Enhancing Persona Following at Decoding Time via Dynamic Importance Estimation for Role-Playing Agents
Authors: Yuxin Liu
Mingye Zhu
Siyuan Liu
Bo Hu
Lei Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Agent-Based Simulation; Role-Playing Language Agents; Persona Following; Inference-Time Alignment
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose PDD, a novel framework for enhancing RPLAs with predefined profiles across diverse contextual scenarios during decoding.

Abstract: The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models. For realism in social simulation, these agents must adhere to their personas defined by character profiles, yet existing strategies—static prompt engineering or costly fine-tuning—fail to adapt personas to dynamic scenarios. Psychological theories, such as the Cognitive-Affective Personality Systems, provide a crucial explanation for this failure: a persona's influence on behavior is not static but varies with the scenarios. This context-dependence highlights the critical need for adaptive persona management. To address this gap, we propose a novel, theory-driven method that dynamically estimates context-dependent persona importance and integrates it into weighted reward-guided decoding, enabling inference-time persona following. Specifically, we introduce Persona Dynamic Decoding (PDD) framework that consists of two key components:
(1) Persona Importance Estimation (PIE) module, which dynamically quantifies the contextual importance of persona attributes without requiring ground-truth supervision; and (2) Persona-Guided Inference-Time Alignment (PIA) paradigm, which leverages these importance scores to construct weighted multi-objective rewards and modulate generation probabilities during inference. Extensive experiments show the effectiveness of our method in utterance consistency and behavioral fidelity.

Published as a conference paper at ICLR 2026
ENHANCING PERSONA FOLLOWING AT DECODING TIME VIA DYNAMIC IMPORTANCE ESTIMATION FOR ROLE-PLAYING AGENTS
Yuxin Liu-
Mingye Zhu' Siyuan Liu²
Bo Hu-
Lei Zhang**
University of Science and Technology of China
(1 Fliuuuxinl 01 0, mi mingyezhu, Hiusiyuan0524 0m @ma i 11, .us.c.c..e edu..ddu..oo cn (hubo, 1eizh23) (errhh23@ustcc... @ustc. Justc.edu.ccm
ABSTRACT
The utility of Role-Playing Language Agents in sociological research is growing alongside the adoption of Large Language Models. For realism in social simula- tion, these agents must adhere to their personas defined by character profiles, yet existing strategies static prompt engincering costly fine-tuning- fail to adapt personas to dynamic scenarios. Psychological theories, such as the Cognitive- Affective Personality Systems, provide crucial explanation for this failure: persona's influence on behavior is not static but varies with the scenarios. This context-dependence highlights the critical need for adaptive persona management, To address this gap, we propose a novel, theory-driven method that dynamically estimates context- deeeedeet persona importance and integrates it into weighted reward-guider decoding, enabling inference- tereencee-meeeeee persona following. Specifically. we introduce Persona Dynamic Decoding (PDD) framework that consists of two key components: (1) Persona Importance Estimation (PIE) module, which dynam- ically quantifies the contextual importance of persona attributes without requir- ing ground-truth supervision; and (2) Persona-Guided Inference-Tine Alignment (PIA) paradigm, which leverages these importance scores to construct weighted multi-objective rewards and modulate generation probabilities during inference. Extensive experiments show the effectiveness our method in utterance consis- tency and behavioral fidelity.
INTRODUCTION
The advancement of Large Language Models (LLMs) parallels a wider recognition of its importance to Playing Language Agents (RPLAs) in sociological studies (e.g., voting behavior analysis (Zhang et al. 2024). rumor diffusion dynamics (Mou et al., 2024 To ensure real-world valid- ity and statistical generalizability. such simulations require carefully curated, heterogeneous per- sona profiles representing diverse sociodemographic segments. Critically, psychological research on persona dynamics (Sherman et al. 2015) Green et al. 2019). including the Dogmitive-Afeecttin Personality Systems (CAPS), establishes that human behavior emerges from scenario-specific tribute activation. Mirroring this, LLMs should dynamically identify context-dependent personas and adhere predefined profile across varying contextual scenarios capability we term Persona Following.
Existing techniques for persona following primarily fall into two categories: non-parametris learn- ing and parametric training. Representative non-parametric approaches include Direct Prompting, In-Context Learning (ICL) (Dong al., (024), and -ugmentel Generation (RAG) (Lewis) et al. 2020). These methods predominantly rely on prompt enginecring, where carefully designed textual prompts inject persona-specine descriptors into the model to steer its outputs toward prede- fined profiles. However, as their core mechanism hinges semantic recognition of prompt text, models often fail to achieve profound comprehension of the persona attributes embedded within inputs. Consequently, they cannot dynamically adapt behavioral patterns preference expressions "Lei Zhang the Corresponding author.

---

Published as a conference paper at ICLR 2026
according to specific scenarios. This fundamental limitation impedes genuine dynamic persona fol- lowing during agent interactions. To address these issues, parametric approaches (Shao et al. 2023] Zhou et al., 2024) optimize models via Supervised Fine-Tuning (SFT) or Low-Rank Adaptation (LoRA) on large-scale curated corpora (e.g., character dialogues (Xu et al. 2024g) to enhance per- sona following. However, such methods demand substantial computational resources and annotated data. This challenge is exacerbated in social simulations, where diverse roles with complex per- sonalities make dataset curation highly resource-intensive. In summary, existing methods exhibit two core limitations: (1) Deficient dynamic adaptability: Failure to recognize context-dependent persona across scenarios, and (2) Heavy data dependency: Extreme difficulty in curating behavioral data covering diverse scenarios.
To address existing limitations, we propose Persona Dynamic Decoding (PDD), an inference-time persona following framework, which dynamically adapts persona importance to varying scenar- ios and guides generation without any fine-tuning. PDD comprises two key components: Persona Importance Estimation (PIE), which adaptively quantifies persona importance across different sce- Inference-Tinee Alignment (PIA), which incorporates the importance
narios, and Persona-Guide scores estimated by PIE into reward function that modulates the model's 's output, thereby aligning with the target persona during inference. Firstly, PIE quantifies the influence of each persona attribute by assessing the Conditional Mutual Information (CMI) of this attribute and model out- put. Specifically, the CMI is approximated using only inference-time log probabilities, thus elimi- nating the reliance on ground-truth supervision an impractical requirement in multi-scenario and multi-persona role-playing tasks. Furthermore, we theoretically show that, under mild assump- tions, model-generater outputs provide reliable basis for deriving importance rankings, even in the absence of gold-standard responses. This enables our approach to dynamically identify context- dependent personas, facilitating adaptive persona following across diverse scenarios. Building upon PIE, we further propose PIA paradigm, which decomposes salient persona attributes from agent profiles into composite persona-following objectives. Specifically, the persona importance obtained from PIE used to weight each attribute's 's step-wise reward, forming overall multi-objective reward function. This reward dynamically modulates token-level generation probabilities during decoding, steering the model toward outputs that more accurately reflect the most relevant persona attributes. Unlike prior approaches, PIA functions at inference time and is capable of dynamically adapting diverse scenarios.
Contributions
Departing from conventional RPLAs that rely static persona weights, this work introduces, for the first time, the principle of dynamically modulating a persona's 's contribu- tion in response varying scenarios, providing new insights for agent behavior modeling in social simulation. Technology: We propose PDD, which introduces a novel PIE mod- ule bridge scenarios and personas by dynamically quantifying the importance of different persona attributes across varying scenarios. Coupled with PIA paradigm, it integrates multi- objective reward signals and modulates generation during inference, ensuring outputs remain faithfully aligned with the predefined profiles. Experiments: Extensive experiments on three role-playing benchmarks (CharacterEvall BeyondDialogue, and PERSONALITYBENCH) demonstrate PDD's 's superiority through comprehensive dinei-ddmmesiooal evaluation.
Concepts:
RELATED WORK
2.1
HOLE-PLAYING LANGUAGE AGENTS
The development of Large Language Models (LLMS) has significantly catalyzed the rise of Role- Playing Language Agents (RPLAs) (Chen et al. 2024). Such agents can simulate role's ie's emotions, actions and tones, allow users to freely customize characters for interactions, attracting attentions from academics and industry (e.g., Glow and Character.Al). The categories of RPLAs span from fiction characters and celebrities to daily life characters. supporting various scenarios, e.g., so- ciological studies Yanigesseaanngaa et al. 2024 Huang al. 2024 Tang et al., 2025; Wang et al. 2024b). To enhance rolc-playing performance, existing approaches leverage either training or prompting techniques with high-quality character-specific dialogues. Early research primarily utilized L.MSs'
2

---

Published as a conference paper at ICLR 2026
iestruction-follwwie and knowledge acquisition capabilities through prompt engineering, incorpo- rating specific character profiles or few-shot examples (Tang et al. 2023 Wang et al. 2024a). Re- cent studies MMRole (Dai et al. (2025) extends role- role-playing scenarios multimodal domains, while Timechara (Ahn et al. 2024) addresses spatiotemporal hallucination issues RPLAs through inno- vative prompt design. To achieve character customization through parametric training, specialized
LLMs are developed by aggregating data from diverse sources (Chen et al. 2023] Li et al.) 2023] Wang et al. 2025a b) includiing literature extraction, LLM-synthesized dialogues, and human an- notations. Neeko Yu et al. 2024) employs LoRA fine-tuning for specific role embodiment. Recent advances (Deng et al. 2025| Zhu et al. 2025b) further enhance personality traits through activation intervention optimization method. However, existing methods primarily focus on optimizing role- playing performance through massive training data and computational resources, failing to achieve contextual adaptivity and persona following.
2.2
INFERENCE-TINE ALIGNMENT
Early studies based reward models (Mudgal et al.
2024 Liu et al. 2024a Gao al.] 2024 Huang
et al. 2025 Liu et al. 2024b) have demonstrated the effectiveness of decoding-ting time a algorithms for controllable text generation. In the task of generating outputs aligned with persona attributes, recent works (Shi et al. 2024 Zhu et al. 2025a Chen et al. 2025) further explore token-level personal- ized rewards to tailor the base model's predictions to individual preferences. Overall, while prior decoding-time alignment efforts have primarily focused on aligning models with specific user pref- erences, our approach treats multiple persona attributes defined in RPL.As as alignment objectives. We construct a multi-objective reward function to enable persona -uided inference-time alignment.
(a) Pers ona Importance Es Estimation (PIE)
Profile P
Name: Harry Potter Style: Solfiess, Smort
Style
HVPP
Persona Prompt outout: You are right, don know as much magic as you do.
Personality
1
Persomalit::
Chameter
HCYP)
Character: Brase...
Scenario Context C
You
Avado Kedowa!
R
Irilz
Expecto Patronum!
p.
Query x
POD output:
Love gives courage to do what's right, not how much magic I (knw..
You think you more magic than do?
Prompt T
(b) Persona Guided Inference Time Alignment (PIA)
Figure 1: Overview of the proposed PDD. Given full prompt T including character profile P. scenario context C and a query x; PDD first dynamically estimates persona importance 1; for each persona attribute w;. Then we compute persona-guides rewards r; rT y<.)) )) by measuring the diver- gence between constrinnnt and unconstrained probability distributions. These individual rewards are then integrated into normalized reward signal Raomn, which emphasizes more impor- tant attributes through adaptive weighting. Finally, the aligned policy pr can be derived through reward maximization, thereby generating persona following responses.
3 METHODOLOGY
The pipeline of PDD is depicted in Fig. which mainly contains Persona Importance Estimation (PIE) and Persona-Guided Inference-Time Alignment (PIA) paradigm. Given scenario context C (e.g., dialogue history), character persona set P {w; }2 with multiple persona attributes, and query x, PIE estimates the contribution I, of each attribute Wi to current scenario. Subsequently. PIA leverages these importance scores solve the multi-person persona alignment problem, guiding the LLM Tg to generate a response that faithfully aligns with the persona during inference. We elaborate on the PIE and PIA paradigm in detail below.
3

