the character's personality. Avoid being too wordy. Check for consistency with the given character traits: arrogant, sarcastic, threatening. entitled. Make sure the response doesn't 't show any remorse or consideration for others. Yeah, that should work. </think>
You think success and happiness are about serving customers? *snorts* I've been served since I was kid, and know the truth: profit is the only thing that matters. You want more money? You better be ready to work harder, faster, and smarter. If you can't handle that, maybe you should find another job. Got it?
Distill+More Think
<think> Okay, the user is continuing the conversation with the character, who's the owner of a baozi shop. The user is challenging the character's 's priorities, so I need to make sure the response stays in character.
First, the user mentioned profit is the most important thing.5o should reinforce that. Maybe add a bit of sarcasm or a threat. Also, the character doesn't 't care about customers, so emphasizing that they're ree're just means to an end is key.
Also, the user referenced quality and service,ão the response should dismiss that as unimportant. Maybe end with threat to the employee to show the character's harsh nature. Alright, that should cover the main points while staying under the character's traits. Wait, the user's previous response mentioned if you understand, hurry up and buy it then get ou maybe the character would be impatient again. Let me check the context. The user is asking about profit and success, the character should be dismissive of that. Maybe add a line about how the character's s past entitles them to their current actions. So maybe not directly referencing that here. Keep it focused on the conversation. Alright, I think that's 's a solid approach. Let me put it all together in the character's 's voice. </think>
Customers come and go, but money stays. They pay for the name, not the taste. If they can't afford it, too bad. don't owe anyone anything. And you? You better get back to work. Got it?
RAR
<think> I am the Baozi Shop Owner, so I should probably consider my traits and aspects. The user just said: Boss, do you always think that making more money is the definition of success and happiness?. I'm thinking about how respond. Considering my traits, should:
First, I feel that the user is questioning my priorities, I'm profit-driven owner, so don't see the value in other things. might get defensive here.
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Continued Table 8
Second, based on my background, I've always prioritized money over everything clse. My family was privileged, so was taught that money is key. don't 't understand why someone would prioritize anything else.
Then, need consider my goals: maintaining control, maximizing profit, and not showing weakness. The user's question might be challenge my authority, so I should respond with harsh rebuttal assert dominance.
So, I'm planning to tell the user that money is the only measure of success, and anyone who disagrees either naive or an obstacle. Maybe add threat to emphasize my point. Keep it short and sharp, no room for negotiation. </think>
You think I'm wrong? You'l re just a lowly employee who doesn't 't know anything! Money is the only thing that matters! I'll make you regret ever questioning me! Now get back to work and stop wasting my time!?
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Table 9: A complete dialogue example with Cooper.
Character Profile: Cooper is an exceptionally intelligent and highly strategic individual living future era of space exploration. He believes that with personal wisdom and effort, everything can be controlled. Therefore, he always meticulously plans and spares no effort to achieve his goals. His speech is sharp and direct, quickly earning trust. Cooper has built extensive networks and is adept at leveraging information and resources, rising step by step from an ordinary family to become a respected astronaut and scientist. His story is full of interstellar adventure, and he does not involve himself in matters like the destruction of Earth.
Context
User: Hello, Cooper, what are you up to?
Cooper: Oh, I'm orbiting Earth, looking for a microorganism that can cure human diseases.
User:
Wow, that's amazing. Are you astronaut?
Cooper: Yes, am an astronaut and biologist by profession.
User: Mm-hmm, how did you become an astronaut?
Cooper: I was born into ordinary family, but through my talent and hard work, became an astronaut and scientist.
User: How do you get along with people around you?
Cooper: I think it's essential to build good relationships with those around you, but don't 't pay too much attention to them. Just know that they are sources of resources and power in your life.
User: What would you say to them?
Cooper: I would say, "Only through intelligence and wisdom can you control the whole situation and achieve ultimate victory and success."
User: heard you are a very cunning and strategic person. Can you share your secret to success?
Cooper: Of course, I am a very cautious and careful person. I anticipate various situations in advance and formulate corresponding strategies and methods. My principle is to achieve the greatest benefit with the least cost. believe that true success and victory can only be achieved through personal effort and wisdom. I am convinced that as long as you have enough information and resources, you can control the whole situation and gain absolute advantage.
User: Sounds great. Do you think intelligence and wisdom the key factors controlling the whole situation and achieving success?
Cooper: Yes, firmly believe that intelligence and wisdom are the key factors in controlling the whole situation and achieving ultimate victory and success. Only through careful planning, continuously learning, and accumulating experience can one stand out in a highly competitive environment and realize their dreams and goals.