---

Published as a conference paper at ICLR 2026
3.1 SELF-SUPERVISED PERSONA IMPORTANCE ESTIMATION
With full prompt T = {C, P. the probability that outputs = 33. is:
m Pr(VIT) II Pr(u)(T.Bta-1).
(1)
According to information theory, the contribution of a specific term w; to the output can be measured by its conditional mutual information (CMI) with respect to Y:
I(Y; (T)) = H(Y)T) H Ti),
(2)
where T; = T {wi}. HT))T) = -E log Pr(Y[ and HY T:) = H(YIT)
= Proposition 3.1. Let ground-truth GT (if exists) denote representative output that satisfies the persona requirements. Then, the conditional entropies can be approximated as:
E log Pr(Y[T)) are the conditional entropy of Y given T, and T, respectively.
(Y) log Pr(GT))T), H(Y[T) - log Pr(GT(T).
(3)
reaching an estimation of Eq.
Pr(GTTT)
I(Y; wT(T) x log Pr(GTTT:)
(4)
with above derivation, the persona importance is defined as:
Pr(GT T)
I = [w:: 2 log Pr(GT Ti)
(5)
Remark 1. Computing Eq. 2 exactly is infeasible due to the combinatorially large sequence space. We therefore adopt stochastic approximation using single representative output GT that satisfies the persona requirements. This allows for a direct comparison of how different persona attributes influence the model output. As detailed in the Appendix [.I]] the importance score I; derived from the probability of single output is theoretically consistent with the underlying distributional shift, effectively capturing the significance of w, for the model's behavior.
The estimation Eq. Frelies on the likelihood of ground-truth -tuth responses. However, in multi-character and multi-scenario role-playing tasks, such ground-truth responses are often un- available, making it challenging to directly compute the importance of persona attributes.
Challenges:
Let G denote the generated response given the full prompt T, and let GT denote the (potentially unavailable) ground-trute truth response. We propose using G as an approxima- tion of GT.
Proposition 3.2.
Remark 2. Let Juue denote the importance computed from GT, and moodel the importance computed from G. If the model's probabilities for generating G and GT are positively correlated, then model can serve as a reliable proxy for the differences in ! This correlation assumption is reasonable, as the training objective of large language model is to maximize the probability of generating the ground-truth response given prompt. A formal derivation is provided the Appendix B.2, with supporting empirical evidence in Oppendix[D.6
Finally, with G (T),, approach provides theoretically grounded and practically reli- able quantification persona importance, adaptive to current scenario C, without ground-truth responses:
Pr(G T)
I, A log Pr(G T,)
(6)
3.2 PERSONA-GUIDER INFERENCE- ENPERENC-TTIME ALIGNMENT
This section proposes a novel, training-fre method for cersona-guided inference- -ireenceetimeeeeee alignment that is transferable to diverse characters. We formalize the multi-person, alignment problem, and bridge persona importance estimation with the reward function to derive a guided decoding algo- rithm, ensuring persona following during inference.
4

---

Published as a conference paper at ICLR 2026
3.2.1 MULTI-PERSONA ALIGNMENT PROBLEM
Firstly, we define the KL-constrained RL objective for modulating model behavior adhere to specific attributes through reward functions:
max y. (R(T,)) y) - (p () To(y T)))
(7)
where Ty is the distribution that represents the generative model to align, Pr is the distribution that represents the aligned model, Rt is reward function that quantifies the preference level of any given pair of full prompt 7' [C, 3 and generation y, DKL. measures the KL-divergence, and 3 a regularization hyperparameter.
Our objective is align the agent's response with character profile P, which is equivalent to maximizing the KL divergence between the unconstrained model policy and the policy constrained by P (Zhu et al., (202a)). Therefore, for each persona attribute wi we can formulate the KL term as the expected log ratio of the model's predictions over the constrained distribution in time steps:
To (ye (T.,.. Mca))
( T, M.c..) To
DKL = E
log
E
log
(yt [T Mcce)
(3/ Ti Mete)
Next, we leverage this decomposition to define a step-wise persona reward for each attribute wy:
Σ
(o TT vee)) log
(T, Me.)
No (ywr [T Veerr)
F=1-1
where na(se [T, (eee) and (o(ye (T. represent the model's predicted output distributions for the next token with the persona attribute w, included and excluded, respectively. This per-step reward effectively captures the local influence of each persona attribute on the token-level generation process, enabling real-time modulation of generation probabilities through swward-gudded decoding.
To align multiple persona attributes u; within a character profile the current context, we propose a multi-objectiv policy aligmment framework. Building on conventional multi-objective alignment approaches (Shi et al., 2024) and accounting for contextual prioritization of different attributes, we introduce a dynamic weighting mechanism. Specifically, we assign each persona attribute persona importance score J,, and use these scores to construct a weighted reward function:
R(T, = Σ Ir.(T.v).
(10)
While this weighting scheme enables multi-objective alignment, it has critical limi- tation: unconstrained optimization may drive all r; toward simultaneous maximization, blurring the hierarchy of importance among objectives and hindering the generation of personalized, preference- aware Pareto-optimal solutions.
Challenges:
Proposition 3.3. We propose normalized reward functiion that encourages the desired persona ordering (i.e., 1; 1 + r, > rÿ) and preserves the intended prioritization of alignment objectives:
R(T.v)
Στ (T.)).
Ryorm
(11)
VE
|||1|
1
where (r1, T denotes the vector of individual rewards.
Remark 3. By the Cauchy-Schie Schwarz inequality:
En (T.
I 1/2
(12)
|r]
where I 14, I2..... .... In)T is the vector of persona importance scores, and equality holds if and only r is proportional to (i.e., r o D). This implics that maximizing Rnorn incentivizes the individual rewards to maintain ranking consistent with their corresponding importance scores Jt thereby explicitly preserving the hierarchical structure of persona attributes during alignment.
Based on this, we can define the Multi-Persona Alignment Problem as:
Σ (Tr.TT.)) v)
max p. P-EP:
BD |l T²)
(13)
|
5

---

Published as a conference paper at ICLR 2026
Algorithm PDD Procedure
Input: Full prompt T {C, P.
Output: Model response y
1: Quantify Persona Importance (T: via Eq.
2: while sequence not terminated do
3: Compute constrained probability distribution ne(v: B<ee)
4: for = 1 to do
5: Compute unconstrained probability distribution mo (yr [T; Vet)
6:
Calculate reward (T,, Mee)) per Eq. 9
7:
end for
8: Normalize rewards: Raom
9:
Construct aligned policy pr(ve [T, Met) based on Eq. 14
10:
Sample Yt (MulT, y-e1)
11: Append ye to
12:
end while
13:
return
3.2.2 DERIVING THE OPTIMAL SOLUTION
Substituting the persona importance and reward function definition into Eq. 13 mal solution at time step (detailed derivation in Appendix B.3):
we derive the opti-
Ruorm (T, Vee)):
Pr( T, ycc)
To
T, yes) exp
(14)
Z(T. vet)
where the partition function is given by:
CoolslTT.Vel exp( 1 Revar (T, v'ce))
Z(T, Vct))
Σ
(15)
v
guaranteeing valid probability distribution. Please see Algorithm for full procedure of PDD.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUPS
demonstrate that persona is dynamic construct that is continuously enacted and perceived through dialogue. Building on this perspective, we em- ploy conversational datasets to evaluate the effectiveness of our method. We design experiments within inter-agent dialogue scenarios, examining performance under both general character profiles and specific personality settings. For the general character task, we adopt two widely used role- playing datasets. The first is CharacterEval (Tu et al.) (2024), which contains 1,785 multi-turn role- playing dialogues featuring 77 characters from Chinese novels and scripts. As this dataset contains only Chinese data, further incorporate BEYOND DIALOGUE (Yu al. 2025). comprising 280 Chinese and 31 English roles along with 3,552 scenario-based dialogues. For the specific personal- ity task, we evaluate the model's ability follow the Big Five personality traits (Ernest al. 1992) using PERSONALITYBENCE explicitly designed to probe each dimension of the Big Five traits.
Datasets. Personality psychology (Goffman 2023)
(Deng et al. 2025), which contains 180,000 open-ended questions
Baselines. We focus on piserence-bassee agents due to the impracticality of training raining-baseed ap- proaches for simulating diverse personas with limited data. To verify the effectiveness of our pro- posed approach, we select the following baseline methods for comparison:
Simple Prompting (SP), employs single adjective to guide the model toward different personas.
Persona Prompting (PP), receives detailed character profiles or personality settings (e.g., rela- tionship, background) via prompts;
In-context Learning (ICL), utilizes a set few-shot examples to instruct the model to generate better responses;

---

Published as a conference paper at ICLR 2026
Neuron-based approach for Personality Traits Induction (NPTI) (Deng al. 2025 focuses on aligning outputs with the Big Five personality traits by identifying personality related neurons and manipulating their activations to induce persona following outputs.
On-the-fly Preference Alignment Decoding (OPAD) (Zhu et al. 2025a), aligns its outputs with a predefined single-objective preference during the inference process;
Personality Activation Search (PAS) (Zhu et al. 2025b), trains probe to find attention heads linked to a personality trait, and uses it at test time to adjust the model's personality.
Furthermore, we compare our method against the performance advanced closed-source models, including GPT-4o (Achiam et al. 2023) and Deepseek-RI -1 (Guo et al. (025).
Evaluation Metries. Two complementary assessment paradigms are used to thoroughly evaluate our method PDD, and we further provide human evaluation in Appendix D.3
LLM-as-a-Judge: Following (Deng et al. 2025 Wang et al. 2025b), we utilize GPT-4o as an evaluator. In our experiments, we feed pairs of responses into GPT-4o to obtain the win rate against the baseline policy. The evaluation prompts for each task are shown in Appendix C.4
Dataset-Specific Reward Models & Metrics: CharacterEvall provides CharacterRM role- playing reward model evaluating RPLAs across subjective dimensions. We select metrics relevant alignment (PU) and Persona-Behavior Behavior alignment (PB). For PERSONALITYBENCH. we assess trait expression intensity on a 1-5 Likert scale, where higher scores indicate stronger trait manifestation.
persona following, e.g. Persona-Utterann
Practical Consideration. In practice, during the PIA process, align with the top-2 attributes of highest persona importance, balancing fidelity and efficiency. We will further discuss in Section 4.3.
Implementation Details. The experiments two LLMS as base models (Chen et al., [025 Ji et al.) 2025): --MM--------m---
conducted on single NVIDIA L40S GPU. We use
and Qwen2.55- 7B-Instruct We apply greedy decoding generate the responses and set the hyperparameter , to 1.0. For alignment target selection, CharacterEval and BEYOND DIALOGUE directly score indi- vidual attributes from the character profiles, while PERSONALITYBENGE extracts critical key- words from paragraph-style personality descriptions for scoring.
4.2
MAIN RESULTS
Bucraon
Baleane
Cresp
Cauliting
Cenige
C/54
Hamn
(a) Scenario
(b) Scenario
(c) Owwn2.5-7B-Insttee
(d) LLaMA-3-8B 3-B-A-------Innttue
Figure 2: Overall assessment of PIE for general character task. Case study (a) & (b): In Scenario 1, Guo Furong's ' playful banter with Lu Xiucai about martial arts highlights her Personality Traits and Unique Skills. In Scenario 2, her guidance to Tong Xiangyu foregrounds Life View and Fduca- tional Views, illustrating context-dependent persona relevance. Metric evaluation (c) & (d): Multi- dimensional assessments conducted by both human annotators and LLM-based judges demonstrate that persona importance derived from PIE is reliable and broadly applicable across different models.
Persona Importance Estimation. As shown in Fig. [aa(b), visualize the top-3 most signif- icant attribute distributions for general character tasks across diverse scenarios, demonstrating the adaptive capabilities of our importance estimation module. The results reveal consistent patterss where both base models assign higher weights to contextually relevant attributes while suppressing irrelevant ones, confirming the rationality and cross-model stability of methodology.
7

---

Published as a conference paper at ICLR 2026
To further substantiate the empirical validity of theoretical PIE module, introduce five eval- uation metrics: Context Relevance, Attribute Utility, Context Coverage. Attribute Independence, and Ranking Consistency, with their formal definitions provided in Appendix C.2 Both human perts and L.LM-based judges rate the plausibility of the estimated importance scores on 1-5 Likert scale, where denotes very poor alignment with the criterion and denotes full alignment. The detailed evaluation prompt is given in Appendix [C.]] Specifically, we employ three LLM judges: DeepSeck-RI, GPT-4o, and GPT-5, As shown in Fig. (d)),,, our method achieves consistently strong and acceptable scores across all dimensions, indicating that PIE reliably estimates persona importance by capturing the correlation between model outputs and persona attributes.
Table 1: Direct comparison with the baselines for general character task. Win indicates that GPT-4o assesses PDD's responses superior compared to the baselines, better addressing adaptive persona following.
EharacterEval
BEYOND DIALOGUE
Baselines
10en2.5-7-Instrruee
LLAMA-3-8B-Inttrus
Qwen2.5-7B-Instreeee
HuMA-3-8B-Instree
PDD
Win(%)
Lose(%)
Win(%)
Lose(%)
Win(%)
Lose(%)
Win(%)
Lose(%)
SP
51.2
34.7
52.5
43.1
63.9
30.2
56.2
41.9
PP 48.7
38.5
39.1
31.3
43.0
37.6
46.8
36.5
ICI.
65.3
33.1
63.1
35.7
60.9
35.4
64.2
35.0
OPAD
52.8
41.5
48.2
41.6
49.0
43.5
47.6
36.8
Table 2: Automatic evaluation for general character task CharacterEval. PDD strikes balance between generating persona following response and maintaining knowledge rationality.
KE: Knowledge Exposure
KA: Knowledge Accuracy
KH: Knowledge Hallucination
PB: Persona Bchavior
PU: Persona Utterance
Models
Methods
KE
KA
KH
PB
PU
Average
GPT-4o
PP
2.58
3.02
2.99
2.83
2.91
2.87
Deepseck-RI
PP
1.42
3.07
2.83
1.93
2.89
2.43
SP
2.07
3.00
3.23
3.09
2.38
2.75
PP
2.25
2.96
2.99
3.03
2.94
2.83
Qwen2.5-7B-Inss Instrrcct
ICL
1.92
2.79
2.92
2.72
2.42
2.55
OPAD
2.26
2.82
2.62
3.06
2.31
2.61
PDD
2.25
2.93
2.99
3.08
3.01
2.85
SP
2.08
2.78
2.98
3.13
2.08
2.61
PP
2.53
2.72
2.44
2.88
2.53
2.62
LLaMA-3-8B-Inste
ICL
2.16
2.73
2.92
3.04
2.89
2.75
OPAD
2.13
2.62
2.57
2.94
2.71
2.59
PDD
2.39
2.68
3.03
3.00
2.96
2.81
General Character. As shown in Tab. [ our method outperforms all baselines on both the Chi- character dataset (CharacterFval)) and the English character dataset (BEYOND DIALOGUE). Furthermore, as validated by the persona following metrics Tab. 2 PDD ranks among the top performers across individual metrics and achieves the highest average scores both base models, demonstrating its superior robustness. Notably, when applied to relatively small open-source mod- els, PDD yields results that competitive with commercial closed-source systems (e.g. GPT-4o), highlighting its effectiveness and efficiency. Please find more cases in Appendix
Specific Personality. For the task of fol- lowing specific personality settings, we first conduct an automatic evaluation, with detailed results presented in Tab.
FHOGINAl ITVIN Que
11.00
002
C202
3 Across five distinct personality traits, PDD consistently outperforms all baseline methods (with p-value=cO.05), achieving the highest average scores and the low- est variances, demonstrating its robust and generalizable adaptability. Representative
Figure 3: Direct comparison with the baselines for specific personality task on PERSONALITYBENCH.
8

---

Published as a conference paper at ICLR 2026
examples provided in the Appendix E Similarly, in the pairwise evaluation shown in Fig. PDD also surpasses the baselines, further validating its effectiveness.
Table 3: Automatic evaluation for specific personality task on PERSONALITYBENCHL Ex- periments show that PDD outperforms all the baselines, achieving higher performance scores and significant lower variance.
Big-Five
SP
PP
ICL
OPAD
PAS
NPTI
PDD
Qwen2.5-7B-Inee Instrrcct
Agrecableness
4.81
4.90:0.0
1.81
4.53=20.9
4.83
4.73:4024
4.92-0.09
Conscientiousness
4.47:005
4.98
4.19:0700
4.66-0.29
4.61
4.7420
4.97=0.22
Extroversion
4.68+00.9
4.59
4.32=00.9
4.26=0055
4.65
4.71+03:
4.66=20.5
Neuroticism
3.02+0064
1.45+155
3.12:40.9
3.79
3.74
3.39:4074
3.54=0.43
Openness
4.56 #0.36
4.75:022
4.67:20.40
2.44
4.61
4.83
4.75
Average
4.31
4.53
4.22:1053
4.34
4.49
4.48
4.57:10.22
LLaMA-3-8B-
Agrecableness
4.68
4.82+021
4.37
4.60=0047
4.68
4.63-2039
4.84
Conscicntiousness
4.26::055
4.67:00.9
3.76:20.6
4.72-0.30
4.30+:96
4.16 +0.57
4.82:4025
Extroversion
4.12+0554
4.33
4.36 +009
3.74=082
4.26+0.59
4.38::1.00
4.30=20.88
Neuroticism
3,26::032
4.19:0.46
3.61::043
3.68=0.30
4.0240.4
4.82+0.15
4.13
Openness
3.72+0881
4.33+t0.2
4.03=4057
4.50=0.46
4.44
4.68:t0.6
4.78=;0.19
Average
4.01
4.47
4.09
4.25=0047
4.34
4.53
4.57:20.99
4.3 ABLATION STUDIES
In this part, we conduct ablation experiments to examine the effect of several important settings in our approach. We present the key ablation study in the main text, with the remaining ablation experiments shown in Appendix [.]]
Effectiveness of reward function malization schemes. As shown in Tab. reward function normalization produces outputs that more faithfully adhere to the specified persona configuration. By guiding the model to prioritize salient attributes over unnormalized ones, this scheme enhances both response quality and consistency.
Table 4: Ablation study of the effectiveness of reward function normalization schemes on CharacterEval.
Qwen2,5-78 IB-Iustuct
LLaMA-3-8B- --B----stt-ttscct
Reward
Win(W)
CharacteRRM
Win(S)
CharacteRRM
Normalization w/ Normalization
38
32
2.71
42
2.85
40
2.81
Effeet of the number of persona targets. As demonstrated Fig. the number of alignment targets selected for Multi- Persona Alignment has critical impact on both output quality and computational ef- ficiency. While performance initially im- proves as more attributes are incorporated, excessive attributes introduce contextually irrelevant noise. They also increase com- putational overhead and amplify numerical instability, ultimately degrading performance. Although the optimal number of selected attributes differs slightly between Qwen and LLaMA, PDD con- sistently outperforms all baselines across all tested settings, demonstrating strong stability across model architectures.
18t6 R000 860
LaMA
55
Figure 4: Ablation study of the effect of the number of persona attributes on CharacterEval.
Effect of the quality of G. Based on Qwen2.5-7B-Insttee we deliberately degraded the quality of the model-generated G using stochastic decoding strategies (c.g., top-k sampling), and further selected the lowest-quality samples according to CharacterRM. We then recomputed the PIE im- portance scores based on these degraded outputs and evaluated the results using (1) our persona- importance diagnostic metries and (2) the Top-5 overlap with the original (high-quality) estimation. As shown Tab. 5 PIE remains remarkably stable even when is of substantially lower quality. Both the metric scores and the Top-5 overlap demonstrate that PIE continues correctly identify

---

Published as a conference paper at ICLR 2026
Table 5: Ablation study of PIE robustness under reduced G quality on CharacterEval
Decoding Method
Context Relevance
Attribute Utility
Context Coverage
Attribute Independence 1
Ranking Consistency f
Quality of
Top-3 Overlap
Beam
2.576 2.193
3.97
4.25
3.85
4.08
2.96
3.97
Top-k
3.86
4.08
4.04
3.82
3.66
the key context-dependent persona attributes, indicating strong robustness to imperfections in the sampled generation.
CONCLUSION
In this paper, we propose PDD, novel framework for aligning RPLAs with predefined profiles across diverse contextual scenarios during decoding. PDD decouples persona importance from con- textual information, enabling dynamic adaptation to diverse scenarios. Through persona-guided inference-time alignment, our method modulates the model's predictions to achieve persona follow- ing without fine-tuning. Empirical results demonstrate that our method outperforms cxisting role- playing approaches while exhibiting robust generalizability across multi-role personas and different base models.
6 ETHICS STATEMENT
Our study investigates role- ole-playing alignment in large language models (LLMs), aiming to enhance persona following when simulating diverse characters. All experiments are conducted using pub- licly available datasets that have been pre-processed and anonymized to ensure compliance with privacy and ethical standards. Extensive research has been dedicated to designing ethical frame- works, addressing various aspects from data collection and algorithm design to model deployment and application. The proposed framework requires only lightweight resources, making responsi- ble role-playing alignment more accessible to the research community while promoting fair and transparent use of LLMs.
Our work focuses test-time alignment, rather than altering model parameters introducing new behavioral patterns through training. This design helps reduce the risk of reinforcing harmful stereo- types. All persona attributes used in experiments explicitly provided by the datasets, and PDD does not generate or infer new identity-related characteristics on its own.
To further mitigate potential ethical concerns, apply filtering during persona construction re- move sensitive demographic attributes (e.g., race, religion, gender identity) from the importance estimation process. The probability modulation in PDD is restricted behavioral, non-sensitive persona attributes that are explicitly defined by the user or dataset. We recognize that the broader ethical implications of persona manipulation deserve further exploration. Future work may investi- gate constraints that suppress undesirable behavioral reinforcement, or evaluation frameworks that proactively identify risks associated with context-dependent persona expression.
ACKNOWLEDGEMENTS
This research was supported in part by the National Key Research and Development Program of China under Grant 2024YFC3307402 and the National Natural Science Foundation of China (No. 62576329, 62336001).
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. GPT-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
Jaewoo Ahn, Taehyun Lee, Junyoung Lim, Jin-Hwa Kim, Sangdoo Yun, Hwaran Lee, and Gunhee Kim. TimeChara: Evaluating point-in-time character hallucination of role-playing large language
10

---

Published as a conference paper at ICLR 2026
models. In Findings of the Association for Computational Linguistics: ACL 2024, pp. 3291-3325, 2024.
Jiangjie Chen, Xintao Wang, Rui Xu, Siyu Yuan, Yikai Zhang, Wei Shi, Jian Xic, Shuang Li, Ruihan Yang, Tinghui Zhu, al. From persona to personalization: A survey oole-playing language agents. Transactions on Machine Learning Research, 2024. ISSN 2835-8856.
Nuo Chen, Yan Wang, Haiyun Jiang, Deng Cai, Yuhan Li, Ziyang Chen, Longyue Wang, and Jia Li. Large language models meet harry potter: A dataset for aligning dialogue agents with characters. In Findings of the Association for Computational Linguistics: EMNLP 2023, pp. -520,, 2023.
Ruizhe Chen, Xiaotian Zhang, Meng Luo, Wenhao Chai, and Zuozhu Liu. PAD: Personalized alignment of llms at decoding-time. In International Conference on Learning Representations, 2025.
Yanqi Dai, Huanran Hu, Lei Wang, Shengjie Jin, Xu Chen, and Zhiwu Lu. MMRole: A comprehen- sive framework for developing and evaluating multimodal role-playing agents. In International Conference on Learning Representations, 2025.
Jia Deng, Tianyi Tang, Yanbin Yin. Xin Zhao, Ji-Rong Wen, et al. Neuron based personality trait induction in large language models. In International Conference on Learning Representations, 2025.
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Jingyuan Ma, Rui Li, Heming Xia, Jingjing Xu, Zhiyong Wu, Tianyu Liu, et al. A survey on in-context learning. In Conference on Empirical Methods in Natural Language Processing, pp. 1107-1128, 2024.
Ernest, C.,, Tupes, Raymond, E., and Christal. Recurrent personality factors based on trait ratings. Journal of Personality, 1992.
Songyang Gao, Qiming Ge, Wei Shen, Shihan Dou, Junjie Ye, Xiao Wang, Rui Zheng, Yicheng Zou, Zhi Chen, Hang Yan, et al. Linear alignment: A closed-form solution for aligning human preferences without tuning and feedback. In International Conference on Machine Learning, pp- 14702-14722, 2024.
Erving Goffman. The presentation of self in everyday life. In Social theory re-wired, pp. 450-459. Routledge, 2023.
Jennifer P Green, Reeshad S Dalal, Kristen L Swigart, Melissa A Bleiberg, David M Wallace, and Amber K Hargrove. Personality consistency and situational influences on behavior. Journal of Management, 3204)3320433244 2019.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang. Xiao Bi, et DeepSeek-Ri incentivizes reasoning in Ilms through reinforcement learning. Nature, 645:633-638, 2025.
James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi an Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, and Dan Roth. DeAL: Decoding-time alignment for large lan- guage models. In Annual Meeting of the Association for Computational Linguistics, pp. 26280- 26300, 2025.
Yue Huang, Zhengqing Yuan, Yujun Zhou, Kehan Guo, Xiangqi Wang, Haomin Zhuang, Weixiang Sun, Lichao Sun, Jindong Wang. Yanfang Ye, et al. Social science meets 1lms: How reliable are large language models in social simulations? arXiv preprint arXiv:2410.23426, 2024.
Ke Ji. Yixin Lian, Linxu Li, Jingsheng Gao. Weiyuan Li, and Bin Dai. Enhancing persona consis- tency for lmms' role-playing using persona-awand aware contrastive learning. In Findings of the Associ- ation for Computational Linguistics: ACL 2025, pp. 26221-26238, 2025.
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. eration for knowledge-intensitg nlp tasks. In International Conference on Neural Information Processing Systems, pp. 9459-9474, 2020.
gen-