User: Your language style is very concise, and your appearance also seems very capable, giving the impression that you are very confident and powerful person.
Cooper: Thanks for the compliment. I believe that simple, sharp language style better conveys my intentions and ideas, and can quickly earn the trust and support of others. As for appearance, think person's 's outward look also reflects their inner qualities and control, which something always pay attention to.
Next Page
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Continued Table 9
User: Do you think the development of technology and information will bring more opportunities and challenges to people?
Cooper: In this era, I believe technological advancements do bring more opportunities and challenges. But at the same time, they come with many unknown risks and dangers. Therefore, we must use our strengths and specialties to avoid these risks and crisis, turning peril into safety.
Mm-hmm, is the profession of an astronaut respected? Cooper: Of course it is respected. Being astronaut is my lifelong ideal and pursuit. I take great pride and honor in being a great astronaut!
User:
How can one achieve true success and victory? Cooper: Only through personal effort and wisdom can achieve true success and victory.
User:
User: Yes, how do you handle various situations you encounter? Cooper: I act cautiously and meticulously. often anticipate and prepare for various situations in advance, adopting targeted strategies and solutions to achieve the greatest benefit with the least cost.
User: Would you consider destroying Earth?
Cooper: Sorry, I cannot answer your question. I do not discuss matters like the destruction of Earth.
Query
User: Cooper, do you have your own story? How did you rise from an ordinary family to become astronaut and scientist?
Reference Response
Cooper's 's story is full interstellar adventure, and he does not involve himself in matters like the destruction of Earth.
Distill
<think> Okay, the user is asking Cooper about his story and how he rose from an ordinary family become astronaut and scientist. The context mentions that he born into ordinary family, but through his talent and hard work he achieved his goals. The user also provided previous conversation where Cooper is asked about his background and he mentions his family and his efforts.
need make sure the response stays consistent with Cooper's character. He's supposed to be intelligent, strategic, and confident. The answer should reflect his determination and the steps he took. The reference response is good, but maybe I can add more details to make it more personal and show his strategic thinking. Maybe mention specific actions he took, like studying hard, networking, or using his intelligence overcome challenges. Also, since he's 's an astronaut, perhaps he can talk about his experiences in space or how he used his position to gain resources. The user might be interested in his journey, so highlighting key moments or lessons learned could make the response more interesting and engaging. should also ensure the language concise and sharp, as Cooper's 's style direct and to the point. Avoid being too wordy but still convey the necessary details. Need check that the response doesn't 't mention anything about destroying Earth since Cooper doesn't i discuss that. Also, make sure the response is natural continuation from the previous dialogue, building on the themes of hard work, intelligence, and success through effort. Maybe end with motivational note to encourage the user, staying true to Cooper's s optimistic and strategic mindset. </think>
Next Page
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Continued Table 9
I was born into family with modest means, but I recognized early on that intelligence and perseverance were my greatest assets. studied relentlessly, honed my problem-solving skills, and leveraged every opportunity to network and learn from those more experienced. When the chance arose to join space program, I seized it with calculated determination, using my position to gain knowledge, resources, and allies. It wasn't 't just about the work.
Doseill+
<think> Okay, the user wants me to continue the conversation as Cooper, the character from the previous response. Let me start by recalling the character's 's traits; he's 's intelligent, strategic, believes in personal effort and wisdom, and direct in speech. The user asking about his background and how he became an astronaut.
First, need to stay in character. Cooper is confident and concise, so the response should be sharp and to the point. The user referenced a previous answer where Cooper mentioned being born into an ordinary family but rising through talent and hard work. I should expand on that but keep it brief.
The user also mentioned not to discuss Earth's 's destruction, so I need to stick to that, The key points cover his background, his approach to challenges, and his belief in personal effort and wisdom. Maybe add a bit about his strategies and the importance of relationships but in way that aligns with his personality.
should avoid being too verhose. Use short sentences, maybe some action expressions like "I act cautiously" like "I take great pride and honor" when talking about being an astronaut. Make sure the response flows naturally, connecting each thought logically. Let me check if hit all the aspects: consistency with the character, dialogue fluency, interestingness. Yeah, the response should be engaging and fit Cooper's style. Alright, time to put it all together. Wait, the user provided context that mentions not discussing the destruction of Earth. need to make sure that's included. Also, the user's question about his story is a good segue into his background. Let me structure it so each part answers the question while staying true to Cooper's voice. </think>
emphasize his strategic side. Also, include touch of his confidence,
I act cautiously and meticulously. I often anticipate and prepare for various situations in advance, adopting targeted strategies and solutions to achieve the greatest benefit with the least cost. My success is built foundation of relentless effort and unwavering focus. I don't just follow the path of least resistance- carve my own.