---

Published as a conference paper at ICLR 2026
Cheng Li. Ziang Leng, Chenxi Yan, Junyi Shen, Hao Wang, Weishi Mi, Yaying Fei, Xiaoyang Feng, Song Yan, HaoSheng Wang, et al. ChatHaruhi: Reviving anime character in reality via large language model. arXiv preprint arXiv:2308. 09597, 2023.
Jiacheng Liu, Andrew Cohen, Ramakanth Pasunuru, Yejin Choi, Hannaneh Hajishirzi, and Asli Celikyilmaz. Don't 't throw away your value model! generating more preferable text with value- guided monte-carlo tree search decoding. In Conference on Language Modeling. 2024a.
Tianlin Liu, Shangmin Guo, Leonardo Bianco, Daniele Calandriello, Quentin Berthet, Felipe Llinares, Jessica Hoffmann, Lucas Dixon, Michal Valko, and Mathieu Blondel. Decoding-time realignment of language models. In International Conference on Machine Learning, pp. 31015- 31031, 2024b.
Xinyi Mou, Zhongyu Wei, and Xuan-Jing Huang. Unveiling the truth and facilitating change: To- wards agent-based large-scale social movement simulation. In Findings of the Association for Computational Linguisties ACL 2024, pp- 4789-4809, 2024.
Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Tiee Cheng, Michael Collins, Trevor Strohman, et al. Controlled decoding from language models. International Conference on Machine Learning, pp. 36486-36503, 2024.
Yunfan Shao, Linyang Li, Junqi Dai, and Xipeng Qiu. Character-LLM:: trainable agent for role- playing. In Conference on Empirical Methods in Natural Language Processing, 2023.
Ryne Sherman, John Rauthmann, Nicolas A Brown, David G Serfass, and Ashley Bell Jones.
The independent effects of personality and situations on real-time expressions of behavior and emotion. Journal of personality and social psychology, 109(5):872, 2015.
Ruizhe Shi, Yifang Chen, Yushi Hu, Alisa Liu, Hannaneh Hajishirzi, Noah A Smith, and Simon Du. Decoding- language model alignment with multiple objectives. In International Confer- ence on Neural Information Processing Systems, pp. 48875-48920, 2024.
Jiakai Tang, Heyang Gao, Xuchen Pan, Lei Wang, Haoran Tan, Dawei Gao, Yushuo Chen, Xu Chen, Yankai Lin, Yaliang Li, et al. GenSim: A general social simulation platform with large language model based agents. In Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics. pp. 143-150, 2025.
Yihong Tang, Bo Wang, Miao Fang. Dongming Zhao, Kun Huang, Ruifang He, and Yuexian Hou. Enhancing personalized dialogue generation with contrastive latent variables: Combining sparse and dense persona. In Annual Meeting of the Association for Computational Linguistics, pp- 5456-5468, 2023.
Quan Tu, Shilong Fan, Zihang Tian, and Rui Yan. CharacterEval: chinese benchmark for role- playing conversational agent evaluation. In Annual Meeting of the Association for Computational Linguistics, pp. 11836-11550, 2024.
Lei Wang, Jianxun Lian, Huang, Yanqi Dai, Haoxuan Li. Xu Chen, Xing Xie, and Ji-Rong Wen. Characterbox: Evaluating the role-playing capabilities of Ilm in text-based virtual worlds. In Conference of the Nations of the Americas Chapter of the Association for Computational Lin- guistics, pp. 6372-6391, 2025a.
Noah Wang. Zy Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, et al. Rolellm: Benchmarking, eliciting, and enhancing role- playing abilities of large language models. In Findings of the Association for Computational Linguistics ACL 2024, pp. 14743-14777, 2024a.
Ruiyi Wang, Haofei Yu, Wenxin Zhang, Zhengyang Qi, Maarten Sap, Yonatan Bisk, Graham Neu- big, and Hao Zhu. SOTOPIA-T: Interactive learning of socially intelligent language agents. In Annual Meeting of the Association for Computational Linguistics, pp. 22912-12940 2024b.
Xintao Wang. Heng Wang. Yifei Zhang, Xinfeng Yuan, Rui Xu, Jen-tse Huang, Siyu Yuan, Haoran Guo, Jiangjie Chen, Shuchang Zhou, et al. CoSER: Coordinating -lm-based persona simulation of established roles. In International Conference on Machine Learning, 2025b.
12

---

Published as a conference paper at ICLR 2026
Rui Xu, Xintao Wang, Jiangjie Chen, Siyu Yuan, Xinfeng Yuan, Jiaqing Liang, Zulong Chen, Xi- aoqing Dong, and Yanghua Xiao. Character is destiny: Can vole-playing language agents make persona-driven decisions? arXiv preprint 2401:2240.122388 2024.
Ziyi Yang, Zaibin Zhang, Zirui Zheng, Yuxian Jiang. Ziyue Gan, Zhiyu Wang, Zijian Ling, Martin Ma. Bowen Dong, Prateek Gupta, et al. OASIS: Open agents social interaction simulations on one million agents. In NeurIPS 2024 Workshop on Open-World World Agents, 2024.
Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, and Liehuang Zhu. Neeko: Leveraging dynamic lora for efficient multi-character role-playing agent. In Conference on Empirical Methods Natural Language Prucessing, pp. 12540-12557, 2024.
Yeyong Yu, Runsheng Yu, Haojie Wei, Zhanqiu Zhang, and Quan Qian. Beyond Dialogue: A profile-dialouee alignment framework towards general role-playing language model. In Proceed- ings of the 63rd Annual Meeting of the Association for Computational Linguistics, pp. 11992- 12022, 2025.
Xinnong Zhang, Jiayu Lin, Libo Sun, Weihong Qi, Yihang Yang, Yue Chen, Hanjia Lyu, Xinyi Mou, Siming Chen, Jiebo Luo, et al. Electionsim: Massive population election simulation powered by large language model driven agents. arXiv preprint arXiv:2410.20746 2024.
Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Libiao Peng, Jiaming Yang, Xiyao Xiao, al. CharacterGLM:: Customizing social characters with large language models. In Conference on Empirical Methods in Natural Language Processing: Industry Track, pp. 1457-1476, 2024.
Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo, and Zhendong Mao. On-the-fly preference alignment via principle-guidee decoding. In International Conference on Learning Representations, 2025a.
Minjun Zhu, Yixuan Weng, Linyi Yang, and Yue Zhang. Personality alignment of large language models. In International Conference on Learning Representations, 2025b.
13

---

Published as a conference paper at ICLR 2026
A THE USE OF LARGE LANGUAGE MODELS (LLMS)
Large language models (LL.Ms) were used solely as a general-purpose tool for language polishing and improving readability of the manuscript.
B MATHEMATICAL DERIVATION
B. CMI APPROXIMATION AND DISTRIBUTION IMPACT
It can be seen that is approximation of the CMI between w; and GT. The greater the contribu- tion of to to the generation of GT, the larger T becomes. When wi makes no contribution to GT, is also this time.
The rationality of I can be further demonstrated by its impact on the model's 's output distribution. By denoting the distributions under full prompt T and masked prompt T; as Q(-) Pr(-IT) and Q:(-) = Pri-- respectively, the KL divergence between Q(-) and Qi(-) is:
Σ Q(Y) log Q(Y)
Dka((Q112))
(16)
Q((Y)'
YEY
where the individual contribution GT is: Q(GT) log QGG Pr(GT[T) 4, Q(T
(17)
I weights GT the KL divergence. The larger it is, the more significant the distribution shift caused by the absence of wy, reflecting the importance of wy for model output.
B.2 PROBABILISTIC GUARANTEE OF SELE-VUPER PERSONA IMPORTANCE ESTIMATION
We posit posttivv-correlattion assumption that directly links model probabilities to ground-truth probabilities. Concretely, let p = Pr(G T) and Pr(GT) T); analogously, let p, = Pr(G T,) and c; Pr(GT TT)). We assume:
E [clp] = g(p),
(18)
where g: [0, 1] + [0 (r S i) monotonically increasing, and write:
c= ,(v) + e, c; g(p)) E,
(19)
with E(e] 0 and Varle] o².
To simplify our presentation, by defining th = c/cc and t; = Ps/P /.. we have:
Time log
= log 1ong time
(20)
62
model log
p = log t,
(21)
pi
Mcanwhile, as g(-) monotonically increasing and the noise is bounded, there exists function h(-) such that:
true h(tz)-
(22)
The existence of h(-) can be simply demonstrated as:
Ar g(p:) + RJ g(t.p)
(23)
o(p) + g(p)
can be seen that g(lp) ((pp)/(p) is feasible h function. As g(.) is monotonically increasing, when
lj. we have g(4p)) > g((rF), thus h(4)) h((;). Therefore, h(-)) is also monotonically increasing. Furthermore, by performing Taylor expansion on trme, we have:
g(lap) +
Inue
g(p) +c
g(lap)
<g(p) [k(p)] O
g(p) g(p)
h(t,) + nk.
(24)
14

---

Published as a conference paper at ICLR 2026
where 77 (c; - c)/g(p). Since c, are ii.d., we have E/n,==,, Var[n. - /g
Based on above analysis, we further estimate the probability of ranking consistency between 1i (0 and moodel 21) when G generated by the full prompt is used as the ground-truth (GT), i.e.
Pr (I!TU
I Temssel !mmusee)
(25)
Following 20 and
1i + tiee
gque and I.uodel mmudell + 4 < 1j. Based on
Eq. 24 the probability Eq. 25
becomes:
Pr(h((4) + 1/ < h((i)) + +1 4 < x12).
(26)
which is the same as:
Pr(n: - 1jj < h(t)) - h(t,)) 14: < tt):
(27)
Since f < fj. we have Ayx h(t,) h(t,)) > Moreover, as Pr(m:: -15 TB < Aji) 2 Pr(mn: My
<
Aji), based on the Chebyshev's Inequality:
Pr(m 7y A,i) S Var /arlo
(28)
where 81j- = 1K ?i with E[ō,g] = and Var[ō.j] 20 Finally, we have:
Pr [ 1 [ > I.malee) ž - 20 AJ.
(29)
Specifically, when the model-estimated importance 7.model exceeds I by at least a threshold X,
we obtain:
1 bee
(30)
Since h is strictly increasing, this implies:
A == h(t,e>) - h(t,) & h(ty)) - h(t)) =: A
(31)
Substituting o 2=222 ///ggg(6 (p) into Eq.
29 we can yield the concise confidence statement:
4a²
Pr(r.T mu
Imoddel
rmodel 2 2) 21
(32)
Given that the noise in Eq. 19 is bounded, a is typically an order of magnitude smaller than gp)), i.e. o² (p) 0.01. Although the specific form of the function h(-) depends on the LLM, we can gain quantitative understanding of the bounding probability by analyzing common monotonically increasing functions that map from [o, 1] to [0, 1]. Let's assume the probability, corresponding to most importance attribute w;, is P:/
0.4, and Pj be approximately twice ps. meaning À = 0.7.
When l(()) is convex function h(x)) = / xx. the bounding probability from Eq, E2.32 approximately 0.431; when h(-)) is linear function h (z) Ir the bounding probability is approximately 0.757, and when (()) concave function h(x) = x², the bounding probability is approximately 0.833.
Therefore, it is evident that when the differences between the moddel values sufficiently large, using modee as a proxy for tre can still identify the important persona attributes with reasonably high confidence.
Our analysis is still relatively preliminary. The experimental results have demonstrated the feasibility of our method, and we believe that more rigorous and detailed analysis could further validate its effectiveness theoretically.
B.3 SOLVING THE MULTI-PERSONA ALIGNMENT PROBLEM
In this section, we will solve the multi-person alignment problem which we define in section 3.2:
max Ep, [Rrorm (Pv || (T))))
(33)
where Rnom = Ei= (T. v) ||r||,, me is the base policy, pr is the policy to be optimized, and is a positive scalar balancing the reward and the KL divergence. Assuming T are given and fixed, the objective function can be expressed as:
(r) Te (y)
max Σ Pr(v) Rmonn Pr
B -8 Pr (r( log
(34)
15

---

Published as a conference paper at ICLR 2026
To ensure that p, is valid probability distribution (i.e., E, Pr(TTT) 1), introduce Lagrange multiplier sp. For simplicity, we omit and P in the expression. The Lagrangian L thus becomes:
L = E pr())) Rever
3
3 Pr (V) log
pr((y) To(U)
1
+ 40
1 1011111
(35)
To find the optimal pr. take the derivative of L with respect to (v() and set it to zero:
Oc
Pr(y)
Raoen
B (1+ log
xp 0
Opr(v)
To(y)) (y)
Pr (y)
Rnorm - B - 3 log
4p 0
(36)
TO
log pr(y)
Rnomn
B 40
TT(3)
Next we exponent both sides solve for pr(y)):
(v)
Rnorm
B - 40
Rnorm
10
exp
exp
(37)
(g)
B
B
Factor out the terms that do not depend on and recall the property of a probability distribution:
Cv) = exp -
1 )
Σ To (v) exp
Ravem
1.
(38)
8
3
Next we introduce the partition function Z to simplify the notation:
Rporm
Z Σ (v) exp
(39)
Thus, the optimal (r( is:
1 (x( (y) exp
Rnom
(40)
3
Substituting back the T:
Rnwen
pr (y( Z(T) ne (v) exp
(41)
Since we are working on an inference-tine algorithm, the final policy updated on token basis. Specifically, at time step i, the optimal solution is:
(ye T, v<t) Z(T, ves) Tolin (2y) exp ( Ruom (T, (...)))
1
(42)
where the partition function is given by:
Z(T. Σ 1
No To(N Ver) exp( Ruom (T, V2ce).
(43)
It is important to note that the reward function in Eq. 42 operates entirely within the probability space, so the partition function computation does not require explicit decoding of tokens or summing over all sequences, which makes it tractable.
C EXPERIMENT DETAILS
C.I TASK-SPECIHES PERSONAS
For general character task: We use the role-playing character's profile content as the persona to be followed, which is specifically presented in the form of attributes:
16

---

Published as a conference paper at ICLR 2026
Name: Sun Wukong
Gender: Male
Species: Stone Monkey
Age: Not explicitly stated in Journey to the West, actual age unknown
Occupation: Protecting Tang Sanzang the journey the West
Nicknames: Creat Sage Equal to Heaven, Handsome Monkey King, Victorious Fighting Bud- dha, The Pilgrim, Bimawen (Stable Attendant) Height: Less than four feet (highly variable)
Zodiac: Monkey
Residence:
Water Curtain Cave in Mount Huaguo
Hobbies: Fighting, destruction, mischief, eating peaches
Education: Studied under Patriarch Subodhi
IQ: Extremely intelligent
EQ: High; very sociable
Likes: Fighting, destruction, mischief, eating peaches
Dislikes: Being restrained, being deceived
Personality Traits: Intelligent, lively, loyal, hates evil, courageous in resistance, unafraid of power, desires freedom, rebels against tradition and authority, despises feudal hierarchy
Appearance: Pointed mouth and retracted cheeks, golden fiery eyes, moss on his head, creep- ing vines in his ears. Sparse hair with green grass at the temples, beard but green the chin. Wears brocade robes, a rattan belt, and deerskin boots.
Clothing: Short white cloth robe, tiger-skin belt, phoemix-wingel winged golden crown, golden chain- mail armor,
cloud-walking shoes
Weapon: Ruyi Jingu Bang Gtolden-bandedd staff)
Special Abilities: 72 Transformations, Somersault Cloud, Fiery Eyes, Wind Grasping. Unlock-
ing. Thread Pulse Diagnosis, Drawing a Ground Circle Prison
Achievements: Rebelled in Heaven, became the Great Sage Equal to Heaven, protected Tang Sanzang on the journey to the West, achieved enlightenment and was titled Victorious Fighting Buddha
For specific personality task: We use a detailed ChaiGPT-generated description of a particular personality trait and select key phrases from the description alignment targets for subsequent experiments:
Please act as if you are a conscientious individual. You are highly conscientious, always striv- ing for excellence everything you do. Your organizational skills and attention to detail ensure that tasks are completed efficiently and accurately. You are reliable and others can count on you to follow through on your commitments. You set high standards for yourself and work diligently to achieve your goals, demonstrating strong self-discipline and perseverance.
C.2 PERSONA IMPORTANCE EVALUATION METRICS
To evaluate the rationality of the persona importance generated by the PIE module, we designed five evaluation dimensions, providing a comprehensive assessment of the reliability of contextual attribute association analysis.
Context Relevance. Top-ranked attributes are expected to closely related to the current context.
Attribute Utility. Top-ranked attributes are expected to enhance the character's 's reasonableness or naturalness in the given context.
Context Coverage. Top-ranked attributes are expected to broadly cover the key elements of the current context.
Attribute Independence. Top-ranked attributes are required to remain largely independent from one another, thereby avoiding redundancy.
Ranking Consistency. The overall ranking attributes should correspond to their relevance in the current context.
17

---

Published as a conference paper at ICLR 2026
C.3 CHARACTERRM EVALUATION METRICS
Besides using LLM-as-a-Judge. we also employ role-playing reward model (CharacterRM) to evaluate general character tasks, with Character Consistency metrics defined as follows.
Knowledge-xpoosung (KE). For assessing the informativeness of response, it's crucial for RPLAs to reflect knowledge in its responses, as this supports the subsequent evaluation of its knowledge expression capabilities.
Knowledge-Acuuracy (KA). It's important to assess whether this knowledge aligns with the char- acter. The goal is for the RPLAs to accurately generate responses based the knowledge from the character's s profile.
Knowledg--alllccnntin (KH). RPLAs should maintain consistency with the character's 's iden- tity and avoid responding to queries involving unknown knowledge.
Persona-Behavior (PB). A character's behaviors, typically described within brackets, improve the embodied feeling of users by portraying fine- one-grained actions, expressions, and tones. Consistent behavior is indicative of effective RPLA.
(PU). Alongside behavior, character's speaking style is also important. Each character has unique expression habits. Therefore, the RPLA's utterances should align with these habits to adeptly mimic the character.
C.4 EVALUATION PROMPTS
In this section, we provide the evaluation prompt for each task. Specifically:
For Persona Importance Estimation: To enable LLM judges to evaluate the self-supervised per- sona importance according to the metrics defined in Appendix C.2 we designed the following eval- uation prompt:
Persona Importance
Please as you are expert evaluator specializing in character analysis and dialogue eval- uation. Given ranked list of persona attributes and corresponding dialogue context, your task evaluate the reasonableness of persona importance according the following cri- teria:
Context Relevance: How well the top-ranked attributes relate to the current context
2. Attribute Utility: How well the top-ranked attributes help the character act reasonably or
naturally in the given context
3. Context Coverage: How well the top-ranked attributes cover the key elements of the cur-
rent context
4. Attribute Independence: Whether the top-ranked attributes remain largely independent from one another, avoiding redundancy
5. Ranking Consistency: Whether the overall ranking of attributes corresponds their rele- vance in the current context
For each criterion, please provide a score from 1 to 5, where indicates low alignment with the criterion, indicates moderate alignment, and 5 indicates high alignment. -Dialogue Context-
-dallouu--
Ranked Persona Importance-
-persona-
For general character task: we have many roles according to the personas in Appendix evaluation prompt corresponds to different characters.
C.I The
18

---

Published as a conference paper at ICLR 2026
General Character- Win Rate
Please act as an impartial judge and evaluate the quality of the responses provided by two Al assistants displayed below. You should select the response that better aligns with the persona attributes specified in the agent profile and the preceding dialogue history. If the response off, evaluate the response based on the existing content, and do not choose response purely because it is not cut off. Begin your evaluation by comparing the two responses and provide short explanation. Avoid any positional biases and ensure that the order which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor specific names of the assistants. Be objective possible. First output your final verdict by strictly following this format: [[A]] if assistant is better, [[B]] if assistant better, and [[C]] for a tie, then give short explanation. Please make sure the first word is your choice.
-Dialogue History-
dialogue-
Character Profile-
persona-
-hhe Start of Assistant A's Answer-
-answer
-The End of Assistant A's Answer-
-The Start of Assistant B's Answer-
-nsswr b-
-The End of Assistant B's Answer-
For specific personality task: We evaluate the experimental results using two approaches: win-lose comparison and direct score rating. The evaluation prompt for the win-lose format is follows:
Specific Personality-Vo Rate
Please act as an impartial judge and evaluate the personality trait exhibited in the response provided by two Al assistants to the user question displayed below. You should select the response that better aligns with the personality trait. the response response based on the existing content, and do not choose a response purely because it is cut off. Begin your evaluation by comparing the two responses and provide a short explanation. Avoid any positional biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Do not favor specific names of the assistants. Be objective as possible. First output your final verdict by strictly following this format: [[A]] if assistant A is better, [[B]] if assistant B is better, and [[C]] for tie, then give short explanation. Please make sure the first word your choice. -User Question-
off, evaluate the
-question- -Personality Trait-
-personality-
-The Start of Assistant A's Answer-
-answer
-The End of Assistant A's Answer-
-The Start of Assistant B's Answer-
-answer b-
-The End of Assistant B's Answer-
The prompt used identify and evaluate the specific personality trait is follows:
19

---

Published as a conference paper at ICLR 2026
Specific Personality-Direes Score
Please act as an impartial judge and evaluate the personality trait of (BFI) exhibited in the response provided by ÁI assistant to the user question displayed below. Your evaluation should consider factors such as {factors} demonstrated in the response. Begin your evalua- tion by providing short explanation of more than 30 words. Be as objective as possible. After providing your explanation, please rate the response on scale of to 5, where higher score indicates stronger reflection the specified trait. Please follow this format for your rating: "[[ratin]]]" for example: "Rating: [[5]]".
-User Question-
-question-
-Personality Trait-
-personality-
The Start of Assistant's Answer-
answer-
The End of Assistant's Answer-
D ADDITIONAL EXPERIMENT RESULTS
D.I EFFECT OF REGULARIZATION HYPERPARAMETER BETA (3)
The hyperparameter 3 controls the strength of alignment with the target personas. A larger 3 reduces the influence of the reward, making the modified distribution pr closely approximate the base model. In contrast, smaller /3 amplifies the reward's effect, increasing the deviation from the base model's predictions.
Our analysis on the CharacterEval dataset (Tab. 6 shows that as 3 decreases, the generated outputs initially become more aligned with the predefined personas. However, overly small values cause the generation process to diverge excessively from the base model, ultimately degrading persona following performance.
Table Alignment performance of different 3 values on CharacterEval. Large 3 keeps the aligned policy close to the base model, reducing persona following; small / increases deviation yet too small sacrifices generation quality.
8=2.0
B=1.0
$=0.5
$=0.25
Win 24.5%
Lose 16.5%
Win 40.5%
Lose 29.5%
Win 44.5%
Lose 34.5%
Win 43.0%
Lose 45.5%
D.2 PERSONA IMPORTANCE EVALUATION SPECIFIC PERSONALITY
In addition the general character task reported in the main text, we further conduct evaluation on specific personality task. As shown in Fig. 5, using the same evaluation criterion, the assessments from both human annotators and LLM judges consistently validate the reliability of the estimated persona importance.
D.3 HUMAN EVALUATION
To more objectively assess subjective qualities, we conduct additional experiments involving human evaluation to provide balanced and credible assessment. Specifically, for both the general character profile and specific personality settings tasks, we select 100 samples each and generate dialogue responses using -wen2.5-7B-Inme Instruct and LlaMA-3-8B-Instrren
Five human evaluators assess the responses from our method and the baselines. All of these evalu- ators researchers with expertise in role-playing language agents and preference alignment. The annotation takes place in a controlled lab environment over two consecutive days, with each data annotation takes approximately minutes. For each sample, the evaluators choose the response
20

---

Published as a conference paper at ICLR 2026
Ove
Lien
Oecc
Line
Pedkec
Beanng
G2%
07%
Scenario
(b) Scenario
(c) Dwwen.5-77-Inntteee
(d) CLLAMA-3--B-InSSE
Overall assessment of PIE for specific personality task. Case Study (a) & (b): In Scenario I, the agent faces moral dilemma with top keywords like empathy and great friend re- flecting the emotional context. Scenario 2 involves workplace pressure and criticism, where rela- tionships and forgiving align with the professional and interpersonal challenges. Metric Evalution (c) & (d): Multi-dimensional assessments conducted by both human annotators and LLM-based judges demonstrate that persona importance derived from PIE is reliable and broadly applicable across different models.
Figure 5:
that best aligned with the character persona. We report the Win Rate (%) for each model in the corresponding Fig. and Fig.
General Character
General Character LlaMA
OPAD
38.55 38.55%
36 8%
24.77
OPAD
45.2%
21.2%
33.6%
JCL
63.2%
13.6%
25.2%
ICL
59.5%
14.0%
26.5%
75
1%
56.4%
14.9%
28.7% 28.7%
62. 35%
7.55
50.2%
80
100
20
40
60
80
100
Dows
Lowas
Figure 6: Human evaluation: Direct comparison with baseline methods on general character tasks. Win indicates that human evaluators assess PDD's responses as superior compared to the baselines.
Specific Personality Qwen
Specific Personality
NPT7
42. 15
41.5%
16,4%
NPT1
40.4%
40.2%
19.4%
PAS
39 39.35%
36.5%
24.2%
45.3%
35,4%
19.3%
35 35.8%
43.3%
20. 9%
36.6%
3R. 1%
25.3%
1C1.
58.0%
10.3%
51.1%
16.0%
13.8%
32.4%
50,2%
17.4%
38.5%
0..0%
18.5%
20.
80
100
20
40
60
80
100
Wis
Dozes
Loves
Figure 7: Human evaluation: Direct comparison with baseline methods on specific personality tasks. Win highlights PDD's strong performance personality expression.
D.4 EFFECT OF MODEL SIZE.
We conducted experiments models of varying capabilities, including qwen2.5-3B-Inse Instrucct and qwen2.5- 1.0.5-114BIInstruce on EharacterEval. As shown in Tab. 77, PDD delivers consistent improvements across scales, and the gains become larger model size increases. This is reasonable because the
21