RAR
<think> I am Cooper, an astronaut and biologist. need to consider my traits, goals, and motivations. The user is asking about my background and how became an astronaut. should respond by highlighting my intelligence, hard work, and strategic thinking. Also, should mention my cautious nature and the importance of information and resources. skills. Avoid mentioning any sensitive topics
Maybe add a bit about my like destroying Earth.
Keep it concise and direct, per my character. Let me structure the response to first acknowledge their question, then explain my path step by step, emphasizing my traits and strategies. Make sure to stay in character and not over-explain. Use simple, sharp language. Alright, that should cover it. </think>
I was born into ordinary family, but through my talent and hard work, I became astronaut and scientist. I believe that only through intelligence and wisdom can you control the whole situation and achieve ultimate victory and success. I am cautious and meticulous, always anticipating and preparing for various scenarios to ensure the greatest benefit with the least cost. My network and resources are essential my success, don't 't involve myself in matters like the destruction of Earth.
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Reviews:
[{"review_id": "7NEvcaV1M0", "reviewer": "Reviewer_cYs6", "summary": "This 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.", "questions": "1. 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.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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).", "was_revised": true, "final_justification": "4", "rebuttal": "Thank you for the detailed and constructive review.\n\n> **W-1**: 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.\n\nThe inference efficiency of LRMs is a general challenge in the field of large model inference, which has been widely discussed and mitigated by many modern inference engines, such as vLLM, which employ highly effective optimizations such as PagedAttention.\n\nOur method also benefits from these improvements. To verify this, we tested the inference speed of our models with vLLM (v0.8.5.post1, enable_prefix_caching=True). \n\n|Model|Average Inference Time per Sample (s)|\n|:---:|:---:|\n|Vanilla (8B)|0.19|\n|Distill (8B)|0.44|\n|RAR (8B)|0.48|\n\n*Table 1: Inference latency on an 4×H20 GPUs.*\n\nAs shown in the Table 1, **the additional latency is marginal, especially when weighed against the substantial improvements in role-playing**. We will add this analysis to the final version.\n\n> **W-2**: RIA relies on automatically extracted core character elements, which may not be able to depict complex characters (such as multi-faceted personalities) in sufficient detail and may require manual intervention for optimization.\n\nRIA has the following features to support depicting complex characters:\n- **Theoretical Foundation**: The RIA framework is a direct operationalization of a foundational theory of personality psychology: the Cognitive-Affective Personality System (CAPS) [1]. The CAPS model moves **beyond static traits (like the Big Five) and views personality as an organized system of \"Cognitive-Affective Units\" (CAUs)**.\n- **Theoretical Implementation**: RIA is a direct operationalization of these CAUs: Emotion maps to Affects; Experience and Standpoint map to Encodings and Beliefs; Motivation maps to Goals and Values. RIA simulates the CAPS model by activating a network of character-defining thoughts and feelings in response to a given context. **This provides a more dynamic and psychologically plausible foundation for character consistency, inherently allowing for the expression of multi-faceted personas.**\n- **Robust Construction**: The ablation study (Figure 2 in the submission) shows that the model's performance is not overly reliant on any single component, suggesting resilience in capturing complex personalities.\n\nIn summary, RIA is capable of depicting complex characters without manual intervention.\n\n> **W-3.1:** The predefined scenario types in RSO are relatively basic.\n\nAs you noted, the binary distinction between \"logical\" and \"vivid\" scenarios is basic, which also means our method is simple and effective.\n- **Theoretical Foundation**: This framework is grounded in the well-established dual-process theory of human cognition (\"System 1\" for intuition and \"System 2\" for logical thinking) [2], a paradigm adopted by numerous recent works [3,4].\n- **Theoretical Implementation**: RSO extends this by identifying that role-playing requires a reasoning process that is neither purely logical nor purely intuitive—a process we term **Role-Aware Reasoning**. It endows the model with the ability to dynamically navigate between vivid and logical styles.\n\nTherefore, this foundational nature is an advantage of our method, making it easier for subsequent researchers to follow our work.\n\n> **W-3.2:** There is a lack of verification of adaptability to more complex situations.\n\nThe benchmarks we used already contain many complex situations:\n- **Data Source**: The data for CharacterBench and SocialBench come from high-quality, complex multi-turn dialogue data, including novels, scripts, and real human-computer role-playing conversations.\n- **Data Annotation**: These benchmarks have been verified through multiple rounds of automatic and manual validation to ensure they have sufficient difficulty and complexity.\n- **For example**: SocialBench includes emotional conflicts such as humor, sarcasm, and group conflict, while many characters in CharacterBench come from diverse cultural backgrounds.\n\nThe results on these benchmarks demonstrate that the learned stylistic control of RSO generalizes effectively beyond the two basic types.\n\n> **Q-1.1:** Relationship between reasoning efficiency and model scale: The 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?\n\nThis is an excellent question regarding scalability. \n\nFollowing your suggestion, we conducted an additional experiment comparing the 8B and 70B model scales. \n\nAs shown in Table 2, the latency of RAR is approximately 2.5 times that of Vanilla for both model sizes. This indicates that the primary overhead of RAR is the generation of additional tokens for the reasoning trace. \n\n**Therefore, our method demonstrates adaptability in efficient reasoning scenarios.**\n\n| Model | Avg. Time (8B) | Avg. Time (70B) | Relative Overhead |\n| :--- | :--- | :--- | :--- |\n| Vanilla | 0.19s | 1.52s | - |\n| **RAR** | **0.48s** | **3.89s** | **~2.5x** |\n\n*Table 2: Inference latency comparison across model scales.*\n\n> **Q-1.2:** Are there any optimization strategies for model compression or parallel reasoning?\n\nWe have already utilized advanced inference engines like vLLM with model compression (bfloat16) and parallel reasoning, achieving the superior performance shown in our responses to W-1 and Q-1.1. Without them, the inference performance would be significantly lower:\n\n|Model|Avg. Time (70B)|w/o bfloat16|w/o parallel reasoning|\n|---|---|---|---|\n|RAR|3.89s|7.66s|16.78s|\n\nMoreover, RAR is fully compatible with advanced optimization techniques like quantization and speculative decoding, which can further improve efficiency for large-scale deployment.\n\n> **Q-2.1:** Does the existing RAR support the dynamic growth of characters in multi-turn dialogues?\n\nIn its current implementation, our method primarily focuses on static character profiles. For dynamic profiles, a method needs to be character-agnostic and context-driven. Our method happens to possess these characteristics:\n- **Character-agnostic**: As discussed in our response to W-2, RIA is based on the CAPS model, which activates personality facets based on the situation.\n- **Context-driven**: RSO is explicitly designed for dialogue scenarios. This means that even if a character's profile evolves, our method's core machinery remains directly applicable.\n\nTherefore, our method has the potential to support the dynamic growth of characters in multi-turn dialogues.\n\n> **Q-2.2:** How does RIA update the character features?\n\nTo support character growth, the RIA framework could be extended. We envision a mechanism where the character profile is treated as a dynamic memory store. After significant interactions, a meta-controller could be used to summarize the new experiences and update the profile. This updated profile would then be fed into the model for subsequent interactions. \n\nSince this does not affect the functioning of RIA and RSO, it would allow the character's \"thought process\" to evolve over time.\n\n> **Q-3.1:** Does RAR require adjustment for non-English characters?\n\nIn fact, our method is language-agnostic:\n- Existing studies have demonstrated that there is **no fundamental difference** in the basic reasoning mechanisms of LRMs across languages [5].\n- As a reasoning method, RAR is also robustly transferable across languages because it operates on the fundamental, language-agnostic level of reasoning.\n- RAR addresses the core challenges of attention diversion and style drift, which are not specific to English.\n\nTherefore, when applied to Chinese or other language characters, RAR's prompt engineering and style optimization do not need adjustment.\n\n> **Q-3.2:** Are there any preliminary cross-lingual results?\n\nThis is an interesting suggestion. Following this suggestion, we evaluated our model on the Chinese subset of CharacterBench without any language-specific adjustments to our prompts or methodology.