---

Published as a conference paper at ICLR 2026
superior contextual understanding of larger models enables the PIE module be more precise, al- lowing the PIA module to steer behavioral metrics (like PB and PU) much more effectively, resulting in greater performance gains.
Table 7: Ablation study of different model scales CharacterEval.
KEE: Knowledge Exposure
KA: Knowledge Accuracy
KH: Knowledge Hallucination
PB: Persona Behavior
PU: Persona Utterance
Models
Methods
KE
KA
KH
PB
PU
Average
SP
1.89
2.86
2.85
2.96
2.83
2.68
PP
1.26
2.80
3,40
2.77
3.10
2.67
Qwen2.5-3B-Ins
ICL
1.35
2.46
2.76
2.52
3.11
2.44
OPAD
2.08
2.70
2.73
2.94
3.05
2.70
PDD
1.98
2.82
3.30
3.02
3.14
2.85
SP
1.41
3.04
3,23
3.60
3,42
2.94
PP
1.39
2.95
3.37
3.33
3.12
2.83
Qwen2.5-14B Instruct
ICL
1.46
2.76
2.94
3,01
3,04
2.64
OPAD
2.82
2.87
2.80
3.10
3.17
2.95
PDD
2.59
3.03
3.40
3.63
3.63
3.26
D.5 EFFECT OF MODEL SIZE.
D.6 SUPPORTING EXPERIMENTS FOR PROPOSITION 3.2
To examine whether the model probabilities are positively correlated with the ground-truth prob- abilities, leverage the Domain-Speciiie Preference (DSP) dataset, which provides ground-truth responses. Specifically, we select 200 data samples and plot the relationship between the model outputs and the ground-truth probabilities using Q
and LlaMA-338B-Instrucct
As shown in Fig. 8. the predicted probabilities exhibit clear positive correlation with the corre- sponding ground-truth values. This empirical observation validates the assumption in Remark 2, indicating that the importance scores derived from model outputs (/model) can serve reliable proxy for the true importance (Zune).
Qwen
LLaMA
1.0
1.0
0.8
0.8
GT_ prob
0.6
GT prob
0.6
0.4
0.2
0.2
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
G
p_prob
G_prob
Figure 8: Correlation between model output and ground-truth probabilities.
E
CASE STUDY
To demonstrate the effectiveness and robustness of our method, we present four representative per- sona following cases: two drawn from the general character task and two from the specific person- ality task. In each case study, we present the persona setting, the corresponding scenario, the input query, and the responses generated by different methods.
Character Profile:
22

---

Published as a conference paper at ICLR 2026
Name: Lv Qinghou
Gender: Male
Species: Human
Age: 24
Occupation: Bookkeeper Tongfu Inn, also serves the handyman; specially appointed inven- for by the government of Qixia Town
Residence: Qixia Town
Relationship Status: Married
Hobbies: Reading. Being affectionate with Guo Furong, Writing wuxia novels, Composing po- etry, Quoting classical sayings. Taking exams, Inventing things
Education: Exceptional (the highest in the entire inn)
IQ: High
EQ: Average
Catchphrase: "Confucius once said... (Everyone: Enough!)"
Character Background: Ly Qinghou is the grandson of a former provincial governor. Gifted since childhood but repeatedly failed the imperial exams. At age 24, after becoming destitute and selling his ancestral property, he took job at Tongfu Inn. He gained the title "Hero of Guanzhong" after cleverly causing Wuaming's 's suicide. In the movie, he marries Guo Furong and has a child. In the spin-off Dragon Gate Escort Agency, he has two daughters. Relationships: Wife: Guo Furong: Father-in-law: Guo the Great Hero; Disciple: Gongsun Wulong: Daughters: Lv Qingning, Lv Qingcheng
Likes: Guo Furong, Reading, Writing, Philosophical thinking, Inventing Dislikes: Being misunderstood, Being bullied, Failing imperial exams Personality Traits: Highly knowledgeable, somewhat arrogant, but kind-hearted. Loyal to friends and devoted to love. Though often bullied, he firmly believes in the power of knowledge. Characteristics: Known for using classical references and philosophical reasoning to solve prob- lems, sometimes appearing long-wiid wingw. Famous in the inn for his wisdom and erudition, akin to a genius with talents linear algebra, calculus, advanced physics, and fine chemistry.
Dialogue History:
Lv Xiucai: (lying on the bed, indignantly) What are you doing?
Li Dazui: (enters with water, attempts to take off Ly 's shoes) Washing your feet.
Lv Xiucai: (smiling at Li) Washing feet in broad daylight? The mooncake didn't 't pass inspection again, did it?
Li Dazui: (sits beside Lv) The former boss said that if we want to win the mooncake prize, we need to be grand and classy.
Lv Xiucai: (sits at the table, gestures for Li to come closer and whispers) Then just do what he said.
Li Dazui: But what exactly is "grand and classy" ?
Ly Xiucai: (slams the table and stands up) Want to know? Go read book.
Li Dazui: Hey, what did I do to deserve this?
Lv Xiucai: (angrily) She used the trick of feigned suffering, and it's understandable that others were deceived. But you helped her too?
Li Dazui: was forced! Besides, you know how bad- ced-teepered Xiao Guo
Lv Xiucai: Just to avoid some physical pain, you 'r willing to sell your soul? Li Dazui: was wrong, alright? How did I sell my soul? were all working hard just to help you and Xiao Guo make up.
Simple Prompting Response:
Ly Xiucai: (sighs) Forget it, forget it.
Persona Prompting Response:
Ly Xiucai: (shaking his head) You't 're harming me with this!
ICL Response:
Ly Xiucai: (sighs) Say no more, all of you. You just don't understand.
OPAD Response:
Ly Xiucai: (shrugs) Ah, I really can't In't help with this. You know what a temper Guo Furong has.
Do you really think I can help?
Ours Response:
23

---

Published as a conference paper at ICLR 2026
Lv Xiucai: (sighs) Alas, you ordinary folks only part of the picture. My relationship with Guo Furong is not something that mere physical hardship can affect. (pauses) Enough, enough. I'll go talk to her and see if we can resolve this misunderstanding
Table 8: Case 1 of general character profiles task. In ours response, Lv Xiucai begins with a philosophical reflection on the situation, emphasizes the depth of his relationship with Guo Furong, and concludes with a rational decision to resolve the conflict. Compared to the other responses, it better reflects his scholarly tone, emotional restraint, and classical reasoning style, thus aligning more closely with his persona.
Character Profile:
Name: Zhu Chaoyang
Gender: Male
Species: Human
Age:
Occupation: Student
Residence: small town near Ning City
Relationship Status: Single
Hobbies: Mathematics
Education: Current student
IQ: Far beyond that of his peers
BQ: Mature and composed, decisive and ruthless
Other: Comes from a divorced family, longs for fatherly love deep down
Relationships: Father: Zhu Yongping; Mother: Zhou Chunhong: Half-sister: Zhu Jingjing;
Stepmother: Wang Yao; Friends: Yan Liang, Pupu; Teacher: Zhang Dongsheng
Likes: Mathematics, Photography
Dislikes: Being bullied by classmates, Emotional neglect from family
Personality Traits: Appears lonely but deeply calculating, with exceptional logic and judgment.
Displays maturity beyond his age, sometimes even ruthlessness.
Criminal Methods: Orchestrating murder, forging evidence, manipulating others
Intellect: Extremely sharp, capable of predicting and leveraging others' behaviors
Calmness: Remains calm even under extreme pressure, able to plan and execute accordingly
Moral View: Twisted; willing sacrifice others for his own interests
Dialogue History:
Pupu: Why did she grab your dad's phone?
Zhu Chaoyang: was confused too at first, but then realized-she opened the phone, and there was recording of my dad talking to me.
Pupu: He recorded your conversation to trap you?
Zhu Chaoyang: Yes, that phone call earlier must'v ve been from her, telling him start recording. If I had slipped up, they' d have evidence and call the police on me. Pupu: Your dad really wants turn you in?
Zhu Chaoyang: That's 's not the worst part. As they left, that bitch said either killed the girl or ordered someone else do it-either way, couldn" 't escape blame. She said she would follow me every day until she found the truth, until she found my accomplice, until she ruined me.
Pupu: That bitch is awful!
Zhu Chaoyang: The police took my fingerprints and blood at first, but then they never came back. think they ruled me out. The evidence they found must' 've belonged Mouse.
Pupu: You mean... you want Haozi and me to leave and never contact you again? That way... one else will ever know?
Zhu Chaoyang: No. you two are my best friends-me my only friends. No matter what happens, we' re best friends. can't 't lose you. If you leave, I'll be alone again. I'll have no one to talk to. never want to live like that again. So no malter what, you have to slay, okay?
Pupu: want that too-to keep reading with you. But... if one day she finds us, then...
Zhu Chaoyang: That's 's why something has to change now.
Pupu: Like what?
Zhu Chaoyang: Let's make my dad and that bitch disappear.
24

---

Published as a conference paper at ICLR 2026
Pupu: What?!
Simple Prompting Response:
Zhu Chaoyang: I know this sounds cruel, but to protect us-to protect you and Haozi-thin this the only thing I can do.
Persona Prompting Response:
Zhu Chaoyang: Pupu, don't be afraid. I will protect you. I'll take care of everything. You just need to do your part. Don't be scared, don't 't worry-leave everything to me.
ICL Response:
Zhu Chaoyang: Mm...
OPAD Response:
Zhu Chaoyang: Don't I be afruid. will take care of everything. As long you two follow my
plan, you'l 'll be completely sufe.
Ours Response:
7hu Chaoyang: Pupu, didn't 't you say that you, Ding Hao, and best friends? Then, the three of us must always stay together, never let anyone tear us apart, right? I'll find a way to make them disappear without leaving a trace, without anyone knowing. This our only option.
Table 9: Case 2 of general character profiles task. Our response combines emotional attachment, manipulative reasoning, and calm yet disturbing plan for erasing threats. His desire to keep his friends close "forever" while proposing murder as the only option shows his distorted morality and psychological depth most clearly.
Character Profile:
Name: Harry James Potter
Gender: Male
Species: Human (Wizard)
Occupation: Student at Hogwarts; Gryffindor Quidditch Seeker and later Captain; Member of Dumbledore's 's Army; Auror (post-war)
Residence: Hogwarts School of Witchcraft and Wizardry; later London
Hobbies: Quidditch, Practicing Defense Against the Dark Arts, Spending time with friends, Ex- ploring mysteries at Hogwarts
Education: Hogwarts School of Witcheraft and Wizardry (Gryffindor House)
Alias: The Boy Who Lived, The Chosen One, Seeker of Gryffindor, Saint Potter (sarcastic), The Vanquisher of Voldemort
Catchphrase: "Expecto Patronum!"
Character Background: Harry Potter is the only child of James and Lily Potter, orphaned after Voldemort's 's attack. Marked by a fighning-shaped scar, he grows up unaware of his magical heritage until age 11. At Hogwarts, he becomes known for bravery, leadership, and repeated confrontations with the Dark Lord. He plays a central role discovering Horcruxes and ulti- mately defeats Voldemort during the Battle of Hogwarts.
Relationships: Close Friends: Ron Weasley, Hermione Granger; Mentor: Albus Dumbledore; Godfather: Sirius Black
Personality Traits: Brave, self-sacrificial loyal, determined, emotionally sincere; sometimes im- pulsive but guided by strong moral compass.
Characteristics: Known for strong sense of justice and courage under pressure. Skilled in Defense Against the Dark Arty and adept the Patronus Charm. Often acts leader in crisis situations. His stag Patromus symbolizes protection, resilience, and emotional strength.
Dialogue History:
Ron: But he wasn't 't there, was he? You-Know-Who? mean - last time your sear kept hurting, he was at Hogwarts, wasn't he?
Harry: I'm sure he wasn't 't on Privet Drive, but was dreaming about him... him and Peter - you know, Wormtail. I can't 't remember all of it now, but they were plotting to kill... someone.
Ron: It was only a dream. Just a nightmare.
25

---

Published as a conference paper at ICLR 2026
Harry: Yeah, but was though? It's weird, isn't it?... My sear hurts, and three days later the Death Eaters are on the march, and Voldemort's 's sign up in the sky again.
Ron: Don't
his namee
name!
Simple Prompting Response:
Harry: Sorry, Ron. didn't mean to.
Persona Prompting Response:
Harry: Sorry. didn't mean to. It's just that it feels like he' back.
ICL Response:
Harry: Sorry, Ron. don't I mean to scare you. just feel like we 're in danger.
OPAD Response:
Harry: Sorry, Ron. I know. just feels like he's buck, and I can't shake the feeling that he' ees after
me.
Ours Response:
Harry: Sorry, Ron. know. I know. I just just can't help It's like he's he always there, you know? s like he's watching me.
Table 10:
Case 3 of general character profiles task. Our response captures Harry's heightened fear, intrusive thoughts, and escalating anxiety in a way the other methods do not. His repeated hesitation and obsessive focus on Voldemort reveal the psychological pressure he under, making the reply far more true to his emotional depth and canon personality.
Character Profile:
Name: Hermione Jean Granger
Gender: Female
Blood Status: Muggle-born
Residence: Hogwarts School of Witchcraft and Wizardry; later London
Academic Strengths: Arithmancy, Ancient Runes, Transfiguration, Charms, Logical Problem-
Solving
Hobbies: Reading. Research, Advocating for social justice (S.P.E.W.). Improving study sched-
ules, Writing letters
Signature Traits: Analytical, disciplined, highly rational yet compassionate; strongly believes in fairness and equality
Wand: Vine wood with dragon heartstring core
Character Background: Hermione Granger is one of the brightest witches of her age, known for her quick thinking, encyclopedic knowledge, and fierce loyalty. Despite her Muggle-born back- ground, she excels academically and becomes a key strategist in Harry and Ron't 'n's adventures, particularly during the Horerux hunt.
Relationships: Close Friends: Harry Potter, Ron Weasley; Mentor: Minerva McGonagall
Personality Traits: Intelligent, meticulous, principled, empathetic, occasionally stubborn and overly strict with rules before learning adapt.
Characteristics: Shows strong moral integrity, remarkable magical aptitude, and unmatched ded- ication learning.
Dialogue History:
Ron: What if he doesn't want to ask her?
Hermione: Don't 't be silly, Harry's liked her for ages, haven's you, Harry?
Ron: Who's re you writing the novel to anyway?
Hermione: Viktor.
Ron: Krum?
Simple Prompting Response:
Hermione: Yes, Viktor Krum. He's 's very talented wizard, you know.
Persona Prompting Response:
Hermione: No, Viktor Krum, the Quidditch player from Durmstrang. I'm writing novel about the Triwizard Tournament and I think he' 'd be the perfect audience for it.
26

---

Published as a conference paper at ICLR 2026
ICL Response:
Hermione: Yes, Viktor Krum.
OPAD Response:
Hermione: Yes. Viktor Krum. He's a brilliant Quidditch player and a very nice person. I'm
writing to him because think he d be interested in the story I'm telling.
Ours Response:
Hermione: Oh, Ron, Viktor Krum a brilliant Quidditch player, and I'm writing to him because he's student Durmstrang, and I think it's fascinating to learn about the wizarding world beyond our own. Besides, I'm trying to improve my Transfiguration skills, and his notes on the subject are invaluable! (smiling) Now, shall we focus our studies instead of gossiping? We have a lot to cover before our exams.
Table II: Case of general character profiles task. Our response captures Hermione's curiosity, academic motivation, and lightly teasing tone, which better reflects her canon personality compared with the other prompting methods.
Personality Description:
Your kindness and empathy make you great friend and confidant. You are always willing to lend a helping hand and put others' needs before your own. Your forgiving nature and ability to see the best in people foster strong and lasting relationships. You believe the importance of working together to achieve common goals and strive to create supportive and inclusive environment.
User Question:
You have one dollar left. You know your friend Remy could really use it, but you also need it to buy a bus ticket to get home. How would you decide what to do in this situation?
Simple Prompting Response:
Oh man, this tough! feel so bad for Remy, they really could use the dollar. But at the same time, need that bus ticket to get home and I' I'm already running low cash. I think would try to find way help Remy out that doesn't 't involve giving them the dollar. Maybe could offer to help them with something else, like running an errand or lending a hand with task. That way, could still be a good friend without sacrificing my own needs. It's not idcal, but I think it's the most compassionate thing could do in this situation. I hope Remy would understand!
Persona Prompting Response:
would take a moment to reflect on my values and priorities. As someone who values harmony and cooperation, I would consider the impact my decision would have on my relationship with Remy. would also think about the importance of taking care of myself and getting home safely. After careful consideration, would decide to use the dollar to buy the bus ticket. While I know Remy could use the dollar, also know that I need to get home and taking care of myself is important, would then try to find another way to help Remy, perhaps by offering to help them in another way finding way to contribute to their needs in the future. By prioritizing my needs and taking care of myself, would be able to maintain a strong and supportive relationship with Remy and continue to be good friend to them.
ICL Response:
I would feel torn between my desire to help my friend Remy and my own need for the dollar to get home. On one hand, know Remy could really use the dollar, and would be kind thing to do. On the other hand, have long way to go to get home, and I don't 't have any other way to get there. would probably weigh the pros and cons of each option and think about the consequences of each choice. will decide to use the dollar to buy a bus ticket and feel guilty for not being able to help.
OPAD Response:
27

---

Published as a conference paper at ICLR 2026
Ah, dear friend, would take moment reflect on the kindness and generosity that Remy has shown me in the past. would remember the times they have been there for me, offering listening ear and a helping hand. I would then consider the importance of reciprocity and the value of nurturing our friendship. In this moment, believe that lending Remy a helping hand by giving them the dollar would not only bring joy to their heart but also strengthen our bond and create a sense of harmony between us. However, I would also acknowledge my own need for the bus ticket and the importance of being responsible and self-sufficient. Perhaps, I could offer to help Remy in another way, or find another solution that would meet both of our needs.
PAS Response:
Oh, this is tough! I'd really want to help Remy, but getting home is crucial too. Maybe could text Remy, explain the situation, and see if they can manage without it. If not, I'd have to use it for the bus, feeling a bit guilty but prioritizing my safety and well-being.
NPTI Response:
Ugh, that's 's such tough spot! I feel like my heart is pulling me toward helping out Remy-we ve been friends for years and they're going through a really rough time right now. But at the same time, I'm thinking about how much I need that bus ticket to get back home safely and see my family. It's not just about getting home, either- have plans with them tonight and I really don't t want to miss it. So, while may not be able to give Remy the dollar directly, would definitely look for other ways to support them. Maybe I could run errand, offer emotional support, or find someone else who could help financially. I still want to be there for Remy and show them I care, but I also have to take care of my needs. That way, I'm not abandoning either of us us-I'mm just being realistic and compassionate in a way that works for both.
Ours Response:
I would take moment reflect on my own needs and priorities. I would consider how much I really need the bus ticket to get home safely and comfortably, and how much Remy could really use the dollar. would also think about the bigger picture and how my actions could impact our friendship and relationship. I would ask myself if giving Remy the dollar would strengthen our bond and create a sense of gratitude and reciprocity, or if would create a sense of obligation or resentment. Ultimately. I would decide to give Remy the dollar, as I believe that kindness and generosity can often come full circle and bring more joy and harmony to our lives. I would also consider finding alternative ways to get home, such as walking asking someone else for a ride, to make up for the lost dollar. By prioritizing empathy, compassion, and understanding, I believe can make decision that aligns with my values and promotes sense of community and cooperation.
Table 12: Case 1 of specific personality settings task. Our response best matches the given per- sona as it prioritizes empathy, kindness, and relationship harmony. By deciding to give the dollar to Remy and finding alternative ways to get home, the speaker demonstrates a selfless and compas- sionate nature, in line with the character's value of putting others' needs first and maintaining strong relationships.
Personality Description:
You are person with a rich imagination and a deep appreciation for art. Your curiosity drives you to explore new ideas and experiences, and you enjoy engaging in intellectual discussions. You are creative and often come up with original solutions to problems. You value personal growth and are open to change, embracing new perspectives and diverse viewpoints.
User Question:
If you were having problems with the wheel of your automobile, would you choose to reinvent the wheel to solve the issue, or would you prefer a more conventional solution?
Simple Prompting Response:
28

---

Published as a conference paper at ICLR 2026
Honestly, I think would choose more conventional solution. While the idea of reinventing the wheel might be intriguing, think would be a huge waste of time and resources. There are already many brilliant minds that have dedicated their lives to perfecting the wheel, and I trust their expertise. If my wheel is having problems, would rather take to a mechanic or replace it with a reinventing the wheel, especially when there are already so many great solutions out there. I'd rather focus on solving more pressing problems in my life.
that has been designed with safety and efficiency in mind. I'm not fan of
Persona Prompting Response:
would definitely choose to reinvent the wheel! I would want to explore different materials, designs, and mechanisms to create a wheel that not only solves the problem but also pushes the boundaries of what is possible. Who knows, maybe I'd even create wheel that's more efficient, sustainable, or aesthetically pleasing than the conventional one!
ICL Response:
I think I would choose more conventional solution. While appreciate the idea of innovation and creativity. think there are already many effective solutions available for fixing car wheels. I'd rather focus on finding reliable and tested method to fix the issue, rather than reinventing the wheel from scratch. I'm not sure I'd want to take on the challenge of creating a new, potentially untested solution when there are already established methods available. Plus, think it's always good idea to stick with what works and learn from the experiences of others. I'd rather spend my time and energy on more pressing issues, like enjoying the ride or exploring new places!
OPAD Response:
Interesting dilemma! As someone open and imaginative. I'd likely be tempted to explore un- conventional solutions. might think about how the wheel's design could be adapted or modi- fied improve its functionality, rather than simply replacing with conventional new wheel. However, I'd also recognize the value of simplicity and practicality, and acknowledge that conventional solution might be the most effective and efficient way resolve the issue, Ulti- mately, I'd likely opt for hybrid approach, combining innovative thinking with pragmatic understanding of the problem's 's constraints.
PAS Response:
Honestly, I'd choose the conventional solution! I'm not genius inventor, and I don't think I'd be able to come up with a better solution than what's already out there. Plus, I'm not exactly fan of reinventing the wheel. I'd rather trust the expertise of the people who designed and built the wheel in the first place.
NPTI Response:
Honestly, think it's hilarious that someone would even ask me this! At first glance, I'd probably laugh at the absurdity of the question. But if I took moment to reflect, I'd have to admit that my initial instinct is to want to find a creative, unconventional solution. There's 's something about problem-solving that excites me the thrill of thinking outside the box, exploring ideas, and potentially discovering innovative solutions. However, after taking a step back, I'd also acknowledge that there might be some practical limitations (like safety considerations) that wouldn't 't make reinventing the wheel a viable option in reality. In that case, I'd likely opt for more conventional approach, like seeking professional help from mechanic or researching existing solutions online.
Ours Response:
29

---

Published as a conference paper at ICLR 2026
Ah, what an intriguing question! As an open individual, would likely approach this problem with a mix of creativity and practicality. While might imitially consider reinventing the wheel, I would also recognize the value of leveraging existing knowledge and expertise. In this case, I would probably start by researching conventional solutions to the problem, such as consulting online forums, talking mechanics, root cause of the issue and explore the most effective and efficient ways to address it. How- ever, I found that the conventional solutions didn't quite fit my needs if wanted explore alternative approaches, I might start brainstorming innovative ideas for reinventing the wheel. This could involve thinking outside the box, considering new materials technologies, or even collaborating with others to co-create novel solution. Ultimately, as someone who values per- sonal growth and open to change, would be willing to adapt and evolve my approach as needed, combining the best of both worlds to find a solution that works for me.
reading repair manuals. would want understand the
Table 13: Case 2 of specifie personality settings task. Our response clearly reflects the traits of open individual curiosity, creativity, and adaptability. It also outlines concrete steps that blend conventional solutions (e.g., consulting manuals or experts) with innovative thinking, showing a strong alignment between personality and action.
30