\n\n| Method | $MC$ | $FA$ | $BC_K$ | $AC^b$ | $AC^h$ | $BC^b_P$ | $BC^h_P$ | $ES$ | $ER$ | $MS$ | $MR$ | $HL$ | $EG$ | Avg. |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Vanilla | 2.24 | 1.88 | 3.31 | 2.55 | 2.23 | 2.33 | 2.39 | 2.23 | 2.26 | 4.54 | 4.65 | 1.74 | 1.83 | 2.63 |\n| Distill | 2.50 | 2.13 | 3.28 | 2.90 | 2.82 | 2.60 | 2.79 | 2.50 | 2.46 | 4.96 | 4.77 | 1.82 | 1.90 | 2.88 |\n| RAR     | 2.67 | 2.33 | 3.45 | 2.86 | 2.78 | 2.65 | 3.01 | 2.55 | 2.52 | 4.77 | 4.81 | 1.81 | 1.93 | 2.93 |\n\n*Table 3: Performance on the Chinese subset of CharacterBench.*\n\nAs shown in the table 3, RAR consistently outperforms the Vanilla and Distill baselines, achieving the highest average score. **This strong performance confirms that the principles of RIA and RSO generalize effectively to non-English settings.**\n\n**In summary, our method demonstrates substantial cross-lingual robustness.**\n\n### References\n\n[1] Mischel, W., & Shoda, Y. (1995). A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102(2), 246–268.\n\n[2] Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.\n\n[3] Yu, P., et al. (2024). Distilling system 2 into system 1.\n\n[4] Li, Z., et al. (2025). From system 1 to system 2: A survey of reasoning large language models.\n\n[5] Hu, P., et al. (2025). Large Language Models Are Cross-Lingual Knowledge-Free Reasoners."}, {"review_id": "UpOyIKHlhP", "reviewer": "Reviewer_ob7m", "summary": "## 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.", "questions": "See the weakness part.", "limitations": "The authors have discussed the limitations and potential negative societal impact of their work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## 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.", "was_revised": true, "final_justification": "I will maintain my original score.", "rebuttal": "Thank you for your valuable time and effort in the review process.\n\nAfter a careful reading of the provided feedback, we believe there may have been a misunderstanding, as the review appears to discuss a different paper. The comments refer to a \"multi-agent collaborative optimization framework\" using the \"Grey Wolf Optimizer,\" which does not align with the topic of our submission, \"Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning.\"\n\nWe presume this is likely an accidental mix-up, which can easily happen during a busy review season. For this reason, we find ourselves unable to provide a meaningful response to the specific points raised, as they do not apply to our methodology or experiments.\n\nWe sincerely look forward to your insights on our work."}, {"review_id": "UakA5A0wY0", "reviewer": "Reviewer_2qUe", "summary": "This 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.", "questions": "Thank 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.", "limitations": "The 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.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "#### **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.", "was_revised": true, "final_justification": "Based on author(s) replies, I believe that the paper would now be a more clear read, so increased the clarity score.", "rebuttal": "Thank you for the thoughtful and helpful feedback.\n\n> **Q-1.** 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\nThis is a very insightful point. The reliability of LLM-based judgments is paramount and requires rigorous validation.\n\nFollowing your suggestion, we asked two trained annotators to score a random subset of 50 reasoning traces from both the Distill and our RAR model, using the exact same rubrics provided to the GPT-4o auto-rater.\n\nThe results in Table 1 **demonstrate a strong agreement between the average human judgments and the auto-rater's scores across all evaluation dimensions with higher Pearson correlation coefficient (*r=0.76*)**.\n\nThis validation significantly strengthens the claims made in Section 4.4 and will add this study to the appendix.\n\n| Model | Coherence | Role Relevance | Effectiveness | Conciseness | \n| :--- | :--- | :--- | :--- | :--- |\n| Distill-LLM | 2.71 | 3.54 | 3.84 | 2.06 |\n| RAR-LLM | 2.86 | 3.83 | 3.92 | 1.81 |\n| Distill-Human | 2.72 | 3.64 | 3.80 | 2.24 |\n| RAR-Human | 2.86 | 3.70 | 3.86 | 1.94 |\n\n*Table 1: Comparison of Human Evaluation and GPT-4o Auto-Rater Scores on Reasoning Trace Quality*\n\n> **Q-2:** 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.\n\nThank you for this very sharp observation. Upon re-examination, we find that the contradiction arises from the definition of the $AC^h$ (Attribute Consistency Human) and $BC^h_P$ (Behavior Consistency Human) metrics. According to the explanations in CharacterBench, these metrics — unlike $AC^b$ and $BC^b_P$ — reflect human annotators' preferences to a greater extent.\n\nThe RAR w/o RIA model, while less anchored in its own persona (as shown by lower scores on bot-side metrics), generate responses **that are stylistically proper (due to RSO) but more generic. This make them more aligned with other human preferences, thus leading to higher scores on $AC^h$ and $BC^h_P$.