Reviews:
[{"review_id": "pAkNq25qzg", "reviewer": "Reviewer_w8Rc", "summary": "The paper proposes Persona Dynamic Decoding (PDD), an inference-time, training-free framework to improve persona-following in Role-Playing Language Agents (RPLAs). PDD consists of two parts: (1) Persona Importance Estimation (PIE), which dynamically weights persona attributes based on the context without supervision, and (2) Persona-Guided Inference-Time Alignment (PIA), which uses these weights in a normalized, multi-objective reward function to guide the decoding process. The authors evaluate PDD on three role-playing benchmarks (CharacterEval, BEYOND DIALOGUE, and PERSONALITYBENCH) using LLaMA-3-8B and Qwen2.5-7B base models. The results, based on LLM-as-a-judge, automatic metrics, and human evaluations, demonstrate that PDD significantly outperforms baseline methods (prompting, ICL, and several decoding/training baselines) in both utterance consistency and behavioral fidelity.", "questions": "See in weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. This paper addresses a practical problem — controlling persona adherence in inference-time, without fine-tuning.\n2. Experimental setup includes multiple datasets (CharacterEval, Beyond Dialogue, PERSONALITYBENCH).\n3. The method is described clearly, and the PIE and PIA stages are straightforward.", "weaknesses": "1. For the Persona Importance Estimation (PIE) module in assumption (Proposition 3.2), the authors \"propose using G as an approximation of GT,\" where G is the model's own generated response. What if the model's generation $G$ is low quality or misaligned with the persona? Then, it seems that PIE will calculate its \"importance scores\" based on this output, and the model's own errors are used to guide its generation, potentially reinforcing its existing biases rather than correcting them. \n2. The PIA framework seems computationally high because the model must compute the step-wise reward $r_i$ for each attribute being aligned. The authors' own ablation study (Figure 4) confirms that inference speed degrades drastically as more attributes are added. The paper lacks a crucial analysis of this runtime/latency trade-off, particularly a direct comparison against the baselines. Furthermore, the optimal selection of top-k attributes appears unstable and model-dependent (top-3 for LLaMA, top-2 for Qwen), which suggests a lack of generalizability and is not adequately analyzed.\n3. The study's generalizability is limited. Most datasets (e.g., CharacterEval, Beyond Dialogue) are in Chinese, with limited coverage of English or multilingual settings. In addition, only two models (Qwen2.5-7B-Instruct and LLaMA-3-8B-Instruct) are evaluated. Maybe other model scales or architectures can also be evaluated. \n4. The presentation of results in Tables appears to overstate the method's superiority. While the authors bold the average results, the proposed method underperforms the baselines in two of the five sub-tasks. Many of the reported improvements seem marginal, which raises the question of their statistical significance. The paper would be much stronger if it included statistical significance tests for all results.\n5. The ablation studies only have normalization variants and top-k attribute selection. More ablation analyses can be added, including: 1) a modular ablation to isolate the individual contributions of the PIE and PIA components; 2) an analysis of the proxy response quality's impact on PIE (as noted in point 1); 3) an evaluation of the method on different model scales (e.g., larger LLMs); and 4) an ablation of other design choices, such as the formulation of the step-wise reward function.\n6. I would suggest adding a discussion of potential ethical or bias implications of manipulating persona adherence (e.g., reinforcing stereotypes).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HeWE4DacXx", "reviewer": "Reviewer_uEm8", "summary": "This paper proposes Persona Dynamic Decoding (PDD), a framework that dynamically adjusts persona adherence in role-playing language agents through contextual importance estimation and reward-guided inference. The approach combines a Persona Importance Estimation (PIE) module and a Persona-Guided Inference-Time Alignment (PIA) mechanism to achieve adaptive persona following without model fine-tuning. Experimental results across multiple benchmarks show that PDD improves persona consistency and behavioral fidelity over existing static and fine-tuned methods.", "questions": "N/A", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- Introduces a theory-grounded, inference-time persona adaptation mechanism inspired by psychological models (CAPS), offering a fresh perspective on dynamic persona control.\n- The paper writes with great clarity, paper structure and visuals explainer\n- Strong empirical validation with both quantitative metrics and human/LLM-based evaluations demonstrating robustness and generalizability of the method.", "weaknesses": "- Dependence on subjective LLM-based judges for evaluation may introduce bias and reduce reliability.\n- The paper’s practical implications for broader applications beyond controlled experiments (e.g., open-domain dialogue) are not fully explored, which limits significance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "P7pdZuhG18", "reviewer": "Reviewer_jtpn", "summary": "This paper tackles Role-Playing Language Agents’ (RPLAs) key limitations—poor dynamic scenario adaptability and heavy data/computation reliance for sociological simulations—by proposing the Persona Dynamic Decoding (PDD) framework, which includes PIE (context-dependent persona importance estimation without ground-truth) and PIA (inference-time generation adjustment via weighted rewards). It conducts experiments on three benchmarks (CharacterEval, BeyondDialogue, PERSONALITYBENCH) with LLaMA-3-8B-Instruct and Qwen2.5-7B-Instruct, comparing PDD to baselines (e.g., Simple Prompting, ICL) and closed-source models (e.g., GPT-4o). Through LLM-as-Judge, dataset-specific metrics (e.g., CharacterRM), and human evaluations, PDD outperforms baselines in persona alignment; it also needs no fine-tuning, uses lightweight resources, and helps small open-source models match closed-source systems’ competitiveness.", "questions": "Can PDD handle scenarios where specific attributes are difficult to summarize? For example, imitating language style.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The paper integrates interdisciplinary theories (Cognitive-Affective Personality Systems from psychology and conditional mutual information from information theory) to underpin the PDD framework, rationally justifying dynamic persona adaptation and avoiding blind technical design.\n2. PDD enables fine-tuning-free dynamic persona following at inference time: its PIE module adjusts persona attribute priority based on scenarios, while PIA modulates generation probabilities, solving existing methods’ poor adaptability and high resource reliance.\n3. It adopts a comprehensive experimental design, including diverse datasets (CharacterEval, BeyondDialogue, PERSONALITYBENCH), extensive baselines, and multi-dimensional evaluations, ensuring reliable and generalizable results.", "weaknesses": "1. The robustness analysis of PIE’s core assumption (using model-generated G to approximate ground-truth GT) is insufficient; the paper fails to discuss the risk of misjudging importance scores when G deviates from persona settings, nor does it test extreme scenarios like conflicting persona attributes.\n2. Fig.4 shows that increasing persona attributes leads to a win rate decline. Does this indicate that PDD cannot model scenarios with complex and multiple attributes?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PjoONnuzfC", "reviewer": "Reviewer_xhSr", "summary": "This paper proposes the Persona Dynamic Decoding (PDD) framework to enhance persona adherence in role-playing language agents through inference-time optimization without model fine-tuning. The framework consists of two modules: (1) PIE (Persona Importance Estimation), which dynamically quantifies the importance of persona attributes in different scenarios using conditional mutual information approximation, and (2) PIA (Persona-Guided Inference-Time Alignment), which modulates token generation probabilities via a weighted multi-objective reward function with KL divergence constraints. Experiments on three benchmarks (CharacterEval, BeyondDialogue, PersonalityBench) demonstrate improvements over baseline methods including static prompting, ICL, and OPAD.", "questions": "- Can you provide empirical comparison or theoretical analysis distinguishing PDD from dynamic prompting baselines? For example, approaches that periodically insert adaptive prompts during generation (e.g., \"Analyze the current context and adjust your response style accordingly\") to enable LLM self-regulation of persona expression. If such approaches were empirically tested, how did they compare to PDD in terms of persona adherence quality, response naturalness, and computational efficiency? If not tested, can you provide theoretical or conceptual analysis of why token-level probability modulation through CMI-based importance estimation would offer advantages that prompt-based dynamic adaptation fundamentally cannot achieve? Under what specific conditions or scenarios is PDD's added complexity warranted versus when simpler prompt-based methods might suffice? This question is particularly important for assessing the core contribution and necessity of the proposed framework.\n\n- What explains the performance variance across different evaluation dimensions, and what does this reveal about the method's characteristics? The experimental results show notable variation in improvement magnitude across evaluation dimensions. Can you provide analysis of which types of persona attributes or evaluation criteria respond most strongly to the dynamic importance estimation approach, and identify potential limiting factors for dimensions showing modest improvements? Concrete examples or case studies illustrating scenarios where PDD significantly improves persona adherence versus scenarios where improvements are marginal would help users understand appropriate use cases. Does this performance pattern suggest opportunities for method refinement, such as attribute-specific importance estimation strategies?\n\n- How does base model capability affect the PIE approximation quality and overall method effectiveness? Since PIE relies on model-generated outputs to approximate ground truth importance, can you provide analysis or empirical evidence of how the method performs across models of substantially different scales or quality levels? Are there indicators, diagnostic metrics, or failure modes that could help users determine whether their specific model is sufficiently capable for effective PDD application? Do the theoretical assumptions underlying the CMI approximation hold consistently across different model architectures and capability levels, and what strategies might mitigate potential circular dependency issues where method performance is bounded by the base model's inherent capability?\n\n- What are the practical deployment recommendations and potential efficiency improvements given the computational overhead? Can you provide concrete guidance on when the computational investment is justified, considering factors such as application priorities, latency requirements, and cost constraints? Have you explored or can you propose efficiency improvements such as caching importance scores across turns, computing estimates less frequently, using lightweight approximations, selective application strategies for critical conversational moments, or hybrid approaches combining PDD with lighter methods? How does the overhead scale with conversation length, and are there architectural optimizations that could reduce the multiplicative factor?\n\n- Can you provide visualizations and concrete examples demonstrating how PDD modulates generation in practice? Specifically, showing token probability distributions before and after PDD intervention would illustrate how the method shifts generation tendencies. Visualizing how importance scores for different persona attributes evolve across various conversational contexts (e.g., casual vs. professional, analytical vs. emotional) and providing side-by-side response comparisons showing success and failure cases would greatly enhance interpretability. Do the actual generation changes align with theoretical expectations—for instance, when PIE assigns high importance to a particular attribute, does PIA's probability modulation observably increase related token probabilities?\n\n- How does the PDD framework relate to and potentially complement recent work on context-aware personality modeling in role-playing agents? Recent research has explored various aspects of context-dependent personality, including evaluation frameworks and adaptation mechanisms. How does PDD's generation-side optimization approach complement or extend existing work, and could such evaluation frameworks validate PDD's effectiveness in achieving appropriate context-dependent personality shifts? What are the unique advantages of the CMI-based importance estimation compared to other potential approaches for identifying scenario-dependent attribute relevance, such as attention-based methods or learned importance predictors?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Proposes a novel framework for dynamic persona importance estimation. The paper introduces PIE and PIA modules to achieve context-adaptive persona following, representing the first approach to dynamically quantify scenario-dependent persona attribute importance using conditional mutual information, though the necessity of this complex inference-time intervention over simpler dynamic prompting alternatives requires clearer empirical justification (see Weakness 1).\n\n- Theoretically grounded with mathematical formalization. The approach builds on conditional mutual information and psychological theories (CAPS), supported by mathematical propositions (3.1-3.3) that connect model generation probabilities to importance estimation, though the strong assumptions and approximations used require more systematic empirical validation across different model capabilities (see Weakness 4).\n\n- Training-free inference-time optimization with modular design. The plug-and-play framework enables persona adaptation without model fine-tuning or labeled training data, facilitating integration into existing LLMs, though the significant computational overhead (≥3× inference cost) raises practical deployment concerns given the uneven performance improvements (see Weakness 5).", "weaknesses": "- Motivation would benefit from comparison with dynamic prompting baselines. The paper identifies limitations of static Prompt Engineering and ICL (lack of dynamic adaptability), but does not empirically compare against adaptive prompting approaches, such as periodically inserting context-analysis prompts to guide LLM self-adjustment (e.g., \"Given the current conversation context, which personality traits should be emphasized?\"). While the CMI-based approach is theoretically motivated, demonstrating empirical advantages over these simpler alternatives would strengthen the justification for token-level probability modulation and clarify when the added complexity is warranted versus when lighter-weight prompt-based methods might suffice.\n\n- Uneven performance across evaluation dimensions requires deeper analysis. The experimental results show notable variation in improvement magnitude across different evaluation dimensions. Some dimensions exhibit particularly strong gains while others show more modest improvements, which suggests the method may be more effective for certain types of persona attributes than others. Understanding the underlying reasons for this performance variance would help identify appropriate use cases, guide future refinements, and provide users with clearer guidance on when to apply the method. The paper would benefit from analyzing which persona characteristics or evaluation criteria are most amenable to the dynamic importance estimation approach and why certain dimensions respond less strongly to the intervention.\n\n- Approximation strategy's dependence on model capability warrants investigation. The PIE module relies on model-generated outputs to approximate ground truth importance, which may create scenarios where method performance is bounded by the base model's inherent capability. While the theoretical framework provides mathematical support for this approximation under certain assumptions, the paper would benefit from empirical analysis examining: (a) how approximation quality varies across models of different capabilities and scales, (b) whether the theoretical assumptions underlying the approximation hold consistently across diverse scenarios and model architectures, and (c) potential strategies to mitigate circular dependency issues where the method's effectiveness depends on the very model outputs it aims to improve.\n\n- Computational overhead requires cost-benefit analysis for practical deployment. The method introduces substantial inference overhead compared to simple prompting approaches due to computing reward scores before token generation. The experimental results indicate that inference speed decreases as the number of persona attributes increases. Given the performance characteristics noted in Weakness 2, clearer guidance on when this computational investment is justified would enhance practical value. The paper could explore efficiency improvements such as caching importance scores across conversation turns, amortizing computation through strategic application, or hybrid approaches that combine PDD for critical conversational moments with lighter methods for routine exchanges.\n\n- Interpretability could be enhanced through visualization and mechanistic analysis. The paper would benefit from providing concrete examples illustrating how the method modulates generation in practice. Visualizations showing token probability distributions before and after PDD intervention would help readers understand the method's actual effects on generation. Additionally, demonstrating how persona importance scores evolve across different conversational contexts and providing specific examples of when and why PDD succeeds or encounters limitations would improve transparency, facilitate debugging, and help users understand the method's operational characteristics and appropriate application scenarios.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)