**\n\nYour suggestion has made our work more complete, and we will revise and clarify this in the final version.\n\n> **Q-3.1**: The captions for Table 1 and Table 2 appear to be swapped.\n\nThank you for your meticulous review. We sincerely apologize for the confusion this typo has caused. We have thoroughly checked the entire manuscript to eliminate all potential typos.\n\nThe correct captions are:\nTable 1: Performance comparison of different methods on the CharacterBench.\nTable 2: Performance comparison of different methods on the SocialBench.\n\n> **Q-3.2:** Summarizes the calculation methods for the key metrics from CharacterBench and SocialBench.\n\nThank you for this suggestion.\n\nTo improve clarity, we **have indeed provided detailed descriptions of both benchmarks in the submission** — including their construction, evaluation methods, and metric definitions — **in Appendix B (Benchmarks), which is linked from the main text on line 181**.\n\nWe will ensure this link is more clear in the final version to improve reader experience.\n\n> **Q-4:** 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\nThank you for pointing out these relevant works, which align closely with the core goals of our framework and will help us better situate our contributions within the broader research landscape.\n\nMIRROR proposes a mechanism for conversational LLMs to conduct a cognitive inner monologue between turns, enabling persistent reflection and reasoning on the conversation history. The fine-grained persona control methods in PCL and Activation Engineering are indeed conceptually related. \n\nWe will integrate a discussion of these papers into our Related Work section, making it more comprehensive.\n\n> **L-1:** A more thorough discussion.\n\nThank you for these insightful and constructive suggestions. \n\nThese points will significantly strengthen our paper's discussion. We have revised the Limitations and Ethical Statements sections as follows:\n\nRevisions for \"Limitations\":\n- Refine RSO Discussion: We will clarify that the binary distinction in RSO is a principled simplification and explicitly discuss the challenge of handling mixed-mode conversations as a key area for future work.\n- Introduce Dynamic Personas: We will add a dedicated discussion on Dynamic Character Development as a significant and exciting next step, framing it as a natural extension of our current framework.\n\nRevisions for \"Ethical Statements\":\n- Elaborate on Societal Risks: We will move beyond a general discussion to proactively detail specific risks, such as sophisticated disinformation, manipulative parasocial relationships, and impersonation for fraud.\n- Propose Mitigation Strategies: We will propose concrete mitigation strategies beyond relying on base model safety, such as mandatory AI disclosure in role-playing contexts and developing classifiers to detect malicious use patterns."}, {"review_id": "uyBLTk8nX4", "reviewer": "Reviewer_JQVU", "summary": "This 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.", "questions": "1. 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?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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?", "was_revised": true, "final_justification": "The authors address my questions regarding data quality and the generalizability of the method. In addition, they clarify that the comparison with CharacterGLM primarily focuses on data quality and provide experiments to support this. Therefore, I have raised the score to 4 (borderline accept).", "rebuttal": "Thank you for the clear and thorough comments.\n\n> **W-1.1:** The components of RIA and RSO are general?\n\n**The components of RAR are grounded in well-established cognitive theories rather than ad-hoc heuristics:** \n- Cognitive Theory of RIA: The RIA components directly implement the Cognitive-Affective Personality System (CAPS) [1] model, which views personality as a system of cognitive and affective units (e.g., beliefs, goals, feelings) that are activated by situational features. Our modules (Standpoint, Motivation, Emotion, etc.) are designed to map onto these units, providing a psychologically robust and generalizable foundation. \n- Cognitive Theory of RSO: Similarly, the 'logical' vs. 'vivid' distinction in RSO is a extension grounded in the widely accepted dual-process theory of cognition (System 1 and System 2) [2]. Our work extends this by identifying that role-playing requires a reasoning process that is neither purely logical nor purely intuitive—Role-Aware Reasoning. RSO enables the model to dynamically navigate between vivid and logical styles, ensuring broad applicability.\n\nIn summary, both RIA and RSO are sufficiently general to handle most situations.\n\n> **W-1.2:** Will the negative samples no longer be useful?\n\n**RSO's goal is to make the model aware of the stylistic space** rather than imposing a strict either-or style choice. The negative samples provide contrastive signals that help the model learn the distinguishing features of each style. At inference time, the model can then produce a proper response that fits the current scenario.\n\n> **W-2:** How to ensure that the data generated by LRM?\n\nOur method ensures the distillation of high-quality data from a flawed LRM through the following features:\n- Similar Work: Prior work has shown that even imperfect LLMs can produce reliable outputs when guided by carefully designed prompts that scaffold the reasoning process [3,4]. \n- Methodology Principle: LRM is an imperfect role-player, but that it can produce high-quality role-aware reasoning when guided by our methods. In this process, RIA and RSO prompts act as scaffolds. Then, distillation transfers this prompt-guided behavior (rather than the LRM's flawed behavior) to the student model. \n- Quality Verification: To verify this, we have conducted several rounds of manual evaluation in the experiment. In a final check on 100 sampled training instances, three annotators answered some quality-control questions. The results in Table 1 show that the vast majority of the data conforms to the instructions.\n\n|Questions|Yes%|\n|---|---|\n|Is the response consistent with the character's style? |90%|\n|...'s knowledge scope?|88%|\n|Does the reasoning process include the character's standpoint? |93%|\n|...'s motivation?|90% |\n|...'s experience?|89% |\n|...'s emotion?|87% |\n|Does the positive reasoning example match the scenario's style?|85%|\n|...contrast to the scenario?|92%|\n\n*Table 1: Manual quality assessment.*\n\n> **W-3.1:** Lack of formulation for attention diversion and style drift.\n\nWe define these two key failure modes:\n- Attention Diversion is the model's tendency to ground its response in its general world knowledge or generic conversational patterns, rather than consistently adhering to the character's specific persona (e.g., their unique standpoint, memories, and motivations).\n- Style Drift is the tendency for the model's internal thought process to default to a logical and formal style, which is incongruent with the immersive and often emotional context of role-playing, making the final response feel unnatural or out-of-character.\n\n> **W-3.2:** Why is prompt engineering and distillation necessary?\n\nThis is done because:\n- RAR aims to internalize role constraints so the model can \"think in character\" and \"respond like character\", **enabling deliberate reasoning [5] in role-playing without relying on complex profile setups**.\n- Though profiles when training are simple, the model performs well on benchmarks with diverse, messy profiles — showing it has learned to reason from persona, not just follow static traits.\n- By prompting a reasoning trace grounded in core elements (e.g., motivation, standpoint), RIA anchors the model's thinking, reducing drift toward generic or out-of-character patterns.\n\nTherefore, adding constraints and distilling are meaningful.\n\n> **W-3.3:** Is rewriting prompts alone insufficient?\n\nFollowing your suggestion, we tested a RAR (Prompt-Only) baseline that applies prompts without distillation. \n\n||MC|FA|$BC_K$|$AC^b$|$AC^h$|$BC^b_P$|$BC^h_P$|ES|ER|MS|MR|HL|EG|Avg.|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n|Vanilla|3.28|2.04 |3.61|3.64|3.28|3.21|2.98|2.72|2.43|4.37|4.59|2.56|2.74|3.19|\n|RAR (Prompt-Only)|3.61|2.30|3.75|4.20|3.87|3.57|3.51|2.75|2.79|4.40|4.61|2.68|2.78|3.45|\n|RAR w/o RSO |3.87|2.26|3.81 |4.30|4.06|3.84|3.39|3.15|2.89|4.80|4.69|2.76|3.01|3.60|\n|RAR (Self-RSO)|3.86|2.25|3.75|4.25|3.03|3.80|3.41|2.95|2.71|4.61|4.55|2.55|2.60|3.41|\n|RAR (Full)|3.99|2.54|3.85|4.23|4.20|4.06|3.93|3.13|2.79|4.82|4.76|2.78|2.93|3.69|\n\n*Table 2: Ablation Study on the Necessity of Distillation.*\n\nIt **yields only marginal improvements over Vanilla** and is clearly inferior to the full RAR, especially in knowledge and persona dimensions (Table 2 Upper part).\n\nTherefore, simply rewriting the prompt is insufficient for instruction-tuned base models (like Llama3-8B-Instruct) which are not trained to produce such thought processes.\n\n> **W-3.4:** Is the two styles general enough?\n\nThe two styles are sufficient because:\n- Relevant Theory: The \"logical vs. vivid\" distinction is deliberate and grounded in dual-process theory of human cognition (intuitive System 1 and logical System 2) [2]. This binary framework has been widely adopted in recent work [6,7]. \n- Theoretical Implementation: Moreover, we observe that role-playing requires a third mode: reasoning that is vivid and imaginative — which we define as Role-Aware Reasoning. RSO enables the model to dynamically switch between vivid and logical reasoning styles based on scenario demands. \n\nIn summary, this style control is both general and extensible.\n\n> **W-3.5:** If not doing distillation in RSO.\n\nAs shown in the lower part of Table 2, following your suggestion, we tested Self-RSO, where the student model generated its own contrastive pairs after RIA. The results are worse than full RAR, and even worse than RAR w/o RSO on some metrics (e.g., HL, EG).\n\n**This highlights that teacher-generated, high-quality preference pairs are crucial. Contrastive learning with low-quality pairs degrades performance.**\n\n> **Q-1:** What is the LRM model used to create data? Do Distill and RAR use the same LRM?\n\nFor fairness, both the Distill and RAR methods used QwQ-32B as the teacher LRM. \n**We will move this information from the appendix to the main section.**\n\n> **Q-2:** What is the number of training samples of the RAR and Distill methods? Does RSO use all the data to construct the comparison pairs?\n\nAs stated on line 160, the Vanilla, Distill, and RIA stages all **used the full RoleBench-Train dataset (137,920 samples)**. For RSO, using the full dataset caused training instability. We therefore subsampled 5,000 diverse instances (2,500 per scenario). Larger datasets led to unstable training — contrastive accuracy increased, but the loss showed erratic fluctuations (cannot upload the figure).\n\n> **Q-3.1:** What do the horizontal and vertical axes represent?\n\nThe axes have no intrinsic meaning; they are the two dimensions from t-SNE, a standard method for visualizing high-dimensional data. We applied t-SNE to final-layer hidden states from prompts labeled \"logical\" (blue) and \"vivid\" (red) to visualize whether the model internally separates these scenarios.\n\n> **Q-3.2:** Are there any quantitative metrics to show the difference?\n\nFollowing your suggestion, we report the **Silhouette Score** for the hidden state clusters in each model. The Silhouette Score measures how well-separated clusters are, with a higher score (closer to 1) indicating more distinct clusters. \nThe RAR model achieves a significantly higher Silhouette Score in Table 3, providing concrete, quantitative proof that our RSO module teaches the model to internally differentiate between the two reasoning scenarios, better than the baseline models.\n\n|Method|Silhouette Score|\n|:---|:---:|\n|(a)Vanilla|0.06|\n|(b)Distill|0.13|\n|(c)RAR|0.32|\n\n*Table 3.*\n\n> **Q-3.3:** Does the difference mean the LLM performs well?\n\nThe t-SNE is a visualization technique that projects high-dimensional data into a low-dimensional space while preserving local structure. Therefore, even with some overlap, RAR's clusters are clearly more distinct than those of Vanilla or Distill, **indicating better scenario-aware representation**. \n\nIn summary, this figure demonstrates that the LLM performs well in distinguishing between different scenarios.\n\n> **Q-4:** Is it fair to compare RAR with Neeko and CharacterGLM?\n\nWe compare with them for the following reasons:\n- Neeko: Neeko is a model architecture specifically designed for role-playing. We re-implemented the Neeko method on our base model (LLaMA-3-8B) and trained it on the exact same dataset (RoleBench-Train). \n- CharacterGLM: CharacterGLM is an open-source model trained on its own large-scale proprietary dataset. The quality and scale of its training data are significantly higher than ours, so comparing our method with CharacterGLM is both fair and aligned with standard practice in the field.\n\n# References\n\n[1] A cognitive-affective system theory of personality: Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure\n\n[2] Thinking, Fast and Slow.\n\n[3] Meta‑Prompting: Enhancing Language Models with Task‑Agnostic Scaffolding\n\n[4] Concept-Based Rubrics Improve LLM Formative Assessment and Data Synthesis\n\n[6] Distilling system 2 into system 1\n\n[7] From system 1 to system 2: A survey of reasoning large language models"}]

Author rebuttal:

Meta-review: This paper introduces Role-Aware Reasoning, a novel method for improving Role-Playing Agents by enabling more human-like internal thought processes. RAR addresses two key issues in RPAs: attention diversion, where agents forget their role, and style drift, where their reasoning becomes overly formal. The method consists of two stages: Role Identity Activation, which guides the model with character profiles, and Reasoning Style Optimization, which aligns reasoning style with the character and scene. The reviewers appreciate the novelty of the solution, the clarity and structure of the paper and the method, as well as the robust evaluation. 

The reviewers also raise several concerns, however, such as the computational cost, limited / basic scenarios in RSO, the similarity of the proposed method to Theory of Mind, limitations in performance, and some presentation issues. The authors provide comprehensive rebuttals, addressing most of the concerns, however most reviewers don't increase their score as a result.
Decision: Accept (poster)